Snippet icon

My Snippet
2.25s
Database
Type

-- Drop for retesting purposes
DROP TABLE student.releasedates;
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
    × 
    INFO  : Compiling command(queryId=hive_20240327071554_8c38e364-65a8-4f9a-aac6-5948e1c73bfe): -- Drop for retesting purposes
DROP TABLE student.releasedates
INFO  : Semantic Analysis Completed
INFO  : Returning Hive schema: Schema(fieldSchemas:null, properties:null)
INFO  : Completed compiling command(queryId=hive_20240327071554_8c38e364-65a8-4f9a-aac6-5948e1c73bfe); Time taken: 0.004 seconds
INFO  : Concurrency mode is disabled, not creating a lock manager
INFO  : Executing command(queryId=hive_20240327071554_8c38e364-65a8-4f9a-aac6-5948e1c73bfe): -- Drop for retesting purposes
DROP TABLE student.releasedates
INFO  : Starting task [Stage-0:DDL] in serial mode
INFO  : Completed executing command(queryId=hive_20240327071554_8c38e364-65a8-4f9a-aac6-5948e1c73bfe); Time taken: 0.023 seconds
INFO  : OK
     
     Success.
    Snippet icon

    My Snippet
    2.90s
    Database
    Type

    -- Current netflix2023 table wasn't uploaded properly and has all null values for `Release Date`
    -- Create table with release dates that contains title and releasedate
    CREATE TABLE student.releasedates (
    title STRING,
    releasedate STRING
    )
    ROW FORMAT DELIMITED
    FIELDS TERMINATED BY ','
    STORED AS TEXTFILE
    LOCATION '/user/student/netflix'
    ;
    XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
      × 
      INFO  : Compiling command(queryId=hive_20240327071559_75841a8c-52eb-4221-8bd5-349a6e79d10e): -- Current netflix2023 table wasn't uploaded properly and has all null values for `Release Date`
-- Create table with release dates that contains title and releasedate
CREATE TABLE student.releasedates (
    title STRING,
    releasedate STRING
)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY ','
STORED AS TEXTFILE
LOCATION '/user/student/netflix'
INFO  : Semantic Analysis Completed
INFO  : Returning Hive schema: Schema(fieldSchemas:null, properties:null)
INFO  : Completed compiling command(queryId=hive_20240327071559_75841a8c-52eb-4221-8bd5-349a6e79d10e); Time taken: 0.002 seconds
INFO  : Concurrency mode is disabled, not creating a lock manager
INFO  : Executing command(queryId=hive_20240327071559_75841a8c-52eb-4221-8bd5-349a6e79d10e): -- Current netflix2023 table wasn't uploaded properly and has all null values for `Release Date`
-- Create table with release dates that contains title and releasedate
CREATE TABLE student.releasedates (
    title STRING,
    releasedate STRING
)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY ','
STORED AS TEXTFILE
LOCATION '/user/student/netflix'
INFO  : Starting task [Stage-0:DDL] in serial mode
INFO  : Completed executing command(queryId=hive_20240327071559_75841a8c-52eb-4221-8bd5-349a6e79d10e); Time taken: 0.023 seconds
INFO  : OK
       
       Success.
      Snippet icon

      My Snippet
      2.5s
      Database
      Type

      -- Check CSV was uploaded properly and values exist in table
      SELECT * FROM student.releasedates LIMIT 5;
      XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
        × 
        INFO  : Compiling command(queryId=hive_20240327071650_6f1bcd71-2da4-41eb-bf48-ebef7629d1a9): -- Check CSV was uploaded properly and values exist in table
SELECT * FROM student.releasedates LIMIT 5
INFO  : Semantic Analysis Completed
INFO  : Returning Hive schema: Schema(fieldSchemas:[FieldSchema(name:releasedates.title, type:string, comment:null), FieldSchema(name:releasedates.releasedate, type:string, comment:null)], properties:null)
INFO  : Completed compiling command(queryId=hive_20240327071650_6f1bcd71-2da4-41eb-bf48-ebef7629d1a9); Time taken: 0.046 seconds
INFO  : Concurrency mode is disabled, not creating a lock manager
INFO  : Executing command(queryId=hive_20240327071650_6f1bcd71-2da4-41eb-bf48-ebef7629d1a9): -- Check CSV was uploaded properly and values exist in table
SELECT * FROM student.releasedates LIMIT 5
INFO  : Completed executing command(queryId=hive_20240327071650_6f1bcd71-2da4-41eb-bf48-ebef7629d1a9); Time taken: 0.001 seconds
INFO  : OK
          releasedates.title releasedates.releasedate
          releasedates.title releasedates.releasedate
        1The Night Agent: Season 12023-03-23
        2Ginny & Georgia: Season 22023-01-05
        3The Glory: Season 1 // 더 글로리: 시즌 12022-12-30
        4Wednesday: Season 12022-11-23
        5Queen Charlotte: A Bridgerton Story2023-05-04
        Snippet icon

        My Snippet
        2.44s
        Database
        Type

        -- Drop for retesting purposes
        DROP VIEW student.corrected_netflix2023;
        XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
          × 
          INFO  : Compiling command(queryId=hive_20240327071657_be4af6d9-1174-4299-a0c6-4124723ad504): -- Drop for retesting purposes
DROP VIEW student.corrected_netflix2023
INFO  : Semantic Analysis Completed
INFO  : Returning Hive schema: Schema(fieldSchemas:null, properties:null)
INFO  : Completed compiling command(queryId=hive_20240327071657_be4af6d9-1174-4299-a0c6-4124723ad504); Time taken: 0.004 seconds
INFO  : Concurrency mode is disabled, not creating a lock manager
INFO  : Executing command(queryId=hive_20240327071657_be4af6d9-1174-4299-a0c6-4124723ad504): -- Drop for retesting purposes
DROP VIEW student.corrected_netflix2023
INFO  : Starting task [Stage-0:DDL] in serial mode
INFO  : Completed executing command(queryId=hive_20240327071657_be4af6d9-1174-4299-a0c6-4124723ad504); Time taken: 0.013 seconds
INFO  : OK
           
           Success.
          Snippet icon

          My Snippet
          2.11s
          Database
          Type

          -- Create view containing all netflix2023 values and releasedates from
          -- releasedates table earlier, joined on the first English part of the title
          -- as joining on full text narrows matching rows due to text upload issues
          CREATE VIEW student.corrected_netflix2023 AS
          SELECT
          n.title,
          n.availableglobally,
          t.releasedate,
          n.hoursviewed,
          n.numberofratings,
          n.rating,
          n.genre,
          n.keywords,
          n.description
          FROM
          default.netflix2023 n
          LEFT OUTER JOIN
          student.releasedates t
          ON
          split(n.title, '//')[0] = split(t.title, '//')[0];
          XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
            × 
            INFO  : Compiling command(queryId=hive_20240327071702_d95f0a40-55f5-44fb-a486-910fb2d6d522): -- Create view containing all netflix2023 values and releasedates from
-- releasedates table earlier, joined on the first English part of the title
-- as joining on full text narrows matching rows due to text upload issues
CREATE VIEW student.corrected_netflix2023 AS
SELECT 
    n.title,
    n.availableglobally,
    t.releasedate,
    n.hoursviewed,
    n.numberofratings,
    n.rating,
    n.genre,
    n.keywords,
    n.description
FROM 
    default.netflix2023 n
LEFT OUTER JOIN 
    student.releasedates t 
ON 
    split(n.title, '//')[0] = split(t.title, '//')[0]
INFO  : Semantic Analysis Completed
INFO  : Returning Hive schema: Schema(fieldSchemas:[FieldSchema(name:title, type:string, comment:null), FieldSchema(name:availableglobally, type:string, comment:null), FieldSchema(name:releasedate, type:string, comment:null), FieldSchema(name:hoursviewed, type:bigint, comment:null), FieldSchema(name:numberofratings, type:bigint, comment:null), FieldSchema(name:rating, type:double, comment:null), FieldSchema(name:genre, type:string, comment:null), FieldSchema(name:keywords, type:string, comment:null), FieldSchema(name:description, type:string, comment:null)], properties:null)
INFO  : Completed compiling command(queryId=hive_20240327071702_d95f0a40-55f5-44fb-a486-910fb2d6d522); Time taken: 0.036 seconds
INFO  : Concurrency mode is disabled, not creating a lock manager
INFO  : Executing command(queryId=hive_20240327071702_d95f0a40-55f5-44fb-a486-910fb2d6d522): -- Create view containing all netflix2023 values and releasedates from
-- releasedates table earlier, joined on the first English part of the title
-- as joining on full text narrows matching rows due to text upload issues
CREATE VIEW student.corrected_netflix2023 AS
SELECT 
    n.title,
    n.availableglobally,
    t.releasedate,
    n.hoursviewed,
    n.numberofratings,
    n.rating,
    n.genre,
    n.keywords,
    n.description
FROM 
    default.netflix2023 n
LEFT OUTER JOIN 
    student.releasedates t 
ON 
    split(n.title, '//')[0] = split(t.title, '//')[0]
INFO  : Starting task [Stage-1:DDL] in serial mode
INFO  : Completed executing command(queryId=hive_20240327071702_d95f0a40-55f5-44fb-a486-910fb2d6d522); Time taken: 0.014 seconds
INFO  : OK
             
             Success.
            Snippet icon

            My Snippet
            7.97s
            Database
            Type

            -- Ensure there are 18332 values
            -- Note there are 18739, but that's okay long as we matched as many dates as possible
            -- and we can filter values out later
            SELECT COUNT(*) FROM student.corrected_netflix2023
            XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
              × 
              INFO  : Compiling command(queryId=hive_20240327071708_f79e47e9-0d8f-4b5f-a100-ba78933f22f8): -- Ensure there are 18332 values
-- Note there are 18739, but that's okay long as we matched as many dates as possible
-- and we can filter values out later
SELECT COUNT(*) FROM student.corrected_netflix2023
INFO  : Semantic Analysis Completed
INFO  : Returning Hive schema: Schema(fieldSchemas:[FieldSchema(name:_c0, type:bigint, comment:null)], properties:null)
INFO  : Completed compiling command(queryId=hive_20240327071708_f79e47e9-0d8f-4b5f-a100-ba78933f22f8); Time taken: 0.045 seconds
INFO  : Concurrency mode is disabled, not creating a lock manager
INFO  : Executing command(queryId=hive_20240327071708_f79e47e9-0d8f-4b5f-a100-ba78933f22f8): -- Ensure there are 18332 values
-- Note there are 18739, but that's okay long as we matched as many dates as possible
-- and we can filter values out later
SELECT COUNT(*) FROM student.corrected_netflix2023
INFO  : Query ID = hive_20240327071708_f79e47e9-0d8f-4b5f-a100-ba78933f22f8
INFO  : Total jobs = 1
INFO  : Launching Job 1 out of 1
INFO  : Starting task [Stage-1:MAPRED] in serial mode
INFO  : Session is already open
INFO  : Dag name: -- Ensure there are 1...orrected_netflix2023(Stage-1)
INFO  : Setting tez.task.scale.memory.reserve-fraction to 0.30000001192092896
INFO  : Status: Running (Executing on YARN cluster with App id application_1634527506680_1335)

INFO  : Map 1: 0/1	Map 3: 0/1	Reducer 2: 0/1	
INFO  : Map 1: 0/1	Map 3: 0(+1)/1	Reducer 2: 0/1
INFO  : Map 1: 0(+1)/1	Map 3: 0(+1)/1	Reducer 2: 0/1	
INFO  : Map 1: 0(+1)/1	Map 3: 1/1	Reducer 2: 0/1
INFO  : Map 1: 1/1	Map 3: 1/1	Reducer 2: 0/1	
INFO  : Map 1: 1/1	Map 3: 1/1	Reducer 2: 0(+1)/1	
INFO  : Map 1: 1/1	Map 3: 1/1	Reducer 2: 1/1	
INFO  : Completed executing command(queryId=hive_20240327071708_f79e47e9-0d8f-4b5f-a100-ba78933f22f8); Time taken: 6.297 seconds
INFO  : OK
                _c0
                _c0
              118739
              Snippet icon

              My Snippet
              4.64s
              Database
              Type

              -- Preview for quality control
              SELECT * FROM student.corrected_netflix2023
              XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
                × 
                INFO  : Compiling command(queryId=hive_20240327071719_feea5644-d6f3-4361-9434-b8f9136aac20): -- Preview for quality control
SELECT * FROM student.corrected_netflix2023
INFO  : Semantic Analysis Completed
INFO  : Returning Hive schema: Schema(fieldSchemas:[FieldSchema(name:corrected_netflix2023.title, type:string, comment:null), FieldSchema(name:corrected_netflix2023.availableglobally, type:string, comment:null), FieldSchema(name:corrected_netflix2023.releasedate, type:string, comment:null), FieldSchema(name:corrected_netflix2023.hoursviewed, type:bigint, comment:null), FieldSchema(name:corrected_netflix2023.numberofratings, type:bigint, comment:null), FieldSchema(name:corrected_netflix2023.rating, type:double, comment:null), FieldSchema(name:corrected_netflix2023.genre, type:string, comment:null), FieldSchema(name:corrected_netflix2023.keywords, type:string, comment:null), FieldSchema(name:corrected_netflix2023.description, type:string, comment:null)], properties:null)
INFO  : Completed compiling command(queryId=hive_20240327071719_feea5644-d6f3-4361-9434-b8f9136aac20); Time taken: 0.038 seconds
INFO  : Concurrency mode is disabled, not creating a lock manager
INFO  : Executing command(queryId=hive_20240327071719_feea5644-d6f3-4361-9434-b8f9136aac20): -- Preview for quality control
SELECT * FROM student.corrected_netflix2023
INFO  : Query ID = hive_20240327071719_feea5644-d6f3-4361-9434-b8f9136aac20
INFO  : Total jobs = 1
INFO  : Launching Job 1 out of 1
INFO  : Starting task [Stage-1:MAPRED] in serial mode
INFO  : Session is already open
INFO  : Dag name: -- Preview for qualit...orrected_netflix2023(Stage-1)
INFO  : Setting tez.task.scale.memory.reserve-fraction to 0.30000001192092896
INFO  : Status: Running (Executing on YARN cluster with App id application_1634527506680_1335)

INFO  : Map 1: 0/1	Map 2: 0(+1)/1	
INFO  : Map 1: 0(+1)/1	Map 2: 1/1
INFO  : Map 1: 1/1	Map 2: 1/1	
INFO  : Completed executing command(queryId=hive_20240327071719_feea5644-d6f3-4361-9434-b8f9136aac20); Time taken: 2.5 seconds
INFO  : OK
                  corrected_netflix2023.title corrected_netflix2023.availableglobally corrected_netflix2023.releasedate corrected_netflix2023.hoursviewed corrected_netflix2023.numberofratings corrected_netflix2023.rating corrected_netflix2023.genre corrected_netflix2023.keywords corrected_netflix2023.description
                  corrected_netflix2023.title corrected_netflix2023.availableglobally corrected_netflix2023.releasedate corrected_netflix2023.hoursviewed corrected_netflix2023.numberofratings corrected_netflix2023.rating corrected_netflix2023.genre corrected_netflix2023.keywords corrected_netflix2023.description
                1Queen Charlotte: A Bridgerton StoryYes2023-05-04503000000456246.6Comedy - Romancetelenovela -drug trafficking -cartel -femme fatale -kidnappingWhile fleeing from dangerous assailants - an assassin comes out of hiding to protect the daughter she left earlier in life.
                2Ginny & Georgia: Season 1Yes2021-02-24302100000441025.6Action - Thrillerclose up of eye -close up of eyes -close up of lips -close up of mouth -childAfter barely surviving his grievous wounds from his mission in Dhaka - Bangladesh - Tyler Rake is back - and his team is ready to take on their next mission.
                3The MotherYes2023-05-1224990000068.6Sportracial tension -black american -couple -love -lifeA young American woman from the Midwest is hired by a marketing firm in Paris to provide them with an American perspective on things.
                4The Diplomat: Season 1Yes2023-04-20214100000608535.5Comedy - Romancefriend -best friend -volunteer -teenager -sonVillain's kidnap a man's daughter in order to get hold of forgery plates which they believe to be in his possession.
                5Luther: The Fallen SunYes2023-03-102097000003007.3Romancefemale protagonist -character name in series title -american in france -location in series title -marketingFinding a ghost named Ernest haunting their new home turns Kevin's family into overnight social media sensations. But when Kevin and Ernest investigate the mystery of Ernest's past - they become a target of the CIA.
                6Fake Profile: Season 1 // Perfil falso: Temporada 1No2023-05-3120650000081658.7Drama - Horror - Thrilleralice character -caterpillar character -the mad hatter character -march hare character -dormouse characterA world -weary detective is hired to investigate the murder of a West Point cadet. Stymied by the cadets' code of silence - he enlists one of their own to help unravel the case  - a young man the world would come to know as Edgar All...
                7XO - Kitty: Season 1YesNULL200700000496.8Comedyrapping animal -rapping penguin -talking animal -talking penguin -sequelShocking tragedies shatter a tight -knit South Carolina community and expose the horrifying secrets of its most powerful family.
                8Outer Banks: Season 1Yes2020-04-151840000001192466.2Adventure - Family - Fantasybritish noir -b movie -forgery plates -kidnapping a girl -rape and sexual intercourse innuendoThe last of a two -part film centered on the life and career of John Reed - the revolutionary communist journalist.
                9Sweet Tooth: Season 2Yes2023-04-27182300000744037.5Action - Crime - Dramaghost -house -chase -car chase -based on short storyTeresa Mendoza returns to Mexico after 8 years to fight with Mexican drug dealers.
                10Perfect Match: Season 1Yes2023-02-141768000003446Adventure - Comedy - Familymurder -year 1830 -1830s -19th century -winterHiding a mysterious past - a mother lives like a nameless fugitive with her daughter as they make hotels their home and see everyone else as a threat.
                11The Marked Heart: Season 2 // Pálpito: Temporada 2Yes2023-04-19174300000118698.4Shortyear 2014 -malaysia airlines flight mh370 -airplane -investigation -missingThree young women join the newly formed Women's Army Corps (WACS) for varied reasons - and make contributions to the war effort.
                12Murder Mystery 2Yes2023-03-311736000004196617.1Comedy - Crime - Dramajunk man -junk -junk wagon -city dump -landfillDocuseries following the FIA Formula One World Championship across multiple seasons.
                13Pablo Escobar - el patrón del mal: Season 1YesNULL168300000279.4Shortprom -sex comedy -female masturbation -female nudity -teen sex comedyTyler Rake - a fearless black market mercenary - embarks on the most deadly extraction of his career when he's enlisted to rescue the kidnapped son of an imprisoned international crime lord.
                14Never Have I Ever: Season 4Yes2023-02-10163000000155.6Animation - Shortmagic -power -young woman -soldier -gay manThings go badly for a hack director and film crew shooting a low budget zombie movie in an abandoned WWII Japanese facility - when they are attacked by real zombies.
                15Your Place or MineYes2022-12-21161100000189.4Drama - Fantasy - Horrorparody comedy -spoofIn the wake of King Edward's death - Uhtred of Bebbanburg and his comrades adventure across a fractured kingdom in the hopes of uniting England at last.
                16Chiquititas (2013)No2022-12-221576000001205.9Crime - Dramaspaghetti western -italo western -eastern -ostern -second partSonic in a high -octane adventure where the fate of a strange new multiverse rests in his gloved hands.
                17Alchemy of Souls: Part 1 // 환혼: 파트 1Yes2022-10-191521000003827.1Documentary - Shortcontroversy -girl -laundry drying on a clothesline -african american -clotheslineA German youth eagerly enters World War I - but his enthusiasm wanes as he gets a firsthand view of the horror.
                18Outer Banks: Season 2Yes2021-06-101514000001183526.6Crime - Horror - Mysterypsychological drama -overprotective mother -cult -hotel -peer pressureAn unusual and touching bond develops when grieving Oona reaches out to a mysterious homeless man - offering him a place to stay in her garden shed.
                19Til Money Do Us Part: Season 1 // Hasta que la plata nos separe: Temporada 1No2023-04-26148600000132166.1Documentarywacs -war widow -war game -male female relationship -father daughter relationshipA single mother who is a renowned hired killer finds it difficult to achieve a balance between her personal and work life.
                20Mr. Queen // 철인왕후No2022-12-1614690000068.6Sportf1 -formula 1 -motor sports -car race -championshipAn Interpol agent successfully tracks down the world's most wanted art thief with help from a rival thief. But nothing is as it seems as a series of double -crosses ensues.
                21Manifest: Season 1No2023-06-0914670000079Short - Dramayear in title -2000s -number in titleThale (17) has just moved with her parents to a small town after her mother has a new job in the local police. After a student is killed brutally at a party Thale attends - she becomes a key witness. Was the killer an animal? A wolf?
                22PAW Patrol: Season 6No2023-03-0114010000098356.8Documentary - Crimedrug dealers -kidnapping -child kidnapping -shot in the head -bangladeshApril 1940. The eyes of the world are on Narvik - a small town in northern Norway - source of the iron ore needed for Hitler's war machinery. Through two months of fierce winter warfare - Hitler is dealt his first defeat.
                23The Good Bad Mother: Limited Series // 나쁜엄마: 리미티드 시리즈Yes2023-06-151399000007407.6Drama - Romancesingle take -zombie -film crew -television broadcast -rooftopWhen a young girl stows away on the ship of a legendary sea monster hunter - they launch an epic journey into uncharted waters  - and make history to boot.
                24The Recruit: Season 1Yes2023-02-221393000004268807Comedylawyer -19th century -legal -legal drama -legal battleAn orphaned boy enrolls in a school of wizardry - where he learns the truth about himself - his family and the terrible evil that haunts the magical world.
                25Bloodhounds: Season 1 // 사냥개들: 시즌 1Yes2020-12-251366000001113617.6Action - Adventure - Dramaanglo saxon -kingdom -exploration -warrior -epicBased on the true story of a father and son who repair their fractured relationship during a forced hike of the Appalachian trail to find their beloved lost dog.
                26Glass Onion: A Knives Out MysteryYes2020-05-311362000001556.4Documentaryfast -based on video game -sonic the hedgehog -anthropomorphic animal -sonic the hedgehog characterCharlie Brandis leads a quiet and uneventful life as a wallflower. His parents trust him - his friends like him - girls are indifferent toward him. Then there's the girl he's watched from afar - Annie Briggs - who doesn't even know he...
                27Black Mirror: Season 6Yes2023-01-271348000001024.9Horrorlawyer -spin off -psychosomatic illness -criminal lawyer -drug tradeIt follows the rise and fall of the American financier and ponzi schemer: Madoff.
                28Triptych: Season 1 // Tríada: Temporada 1Yes2022-05-271336000001746.4Drama - Western1910s -anti war -shell shock -ptsd post traumatic stress disorder -depressionShort documentary about making the second season of The Witcher (2019).
                29Bridgerton: Season 1Yes2022-03-25133400000145919.2Drama - Horror -  SciFititle co written by female -title co directed by female -f ratedCharlie Brandis leads a quiet and uneventful life as a wallflower. His parents trust him - his friends like him - girls are indifferent toward him. Then there's the girl he's watched from afar - Annie Briggs - who doesn't even know he...
                30The Marked Heart: Season 1 // Pálpito: Temporada 1Yes2023-01-06120500000483708.5Documentary - Sportnon fictionMarion and Jack try to rekindle their relationship with a visit to Paris - home of Marion's parents  - - and several of her ex -boyfriends.
                31Little Angel: Volume 1Yes2023-04-281200000001631916Action - Comedy - Crimehomosexual -gay serial killer -murder -serial killer -homosexualityIn celebration of Season 2 being released soon - the Glitch Productions team put all of Season 1 into a single movie to watch in one go.
                32PAW Patrol: Season 5No2023-03-081189000002511.6Short - Comedynorwegian army -nazi invasion of norway -winter -year 1940 -man in uniformThree young women looking for adventure get jobs on a dude ranch.
                33Sex/Life: Season 1Yes2023-05-1911580000070577.4Biography - Crime - Dramacgi animation -bounty hunter -alien -danger -laser gunThe trials and tribulations of criminal lawyer Jimmy McGill in the years leading up to his fateful run -in with Walter White and Jesse Pinkman.
                34We Have a GhostYes2023-01-27113600000331796.9Action - Drama - Historydog -search -find -journey -fatherAn executive goes through an unexpected breakup - then accepting an assignment to go undercover and learn about the tourist industry in Vietnam.
                35Crash Landing on You: Season 1 // 사랑의 불시착: 시즌 1Yes2017-10-03102800000667508.1Drama - Wartv specialA lawyer defending a wealthy man begins to believe his client is guilty of more than just one crime.
                36MH370: The Plane That Disappeared: Limited SeriesYes2021-12-2910170000068865.5Action - Adventure - Dramaparty -teenager -sex comedyFollows the tragedy in which terrorists detonated a bomb at the Boston Marathon's finish line; they carried out the attack by placing two homemade pressure -cooker bombs that resulted in three fatalities and numerous injuries.
                37Breaking Bad: Season 2No2022-11-23990000002595.9Short - Biography - DramaprisonIm Hwa Ryeong - a prickly - sensitive and hot -tempered queen - tries to turn her trouble making princes into proper crown princes.
                38Lockwood & Co.: Season 1Yes2022-12-2197800000267.1Comedy - Talk -Showparty -teenager -sex comedyIt's 1940's Australia and siblings Maggie and Charles must endure taunts of newly enlisted teenagers - grapple with the fact that neither of them can fight in the war and resort to chess in order to pass the time.
                39You: Season 3Yes2022-05-209760000078.7Animation - Comedy -  SciFifemale full frontal nudity -female nudity -female frontal nudity -sex scene -country in titleElliott - a young fisherman with an extraordinary voice - gets the chance of a lifetime when high -profile music manager Suzanne discovers him at a party.
                40Breaking Bad: Season 5No2023-01-19951000008407.5Dramafrench -vacation -europe -chest hair -male nudityElliott - a young fisherman with an extraordinary voice - gets the chance of a lifetime when high -profile music manager Suzanne discovers him at a party.
                41Welcome to Eden: Season 2 // Bienvenidos a Edén: Temporada 2Yes2022-01-28946000002379.3Documentary - Shortbikini -women -young -f rated -best friendThe relationship of a well -known journalist and a down -to -earth teacher goes through hard times when she takes a new job.
                42CoComelon: Season 2No2023-03-2492900000437824.9Action - Adventure - Dramalawyer -spin off -psychosomatic illness -criminal lawyer -drug tradeIn spite of their many differences - Cassie - a struggling singer -songwriter - and Luke - a troubled Marine - agree to marry solely for military benefits - but when tragedy strikes - the line between real and pretend begins to blur.
                43The Blacklist: Season 1No2020-12-10922000001484057.9Biography - Crime - Dramamale nudity -quirky comedy -love -island -escapeTwo rival newsreel photographers join forces to find an aviatrix's missing brother - who has disappeared in the Amazon rainforest.
                44Shadow and Bone: Season 1Yes2021-05-3191400000161096.6Drama - History - Waranglo saxon -kingdom -exploration -warrior -epicA woman's life is turned upside -down when a dangerous man gets hold of her lost cell phone and uses it to track her every move.
                45The Unbroken Voice: Season 1 // Canto para no llorar - Arelys Henao: Temporada 1NoNULL91200000559797Animation - Adventure - ComedybusinessmanA woman's life is turned upside -down when a dangerous man gets hold of her lost cell phone and uses it to track her every move.
                46Demon Slayer: Kimetsu no Yaiba: Tanjiro Kamado - Unwavering Resolve Arc // 鬼滅の刃: 竈門炭治郎 立志編NoNULL87200000496.8Comedyboston marathon -boston marathon bombing -bomb -year 2013 -manhuntTen gorgeous singles meet in a tropical paradise. Little do they know that to win the EUR200 -000 prize - they'll have to completely give up sex.
                47That 90s Show: Part 1YesNULL86100000566.3Short - Horrorcharacter name as title -22nd century -future -robot -futuristicThis docuseries examining the chilling true stories of four Korean leaders claiming to be prophets exposes the dark side of unquestioning belief.
                48You: Season 2Yes2017-08-3186100000118698.4Shortschool -hero -academy -master -witchThe trials and tribulations of criminal lawyer Jimmy McGill in the years leading up to his fateful run -in with Walter White and Jesse Pinkman.
                49All of Us Are Dead: Season 1 // 지금 우리 학교는: 시즌 1Yes2022-11-15854000001936.6Documentarydonghua -chinese animation -chinese anime -team sports -basketballThis shocking documentary chronicles a happy -go -lucky nomad's ascent to viral stardom and the steep downward spiral that resulted in his imprisonment.
                50Black Knight: Season 1 // 택배기사: 시즌 1Yes2023-04-1384600000496.8Comedyperformer -fisherman -song -life -managerA quirky - dysfunctional family's road trip is upended when they find themselves in the middle of the robot apocalypse and suddenly become humanity's unlikeliest last hope.
                51Breaking Bad: Season 4No2017-07-2384400000118698.4Shortperformer -fisherman -song -life -managerDeep in the Dovre mountain - something gigantic wakes up after a thousand years in captivity. The creature destroys everything in its path and quickly approaches Oslo.
                52Obsession: Limited SeriesYes2023-01-208360000089536.8Adventure - Biography - Dramaeducational film -world war two -enemy -japanese soldier -training filmComedian Chris Rock performs a live stand -up special in Baltimore - Maryland.
                53Who Were We Running From?: Limited Series // Biz Kimden Kaçıyorduk Anne?: Mini DiziYes2023-04-1483200000111884.8Drama - Thrillerheist -robbery -spain -mint -professorAfter finding out their babies were switched at birth - two women develop a plan to adjust to their new lives creating a single and very peculiar family.
                54Hunger // คนหิว เกมกระหายYes2021-10-14825000002379.3Documentary - Shortjournalist -teacherWhat if everything we know about prehistory is wrong? Journalist Graham Hancock visits archaeological sites around the world investigating if a civilization far more advanced than we ever believed possible existed thousands of yea...
                55Alice in Borderland: Season 1 // 今際の国のアリス: シーズン1Yes2022-12-1582100000118698.4Shortanglo saxon -kingdom -exploration -warrior -epicFramed for a corporate crime - an adult Ted Templeton turns back into the Boss Baby to live undercover with his brother - Tim - posing as one of his kids.
                56CoComelon: Season 3No2021-08-218170000010927.8Dramasequel -second part -wattpad -love -windowSpinoff of Bling Empire (2021) series. Follows a group of humorous - sophisticated and rich Asian -Americans from New York City.
                57Firefly Lane: Season 1Yes2022-12-2581000000875.7Short - Comedy - Musicallove -singer -heart -two word title -color in titleA travelling monk and his followers find themselves trapped in a land inhabited by only women.
                58S.W.A.T. (2017): Season 1No2022-12-3078200000331796.9Action - Drama - Historyplot twist -criminal -smartphone -worker -cell phoneFood competition that follows the country's best backyard smokers and competitive barbecuers as they compete for the title of American Barbecue Champion.
                59Murder MysteryYes2023-01-2678200000368.5Animation - Action - Adventureplot twist -criminal -smartphone -worker -cell phoneRaquel's longtime crush on her next -door neighbor turns into something more when he starts developing feelings for her - despite his family's objections.
                60True Beauty // 여신강림No2023-04-07778000003265.7Comedy - Dramaactress -love -reference to pamela anderson -personal -careerWhen a psychiatrist shelters a mysterious cult escapee - her world is turned upside down as the girl's arrival threatens to tear her own family apart.
                61Squid Game: Season 1 // 오징어 게임: 시즌 1Yes2016-07-1577800000167064.7Action - Comedy - Dramaairplane -heist -diamond -accident -planeThis Sportscope short features the sport of kayaking. Participants ride river rapids and show their skill as they maneuver through special 'water slalom' courses.
                62Little Angel: Volume 2Yes2022-11-0976300000167084.7Action - Comedy - Dramaanthology -midnight -horrific -horrifying -sinisterHospital Playlist tells the story of five doctors who have been friends since they entered medical school in 1999.
                63Manifest: Season 2No2023-03-03757000006201439Crime - Dramalawyer -spin off -psychosomatic illness -criminal lawyer -drug tradeWhen a stay -at -home dad who dedicates all his time to his children is persuaded to take time off for himself - he gets mixed up in the wild shenanigans of his childhood friend who's celebrating his 44th birthday.
                64ExtractionYes2020-08-2175200000118698.4Shortanglo saxon -kingdom -exploration -warrior -epicThe Seven Deadly Sins travel to the Sky Temple in search of an elusive ingredient.
                65Lucifer: Season 1No2021-01-05716000003656.3Animation - Short - Horrordonghua -chinese animation -chinese anime -team sports -basketballMatchmaker Sima Taparia guides clients in the U.S. and India in the arranged marriage process - offering an inside look at the custom in a modern era.
                66The Last Kingdom: Seven Kings Must DieYes2023-03-046950000054005.8Thrilleryacht -the future -ensemble cast -italy -florence italyAfter the death of their father - two half -brothers find themselves on opposite sides of an escalating conflict with tragic consequences.
                67CoComelon: Season 4No2021-08-28690000002505327.3Crime - Drama - Mysterysnake -crocodile -spider -koala -animalAfter accidentally crash -landing in 2022 - time -traveling fighter pilot Adam Reed teams up with his 12 -year -old self for a mission to save the future.
                68Sam & Cat: Season 1No2023-03-316850000091097Adventure - Comedy - Familyrobot -road trip -dog -furby -family road tripA policeman and his doctor wife have some marriage problems and the son blames the mother. For his job - the policeman investigates a case of a missing boy. The possible kidnapping looks like some cases from a few years ago.
                69Gilmore Girls: Season 1Yes2021-11-126810000052866.5Animation - Adventure - Comedyolder woman younger man sex -deep cleavage -large breasts -female topless nudity -cuckolded husbandA live -action adaptation of Nickelodeon's Winx Club (2004). It follows Bloom as she adjusts to life in the Otherworld - where she must learn to control her dangerous magical powers.
                70Sonic Prime: Season 1Yes2021-11-176780000062827.5Documentary - Crime - Historyfather daughter relationship -mountain -oslo norway -folklore -mythological creatureAn astronaut's return after a 30 -year disappearance rekindles a lost love and sparks interest from a corporation determined to learn why he hasn't aged.
                71Manifest: Season 3No2017-09-2867200000102065.5Action - Adventure - Dramamotherhood -switched at birth -female female kiss -female nudity -female friendsWhen an army of powerful alien beings is unleashed on Earth threatening life as we know it - a brand -new team of Power Rangers - fueled by the prehistoric power of the dinosaurs - are recruited to deal with the threat.
                72Roald Dahls Matilda The MusicalYesNULL67000000343275.9Action - Comedy - Dramacardboard cutout -watching tv -watching oneself on tv -boxer -boxingThe world's deadliest assassin and New York's biggest screw -up are mistaken for each other at an Airbnb rental.
                73Sing (2016)No2022-12-136590000023968.5Comedy - Dramaworld -civilizationFrom war -torn Syria to the 2016 Rio Olympics - two young sisters embark on a harrowing journey as refugees - putting both their hearts and champion swimming skills to heroic use.
                74Hajime no Ippo: The Fighting!: Season 1 // はじめの一歩: シーズン1No2022-12-2565300000148.4Animation - Drama - Sportsequel -sequel to tv series -based on film -baby -bossFrom war -torn Syria to the 2016 Rio Olympics - two young sisters embark on a harrowing journey as refugees - putting both their hearts and champion swimming skills to heroic use.
                75Sky High: The Series: Season 1 // Hasta el cielo: La serie: Season 1Yes2022-10-136440000066766.4Drama - Music - Romancemonk -journey to the west -sun wukong the monkey king character -3d -3 dimensionalA propaganda film - made in the early months of World War II - dramatizing a new group of U.S. Army Air Force pilots receiving their wings from Lt. General H.H. Arnold: on off -screen narrator introduces four of them to us - we see th...
                76Record of Ragnarok: Season 2 // 終末のワルキューレ: シーズン２No2022-09-2163500000265.9Short - Documentaryrepeat sequel -question mark in title -returning character killed off -writer director -sequelKyra Berry - a 14 year old USA gymnast arrives in Australia to try and win a scholarship at an elite Gymnastics Academy. It's a second chance but also her last.
                77Paw Patrol: The MovieNo2022-12-08620000003516.5DramarevengeIvan Locke - a dedicated family man and successful construction manager - receives a phone call on the eve of the biggest challenge of his career that sets in motion a series of events that threaten his carefully cultivated existence.
                78Next in Fashion: Season 2Yes2021-10-0161500000118698.4Shortwattpad -love -young -teenage girl -teenage boyAfter the murder of his parents when he was a little kid - Mexican Miguel Garza is sent away to Japan. 20 years later - he has to go back to his home country as the new heir of his family's cartel.
                79Gilmore Girls: Season 2Yes2014-09-3061100000288.1Shortcult -psychiatrist -tv mini series -devil -location in titleAfter her mother disappears - Clary must venture into the dark world of demon hunting - and embrace her new role among the Shadowhunters.
                80Treason: Limited SeriesYes2023-02-1560600000484356.7Drama - Music - Romancedoctor -medical -friendship -colleagues -workAfter moving his family into his childhood home - a man's investigation into a local factory accident connected to his father unveils dark family secrets.
                81TrollsNo2023-03-2960100000331796.9Action - Drama - Historymale nudity -male star appears nude -father -life -friendAfter moving his family into his childhood home - a man's investigation into a local factory accident connected to his father unveils dark family secrets.
                82Blood Ties: Season 1 // Las Villamizar: Season 1No2015-08-285990000071467Documentary - Crimebased on manga -based on anime -holy knight -based on tv series -based on anime seriesThe Seven Deadly Sins travel to the Sky Temple in search of an elusive ingredient.
                83Money Heist: Part 1 // La casa de papel: Parte 1Yes2023-01-045960000016486.6Adventure - Comedy - Dramaboat -village -fishing -yorkshire england -lifeboatA look at the 2001 Seattle Mariners who tied Major League Baseballs modern day record for most wins in a season with 116.
                84Rough Diamonds: Season 1Yes2021-12-17593000006847.6Documentary - Shortspongebob squarepants character -sequel -king -kidnapping -friendshipThis RKO -Pathe short film promotes the need for cooperation and neighborliness in the event of a nuclear disaster and associated civil defense procedures. After preaching the power of modern (for 1956) atomic weapons - civil defens...
                85The Walking Dead: Season 10No2022-08-125830000090116.4Crime - Drama - Mysteryottoman empire -christian -constantinople turkey -medieval times -1400sAn unhappily married aristocrat begins a torrid affair with the gamekeeper on her husband's country estate.
                86The Witcher: Season 1Yes2022-07-2258300000122557.2Documentary - Biographycinepanettone -politician -ancient romeTwo inseparable friends move to Kyoto to chase their dreams of becoming maiko - but decide to pursue different passions while living under the same roof.
                87The Walking Dead: Season 3No2022-08-055670000078.7Animation - Comedy -  SciFidetective -runaway -fight -teenager -teenage girlA storm rages. A young girl is kidnapped. Her mother teams up with the mysterious woman next door to pursue the kidnapper - a journey that tests their limits and exposes shocking secrets from their pasts.
                88Gilmore Girls: Season 3Yes2022-03-31566000006201439Crime - Dramaheist -robbery -spain -mint -professorA young girl discovers a secret map to the dreamworld of Slumberland - and with the help of an eccentric outlaw - she traverses dreams and flees nightmares - with the hope that she will be able to see her late father again.
                89Red Rose: Season 1Yes2023-02-2256500000331796.9Action - Drama - Historylove -life -airport -flight -coupleGirl Luba had a dream. She - the daughter of a chess king in a fairyland - became the victim of a cunning political conspiracy of two cards  - the Peak Jack Krivello and the Cross Dame Dvuliche.
                90Gilmore Girls: Season 5Yes2023-02-14552000008107.1Documentary - Crimebased on series of novels -based on tv series -innocent cop framed -ex cop -police corruptionA live -action adaptation of Nickelodeon's Winx Club (2004). It follows Bloom as she adjusts to life in the Otherworld - where she must learn to control her dangerous magical powers.
                91Stranger Things 2Yes2023-05-2453800000646.6Short - Sportdigoxin -mercy -murder of wife -old woman -hospitalWith a little help from his brother and accomplice - Tim - Boss Baby tries to balance family life with his job at Baby Corp headquarters.
                92Red NoticeYes2022-05-0653000000357.6Short - Comedydinosaur adventure -superhero -superhero team -morphing -villainA young man is magically turned a merman - and discovers his underwater origins - after he comes in contact with the magic waters at the mysterious Mako Island guarded by a trio of mermaids.
                93Henry Danger: Season 1No2020-12-25528000002379.3Documentary - Shortmistaken identity -buddy comedy -toronto ontario canada -evil woman -final showdownA family of former child heroes - now grown apart - must reunite to continue to protect the world.
                94The Queen of Flow: Season 2 // La reina del flow: Temporada 2No2020-12-25528000008874.6Action - Comedynonlinear timeline -criminal as protagonist -killer as protagonist -gangster -truck driverSawako Kuronuma is misunderstood due to her resemblance to the ghost girl from The Ring. But one day the nicest boy in the class - Kazehaya befriends her and everything changed after that and also everyone perspective of Sawako but...
                95The Walking Dead: Season 2No2023-06-0752500000569.3Talk -Showboy -city -career -boat -olympianAfter failing to find success on Broadway - April returns to her hometown and reluctantly is recruited to train a misfit group of young dancers for a big competition.
                96Designated Survivor: Season 2Yes2021-10-11524000001037.3Short - Drama - Thrillerspaceship -robot -alien -sabotage -scientistIt follows singles in the US and Israel as they turn their dating life over to a top Jewish matchmaker.
                97Bebefinn: Season 1Yes2023-05-1052200000569.3Talk -Showpatriotism -military pilot -u.s. military -propaganda -u.s. army air corpsA team of rapid -fire renovators takes big risks and makes painstaking plans to transform families' homes from top to bottom in just 12 hours.
                98Hotel Transylvania 2No2018-10-2452000000474695.8Action - Adventure - Dramanon fictionTwo meddling grannies trick their adult grandkids into a meet -cute that reignites a childhood crush and old grudges.
                99Stranger Things 3Yes2022-11-1751800000111146.8Comedyprequel -zombie -safe -apocalypse -heistA high school student is forced to confront her secret crush at a kissing booth.
                100Outlander: Season 1No2020-06-20518000002379.3Documentary - Shortbased on film -spin off -gymnastic -athlete -eliteI AM NO ONE is a documentary film written - directed - and edited by Jason Hoover about a man named Charles Lake who moonlights as a serial killer in Chicago - IL.
                Snippet icon

                My Snippet
                2.94s
                Database
                Type

                -- Drop for retesting purposes
                DROP TABLE student.netflix2023
                XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
                  × 
                  INFO  : Compiling command(queryId=hive_20240327071726_ff01dd9a-b070-4c6c-8baf-9fe7d64d4e06): -- Drop for retesting purposes
DROP TABLE student.netflix2023
INFO  : Semantic Analysis Completed
INFO  : Returning Hive schema: Schema(fieldSchemas:null, properties:null)
INFO  : Completed compiling command(queryId=hive_20240327071726_ff01dd9a-b070-4c6c-8baf-9fe7d64d4e06); Time taken: 0.005 seconds
INFO  : Concurrency mode is disabled, not creating a lock manager
INFO  : Executing command(queryId=hive_20240327071726_ff01dd9a-b070-4c6c-8baf-9fe7d64d4e06): -- Drop for retesting purposes
DROP TABLE student.netflix2023
INFO  : Starting task [Stage-0:DDL] in serial mode
INFO  : Completed executing command(queryId=hive_20240327071726_ff01dd9a-b070-4c6c-8baf-9fe7d64d4e06); Time taken: 0.025 seconds
INFO  : OK
                   
                   Success.
                  Snippet icon

                  My Snippet
                  11.21s
                  Database
                  Type

                  -- TEXT CLEANUP
                  /* Finally, create a cleanedup version optimized for later analysis
                  Text Cleanup: cleaned up keywords and description a bit.
                  Removed rows with invalid dates */
                  CREATE TABLE student.netflix2023 AS
                  SELECT
                  title,
                  availableglobally,
                  TO_DATE(releasedate) AS `releasedate`,
                  hoursviewed,
                  numberofratings,
                  rating,
                  genre,
                  REPLACE(keywords, ' -', ', ') as keywords,
                  REPLACE(REPLACE(REPLACE(description, ' - ', ', '), ' -', '-'), ''', '\'') as description
                  FROM
                  corrected_netflix2023
                  WHERE
                  TO_DATE(releasedate) LIKE "20%" -- Necessary, as will be seen later
                  XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
                    × 
                    INFO  : Compiling command(queryId=hive_20240327071731_bd8949f7-938e-4ef8-b2ec-8e644a2928e1): -- TEXT CLEANUP
/* Finally, create a cleanedup version optimized for later analysis
Text Cleanup: cleaned up keywords and description a bit.
Removed rows with invalid dates */
CREATE TABLE student.netflix2023 AS
SELECT
    title,
    availableglobally,
    TO_DATE(releasedate) AS `releasedate`,
    hoursviewed,
    numberofratings,
    rating,
    genre,
    REPLACE(keywords, ' -', ', ') as keywords,  
    REPLACE(REPLACE(REPLACE(description, ' - ', ', '), ' -', '-'), ''', '\'') as description
FROM
    corrected_netflix2023
WHERE
    TO_DATE(releasedate) LIKE "20%" -- Necessary, as can be seen later
INFO  : Semantic Analysis Completed
INFO  : Returning Hive schema: Schema(fieldSchemas:[FieldSchema(name:title, type:string, comment:null), FieldSchema(name:availableglobally, type:string, comment:null), FieldSchema(name:releasedate, type:date, comment:null), FieldSchema(name:hoursviewed, type:bigint, comment:null), FieldSchema(name:numberofratings, type:bigint, comment:null), FieldSchema(name:rating, type:double, comment:null), FieldSchema(name:genre, type:string, comment:null), FieldSchema(name:keywords, type:string, comment:null), FieldSchema(name:description, type:string, comment:null)], properties:null)
INFO  : Completed compiling command(queryId=hive_20240327071731_bd8949f7-938e-4ef8-b2ec-8e644a2928e1); Time taken: 0.048 seconds
INFO  : Concurrency mode is disabled, not creating a lock manager
INFO  : Executing command(queryId=hive_20240327071731_bd8949f7-938e-4ef8-b2ec-8e644a2928e1): -- TEXT CLEANUP
/* Finally, create a cleanedup version optimized for later analysis
Text Cleanup: cleaned up keywords and description a bit.
Removed rows with invalid dates */
CREATE TABLE student.netflix2023 AS
SELECT
    title,
    availableglobally,
    TO_DATE(releasedate) AS `releasedate`,
    hoursviewed,
    numberofratings,
    rating,
    genre,
    REPLACE(keywords, ' -', ', ') as keywords,  
    REPLACE(REPLACE(REPLACE(description, ' - ', ', '), ' -', '-'), ''', '\'') as description
FROM
    corrected_netflix2023
WHERE
    TO_DATE(releasedate) LIKE "20%" -- Necessary, as can be seen later
INFO  : Query ID = hive_20240327071731_bd8949f7-938e-4ef8-b2ec-8e644a2928e1
INFO  : Total jobs = 1
INFO  : Launching Job 1 out of 1
INFO  : Starting task [Stage-1:MAPRED] in serial mode
INFO  : Session is already open
INFO  : Dag name: -- TEXT CLEANUP
/* Finally, create a...later(Stage-1)
INFO  : Setting tez.task.scale.memory.reserve-fraction to 0.30000001192092896
INFO  : Status: Running (Executing on YARN cluster with App id application_1634527506680_1335)

INFO  : Map 1: 0/1	Map 2: 0/1
INFO  : Map 1: 0/1	Map 2: 0(+1)/1
INFO  : Map 1: 0(+1)/1	Map 2: 0(+1)/1	
INFO  : Map 1: 0(+1)/1	Map 2: 1/1
INFO  : Map 1: 0(+1)/1	Map 2: 1/1	
INFO  : Map 1: 1/1	Map 2: 1/1	
INFO  : Starting task [Stage-2:DEPENDENCY_COLLECTION] in serial mode
INFO  : Starting task [Stage-0:MOVE] in serial mode
INFO  : Moving data to directory hdfs://ip-172-31-2-172.us-west-1.compute.internal:8020/user/hive/warehouse/student.db/netflix2023 from hdfs://ip-172-31-2-172.us-west-1.compute.internal:8020/user/hive/warehouse/student.db/.hive-staging_hive_2024-03-27_07-17-31_639_2411937544670865735-4/-ext-10002
INFO  : Starting task [Stage-4:DDL] in serial mode
INFO  : Starting task [Stage-3:STATS] in serial mode
INFO  : Completed executing command(queryId=hive_20240327071731_bd8949f7-938e-4ef8-b2ec-8e644a2928e1); Time taken: 8.02 seconds
INFO  : OK
                     
                     Success.
                    Snippet icon

                    My Snippet
                    2.64s
                    Database
                    Type

                    -- COLUMN DATA TYPES
                    DESCRIBE student.netflix2023
                    XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
                      × 
                      INFO  : Compiling command(queryId=hive_20240327071748_2df8f11c-6223-4d28-badd-c25fc64310ea): -- COLUMN DATA TYPES
DESCRIBE student.netflix2023
INFO  : Semantic Analysis Completed
INFO  : Returning Hive schema: Schema(fieldSchemas:[FieldSchema(name:col_name, type:string, comment:from deserializer), FieldSchema(name:data_type, type:string, comment:from deserializer), FieldSchema(name:comment, type:string, comment:from deserializer)], properties:null)
INFO  : Completed compiling command(queryId=hive_20240327071748_2df8f11c-6223-4d28-badd-c25fc64310ea); Time taken: 0.005 seconds
INFO  : Concurrency mode is disabled, not creating a lock manager
INFO  : Executing command(queryId=hive_20240327071748_2df8f11c-6223-4d28-badd-c25fc64310ea): -- COLUMN DATA TYPES
DESCRIBE student.netflix2023
INFO  : Starting task [Stage-0:DDL] in serial mode
INFO  : Completed executing command(queryId=hive_20240327071748_2df8f11c-6223-4d28-badd-c25fc64310ea); Time taken: 0.004 seconds
INFO  : OK
                        col_name data_type comment
                        col_name data_type comment
                      1titlestring
                      2availablegloballystring
                      3releasedatedate
                      4hoursviewedbigint
                      5numberofratingsbigint
                      6ratingdouble
                      7genrestring
                      8keywordsstring
                      9descriptionstring
                      Snippet icon

                      My Snippet
                      12.32s
                      Database
                      Type

                      -- INSPECT NULL COUNT
                      -- Count null values and account for later if/when necessary
                      SELECT
                      SUM(CASE WHEN title IS NULL THEN 1 ELSE 0 END) AS null_titles,
                      SUM(CASE WHEN availableglobally IS NULL THEN 1 ELSE 0 END) AS null_availableglobally,
                      SUM(CASE WHEN releasedate IS NULL THEN 1 ELSE 0 END) AS null_releasedate,
                      SUM(CASE WHEN hoursviewed IS NULL THEN 1 ELSE 0 END) AS null_hoursviewed,
                      SUM(CASE WHEN numberofratings IS NULL THEN 1 ELSE 0 END) AS null_numberofratings,
                      SUM(CASE WHEN rating IS NULL THEN 1 ELSE 0 END) AS null_rating,
                      SUM(CASE WHEN genre IS NULL THEN 1 ELSE 0 END) AS null_genre,
                      SUM(CASE WHEN keywords IS NULL THEN 1 ELSE 0 END) AS null_keywords,
                      SUM(CASE WHEN description IS NULL THEN 1 ELSE 0 END) AS null_description
                      FROM
                      student.netflix2023;
                      XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
                        × 
                        INFO  : Compiling command(queryId=hive_20240327070839_862025f3-1df5-42ba-ba8c-fa9d18567330): -- INSPECT NULL COUNT
-- Count null values and account for later if/when necessary
SELECT
  SUM(CASE WHEN title IS NULL THEN 1 ELSE 0 END) AS null_titles,
  SUM(CASE WHEN availableglobally IS NULL THEN 1 ELSE 0 END) AS null_availableglobally,
  SUM(CASE WHEN releasedate IS NULL THEN 1 ELSE 0 END) AS null_releasedate,
  SUM(CASE WHEN hoursviewed IS NULL THEN 1 ELSE 0 END) AS null_hoursviewed,
  SUM(CASE WHEN numberofratings IS NULL THEN 1 ELSE 0 END) AS null_numberofratings,
  SUM(CASE WHEN rating IS NULL THEN 1 ELSE 0 END) AS null_rating,
  SUM(CASE WHEN genre IS NULL THEN 1 ELSE 0 END) AS null_genre,
  SUM(CASE WHEN keywords IS NULL THEN 1 ELSE 0 END) AS null_keywords,
  SUM(CASE WHEN description IS NULL THEN 1 ELSE 0 END) AS null_description
FROM
  student.netflix2023
INFO  : Semantic Analysis Completed
INFO  : Returning Hive schema: Schema(fieldSchemas:[FieldSchema(name:null_titles, type:bigint, comment:null), FieldSchema(name:null_availableglobally, type:bigint, comment:null), FieldSchema(name:null_releasedate, type:bigint, comment:null), FieldSchema(name:null_hoursviewed, type:bigint, comment:null), FieldSchema(name:null_numberofratings, type:bigint, comment:null), FieldSchema(name:null_rating, type:bigint, comment:null), FieldSchema(name:null_genre, type:bigint, comment:null), FieldSchema(name:null_keywords, type:bigint, comment:null), FieldSchema(name:null_description, type:bigint, comment:null)], properties:null)
INFO  : Completed compiling command(queryId=hive_20240327070839_862025f3-1df5-42ba-ba8c-fa9d18567330); Time taken: 0.054 seconds
INFO  : Concurrency mode is disabled, not creating a lock manager
INFO  : Executing command(queryId=hive_20240327070839_862025f3-1df5-42ba-ba8c-fa9d18567330): -- INSPECT NULL COUNT
-- Count null values and account for later if/when necessary
SELECT
  SUM(CASE WHEN title IS NULL THEN 1 ELSE 0 END) AS null_titles,
  SUM(CASE WHEN availableglobally IS NULL THEN 1 ELSE 0 END) AS null_availableglobally,
  SUM(CASE WHEN releasedate IS NULL THEN 1 ELSE 0 END) AS null_releasedate,
  SUM(CASE WHEN hoursviewed IS NULL THEN 1 ELSE 0 END) AS null_hoursviewed,
  SUM(CASE WHEN numberofratings IS NULL THEN 1 ELSE 0 END) AS null_numberofratings,
  SUM(CASE WHEN rating IS NULL THEN 1 ELSE 0 END) AS null_rating,
  SUM(CASE WHEN genre IS NULL THEN 1 ELSE 0 END) AS null_genre,
  SUM(CASE WHEN keywords IS NULL THEN 1 ELSE 0 END) AS null_keywords,
  SUM(CASE WHEN description IS NULL THEN 1 ELSE 0 END) AS null_description
FROM
  student.netflix2023
INFO  : Query ID = hive_20240327070839_862025f3-1df5-42ba-ba8c-fa9d18567330
INFO  : Total jobs = 1
INFO  : Launching Job 1 out of 1
INFO  : Starting task [Stage-1:MAPRED] in serial mode
INFO  : Session is already open
INFO  : Dag name: -- INSPECT NULL COUNT
...student.netflix2023(Stage-1)
INFO  : Tez session was closed. Reopening...
INFO  : Session re-established.
INFO  : Status: Running (Executing on YARN cluster with App id application_1634527506680_1335)

INFO  : Map 1: 0/1	Reducer 2: 0/1
INFO  : Map 1: 0(+1)/1	Reducer 2: 0/1
INFO  : Map 1: 1/1	Reducer 2: 0(+1)/1	
INFO  : Map 1: 1/1	Reducer 2: 1/1	
INFO  : Completed executing command(queryId=hive_20240327070839_862025f3-1df5-42ba-ba8c-fa9d18567330); Time taken: 9.718 seconds
INFO  : OK
                        columns (10)
                        int
                        null_titles bigint
                        null_availableglobally bigint
                        null_releasedate bigint
                        null_hoursviewed bigint
                        null_numberofratings bigint
                        null_rating bigint
                        null_genre bigint
                        null_keywords bigint
                        null_description bigint
                          null_titles null_availableglobally null_releasedate null_hoursviewed null_numberofratings null_rating null_genre null_keywords null_description
                          null_titles null_availableglobally null_releasedate null_hoursviewed null_numberofratings null_rating null_genre null_keywords null_description
                        1000044000
                        Snippet icon

                        My Snippet
                        3.44s
                        Database
                        Type

                        -- Drop for retesting purposes
                        DROP VIEW student.netflix2023_genre_exploded;
                        XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
                          × 
                          INFO  : Compiling command(queryId=hive_20240327070854_9e6cb954-2820-4b19-89e3-eed2e5f7beb7): -- Drop for retesting purposes
DROP VIEW student.netflix2023_genre_exploded
INFO  : Semantic Analysis Completed
INFO  : Returning Hive schema: Schema(fieldSchemas:null, properties:null)
INFO  : Completed compiling command(queryId=hive_20240327070854_9e6cb954-2820-4b19-89e3-eed2e5f7beb7); Time taken: 0.003 seconds
INFO  : Concurrency mode is disabled, not creating a lock manager
INFO  : Executing command(queryId=hive_20240327070854_9e6cb954-2820-4b19-89e3-eed2e5f7beb7): -- Drop for retesting purposes
DROP VIEW student.netflix2023_genre_exploded
INFO  : Starting task [Stage-0:DDL] in serial mode
INFO  : Completed executing command(queryId=hive_20240327070854_9e6cb954-2820-4b19-89e3-eed2e5f7beb7); Time taken: 0.017 seconds
INFO  : OK
                           
                           Success.
                          Snippet icon

                          My Snippet
                          2.22s
                          Database
                          Type

                          -- Create separate view of netflix2023 where genre is exploded into multiple rows
                          CREATE VIEW student.netflix2023_genre_exploded AS
                          SELECT
                          title,
                          availableglobally,
                          releasedate,
                          hoursviewed,
                          numberofratings,
                          rating,
                          REPLACE(genre_exploded, ' -', '-') AS genre,
                          keywords,
                          description
                          FROM
                          student.netflix2023
                          LATERAL VIEW
                          EXPLODE(SPLIT(genre, ' - ')) exploded_table AS genre_exploded
                          WHERE
                          genre_exploded <> 'Genre'
                          AND genre_exploded <> 'TBD';
                          XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
                            × 
                            INFO  : Compiling command(queryId=hive_20240327070859_5d232a78-16f6-4882-92d5-e885e9907c54): -- Create separate view of netflix2023 where genre is exploded into multiple rows
CREATE VIEW student.netflix2023_genre_exploded AS
SELECT
    title,
    availableglobally,
    releasedate,
    hoursviewed,
    numberofratings,
    rating,
    REPLACE(genre_exploded, ' -', '-') AS genre,
    keywords,
    description
FROM
    student.netflix2023
LATERAL VIEW
    EXPLODE(SPLIT(genre, ' - ')) exploded_table AS genre_exploded
WHERE
    genre_exploded <> 'Genre'
    AND genre_exploded <> 'TBD'
INFO  : Semantic Analysis Completed
INFO  : Returning Hive schema: Schema(fieldSchemas:[FieldSchema(name:title, type:string, comment:null), FieldSchema(name:availableglobally, type:string, comment:null), FieldSchema(name:releasedate, type:date, comment:null), FieldSchema(name:hoursviewed, type:bigint, comment:null), FieldSchema(name:numberofratings, type:bigint, comment:null), FieldSchema(name:rating, type:double, comment:null), FieldSchema(name:genre, type:string, comment:null), FieldSchema(name:keywords, type:string, comment:null), FieldSchema(name:description, type:string, comment:null)], properties:null)
INFO  : Completed compiling command(queryId=hive_20240327070859_5d232a78-16f6-4882-92d5-e885e9907c54); Time taken: 0.014 seconds
INFO  : Concurrency mode is disabled, not creating a lock manager
INFO  : Executing command(queryId=hive_20240327070859_5d232a78-16f6-4882-92d5-e885e9907c54): -- Create separate view of netflix2023 where genre is exploded into multiple rows
CREATE VIEW student.netflix2023_genre_exploded AS
SELECT
    title,
    availableglobally,
    releasedate,
    hoursviewed,
    numberofratings,
    rating,
    REPLACE(genre_exploded, ' -', '-') AS genre,
    keywords,
    description
FROM
    student.netflix2023
LATERAL VIEW
    EXPLODE(SPLIT(genre, ' - ')) exploded_table AS genre_exploded
WHERE
    genre_exploded <> 'Genre'
    AND genre_exploded <> 'TBD'
INFO  : Starting task [Stage-0:DDL] in serial mode
INFO  : Completed executing command(queryId=hive_20240327070859_5d232a78-16f6-4882-92d5-e885e9907c54); Time taken: 0.01 seconds
INFO  : OK
                             
                             Success.
                            Snippet icon

                            My Snippet
                            3.89s
                            Database
                            Type

                            -- Ensure genre exploded table was made correctly
                            SELECT * FROM student.netflix2023_genre_exploded
                            XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
                              × 
                              INFO  : Compiling command(queryId=hive_20240327070922_effaba5f-c2c2-4c20-b6f3-4079d1646b20): -- Ensure genre exploded table was made correctly
SELECT * FROM student.netflix2023_genre_exploded
INFO  : Semantic Analysis Completed
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INFO  : Completed compiling command(queryId=hive_20240327070922_effaba5f-c2c2-4c20-b6f3-4079d1646b20); Time taken: 0.03 seconds
INFO  : Concurrency mode is disabled, not creating a lock manager
INFO  : Executing command(queryId=hive_20240327070922_effaba5f-c2c2-4c20-b6f3-4079d1646b20): -- Ensure genre exploded table was made correctly
SELECT * FROM student.netflix2023_genre_exploded
INFO  : Completed executing command(queryId=hive_20240327070922_effaba5f-c2c2-4c20-b6f3-4079d1646b20); Time taken: 0.0 seconds
INFO  : OK
                                netflix2023_genre_exploded.title netflix2023_genre_exploded.availableglobally netflix2023_genre_exploded.releasedate netflix2023_genre_exploded.hoursviewed netflix2023_genre_exploded.numberofratings netflix2023_genre_exploded.rating netflix2023_genre_exploded.genre netflix2023_genre_exploded.keywords netflix2023_genre_exploded.description
                                netflix2023_genre_exploded.title netflix2023_genre_exploded.availableglobally netflix2023_genre_exploded.releasedate netflix2023_genre_exploded.hoursviewed netflix2023_genre_exploded.numberofratings netflix2023_genre_exploded.rating netflix2023_genre_exploded.genre netflix2023_genre_exploded.keywords netflix2023_genre_exploded.description
                              1Queen Charlotte: A Bridgerton StoryYes2023-05-04503000000456246.6Comedytelenovela, drug trafficking, cartel, femme fatale, kidnappingWhile fleeing from dangerous assailants, an assassin comes out of hiding to protect the daughter she left earlier in life.
                              2Queen Charlotte: A Bridgerton StoryYes2023-05-04503000000456246.6Romancetelenovela, drug trafficking, cartel, femme fatale, kidnappingWhile fleeing from dangerous assailants, an assassin comes out of hiding to protect the daughter she left earlier in life.
                              3Ginny & Georgia: Season 1Yes2021-02-24302100000441025.6Actionclose up of eye, close up of eyes, close up of lips, close up of mouth, childAfter barely surviving his grievous wounds from his mission in Dhaka, Bangladesh, Tyler Rake is back, and his team is ready to take on their next mission.
                              4Ginny & Georgia: Season 1Yes2021-02-24302100000441025.6Thrillerclose up of eye, close up of eyes, close up of lips, close up of mouth, childAfter barely surviving his grievous wounds from his mission in Dhaka, Bangladesh, Tyler Rake is back, and his team is ready to take on their next mission.
                              5The MotherYes2023-05-1224990000068.6Sportracial tension, black american, couple, love, lifeA young American woman from the Midwest is hired by a marketing firm in Paris to provide them with an American perspective on things.
                              6The Diplomat: Season 1Yes2023-04-20214100000608535.5Comedyfriend, best friend, volunteer, teenager, sonVillain's kidnap a man's daughter in order to get hold of forgery plates which they believe to be in his possession.
                              7The Diplomat: Season 1Yes2023-04-20214100000608535.5Romancefriend, best friend, volunteer, teenager, sonVillain's kidnap a man's daughter in order to get hold of forgery plates which they believe to be in his possession.
                              8Luther: The Fallen SunYes2023-03-102097000003007.3Romancefemale protagonist, character name in series title, american in france, location in series title, marketingFinding a ghost named Ernest haunting their new home turns Kevin's family into overnight social media sensations. But when Kevin and Ernest investigate the mystery of Ernest's past, they become a target of the CIA.
                              9Fake Profile: Season 1 // Perfil falso: Temporada 1No2023-05-3120650000081658.7Dramaalice character, caterpillar character, the mad hatter character, march hare character, dormouse characterA world-weary detective is hired to investigate the murder of a West Point cadet. Stymied by the cadets' code of silence, he enlists one of their own to help unravel the case , a young man the world would come to know as Edgar All...
                              10Fake Profile: Season 1 // Perfil falso: Temporada 1No2023-05-3120650000081658.7Horroralice character, caterpillar character, the mad hatter character, march hare character, dormouse characterA world-weary detective is hired to investigate the murder of a West Point cadet. Stymied by the cadets' code of silence, he enlists one of their own to help unravel the case , a young man the world would come to know as Edgar All...
                              11Fake Profile: Season 1 // Perfil falso: Temporada 1No2023-05-3120650000081658.7Thrilleralice character, caterpillar character, the mad hatter character, march hare character, dormouse characterA world-weary detective is hired to investigate the murder of a West Point cadet. Stymied by the cadets' code of silence, he enlists one of their own to help unravel the case , a young man the world would come to know as Edgar All...
                              12Outer Banks: Season 1Yes2020-04-151840000001192466.2Adventurebritish noir, b movie, forgery plates, kidnapping a girl, rape and sexual intercourse innuendoThe last of a two-part film centered on the life and career of John Reed, the revolutionary communist journalist.
                              13Outer Banks: Season 1Yes2020-04-151840000001192466.2Familybritish noir, b movie, forgery plates, kidnapping a girl, rape and sexual intercourse innuendoThe last of a two-part film centered on the life and career of John Reed, the revolutionary communist journalist.
                              14Outer Banks: Season 1Yes2020-04-151840000001192466.2Fantasybritish noir, b movie, forgery plates, kidnapping a girl, rape and sexual intercourse innuendoThe last of a two-part film centered on the life and career of John Reed, the revolutionary communist journalist.
                              15Sweet Tooth: Season 2Yes2023-04-27182300000744037.5Actionghost, house, chase, car chase, based on short storyTeresa Mendoza returns to Mexico after 8 years to fight with Mexican drug dealers.
                              16Sweet Tooth: Season 2Yes2023-04-27182300000744037.5Crimeghost, house, chase, car chase, based on short storyTeresa Mendoza returns to Mexico after 8 years to fight with Mexican drug dealers.
                              17Sweet Tooth: Season 2Yes2023-04-27182300000744037.5Dramaghost, house, chase, car chase, based on short storyTeresa Mendoza returns to Mexico after 8 years to fight with Mexican drug dealers.
                              18Perfect Match: Season 1Yes2023-02-141768000003446Adventuremurder, year 1830, 1830s, 19th century, winterHiding a mysterious past, a mother lives like a nameless fugitive with her daughter as they make hotels their home and see everyone else as a threat.
                              19Perfect Match: Season 1Yes2023-02-141768000003446Comedymurder, year 1830, 1830s, 19th century, winterHiding a mysterious past, a mother lives like a nameless fugitive with her daughter as they make hotels their home and see everyone else as a threat.
                              20Perfect Match: Season 1Yes2023-02-141768000003446Familymurder, year 1830, 1830s, 19th century, winterHiding a mysterious past, a mother lives like a nameless fugitive with her daughter as they make hotels their home and see everyone else as a threat.
                              21The Marked Heart: Season 2 // Pálpito: Temporada 2Yes2023-04-19174300000118698.4Shortyear 2014, malaysia airlines flight mh370, airplane, investigation, missingThree young women join the newly formed Women's Army Corps (WACS) for varied reasons, and make contributions to the war effort.
                              22Murder Mystery 2Yes2023-03-311736000004196617.1Comedyjunk man, junk, junk wagon, city dump, landfillDocuseries following the FIA Formula One World Championship across multiple seasons.
                              23Murder Mystery 2Yes2023-03-311736000004196617.1Crimejunk man, junk, junk wagon, city dump, landfillDocuseries following the FIA Formula One World Championship across multiple seasons.
                              24Murder Mystery 2Yes2023-03-311736000004196617.1Dramajunk man, junk, junk wagon, city dump, landfillDocuseries following the FIA Formula One World Championship across multiple seasons.
                              25Never Have I Ever: Season 4Yes2023-02-10163000000155.6Animationmagic, power, young woman, soldier, gay manThings go badly for a hack director and film crew shooting a low budget zombie movie in an abandoned WWII Japanese facility, when they are attacked by real zombies.
                              26Never Have I Ever: Season 4Yes2023-02-10163000000155.6Shortmagic, power, young woman, soldier, gay manThings go badly for a hack director and film crew shooting a low budget zombie movie in an abandoned WWII Japanese facility, when they are attacked by real zombies.
                              27Your Place or MineYes2022-12-21161100000189.4Dramaparody comedy, spoofIn the wake of King Edward's death, Uhtred of Bebbanburg and his comrades adventure across a fractured kingdom in the hopes of uniting England at last.
                              28Your Place or MineYes2022-12-21161100000189.4Fantasyparody comedy, spoofIn the wake of King Edward's death, Uhtred of Bebbanburg and his comrades adventure across a fractured kingdom in the hopes of uniting England at last.
                              29Your Place or MineYes2022-12-21161100000189.4Horrorparody comedy, spoofIn the wake of King Edward's death, Uhtred of Bebbanburg and his comrades adventure across a fractured kingdom in the hopes of uniting England at last.
                              30Chiquititas (2013)No2022-12-221576000001205.9Crimespaghetti western, italo western, eastern, ostern, second partSonic in a high-octane adventure where the fate of a strange new multiverse rests in his gloved hands.
                              31Chiquititas (2013)No2022-12-221576000001205.9Dramaspaghetti western, italo western, eastern, ostern, second partSonic in a high-octane adventure where the fate of a strange new multiverse rests in his gloved hands.
                              32Alchemy of Souls: Part 1 // 환혼: 파트 1Yes2022-10-191521000003827.1Documentarycontroversy, girl, laundry drying on a clothesline, african american, clotheslineA German youth eagerly enters World War I, but his enthusiasm wanes as he gets a firsthand view of the horror.
                              33Alchemy of Souls: Part 1 // 환혼: 파트 1Yes2022-10-191521000003827.1Shortcontroversy, girl, laundry drying on a clothesline, african american, clotheslineA German youth eagerly enters World War I, but his enthusiasm wanes as he gets a firsthand view of the horror.
                              34Outer Banks: Season 2Yes2021-06-101514000001183526.6Crimepsychological drama, overprotective mother, cult, hotel, peer pressureAn unusual and touching bond develops when grieving Oona reaches out to a mysterious homeless man, offering him a place to stay in her garden shed.
                              35Outer Banks: Season 2Yes2021-06-101514000001183526.6Horrorpsychological drama, overprotective mother, cult, hotel, peer pressureAn unusual and touching bond develops when grieving Oona reaches out to a mysterious homeless man, offering him a place to stay in her garden shed.
                              36Outer Banks: Season 2Yes2021-06-101514000001183526.6Mysterypsychological drama, overprotective mother, cult, hotel, peer pressureAn unusual and touching bond develops when grieving Oona reaches out to a mysterious homeless man, offering him a place to stay in her garden shed.
                              37Til Money Do Us Part: Season 1 // Hasta que la plata nos separe: Temporada 1No2023-04-26148600000132166.1Documentarywacs, war widow, war game, male female relationship, father daughter relationshipA single mother who is a renowned hired killer finds it difficult to achieve a balance between her personal and work life.
                              38Mr. Queen // 철인왕후No2022-12-1614690000068.6Sportf1, formula 1, motor sports, car race, championshipAn Interpol agent successfully tracks down the world's most wanted art thief with help from a rival thief. But nothing is as it seems as a series of double-crosses ensues.
                              39Manifest: Season 1No2023-06-0914670000079Shortyear in title, 2000s, number in titleThale (17) has just moved with her parents to a small town after her mother has a new job in the local police. After a student is killed brutally at a party Thale attends, she becomes a key witness. Was the killer an animal? A wolf?
                              40Manifest: Season 1No2023-06-0914670000079Dramayear in title, 2000s, number in titleThale (17) has just moved with her parents to a small town after her mother has a new job in the local police. After a student is killed brutally at a party Thale attends, she becomes a key witness. Was the killer an animal? A wolf?
                              41PAW Patrol: Season 6No2023-03-0114010000098356.8Documentarydrug dealers, kidnapping, child kidnapping, shot in the head, bangladeshApril 1940. The eyes of the world are on Narvik, a small town in northern Norway, source of the iron ore needed for Hitler's war machinery. Through two months of fierce winter warfare, Hitler is dealt his first defeat.
                              42PAW Patrol: Season 6No2023-03-0114010000098356.8Crimedrug dealers, kidnapping, child kidnapping, shot in the head, bangladeshApril 1940. The eyes of the world are on Narvik, a small town in northern Norway, source of the iron ore needed for Hitler's war machinery. Through two months of fierce winter warfare, Hitler is dealt his first defeat.
                              43The Good Bad Mother: Limited Series // 나쁜엄마: 리미티드 시리즈Yes2023-06-151399000007407.6Dramasingle take, zombie, film crew, television broadcast, rooftopWhen a young girl stows away on the ship of a legendary sea monster hunter, they launch an epic journey into uncharted waters , and make history to boot.
                              44The Good Bad Mother: Limited Series // 나쁜엄마: 리미티드 시리즈Yes2023-06-151399000007407.6Romancesingle take, zombie, film crew, television broadcast, rooftopWhen a young girl stows away on the ship of a legendary sea monster hunter, they launch an epic journey into uncharted waters , and make history to boot.
                              45The Recruit: Season 1Yes2023-02-221393000004268807Comedylawyer, 19th century, legal, legal drama, legal battleAn orphaned boy enrolls in a school of wizardry, where he learns the truth about himself, his family and the terrible evil that haunts the magical world.
                              46Bloodhounds: Season 1 // 사냥개들: 시즌 1Yes2020-12-251366000001113617.6Actionanglo saxon, kingdom, exploration, warrior, epicBased on the true story of a father and son who repair their fractured relationship during a forced hike of the Appalachian trail to find their beloved lost dog.
                              47Bloodhounds: Season 1 // 사냥개들: 시즌 1Yes2020-12-251366000001113617.6Adventureanglo saxon, kingdom, exploration, warrior, epicBased on the true story of a father and son who repair their fractured relationship during a forced hike of the Appalachian trail to find their beloved lost dog.
                              48Bloodhounds: Season 1 // 사냥개들: 시즌 1Yes2020-12-251366000001113617.6Dramaanglo saxon, kingdom, exploration, warrior, epicBased on the true story of a father and son who repair their fractured relationship during a forced hike of the Appalachian trail to find their beloved lost dog.
                              49Glass Onion: A Knives Out MysteryYes2020-05-311362000001556.4Documentaryfast, based on video game, sonic the hedgehog, anthropomorphic animal, sonic the hedgehog characterCharlie Brandis leads a quiet and uneventful life as a wallflower. His parents trust him, his friends like him, girls are indifferent toward him. Then there's the girl he's watched from afar, Annie Briggs, who doesn't even know he...
                              50Black Mirror: Season 6Yes2023-01-271348000001024.9Horrorlawyer, spin off, psychosomatic illness, criminal lawyer, drug tradeIt follows the rise and fall of the American financier and ponzi schemer: Madoff.
                              51Triptych: Season 1 // Tríada: Temporada 1Yes2022-05-271336000001746.4Drama1910s, anti war, shell shock, ptsd post traumatic stress disorder, depressionShort documentary about making the second season of The Witcher (2019).
                              52Triptych: Season 1 // Tríada: Temporada 1Yes2022-05-271336000001746.4Western1910s, anti war, shell shock, ptsd post traumatic stress disorder, depressionShort documentary about making the second season of The Witcher (2019).
                              53Bridgerton: Season 1Yes2022-03-25133400000145919.2Dramatitle co written by female, title co directed by female, f ratedCharlie Brandis leads a quiet and uneventful life as a wallflower. His parents trust him, his friends like him, girls are indifferent toward him. Then there's the girl he's watched from afar, Annie Briggs, who doesn't even know he...
                              54Bridgerton: Season 1Yes2022-03-25133400000145919.2Horrortitle co written by female, title co directed by female, f ratedCharlie Brandis leads a quiet and uneventful life as a wallflower. His parents trust him, his friends like him, girls are indifferent toward him. Then there's the girl he's watched from afar, Annie Briggs, who doesn't even know he...
                              55Bridgerton: Season 1Yes2022-03-25133400000145919.2 SciFititle co written by female, title co directed by female, f ratedCharlie Brandis leads a quiet and uneventful life as a wallflower. His parents trust him, his friends like him, girls are indifferent toward him. Then there's the girl he's watched from afar, Annie Briggs, who doesn't even know he...
                              56The Marked Heart: Season 1 // Pálpito: Temporada 1Yes2023-01-06120500000483708.5Documentarynon fictionMarion and Jack try to rekindle their relationship with a visit to Paris, home of Marion's parents ,- and several of her ex-boyfriends.
                              57The Marked Heart: Season 1 // Pálpito: Temporada 1Yes2023-01-06120500000483708.5Sportnon fictionMarion and Jack try to rekindle their relationship with a visit to Paris, home of Marion's parents ,- and several of her ex-boyfriends.
                              58Little Angel: Volume 1Yes2023-04-281200000001631916Actionhomosexual, gay serial killer, murder, serial killer, homosexualityIn celebration of Season 2 being released soon, the Glitch Productions team put all of Season 1 into a single movie to watch in one go.
                              59Little Angel: Volume 1Yes2023-04-281200000001631916Comedyhomosexual, gay serial killer, murder, serial killer, homosexualityIn celebration of Season 2 being released soon, the Glitch Productions team put all of Season 1 into a single movie to watch in one go.
                              60Little Angel: Volume 1Yes2023-04-281200000001631916Crimehomosexual, gay serial killer, murder, serial killer, homosexualityIn celebration of Season 2 being released soon, the Glitch Productions team put all of Season 1 into a single movie to watch in one go.
                              61PAW Patrol: Season 5No2023-03-081189000002511.6Shortnorwegian army, nazi invasion of norway, winter, year 1940, man in uniformThree young women looking for adventure get jobs on a dude ranch.
                              62PAW Patrol: Season 5No2023-03-081189000002511.6Comedynorwegian army, nazi invasion of norway, winter, year 1940, man in uniformThree young women looking for adventure get jobs on a dude ranch.
                              63Sex/Life: Season 1Yes2023-05-1911580000070577.4Biographycgi animation, bounty hunter, alien, danger, laser gunThe trials and tribulations of criminal lawyer Jimmy McGill in the years leading up to his fateful run-in with Walter White and Jesse Pinkman.
                              64Sex/Life: Season 1Yes2023-05-1911580000070577.4Crimecgi animation, bounty hunter, alien, danger, laser gunThe trials and tribulations of criminal lawyer Jimmy McGill in the years leading up to his fateful run-in with Walter White and Jesse Pinkman.
                              65Sex/Life: Season 1Yes2023-05-1911580000070577.4Dramacgi animation, bounty hunter, alien, danger, laser gunThe trials and tribulations of criminal lawyer Jimmy McGill in the years leading up to his fateful run-in with Walter White and Jesse Pinkman.
                              66We Have a GhostYes2023-01-27113600000331796.9Actiondog, search, find, journey, fatherAn executive goes through an unexpected breakup, then accepting an assignment to go undercover and learn about the tourist industry in Vietnam.
                              67We Have a GhostYes2023-01-27113600000331796.9Dramadog, search, find, journey, fatherAn executive goes through an unexpected breakup, then accepting an assignment to go undercover and learn about the tourist industry in Vietnam.
                              68We Have a GhostYes2023-01-27113600000331796.9Historydog, search, find, journey, fatherAn executive goes through an unexpected breakup, then accepting an assignment to go undercover and learn about the tourist industry in Vietnam.
                              69Crash Landing on You: Season 1 // 사랑의 불시착: 시즌 1Yes2017-10-03102800000667508.1Dramatv specialA lawyer defending a wealthy man begins to believe his client is guilty of more than just one crime.
                              70Crash Landing on You: Season 1 // 사랑의 불시착: 시즌 1Yes2017-10-03102800000667508.1Wartv specialA lawyer defending a wealthy man begins to believe his client is guilty of more than just one crime.
                              71MH370: The Plane That Disappeared: Limited SeriesYes2021-12-2910170000068865.5Actionparty, teenager, sex comedyFollows the tragedy in which terrorists detonated a bomb at the Boston Marathon's finish line; they carried out the attack by placing two homemade pressure-cooker bombs that resulted in three fatalities and numerous injuries.
                              72MH370: The Plane That Disappeared: Limited SeriesYes2021-12-2910170000068865.5Adventureparty, teenager, sex comedyFollows the tragedy in which terrorists detonated a bomb at the Boston Marathon's finish line; they carried out the attack by placing two homemade pressure-cooker bombs that resulted in three fatalities and numerous injuries.
                              73MH370: The Plane That Disappeared: Limited SeriesYes2021-12-2910170000068865.5Dramaparty, teenager, sex comedyFollows the tragedy in which terrorists detonated a bomb at the Boston Marathon's finish line; they carried out the attack by placing two homemade pressure-cooker bombs that resulted in three fatalities and numerous injuries.
                              74Breaking Bad: Season 2No2022-11-23990000002595.9ShortprisonIm Hwa Ryeong, a prickly, sensitive and hot-tempered queen, tries to turn her trouble making princes into proper crown princes.
                              75Breaking Bad: Season 2No2022-11-23990000002595.9BiographyprisonIm Hwa Ryeong, a prickly, sensitive and hot-tempered queen, tries to turn her trouble making princes into proper crown princes.
                              76Breaking Bad: Season 2No2022-11-23990000002595.9DramaprisonIm Hwa Ryeong, a prickly, sensitive and hot-tempered queen, tries to turn her trouble making princes into proper crown princes.
                              77Lockwood & Co.: Season 1Yes2022-12-2197800000267.1Comedyparty, teenager, sex comedyIt's 1940's Australia and siblings Maggie and Charles must endure taunts of newly enlisted teenagers, grapple with the fact that neither of them can fight in the war and resort to chess in order to pass the time.
                              78Lockwood & Co.: Season 1Yes2022-12-2197800000267.1Talk-Showparty, teenager, sex comedyIt's 1940's Australia and siblings Maggie and Charles must endure taunts of newly enlisted teenagers, grapple with the fact that neither of them can fight in the war and resort to chess in order to pass the time.
                              79You: Season 3Yes2022-05-209760000078.7Animationfemale full frontal nudity, female nudity, female frontal nudity, sex scene, country in titleElliott, a young fisherman with an extraordinary voice, gets the chance of a lifetime when high-profile music manager Suzanne discovers him at a party.
                              80You: Season 3Yes2022-05-209760000078.7Comedyfemale full frontal nudity, female nudity, female frontal nudity, sex scene, country in titleElliott, a young fisherman with an extraordinary voice, gets the chance of a lifetime when high-profile music manager Suzanne discovers him at a party.
                              81You: Season 3Yes2022-05-209760000078.7 SciFifemale full frontal nudity, female nudity, female frontal nudity, sex scene, country in titleElliott, a young fisherman with an extraordinary voice, gets the chance of a lifetime when high-profile music manager Suzanne discovers him at a party.
                              82Breaking Bad: Season 5No2023-01-19951000008407.5Dramafrench, vacation, europe, chest hair, male nudityElliott, a young fisherman with an extraordinary voice, gets the chance of a lifetime when high-profile music manager Suzanne discovers him at a party.
                              83Welcome to Eden: Season 2 // Bienvenidos a Edén: Temporada 2Yes2022-01-28946000002379.3Documentarybikini, women, young, f rated, best friendThe relationship of a well-known journalist and a down-to-earth teacher goes through hard times when she takes a new job.
                              84Welcome to Eden: Season 2 // Bienvenidos a Edén: Temporada 2Yes2022-01-28946000002379.3Shortbikini, women, young, f rated, best friendThe relationship of a well-known journalist and a down-to-earth teacher goes through hard times when she takes a new job.
                              85CoComelon: Season 2No2023-03-2492900000437824.9Actionlawyer, spin off, psychosomatic illness, criminal lawyer, drug tradeIn spite of their many differences, Cassie, a struggling singer-songwriter, and Luke, a troubled Marine, agree to marry solely for military benefits, but when tragedy strikes, the line between real and pretend begins to blur.
                              86CoComelon: Season 2No2023-03-2492900000437824.9Adventurelawyer, spin off, psychosomatic illness, criminal lawyer, drug tradeIn spite of their many differences, Cassie, a struggling singer-songwriter, and Luke, a troubled Marine, agree to marry solely for military benefits, but when tragedy strikes, the line between real and pretend begins to blur.
                              87CoComelon: Season 2No2023-03-2492900000437824.9Dramalawyer, spin off, psychosomatic illness, criminal lawyer, drug tradeIn spite of their many differences, Cassie, a struggling singer-songwriter, and Luke, a troubled Marine, agree to marry solely for military benefits, but when tragedy strikes, the line between real and pretend begins to blur.
                              88The Blacklist: Season 1No2020-12-10922000001484057.9Biographymale nudity, quirky comedy, love, island, escapeTwo rival newsreel photographers join forces to find an aviatrix's missing brother, who has disappeared in the Amazon rainforest.
                              89The Blacklist: Season 1No2020-12-10922000001484057.9Crimemale nudity, quirky comedy, love, island, escapeTwo rival newsreel photographers join forces to find an aviatrix's missing brother, who has disappeared in the Amazon rainforest.
                              90The Blacklist: Season 1No2020-12-10922000001484057.9Dramamale nudity, quirky comedy, love, island, escapeTwo rival newsreel photographers join forces to find an aviatrix's missing brother, who has disappeared in the Amazon rainforest.
                              91Shadow and Bone: Season 1Yes2021-05-3191400000161096.6Dramaanglo saxon, kingdom, exploration, warrior, epicA woman's life is turned upside-down when a dangerous man gets hold of her lost cell phone and uses it to track her every move.
                              92Shadow and Bone: Season 1Yes2021-05-3191400000161096.6Historyanglo saxon, kingdom, exploration, warrior, epicA woman's life is turned upside-down when a dangerous man gets hold of her lost cell phone and uses it to track her every move.
                              93Shadow and Bone: Season 1Yes2021-05-3191400000161096.6Waranglo saxon, kingdom, exploration, warrior, epicA woman's life is turned upside-down when a dangerous man gets hold of her lost cell phone and uses it to track her every move.
                              94You: Season 2Yes2017-08-3186100000118698.4Shortschool, hero, academy, master, witchThe trials and tribulations of criminal lawyer Jimmy McGill in the years leading up to his fateful run-in with Walter White and Jesse Pinkman.
                              95All of Us Are Dead: Season 1 // 지금 우리 학교는: 시즌 1Yes2022-11-15854000001936.6Documentarydonghua, chinese animation, chinese anime, team sports, basketballThis shocking documentary chronicles a happy-go-lucky nomad's ascent to viral stardom and the steep downward spiral that resulted in his imprisonment.
                              96Black Knight: Season 1 // 택배기사: 시즌 1Yes2023-04-1384600000496.8Comedyperformer, fisherman, song, life, managerA quirky, dysfunctional family's road trip is upended when they find themselves in the middle of the robot apocalypse and suddenly become humanity's unlikeliest last hope.
                              97Breaking Bad: Season 4No2017-07-2384400000118698.4Shortperformer, fisherman, song, life, managerDeep in the Dovre mountain, something gigantic wakes up after a thousand years in captivity. The creature destroys everything in its path and quickly approaches Oslo.
                              98Obsession: Limited SeriesYes2023-01-208360000089536.8Adventureeducational film, world war two, enemy, japanese soldier, training filmComedian Chris Rock performs a live stand-up special in Baltimore, Maryland.
                              99Obsession: Limited SeriesYes2023-01-208360000089536.8Biographyeducational film, world war two, enemy, japanese soldier, training filmComedian Chris Rock performs a live stand-up special in Baltimore, Maryland.
                              100Obsession: Limited SeriesYes2023-01-208360000089536.8Dramaeducational film, world war two, enemy, japanese soldier, training filmComedian Chris Rock performs a live stand-up special in Baltimore, Maryland.
                              Snippet icon

                              My Snippet
                              Database
                              Type

                              -- START OF ANALYSIS
                              XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
                                ×
                                Snippet icon

                                My Snippet
                                6.90s
                                Database
                                Type

                                -- Number of shows released per year
                                SELECT
                                YEAR(releasedate) AS year,
                                COUNT(title) AS number_of_shows
                                FROM
                                student.netflix2023
                                GROUP BY
                                YEAR(releasedate)
                                ORDER BY
                                year ASC;
                                XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
                                  × 
                                  INFO  : Compiling command(queryId=hive_20240327070912_4da3cfbb-0244-4969-b691-70910314a8d2): -- Number of shows released per year
SELECT
    YEAR(releasedate) AS year,
    COUNT(title) AS number_of_shows
FROM
    student.netflix2023
GROUP BY
    YEAR(releasedate)
ORDER BY
    year ASC
INFO  : Semantic Analysis Completed
INFO  : Returning Hive schema: Schema(fieldSchemas:[FieldSchema(name:year, type:int, comment:null), FieldSchema(name:number_of_shows, type:bigint, comment:null)], properties:null)
INFO  : Completed compiling command(queryId=hive_20240327070912_4da3cfbb-0244-4969-b691-70910314a8d2); Time taken: 0.051 seconds
INFO  : Concurrency mode is disabled, not creating a lock manager
INFO  : Executing command(queryId=hive_20240327070912_4da3cfbb-0244-4969-b691-70910314a8d2): -- Number of shows released per year
SELECT
    YEAR(releasedate) AS year,
    COUNT(title) AS number_of_shows
FROM
    student.netflix2023
GROUP BY
    YEAR(releasedate)
ORDER BY
    year ASC
INFO  : Query ID = hive_20240327070912_4da3cfbb-0244-4969-b691-70910314a8d2
INFO  : Total jobs = 1
INFO  : Launching Job 1 out of 1
INFO  : Starting task [Stage-1:MAPRED] in serial mode
INFO  : Session is already open
INFO  : Dag name: -- Number of shows released per year
S...ASC(Stage-1)
INFO  : Status: Running (Executing on YARN cluster with App id application_1634527506680_1335)

INFO  : Map 1: 0/1	Reducer 2: 0/1	Reducer 3: 0/1
INFO  : Map 1: 0(+1)/1	Reducer 2: 0/1	Reducer 3: 0/1
INFO  : Map 1: 1/1	Reducer 2: 0(+1)/1	Reducer 3: 0/1	
INFO  : Map 1: 1/1	Reducer 2: 1/1	Reducer 3: 0(+1)/1	
INFO  : Map 1: 1/1	Reducer 2: 1/1	Reducer 3: 1/1	
INFO  : Completed executing command(queryId=hive_20240327070912_4da3cfbb-0244-4969-b691-70910314a8d2); Time taken: 5.008 seconds
INFO  : OK
                                  • type
                                  • x-axis
                                  • y-axis
                                  • group
                                  • limit
                                  • sorting

                                  Select the chart parameters on the left

                                  2,0102,0122,0142,0162,0182,0202,0225001k1.5k2k2.5k32.751k
                                  number_of_showsnumber_of_shows
                                  Snippet icon

                                  My Snippet
                                  6.75s
                                  Database
                                  Type

                                  -- Total Number of Ratings per Genre
                                  SELECT
                                  genre,
                                  SUM(numberofratings) AS total_ratings,
                                  SUM(hoursviewed) AS total_hours_viewed
                                  FROM
                                  netflix2023_genre_exploded
                                  GROUP BY
                                  genre
                                  ORDER BY
                                  total_ratings DESC;
                                  XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
                                    × 
                                    INFO  : Compiling command(queryId=hive_20240327070937_4fc4905b-1b11-4a47-838b-d17075297059): -- Total Number of Ratings per Genre
SELECT
    genre,
    SUM(numberofratings) AS total_ratings,
    SUM(hoursviewed) AS total_hours_viewed
FROM
    netflix2023_genre_exploded
GROUP BY
    genre
ORDER BY
    total_ratings DESC
INFO  : Semantic Analysis Completed
INFO  : Returning Hive schema: Schema(fieldSchemas:[FieldSchema(name:genre, type:string, comment:null), FieldSchema(name:total_ratings, type:bigint, comment:null), FieldSchema(name:total_hours_viewed, type:bigint, comment:null)], properties:null)
INFO  : Completed compiling command(queryId=hive_20240327070937_4fc4905b-1b11-4a47-838b-d17075297059); Time taken: 0.026 seconds
INFO  : Concurrency mode is disabled, not creating a lock manager
INFO  : Executing command(queryId=hive_20240327070937_4fc4905b-1b11-4a47-838b-d17075297059): -- Total Number of Ratings per Genre
SELECT
    genre,
    SUM(numberofratings) AS total_ratings,
    SUM(hoursviewed) AS total_hours_viewed
FROM
    netflix2023_genre_exploded
GROUP BY
    genre
ORDER BY
    total_ratings DESC
INFO  : Query ID = hive_20240327070937_4fc4905b-1b11-4a47-838b-d17075297059
INFO  : Total jobs = 1
INFO  : Launching Job 1 out of 1
INFO  : Starting task [Stage-1:MAPRED] in serial mode
INFO  : Session is already open
INFO  : Dag name: -- Total Number of Ratings per Genre
...DESC(Stage-1)
INFO  : Status: Running (Executing on YARN cluster with App id application_1634527506680_1335)

INFO  : Map 1: 0/1	Reducer 2: 0/1	Reducer 3: 0/1
INFO  : Map 1: 0(+1)/1	Reducer 2: 0/1	Reducer 3: 0/1
INFO  : Map 1: 1/1	Reducer 2: 0/1	Reducer 3: 0/1	
INFO  : Map 1: 1/1	Reducer 2: 0(+1)/1	Reducer 3: 0/1	
INFO  : Map 1: 1/1	Reducer 2: 1/1	Reducer 3: 0(+1)/1	
INFO  : Map 1: 1/1	Reducer 2: 1/1	Reducer 3: 1/1	
INFO  : Completed executing command(queryId=hive_20240327070937_4fc4905b-1b11-4a47-838b-d17075297059); Time taken: 5.225 seconds
INFO  : OK
                                    columns (4)
                                    int
                                    genre string
                                    total_ratings bigint
                                    total_hours_viewed bigint
                                      genre total_ratings total_hours_viewed
                                      genre total_ratings total_hours_viewed
                                    1Drama11348030453497900000
                                    2Comedy9210741951425000000
                                    3Action5963798221657800000
                                    4Short5429817827529600000
                                    5Adventure4632555019450000000
                                    6Crime4417766321016100000
                                    7Documentary4147653323739200000
                                    8Animation3834911221014100000
                                    9Romance2149026516111500000
                                    10Thriller2089267110647000000
                                    11Family189479748958800000
                                    12Horror189228419596800000
                                    13Mystery159707107194600000
                                    14Biography136332057119600000
                                    15History127204525780700000
                                    16Sport126339594401600000
                                    17Fantasy113493324877300000
                                    18Music73009203948700000
                                    19 SciFi68045013320700000
                                    20RealityTV57738874736900000
                                    21War44667891307900000
                                    22Musical40762072720700000
                                    23Talk-Show36317991499800000
                                    24Game-Show22471901511200000
                                    25Western1268046863200000
                                    26News760307291800000
                                    Snippet icon

                                    My Snippet
                                    2.36s
                                    Database
                                    Type

                                    -- Impact of Global Availability on Viewership and Ratings
                                    SELECT
                                    availableglobally,
                                    AVG(hoursviewed) AS average_hours_viewed,
                                    AVG(rating) AS average_rating
                                    FROM
                                    student.netflix2023
                                    GROUP BY
                                    availableglobally;
                                    XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
                                      × 
                                      INFO  : Compiling command(queryId=hive_20240327070947_a8005607-0112-4712-aa3f-7abf95207355): -- Impact of Global Availability on Viewership and Ratings
SELECT 
    availableglobally,
    AVG(hoursviewed) AS average_hours_viewed,
    AVG(rating) AS average_rating
FROM
    student.netflix2023
GROUP BY 
    availableglobally
INFO  : Semantic Analysis Completed
INFO  : Returning Hive schema: Schema(fieldSchemas:[FieldSchema(name:availableglobally, type:string, comment:null), FieldSchema(name:average_hours_viewed, type:double, comment:null), FieldSchema(name:average_rating, type:double, comment:null)], properties:null)
INFO  : Completed compiling command(queryId=hive_20240327070947_a8005607-0112-4712-aa3f-7abf95207355); Time taken: 0.049 seconds
INFO  : Concurrency mode is disabled, not creating a lock manager
INFO  : Executing command(queryId=hive_20240327070947_a8005607-0112-4712-aa3f-7abf95207355): -- Impact of Global Availability on Viewership and Ratings
SELECT 
    availableglobally,
    AVG(hoursviewed) AS average_hours_viewed,
    AVG(rating) AS average_rating
FROM
    student.netflix2023
GROUP BY 
    availableglobally
INFO  : Query ID = hive_20240327070947_a8005607-0112-4712-aa3f-7abf95207355
INFO  : Total jobs = 1
INFO  : Launching Job 1 out of 1
INFO  : Starting task [Stage-1:MAPRED] in serial mode
INFO  : Session is already open
INFO  : Dag name: -- Impact of Global Avai...availableglobally(Stage-1)
INFO  : Status: Running (Executing on YARN cluster with App id application_1634527506680_1335)

INFO  : Map 1: 0(+1)/1	Reducer 2: 0/1	
INFO  : Map 1: 1/1	Reducer 2: 0(+1)/1	
INFO  : Map 1: 1/1	Reducer 2: 1/1	
INFO  : Completed executing command(queryId=hive_20240327070947_a8005607-0112-4712-aa3f-7abf95207355); Time taken: 0.631 seconds
INFO  : OK
                                      columns (4)
                                      int
                                      availableglobally string
                                      average_hours_viewed double
                                      average_rating double
                                        availableglobally average_hours_viewed average_rating
                                        availableglobally average_hours_viewed average_rating
                                      1No9093003.7033336.6432862721538
                                      2Yes15299415.947181316.611503301168082
                                      Snippet icon

                                      My Snippet
                                      2.75s
                                      Database
                                      Type

                                      -- Attempt at creating buckets for rating categories
                                      SELECT
                                      availableglobally,
                                      CASE
                                      WHEN rating >= 8 THEN 'High'
                                      WHEN rating >= 5 THEN 'Medium'
                                      ELSE 'Low'
                                      END AS rating_category,
                                      COUNT(*) AS count
                                      FROM
                                      student.netflix2023
                                      GROUP BY
                                      availableglobally,
                                      CASE
                                      WHEN rating >= 8 THEN 'High'
                                      WHEN rating >= 5 THEN 'Medium'
                                      ELSE 'Low'
                                      END
                                      ORDER BY
                                      availableglobally,
                                      rating_category
                                      XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
                                        × 
                                        INFO  : Compiling command(queryId=hive_20240327070953_1cecf51d-c9e5-45a6-ad14-1c35743ebba4): SELECT
    availableglobally,
    CASE 
        WHEN rating >= 8 THEN 'High'
        WHEN rating >= 5 THEN 'Medium'
        ELSE 'Low'
    END AS rating_category,
    COUNT(*) AS count
FROM 
    student.netflix2023
GROUP BY 
    availableglobally,
    CASE 
        WHEN rating >= 8 THEN 'High'
        WHEN rating >= 5 THEN 'Medium'
        ELSE 'Low'
    END
ORDER BY 
    availableglobally,
    rating_category
INFO  : Semantic Analysis Completed
INFO  : Returning Hive schema: Schema(fieldSchemas:[FieldSchema(name:availableglobally, type:string, comment:null), FieldSchema(name:rating_category, type:string, comment:null), FieldSchema(name:count, type:bigint, comment:null)], properties:null)
INFO  : Completed compiling command(queryId=hive_20240327070953_1cecf51d-c9e5-45a6-ad14-1c35743ebba4); Time taken: 0.08 seconds
INFO  : Concurrency mode is disabled, not creating a lock manager
INFO  : Executing command(queryId=hive_20240327070953_1cecf51d-c9e5-45a6-ad14-1c35743ebba4): SELECT
    availableglobally,
    CASE 
        WHEN rating >= 8 THEN 'High'
        WHEN rating >= 5 THEN 'Medium'
        ELSE 'Low'
    END AS rating_category,
    COUNT(*) AS count
FROM 
    student.netflix2023
GROUP BY 
    availableglobally,
    CASE 
        WHEN rating >= 8 THEN 'High'
        WHEN rating >= 5 THEN 'Medium'
        ELSE 'Low'
    END
ORDER BY 
    availableglobally,
    rating_category
INFO  : Query ID = hive_20240327070953_1cecf51d-c9e5-45a6-ad14-1c35743ebba4
INFO  : Total jobs = 1
INFO  : Launching Job 1 out of 1
INFO  : Starting task [Stage-1:MAPRED] in serial mode
INFO  : Session is already open
INFO  : Dag name: SELECT
    availableglobal...rating_category(Stage-1)
INFO  : Status: Running (Executing on YARN cluster with App id application_1634527506680_1335)

INFO  : Map 1: 0(+1)/1	Reducer 2: 0/1	Reducer 3: 0/1	
INFO  : Map 1: 1/1	Reducer 2: 0/1	Reducer 3: 0/1	
INFO  : Map 1: 1/1	Reducer 2: 1/1	Reducer 3: 0/1	
INFO  : Map 1: 1/1	Reducer 2: 1/1	Reducer 3: 1/1	
INFO  : Completed executing command(queryId=hive_20240327070953_1cecf51d-c9e5-45a6-ad14-1c35743ebba4); Time taken: 0.839 seconds
INFO  : OK
                                          availableglobally rating_category count
                                          availableglobally rating_category count
                                        1NoHigh1575
                                        2NoLow833
                                        3NoMedium7583
                                        4YesHigh631
                                        5YesLow331
                                        6YesMedium2976
                                        Snippet icon

                                        My Snippet
                                        8.43s
                                        Database
                                        Type

                                        -- Most popular show per year by Hours Viewed
                                        -- Uses Common Table Expression (CTE) for convenience and code reproducibility
                                        -- Note: I used Row_number instead of Rank as many rows strangely had the
                                        -- same hoursviewed
                                        WITH RankedShows AS (
                                        SELECT
                                        YEAR(releasedate) AS year,
                                        title,
                                        hoursviewed,
                                        ROW_NUMBER() OVER (PARTITION BY YEAR(releasedate) ORDER BY hoursviewed DESC) AS rank
                                        FROM
                                        student.netflix2023
                                        )
                                        SELECT
                                        year,
                                        title,
                                        hoursviewed
                                        FROM
                                        RankedShows
                                        WHERE
                                        rank = 1
                                        ORDER BY
                                        year ASC;
                                        XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
                                          × 
                                          INFO  : Compiling command(queryId=hive_20240327071038_fcb9cd22-9b76-4d43-881c-7c39d079ed48): -- Most popular show per year by Hours Viewed
-- Uses Common Table Expression (CTE) for convenience and code reproducibility
-- Note: I used Row_number instead of Rank as many rows strangely had the 
-- same hoursviewed
WITH RankedShows AS (
    SELECT 
        YEAR(releasedate) AS year,
        title,
        hoursviewed,
        ROW_NUMBER() OVER (PARTITION BY YEAR(releasedate) ORDER BY hoursviewed DESC) AS rank
    FROM 
        student.netflix2023
)
SELECT 
    year,
    title,
    hoursviewed
FROM 
    RankedShows
WHERE 
    rank = 1
ORDER BY
    year ASC
INFO  : Semantic Analysis Completed
INFO  : Returning Hive schema: Schema(fieldSchemas:[FieldSchema(name:year, type:int, comment:null), FieldSchema(name:title, type:string, comment:null), FieldSchema(name:hoursviewed, type:bigint, comment:null)], properties:null)
INFO  : Completed compiling command(queryId=hive_20240327071038_fcb9cd22-9b76-4d43-881c-7c39d079ed48); Time taken: 0.061 seconds
INFO  : Concurrency mode is disabled, not creating a lock manager
INFO  : Executing command(queryId=hive_20240327071038_fcb9cd22-9b76-4d43-881c-7c39d079ed48): -- Most popular show per year by Hours Viewed
-- Uses Common Table Expression (CTE) for convenience and code reproducibility
-- Note: I used Row_number instead of Rank as many rows strangely had the 
-- same hoursviewed
WITH RankedShows AS (
    SELECT 
        YEAR(releasedate) AS year,
        title,
        hoursviewed,
        ROW_NUMBER() OVER (PARTITION BY YEAR(releasedate) ORDER BY hoursviewed DESC) AS rank
    FROM 
        student.netflix2023
)
SELECT 
    year,
    title,
    hoursviewed
FROM 
    RankedShows
WHERE 
    rank = 1
ORDER BY
    year ASC
INFO  : Query ID = hive_20240327071038_fcb9cd22-9b76-4d43-881c-7c39d079ed48
INFO  : Total jobs = 1
INFO  : Launching Job 1 out of 1
INFO  : Starting task [Stage-1:MAPRED] in serial mode
INFO  : Session is already open
INFO  : Dag name: -- Most popular show per year by Hours...ASC(Stage-1)
INFO  : Status: Running (Executing on YARN cluster with App id application_1634527506680_1335)

INFO  : Map 1: 0/1	Reducer 2: 0/1	Reducer 3: 0/1
INFO  : Map 1: 0(+1)/1	Reducer 2: 0/1	Reducer 3: 0/1
INFO  : Map 1: 1/1	Reducer 2: 0/1	Reducer 3: 0/1	
INFO  : Map 1: 1/1	Reducer 2: 0(+1)/1	Reducer 3: 0/1	
INFO  : Map 1: 1/1	Reducer 2: 1/1	Reducer 3: 1/1	
INFO  : Completed executing command(queryId=hive_20240327071038_fcb9cd22-9b76-4d43-881c-7c39d079ed48); Time taken: 5.359 seconds
INFO  : OK
                                            year title hoursviewed
                                            year title hoursviewed
                                          12010Chi mon chaton: Season 117600000
                                          22011School Tales The Series: Season 1 // โรงเรียนผีมีอยู่ว่า...: ซีซั่น 194700000
                                          32012Mallesham3500000
                                          42013DAHMER: Monster: The Jeffrey Dahmer Story48800000
                                          52014Ya Boy Kongming!: Season 1 // パリピ孔明: シーズン161100000
                                          62015Pinky Malinky: Part 359900000
                                          72016Muted: Limited Series // El silencio: Miniserie98500000
                                          82017Crash Landing on You: Season 1 // 사랑의 불시착: 시즌 1102800000
                                          92018Jack the Giant Slayer123500000
                                          102019Bridgerton: Season 2120300000
                                          112020Stretch Armstrong & the Flex Fighters: Season 2184000000
                                          122021Ginny & Georgia: Season 1302100000
                                          132022Sexy Beasts: Season 1622800000
                                          142023The Night Agent: Season 1812100000
                                          Snippet icon

                                          My Snippet
                                          2.98s
                                          Database
                                          Type

                                          -- Most popular show per year by rating
                                          WITH RankedShows AS (
                                          SELECT
                                          title,
                                          YEAR(releasedate) AS year,
                                          rating,
                                          RANK() OVER (PARTITION BY YEAR(releasedate) ORDER BY rating DESC) AS rank
                                          FROM
                                          netflix2023
                                          )
                                          SELECT
                                          year,
                                          title,
                                          rating
                                          FROM
                                          RankedShows
                                          WHERE
                                          rank = 1
                                          ORDER BY
                                          year ASC;
                                          XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
                                            × 
                                            INFO  : Compiling command(queryId=hive_20240327071050_5ce4c4a1-849a-4b4d-a62c-7a0dd10925bd): -- Most popular show per year by rating
WITH RankedShows AS (
    SELECT
        title,
        YEAR(releasedate) AS year,
        rating,
        RANK() OVER (PARTITION BY YEAR(releasedate) ORDER BY rating DESC) AS rank
    FROM
        netflix2023
)
SELECT
    year,
    title,
    rating
FROM
    RankedShows
WHERE
    rank = 1
ORDER BY
    year ASC
INFO  : Semantic Analysis Completed
INFO  : Returning Hive schema: Schema(fieldSchemas:[FieldSchema(name:year, type:int, comment:null), FieldSchema(name:title, type:string, comment:null), FieldSchema(name:rating, type:double, comment:null)], properties:null)
INFO  : Completed compiling command(queryId=hive_20240327071050_5ce4c4a1-849a-4b4d-a62c-7a0dd10925bd); Time taken: 0.062 seconds
INFO  : Concurrency mode is disabled, not creating a lock manager
INFO  : Executing command(queryId=hive_20240327071050_5ce4c4a1-849a-4b4d-a62c-7a0dd10925bd): -- Most popular show per year by rating
WITH RankedShows AS (
    SELECT
        title,
        YEAR(releasedate) AS year,
        rating,
        RANK() OVER (PARTITION BY YEAR(releasedate) ORDER BY rating DESC) AS rank
    FROM
        netflix2023
)
SELECT
    year,
    title,
    rating
FROM
    RankedShows
WHERE
    rank = 1
ORDER BY
    year ASC
INFO  : Query ID = hive_20240327071050_5ce4c4a1-849a-4b4d-a62c-7a0dd10925bd
INFO  : Total jobs = 1
INFO  : Launching Job 1 out of 1
INFO  : Starting task [Stage-1:MAPRED] in serial mode
INFO  : Session is already open
INFO  : Dag name: -- Most popular show per year by ratin...ASC(Stage-1)
INFO  : Status: Running (Executing on YARN cluster with App id application_1634527506680_1335)

INFO  : Map 1: 0(+1)/1	Reducer 2: 0/1	Reducer 3: 0/1	
INFO  : Map 1: 1/1	Reducer 2: 0/1	Reducer 3: 0/1	
INFO  : Map 1: 1/1	Reducer 2: 0(+1)/1	Reducer 3: 0/1	
INFO  : Map 1: 1/1	Reducer 2: 1/1	Reducer 3: 0(+1)/1	
INFO  : Map 1: 1/1	Reducer 2: 1/1	Reducer 3: 1/1	
INFO  : Completed executing command(queryId=hive_20240327071050_5ce4c4a1-849a-4b4d-a62c-7a0dd10925bd); Time taken: 0.972 seconds
INFO  : OK
                                              year title rating
                                              year title rating
                                            12010Guardian: The Lonely and Great God: Season 1 // 도깨비: 시즌18.4
                                            22011The Myth // 神話 // 神话8.1
                                            32012African Queens: Njinga: Limited Series8.4
                                            42013On Children: Season 1 // 你的孩子不是你的孩子: 第 1 季8.9
                                            52013Story of My Family!!!: Season 1 // 俺の家の話: シーズン18.9
                                            62014Shiny_Flakes: The Teenage Drug Lord9.7
                                            72015Naruto Shippuden: Season 10 // NARUTO -ナルト - 疾風伝: 五影編9.3
                                            82016The Dig9.9
                                            92017Switch // ドラマスペシャル「スイッチ」9.7
                                            102017Confessions of an Invisible Girl // Confissões de uma Garota Excluída9.7
                                            112017Overlord IV // オーバーロードⅣ9.7
                                            122018Once Upon A Time: Season 1 // Pada Zaman Dahulu: Musim 110
                                            132019Battle: Freestyle10
                                            142020Geng: The Adventure Begins // Geng: Pengembaraan bermula9.9
                                            152021XXX: State of the Union10
                                            162022Barbie Epic Road Trip10
                                            172022School of Roars: Season 110
                                            182022Everything But a Man10
                                            192023Switch // ドラマスペシャル「スイッチ」9.7
                                            Snippet icon

                                            My Snippet
                                            7.31s
                                            Database
                                            Type

                                            -- Rank by rating then hoursviewed
                                            WITH RankedMovies AS (
                                            SELECT
                                            title,
                                            YEAR(releasedate) AS year,
                                            rating,
                                            hoursviewed,
                                            ROW_NUMBER() OVER (PARTITION BY YEAR(releasedate) ORDER BY rating DESC, hoursviewed DESC) as rank
                                            FROM
                                            netflix2023
                                            )
                                            SELECT
                                            year,
                                            title,
                                            rating,
                                            hoursviewed
                                            FROM
                                            RankedMovies
                                            WHERE
                                            rank = 1
                                            ORDER BY
                                            year ASC;
                                            XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
                                              × 
                                              INFO  : Compiling command(queryId=hive_20240327071056_bed42334-d4c6-4a12-8b68-3364e8257ee1): -- Rank by rating then hoursviewed
WITH RankedMovies AS (
    SELECT
        title,
        YEAR(releasedate) AS year,
        rating,
        hoursviewed,
        ROW_NUMBER() OVER (PARTITION BY YEAR(releasedate) ORDER BY rating DESC, hoursviewed DESC) as rank
    FROM
        netflix2023
)
SELECT
    year,
    title,
    rating,
    hoursviewed
FROM
    RankedMovies
WHERE
    rank = 1
ORDER BY
    year ASC
INFO  : Semantic Analysis Completed
INFO  : Returning Hive schema: Schema(fieldSchemas:[FieldSchema(name:year, type:int, comment:null), FieldSchema(name:title, type:string, comment:null), FieldSchema(name:rating, type:double, comment:null), FieldSchema(name:hoursviewed, type:bigint, comment:null)], properties:null)
INFO  : Completed compiling command(queryId=hive_20240327071056_bed42334-d4c6-4a12-8b68-3364e8257ee1); Time taken: 0.056 seconds
INFO  : Concurrency mode is disabled, not creating a lock manager
INFO  : Executing command(queryId=hive_20240327071056_bed42334-d4c6-4a12-8b68-3364e8257ee1): -- Rank by rating then hoursviewed
WITH RankedMovies AS (
    SELECT
        title,
        YEAR(releasedate) AS year,
        rating,
        hoursviewed,
        ROW_NUMBER() OVER (PARTITION BY YEAR(releasedate) ORDER BY rating DESC, hoursviewed DESC) as rank
    FROM
        netflix2023
)
SELECT
    year,
    title,
    rating,
    hoursviewed
FROM
    RankedMovies
WHERE
    rank = 1
ORDER BY
    year ASC
INFO  : Query ID = hive_20240327071056_bed42334-d4c6-4a12-8b68-3364e8257ee1
INFO  : Total jobs = 1
INFO  : Launching Job 1 out of 1
INFO  : Starting task [Stage-1:MAPRED] in serial mode
INFO  : Session is already open
INFO  : Dag name: -- Rank by rating then hoursviewed
WIT...ASC(Stage-1)
INFO  : Status: Running (Executing on YARN cluster with App id application_1634527506680_1335)

INFO  : Map 1: 0/1	Reducer 2: 0/1	Reducer 3: 0/1
INFO  : Map 1: 0(+1)/1	Reducer 2: 0/1	Reducer 3: 0/1
INFO  : Map 1: 1/1	Reducer 2: 0(+1)/1	Reducer 3: 0/1	
INFO  : Map 1: 1/1	Reducer 2: 1/1	Reducer 3: 0(+1)/1	
INFO  : Map 1: 1/1	Reducer 2: 1/1	Reducer 3: 1/1	
INFO  : Completed executing command(queryId=hive_20240327071056_bed42334-d4c6-4a12-8b68-3364e8257ee1); Time taken: 5.522 seconds
INFO  : OK
                                                year title rating hoursviewed
                                                year title rating hoursviewed
                                              12010Guardian: The Lonely and Great God: Season 1 // 도깨비: 시즌18.411300000
                                              22011The Myth // 神話 // 神话8.194700000
                                              32012African Queens: Njinga: Limited Series8.43500000
                                              42013Story of My Family!!!: Season 1 // 俺の家の話: シーズン18.924700000
                                              52014Shiny_Flakes: The Teenage Drug Lord9.74600000
                                              62015Naruto Shippuden: Season 10 // NARUTO -ナルト - 疾風伝: 五影編9.3800000
                                              72016The Dig9.9600000
                                              82017Overlord IV // オーバーロードⅣ9.71400000
                                              92018Once Upon A Time: Season 1 // Pada Zaman Dahulu: Musim 110600000
                                              102019Battle: Freestyle105100000
                                              112020Geng: The Adventure Begins // Geng: Pengembaraan bermula9.92600000
                                              122021XXX: State of the Union106000000
                                              132022Everything But a Man1032100000
                                              142023Switch // ドラマスペシャル「スイッチ」9.7300000
                                              Snippet icon

                                              My Snippet
                                              2.77s
                                              Database
                                              Type

                                              -- Total Hours Viewed over the years
                                              SELECT
                                              YEAR(releasedate) AS year,
                                              SUM(hoursviewed) AS `Total Hours Viewed`
                                              FROM
                                              student.netflix2023
                                              GROUP BY
                                              YEAR(releasedate)
                                              XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
                                                × 
                                                INFO  : Compiling command(queryId=hive_20240327071106_97da438d-0215-44f6-88d0-e0344e680326): -- Total Hours Viewed over the years
SELECT 
    YEAR(releasedate) AS year,
    SUM(hoursviewed) AS `Total Hours Viewed`
FROM 
    student.netflix2023
GROUP BY 
    YEAR(releasedate)
INFO  : Semantic Analysis Completed
INFO  : Returning Hive schema: Schema(fieldSchemas:[FieldSchema(name:year, type:int, comment:null), FieldSchema(name:total hours viewed, type:bigint, comment:null)], properties:null)
INFO  : Completed compiling command(queryId=hive_20240327071106_97da438d-0215-44f6-88d0-e0344e680326); Time taken: 0.048 seconds
INFO  : Concurrency mode is disabled, not creating a lock manager
INFO  : Executing command(queryId=hive_20240327071106_97da438d-0215-44f6-88d0-e0344e680326): -- Total Hours Viewed over the years
SELECT 
    YEAR(releasedate) AS year,
    SUM(hoursviewed) AS `Total Hours Viewed`
FROM 
    student.netflix2023
GROUP BY 
    YEAR(releasedate)
INFO  : Query ID = hive_20240327071106_97da438d-0215-44f6-88d0-e0344e680326
INFO  : Total jobs = 1
INFO  : Launching Job 1 out of 1
INFO  : Starting task [Stage-1:MAPRED] in serial mode
INFO  : Session is already open
INFO  : Dag name: -- Total Hours Viewed ov...YEAR(releasedate)(Stage-1)
INFO  : Status: Running (Executing on YARN cluster with App id application_1634527506680_1335)

INFO  : Map 1: 0(+1)/1	Reducer 2: 0/1	
INFO  : Map 1: 1/1	Reducer 2: 0(+1)/1	
INFO  : Map 1: 1/1	Reducer 2: 1/1	
INFO  : Completed executing command(queryId=hive_20240327071106_97da438d-0215-44f6-88d0-e0344e680326); Time taken: 0.744 seconds
INFO  : OK
                                                • type
                                                • x-axis
                                                • y-axis
                                                • group
                                                • limit
                                                • sorting

                                                Select the chart parameters on the left

                                                2,0102,0122,0142,0162,0182,0202,02210G20G30G40G10.5M48.823G
                                                total hours viewedtotal hours viewed
                                                Snippet icon

                                                My Snippet
                                                2.77s
                                                Database
                                                Type

                                                -- Common Table Expression (CTE)
                                                WITH RankedMovies AS (
                                                SELECT
                                                title,
                                                YEAR(releasedate) AS year,
                                                rating,
                                                RANK() OVER (PARTITION BY YEAR(releasedate) ORDER BY rating DESC) AS rank
                                                FROM
                                                netflix2023
                                                )
                                                SELECT
                                                year,
                                                title,
                                                rating
                                                FROM
                                                RankedMovies
                                                WHERE
                                                rank = 1
                                                ORDER BY
                                                year ASC;
                                                XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
                                                  × 
                                                  INFO  : Compiling command(queryId=hive_20240327071113_342d1747-9d11-4822-b38a-e4b25f410eff): -- Common Table Expression (CTE)
WITH RankedMovies AS (
    SELECT
        title,
        YEAR(releasedate) AS year,
        rating,
        RANK() OVER (PARTITION BY YEAR(releasedate) ORDER BY rating DESC) AS rank
    FROM
        netflix2023
)
SELECT
    year,
    title,
    rating
FROM
    RankedMovies
WHERE
    rank = 1
ORDER BY
    year ASC
INFO  : Semantic Analysis Completed
INFO  : Returning Hive schema: Schema(fieldSchemas:[FieldSchema(name:year, type:int, comment:null), FieldSchema(name:title, type:string, comment:null), FieldSchema(name:rating, type:double, comment:null)], properties:null)
INFO  : Completed compiling command(queryId=hive_20240327071113_342d1747-9d11-4822-b38a-e4b25f410eff); Time taken: 0.052 seconds
INFO  : Concurrency mode is disabled, not creating a lock manager
INFO  : Executing command(queryId=hive_20240327071113_342d1747-9d11-4822-b38a-e4b25f410eff): -- Common Table Expression (CTE)
WITH RankedMovies AS (
    SELECT
        title,
        YEAR(releasedate) AS year,
        rating,
        RANK() OVER (PARTITION BY YEAR(releasedate) ORDER BY rating DESC) AS rank
    FROM
        netflix2023
)
SELECT
    year,
    title,
    rating
FROM
    RankedMovies
WHERE
    rank = 1
ORDER BY
    year ASC
INFO  : Query ID = hive_20240327071113_342d1747-9d11-4822-b38a-e4b25f410eff
INFO  : Total jobs = 1
INFO  : Launching Job 1 out of 1
INFO  : Starting task [Stage-1:MAPRED] in serial mode
INFO  : Session is already open
INFO  : Dag name: -- Common Table Expression (CTE)
WITH ...ASC(Stage-1)
INFO  : Status: Running (Executing on YARN cluster with App id application_1634527506680_1335)

INFO  : Map 1: 0(+1)/1	Reducer 2: 0/1	Reducer 3: 0/1	
INFO  : Map 1: 1/1	Reducer 2: 0/1	Reducer 3: 0/1	
INFO  : Map 1: 1/1	Reducer 2: 0(+1)/1	Reducer 3: 0/1	
INFO  : Map 1: 1/1	Reducer 2: 1/1	Reducer 3: 0(+1)/1	
INFO  : Map 1: 1/1	Reducer 2: 1/1	Reducer 3: 1/1	
INFO  : Completed executing command(queryId=hive_20240327071113_342d1747-9d11-4822-b38a-e4b25f410eff); Time taken: 1.007 seconds
INFO  : OK
                                                    year title rating
                                                    year title rating
                                                  12010Guardian: The Lonely and Great God: Season 1 // 도깨비: 시즌18.4
                                                  22011The Myth // 神話 // 神话8.1
                                                  32012African Queens: Njinga: Limited Series8.4
                                                  42013On Children: Season 1 // 你的孩子不是你的孩子: 第 1 季8.9
                                                  52013Story of My Family!!!: Season 1 // 俺の家の話: シーズン18.9
                                                  62014Shiny_Flakes: The Teenage Drug Lord9.7
                                                  72015Naruto Shippuden: Season 10 // NARUTO -ナルト - 疾風伝: 五影編9.3
                                                  82016The Dig9.9
                                                  92017Switch // ドラマスペシャル「スイッチ」9.7
                                                  102017Confessions of an Invisible Girl // Confissões de uma Garota Excluída9.7
                                                  112017Overlord IV // オーバーロードⅣ9.7
                                                  122018Once Upon A Time: Season 1 // Pada Zaman Dahulu: Musim 110
                                                  132019Battle: Freestyle10
                                                  142020Geng: The Adventure Begins // Geng: Pengembaraan bermula9.9
                                                  152021XXX: State of the Union10
                                                  162022Barbie Epic Road Trip10
                                                  172022School of Roars: Season 110
                                                  182022Everything But a Man10
                                                  192023Switch // ドラマスペシャル「スイッチ」9.7
                                                  Snippet icon

                                                  My Snippet
                                                  3.22s
                                                  Database
                                                  Type

                                                  -- Hours Viewed per Genre
                                                  SELECT
                                                  genre,
                                                  SUM(hoursviewed) AS total_hours_viewed
                                                  FROM
                                                  netflix2023_genre_exploded
                                                  GROUP BY
                                                  genre
                                                  ORDER BY
                                                  total_hours_viewed DESC;
                                                  XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
                                                    × 
                                                    INFO  : Compiling command(queryId=hive_20240327071119_d9dfe93b-53ef-4faa-bfa2-ce399a142b8f): -- Hours Viewed per Genre
SELECT
    genre,
    SUM(hoursviewed) AS total_hours_viewed
FROM
    netflix2023_genre_exploded
GROUP BY
    genre
ORDER BY
    total_hours_viewed DESC
INFO  : Semantic Analysis Completed
INFO  : Returning Hive schema: Schema(fieldSchemas:[FieldSchema(name:genre, type:string, comment:null), FieldSchema(name:total_hours_viewed, type:bigint, comment:null)], properties:null)
INFO  : Completed compiling command(queryId=hive_20240327071119_d9dfe93b-53ef-4faa-bfa2-ce399a142b8f); Time taken: 0.025 seconds
INFO  : Concurrency mode is disabled, not creating a lock manager
INFO  : Executing command(queryId=hive_20240327071119_d9dfe93b-53ef-4faa-bfa2-ce399a142b8f): -- Hours Viewed per Genre
SELECT
    genre,
    SUM(hoursviewed) AS total_hours_viewed
FROM
    netflix2023_genre_exploded
GROUP BY
    genre
ORDER BY
    total_hours_viewed DESC
INFO  : Query ID = hive_20240327071119_d9dfe93b-53ef-4faa-bfa2-ce399a142b8f
INFO  : Total jobs = 1
INFO  : Launching Job 1 out of 1
INFO  : Starting task [Stage-1:MAPRED] in serial mode
INFO  : Session is already open
INFO  : Dag name: -- Hours Viewed per Genre
SELECT
    ...DESC(Stage-1)
INFO  : Status: Running (Executing on YARN cluster with App id application_1634527506680_1335)

INFO  : Map 1: 0(+1)/1	Reducer 2: 0/1	Reducer 3: 0/1	
INFO  : Map 1: 1/1	Reducer 2: 0(+1)/1	Reducer 3: 0/1	
INFO  : Map 1: 1/1	Reducer 2: 1/1	Reducer 3: 1/1	
INFO  : Completed executing command(queryId=hive_20240327071119_d9dfe93b-53ef-4faa-bfa2-ce399a142b8f); Time taken: 1.038 seconds
INFO  : OK
                                                    • value
                                                    • legend
                                                    • limit
                                                    • sorting

                                                    Select the chart parameters on the left

                                                    DramaComedyShortDocumentaryActionCrimeAnimationAdventureRomanceThrillerHorrorFamilyMysteryBiography
                                                    Snippet icon

                                                    My Snippet
                                                    3.33s
                                                    Database
                                                    Type

                                                    -- Most popular genre by "Total Hours Viewed"
                                                    WITH YearlyGenreStats AS (
                                                    SELECT
                                                    YEAR(releasedate) AS year,
                                                    genre,
                                                    SUM(hoursviewed) AS total_hours_viewed,
                                                    ROW_NUMBER() OVER (PARTITION BY YEAR(releasedate) ORDER BY SUM(hoursviewed) DESC) AS genre_rank
                                                    FROM
                                                    student.netflix2023_genre_exploded
                                                    GROUP BY
                                                    YEAR(releasedate),
                                                    genre
                                                    )
                                                    SELECT
                                                    year,
                                                    genre,
                                                    total_hours_viewed
                                                    FROM
                                                    YearlyGenreStats
                                                    WHERE
                                                    genre_rank = 1
                                                    ORDER BY
                                                    year ASC;
                                                    XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
                                                      × 
                                                      INFO  : Compiling command(queryId=hive_20240327071126_9d847c71-9472-444a-9aee-79c5a72cd6ad): -- Most popular genre by "Total Hours Viewed"
WITH YearlyGenreStats AS (
    SELECT
        YEAR(releasedate) AS year,
        genre,
        SUM(hoursviewed) AS total_hours_viewed,
        ROW_NUMBER() OVER (PARTITION BY YEAR(releasedate) ORDER BY SUM(hoursviewed) DESC) AS genre_rank
    FROM 
        student.netflix2023_genre_exploded
    GROUP BY
        YEAR(releasedate),
        genre
)
SELECT
    year,
    genre,
    total_hours_viewed
FROM
    YearlyGenreStats
WHERE
    genre_rank = 1
ORDER BY
    year ASC
INFO  : Semantic Analysis Completed
INFO  : Returning Hive schema: Schema(fieldSchemas:[FieldSchema(name:year, type:int, comment:null), FieldSchema(name:genre, type:string, comment:null), FieldSchema(name:total_hours_viewed, type:bigint, comment:null)], properties:null)
INFO  : Completed compiling command(queryId=hive_20240327071126_9d847c71-9472-444a-9aee-79c5a72cd6ad); Time taken: 0.029 seconds
INFO  : Concurrency mode is disabled, not creating a lock manager
INFO  : Executing command(queryId=hive_20240327071126_9d847c71-9472-444a-9aee-79c5a72cd6ad): -- Most popular genre by "Total Hours Viewed"
WITH YearlyGenreStats AS (
    SELECT
        YEAR(releasedate) AS year,
        genre,
        SUM(hoursviewed) AS total_hours_viewed,
        ROW_NUMBER() OVER (PARTITION BY YEAR(releasedate) ORDER BY SUM(hoursviewed) DESC) AS genre_rank
    FROM 
        student.netflix2023_genre_exploded
    GROUP BY
        YEAR(releasedate),
        genre
)
SELECT
    year,
    genre,
    total_hours_viewed
FROM
    YearlyGenreStats
WHERE
    genre_rank = 1
ORDER BY
    year ASC
INFO  : Query ID = hive_20240327071126_9d847c71-9472-444a-9aee-79c5a72cd6ad
INFO  : Total jobs = 1
INFO  : Launching Job 1 out of 1
INFO  : Starting task [Stage-1:MAPRED] in serial mode
INFO  : Session is already open
INFO  : Dag name: -- Most popular genre by "Total Hours ...ASC(Stage-1)
INFO  : Status: Running (Executing on YARN cluster with App id application_1634527506680_1335)

INFO  : Map 1: 0(+1)/1	Reducer 2: 0/1	Reducer 3: 0/1	Reducer 4: 0/1	
INFO  : Map 1: 1/1	Reducer 2: 1/1	Reducer 3: 0(+1)/1	Reducer 4: 0/1	
INFO  : Map 1: 1/1	Reducer 2: 1/1	Reducer 3: 1/1	Reducer 4: 1/1	
INFO  : Completed executing command(queryId=hive_20240327071126_9d847c71-9472-444a-9aee-79c5a72cd6ad); Time taken: 0.887 seconds
INFO  : OK
                                                      • type
                                                      • x-axis
                                                      • y-axis
                                                      • group
                                                      • limit
                                                      • sorting

                                                      Select the chart parameters on the left

                                                      2,0102,0122,0142,0162,0182,0202,0225G10G15G3.5M18.9414G
                                                      total_hours_viewedtotal_hours_viewed
                                                      Snippet icon

                                                      My Snippet
                                                      6.99s
                                                      Database
                                                      Type

                                                      -- Average Ratings per Genre
                                                      SELECT
                                                      genre,
                                                      AVG(rating) AS average_rating
                                                      FROM
                                                      student.netflix2023_genre_exploded
                                                      GROUP BY
                                                      genre
                                                      ORDER BY
                                                      average_rating DESC;
                                                      XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
                                                        × 
                                                        INFO  : Compiling command(queryId=hive_20240327071150_c84ce789-ad28-4e7a-ac90-6d6f128f41ec): -- Average Ratings per Genre

SELECT
    genre,
    AVG(rating) AS average_rating
FROM
    student.netflix2023_genre_exploded
GROUP BY
    genre
ORDER BY
    average_rating DESC
INFO  : Semantic Analysis Completed
INFO  : Returning Hive schema: Schema(fieldSchemas:[FieldSchema(name:genre, type:string, comment:null), FieldSchema(name:average_rating, type:double, comment:null)], properties:null)
INFO  : Completed compiling command(queryId=hive_20240327071150_c84ce789-ad28-4e7a-ac90-6d6f128f41ec); Time taken: 0.026 seconds
INFO  : Concurrency mode is disabled, not creating a lock manager
INFO  : Executing command(queryId=hive_20240327071150_c84ce789-ad28-4e7a-ac90-6d6f128f41ec): -- Average Ratings per Genre

SELECT
    genre,
    AVG(rating) AS average_rating
FROM
    student.netflix2023_genre_exploded
GROUP BY
    genre
ORDER BY
    average_rating DESC
INFO  : Query ID = hive_20240327071150_c84ce789-ad28-4e7a-ac90-6d6f128f41ec
INFO  : Total jobs = 1
INFO  : Launching Job 1 out of 1
INFO  : Starting task [Stage-1:MAPRED] in serial mode
INFO  : Session is already open
INFO  : Dag name: -- Average Ratings per Genre

SELECT
...DESC(Stage-1)
INFO  : Status: Running (Executing on YARN cluster with App id application_1634527506680_1335)

INFO  : Map 1: 0/1	Reducer 2: 0/1	Reducer 3: 0/1
INFO  : Map 1: 0(+1)/1	Reducer 2: 0/1	Reducer 3: 0/1
INFO  : Map 1: 1/1	Reducer 2: 0/1	Reducer 3: 0/1	
INFO  : Map 1: 1/1	Reducer 2: 0(+1)/1	Reducer 3: 0/1	
INFO  : Map 1: 1/1	Reducer 2: 1/1	Reducer 3: 0(+1)/1	
INFO  : Map 1: 1/1	Reducer 2: 1/1	Reducer 3: 1/1	
INFO  : Completed executing command(queryId=hive_20240327071150_c84ce789-ad28-4e7a-ac90-6d6f128f41ec); Time taken: 5.194 seconds
INFO  : OK
                                                        • type
                                                        • x-axis
                                                        • y-axis
                                                        • group
                                                        • limit
                                                        • sorting

                                                        Select the chart parameters on the left

                                                        Talk...We...Doc...Ro...Bio...Dra...Thri...Adv...Hor...Fa... SciFiMys...Ga...012345606.839259
                                                        average_ratingaverage_rating
                                                        Snippet icon

                                                        My Snippet
                                                        8.69s
                                                        Database
                                                        Type

                                                        -- Most popular genre per year by "Average rating"
                                                        WITH YearlyGenreRatings AS (
                                                        SELECT
                                                        YEAR(releasedate) AS year,
                                                        genre,
                                                        ROUND(AVG(rating), 2) AS average_rating,
                                                        ROW_NUMBER() OVER (PARTITION BY YEAR(releasedate) ORDER BY AVG(rating) DESC) AS rank
                                                        FROM
                                                        student.netflix2023_genre_exploded
                                                        GROUP BY
                                                        YEAR(releasedate),
                                                        genre
                                                        )
                                                        SELECT
                                                        year,
                                                        genre,
                                                        average_rating
                                                        FROM
                                                        YearlyGenreRatings
                                                        WHERE
                                                        rank = 1
                                                        ORDER BY
                                                        year ASC;
                                                        XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
                                                          × 
                                                          INFO  : Compiling command(queryId=hive_20240327071205_798f7826-99a7-4d05-ab3d-09601f010832): -- Most popular genre per year by "Average rating"
WITH YearlyGenreRatings AS (
    SELECT
        YEAR(releasedate) AS year,
        genre,
        ROUND(AVG(rating), 2) AS average_rating,
        ROW_NUMBER() OVER (PARTITION BY YEAR(releasedate) ORDER BY AVG(rating) DESC) AS rank
    FROM 
        student.netflix2023_genre_exploded
    GROUP BY
        YEAR(releasedate),
        genre
)
SELECT
    year,
    genre,
    average_rating
FROM
    YearlyGenreRatings
WHERE
    rank = 1
ORDER BY
    year ASC
INFO  : Semantic Analysis Completed
INFO  : Returning Hive schema: Schema(fieldSchemas:[FieldSchema(name:year, type:int, comment:null), FieldSchema(name:genre, type:string, comment:null), FieldSchema(name:average_rating, type:double, comment:null)], properties:null)
INFO  : Completed compiling command(queryId=hive_20240327071205_798f7826-99a7-4d05-ab3d-09601f010832); Time taken: 0.038 seconds
INFO  : Concurrency mode is disabled, not creating a lock manager
INFO  : Executing command(queryId=hive_20240327071205_798f7826-99a7-4d05-ab3d-09601f010832): -- Most popular genre per year by "Average rating"
WITH YearlyGenreRatings AS (
    SELECT
        YEAR(releasedate) AS year,
        genre,
        ROUND(AVG(rating), 2) AS average_rating,
        ROW_NUMBER() OVER (PARTITION BY YEAR(releasedate) ORDER BY AVG(rating) DESC) AS rank
    FROM 
        student.netflix2023_genre_exploded
    GROUP BY
        YEAR(releasedate),
        genre
)
SELECT
    year,
    genre,
    average_rating
FROM
    YearlyGenreRatings
WHERE
    rank = 1
ORDER BY
    year ASC
INFO  : Query ID = hive_20240327071205_798f7826-99a7-4d05-ab3d-09601f010832
INFO  : Total jobs = 1
INFO  : Launching Job 1 out of 1
INFO  : Starting task [Stage-1:MAPRED] in serial mode
INFO  : Session is already open
INFO  : Dag name: -- Most popular genre per year by "Ave...ASC(Stage-1)
INFO  : Status: Running (Executing on YARN cluster with App id application_1634527506680_1335)

INFO  : Map 1: 0/1	Reducer 2: 0/1	Reducer 3: 0/1	Reducer 4: 0/1
INFO  : Map 1: 0(+1)/1	Reducer 2: 0/1	Reducer 3: 0/1	Reducer 4: 0/1
INFO  : Map 1: 1/1	Reducer 2: 0/1	Reducer 3: 0/1	Reducer 4: 0/1	
INFO  : Map 1: 1/1	Reducer 2: 0(+1)/1	Reducer 3: 0/1	Reducer 4: 0/1	
INFO  : Map 1: 1/1	Reducer 2: 1/1	Reducer 3: 0(+1)/1	Reducer 4: 0/1	
INFO  : Map 1: 1/1	Reducer 2: 1/1	Reducer 3: 1/1	Reducer 4: 1/1	
INFO  : Completed executing command(queryId=hive_20240327071205_798f7826-99a7-4d05-ab3d-09601f010832); Time taken: 5.77 seconds
INFO  : OK
                                                          columns (4)
                                                          int
                                                          year int
                                                          genre string
                                                          average_rating double
                                                            year genre average_rating
                                                            year genre average_rating
                                                          12010History7.8
                                                          22011Family8.1
                                                          32012Biography8.4
                                                          42013Mystery8.2
                                                          52014Music7.93
                                                          62015Western7.85
                                                          72016Talk-Show7.3
                                                          82017Music7.01
                                                          92018Western7.22
                                                          102019Talk-Show6.98
                                                          112020News6.95
                                                          122021Western7.04
                                                          132022Western7.06
                                                          142023Talk-Show8.11
                                                          Snippet icon

                                                          My Snippet
                                                          5.5s
                                                          Database
                                                          Type

                                                          -- Genre Popularity Over Time: Comedy
                                                          SELECT
                                                          YEAR(releasedate) AS year,
                                                          SUM(hoursviewed) AS total_hours_viewed
                                                          FROM
                                                          student.netflix2023_genre_exploded
                                                          GROUP BY
                                                          YEAR(releasedate),
                                                          genre
                                                          HAVING
                                                          genre = "Comedy"
                                                          ORDER BY
                                                          year ASC,
                                                          total_hours_viewed DESC;
                                                          XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
                                                            × 
                                                            INFO  : Compiling command(queryId=hive_20240327071217_93cc1833-b796-474b-831c-88ef53f697dc): -- Genre Popularity Over Time: Comedy
SELECT 
    YEAR(releasedate) AS year, 
    SUM(hoursviewed) AS total_hours_viewed
FROM 
    student.netflix2023_genre_exploded
GROUP BY 
    YEAR(releasedate), 
    genre
HAVING
    genre = "Comedy"
ORDER BY 
    year ASC, 
    total_hours_viewed DESC
INFO  : Semantic Analysis Completed
INFO  : Returning Hive schema: Schema(fieldSchemas:[FieldSchema(name:year, type:int, comment:null), FieldSchema(name:total_hours_viewed, type:bigint, comment:null)], properties:null)
INFO  : Completed compiling command(queryId=hive_20240327071217_93cc1833-b796-474b-831c-88ef53f697dc); Time taken: 0.026 seconds
INFO  : Concurrency mode is disabled, not creating a lock manager
INFO  : Executing command(queryId=hive_20240327071217_93cc1833-b796-474b-831c-88ef53f697dc): -- Genre Popularity Over Time: Comedy
SELECT 
    YEAR(releasedate) AS year, 
    SUM(hoursviewed) AS total_hours_viewed
FROM 
    student.netflix2023_genre_exploded
GROUP BY 
    YEAR(releasedate), 
    genre
HAVING
    genre = "Comedy"
ORDER BY 
    year ASC, 
    total_hours_viewed DESC
INFO  : Query ID = hive_20240327071217_93cc1833-b796-474b-831c-88ef53f697dc
INFO  : Total jobs = 1
INFO  : Launching Job 1 out of 1
INFO  : Starting task [Stage-1:MAPRED] in serial mode
INFO  : Session is already open
INFO  : Dag name: -- Genre Popularity Over Time: Comedy...DESC(Stage-1)
INFO  : Status: Running (Executing on YARN cluster with App id application_1634527506680_1335)

INFO  : Map 1: 0(+1)/1	Reducer 2: 0/1	Reducer 3: 0/1	
INFO  : Map 1: 1/1	Reducer 2: 0/1	Reducer 3: 0/1	
INFO  : Map 1: 1/1	Reducer 2: 1/1	Reducer 3: 0(+1)/1	
INFO  : Map 1: 1/1	Reducer 2: 1/1	Reducer 3: 1/1	
INFO  : Completed executing command(queryId=hive_20240327071217_93cc1833-b796-474b-831c-88ef53f697dc); Time taken: 1.201 seconds
INFO  : OK
                                                            columns (3)
                                                            int
                                                            year int
                                                            total_hours_viewed bigint
                                                              year total_hours_viewed
                                                              year total_hours_viewed
                                                            1201077000000
                                                            22011111800000
                                                            320123500000
                                                            42013196200000
                                                            52014156000000
                                                            62015597500000
                                                            720161296200000
                                                            820172226100000
                                                            920182475200000
                                                            1020193533700000
                                                            1120204579900000
                                                            1220215746600000
                                                            13202211483900000
                                                            14202318941400000
                                                            Snippet icon

                                                            My Snippet
                                                            7.7s
                                                            Database
                                                            Type

                                                            -- Genre Popularity Over Time: Drama
                                                            SELECT
                                                            YEAR(releasedate) AS year,
                                                            genre,
                                                            SUM(hoursviewed) AS total_hours_viewed
                                                            FROM
                                                            student.netflix2023_genre_exploded
                                                            GROUP BY
                                                            YEAR(releasedate),
                                                            genre
                                                            HAVING
                                                            genre = "Drama"
                                                            ORDER BY
                                                            year ASC,
                                                            total_hours_viewed DESC;
                                                            XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
                                                              × 
                                                              INFO  : Compiling command(queryId=hive_20240327071241_b1349af3-cbff-437e-b499-75d08a994795): -- Genre Popularity Over Time: Drama
SELECT 
    YEAR(releasedate) AS year, 
    genre, 
    SUM(hoursviewed) AS total_hours_viewed
FROM 
    student.netflix2023_genre_exploded
GROUP BY 
    YEAR(releasedate), 
    genre
HAVING
    genre = "Drama"
ORDER BY 
    year ASC, 
    total_hours_viewed DESC
INFO  : Semantic Analysis Completed
INFO  : Returning Hive schema: Schema(fieldSchemas:[FieldSchema(name:year, type:int, comment:null), FieldSchema(name:genre, type:string, comment:null), FieldSchema(name:total_hours_viewed, type:bigint, comment:null)], properties:null)
INFO  : Completed compiling command(queryId=hive_20240327071241_b1349af3-cbff-437e-b499-75d08a994795); Time taken: 0.026 seconds
INFO  : Concurrency mode is disabled, not creating a lock manager
INFO  : Executing command(queryId=hive_20240327071241_b1349af3-cbff-437e-b499-75d08a994795): -- Genre Popularity Over Time: Drama
SELECT 
    YEAR(releasedate) AS year, 
    genre, 
    SUM(hoursviewed) AS total_hours_viewed
FROM 
    student.netflix2023_genre_exploded
GROUP BY 
    YEAR(releasedate), 
    genre
HAVING
    genre = "Drama"
ORDER BY 
    year ASC, 
    total_hours_viewed DESC
INFO  : Query ID = hive_20240327071241_b1349af3-cbff-437e-b499-75d08a994795
INFO  : Total jobs = 1
INFO  : Launching Job 1 out of 1
INFO  : Starting task [Stage-1:MAPRED] in serial mode
INFO  : Session is already open
INFO  : Dag name: -- Genre Popularity Over Time: Drama
...DESC(Stage-1)
INFO  : Status: Running (Executing on YARN cluster with App id application_1634527506680_1335)

INFO  : Map 1: 0/1	Reducer 2: 0/1	Reducer 3: 0/1
INFO  : Map 1: 0(+1)/1	Reducer 2: 0/1	Reducer 3: 0/1
INFO  : Map 1: 1/1	Reducer 2: 0/1	Reducer 3: 0/1	
INFO  : Map 1: 1/1	Reducer 2: 0(+1)/1	Reducer 3: 0/1	
INFO  : Map 1: 1/1	Reducer 2: 1/1	Reducer 3: 0(+1)/1	
INFO  : Map 1: 1/1	Reducer 2: 1/1	Reducer 3: 1/1	
INFO  : Completed executing command(queryId=hive_20240327071241_b1349af3-cbff-437e-b499-75d08a994795); Time taken: 5.447 seconds
INFO  : OK
                                                                year genre total_hours_viewed
                                                                year genre total_hours_viewed
                                                              12010Drama18300000
                                                              22011Drama107000000
                                                              32012Drama3500000
                                                              42013Drama104600000
                                                              52014Drama226300000
                                                              62015Drama573800000
                                                              72016Drama1320200000
                                                              82017Drama2038900000
                                                              92018Drama2790800000
                                                              102019Drama3280600000
                                                              112020Drama4884300000
                                                              122021Drama5705100000
                                                              132022Drama15027600000
                                                              142023Drama17416900000
                                                              Snippet icon

                                                              My Snippet
                                                              2.49s
                                                              Database
                                                              Type

                                                              -- Keyword Analysis for High-Performing Titles
                                                              WITH HighPerformingTitles AS (
                                                              SELECT
                                                              keywords,
                                                              RANK() OVER (ORDER BY hoursviewed DESC) AS rank
                                                              FROM
                                                              student.netflix2023
                                                              )
                                                              SELECT
                                                              keywords
                                                              FROM
                                                              HighPerformingTitles
                                                              WHERE
                                                              rank <= 10;
                                                              XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
                                                                × 
                                                                INFO  : Compiling command(queryId=hive_20240327071252_7f460b04-1642-40a3-b1a8-e2adb28965ef): -- Keyword Analysis for High-Performing Titles
WITH HighPerformingTitles AS (
    SELECT 
        keywords,
        RANK() OVER (ORDER BY hoursviewed DESC) AS rank
    FROM 
        student.netflix2023
)
SELECT 
    keywords
FROM 
    HighPerformingTitles
WHERE 
    rank <= 10
INFO  : Semantic Analysis Completed
INFO  : Returning Hive schema: Schema(fieldSchemas:[FieldSchema(name:keywords, type:string, comment:null)], properties:null)
INFO  : Completed compiling command(queryId=hive_20240327071252_7f460b04-1642-40a3-b1a8-e2adb28965ef); Time taken: 0.051 seconds
INFO  : Concurrency mode is disabled, not creating a lock manager
INFO  : Executing command(queryId=hive_20240327071252_7f460b04-1642-40a3-b1a8-e2adb28965ef): -- Keyword Analysis for High-Performing Titles
WITH HighPerformingTitles AS (
    SELECT 
        keywords,
        RANK() OVER (ORDER BY hoursviewed DESC) AS rank
    FROM 
        student.netflix2023
)
SELECT 
    keywords
FROM 
    HighPerformingTitles
WHERE 
    rank <= 10
INFO  : Query ID = hive_20240327071252_7f460b04-1642-40a3-b1a8-e2adb28965ef
INFO  : Total jobs = 1
INFO  : Launching Job 1 out of 1
INFO  : Starting task [Stage-1:MAPRED] in serial mode
INFO  : Session is already open
INFO  : Dag name: -- Keyword Analysis for High-Performing...10(Stage-1)
INFO  : Status: Running (Executing on YARN cluster with App id application_1634527506680_1335)

INFO  : Map 1: 0(+1)/1	Reducer 2: 0/1	
INFO  : Map 1: 1/1	Reducer 2: 0(+1)/1	
INFO  : Map 1: 1/1	Reducer 2: 1/1	
INFO  : Completed executing command(queryId=hive_20240327071252_7f460b04-1642-40a3-b1a8-e2adb28965ef); Time taken: 0.608 seconds
INFO  : OK
                                                                  keywords
                                                                  keywords
                                                                1dystopia, rebellion, revolution, brainwashing, execution
                                                                2intruder, executive, losing a job, berserk, jealousy
                                                                3persian empire, empire, 5th century b.c., achaemenid empire, persia
                                                                4producer, three word title, headstrong, arranged marriage, mother
                                                                5revenge, vengeance, lesbian, musician, female musical prodigy
                                                                6tv special, halloween, reenactment, halloween costume, trick or treating
                                                                7poetry, spain
                                                                8prequel, queen, historical, england, queen charlotte character
                                                                9dinosaur, jurassic park, pink haired girl, computer animation, black haired girl
                                                                10christmas, coming out, holidays, lesbian relationship, lesbian
                                                                11money
                                                                Snippet icon

                                                                My Snippet
                                                                4.49s
                                                                Database
                                                                Type

                                                                -- "How does the release timing of shows and movies (by season or quarter) affect their viewership and ratings, and how does this vary across different genres?"
                                                                -- extracting the release quarter, aggregating viewership and ratings data by quarter and genre,
                                                                -- and then analyzing the results to identify trends or patterns
                                                                -- Note: this is only an initial exploration, and not a fully fleshed out answer (which would take deeper investigation)
                                                                SELECT
                                                                genre,
                                                                CONCAT('Q', CEIL(MONTH(releasedate)/3)) AS release_quarter, -- Determine the release quarter
                                                                YEAR(releasedate) AS year,
                                                                AVG(hoursviewed) AS average_hours_viewed,
                                                                AVG(rating) AS average_rating,
                                                                COUNT(*) AS number_of_titles
                                                                FROM
                                                                student.netflix2023_genre_exploded
                                                                GROUP BY
                                                                genre,
                                                                YEAR(releasedate),
                                                                CONCAT('Q', CEIL(MONTH(releasedate)/3))
                                                                ORDER BY
                                                                genre,
                                                                year,
                                                                release_quarter;
                                                                XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
                                                                  × 
                                                                  INFO  : Compiling command(queryId=hive_20240327071300_385eaf27-9a18-4f92-92cb-baf3bc3f9f53): -- "How does the release timing of shows and movies (by season or quarter) affect their viewership and ratings, and how does this vary across different genres?"
-- extracting the release quarter, aggregating viewership and ratings data by quarter and genre, 
-- and then analyzing the results to identify trends or patterns
SELECT
    genre,
    CONCAT('Q', CEIL(MONTH(releasedate)/3)) AS release_quarter, -- Determine the release quarter
    YEAR(releasedate) AS year,
    AVG(hoursviewed) AS average_hours_viewed,
    AVG(rating) AS average_rating,
    COUNT(*) AS number_of_titles
FROM
    student.netflix2023_genre_exploded
GROUP BY
    genre,
    YEAR(releasedate),
    CONCAT('Q', CEIL(MONTH(releasedate)/3))
ORDER BY
    genre,
    year,
    release_quarter
INFO  : Semantic Analysis Completed
INFO  : Returning Hive schema: Schema(fieldSchemas:[FieldSchema(name:genre, type:string, comment:null), FieldSchema(name:release_quarter, type:string, comment:null), FieldSchema(name:year, type:int, comment:null), FieldSchema(name:average_hours_viewed, type:double, comment:null), FieldSchema(name:average_rating, type:double, comment:null), FieldSchema(name:number_of_titles, type:bigint, comment:null)], properties:null)
INFO  : Completed compiling command(queryId=hive_20240327071300_385eaf27-9a18-4f92-92cb-baf3bc3f9f53); Time taken: 0.025 seconds
INFO  : Concurrency mode is disabled, not creating a lock manager
INFO  : Executing command(queryId=hive_20240327071300_385eaf27-9a18-4f92-92cb-baf3bc3f9f53): -- "How does the release timing of shows and movies (by season or quarter) affect their viewership and ratings, and how does this vary across different genres?"
-- extracting the release quarter, aggregating viewership and ratings data by quarter and genre, 
-- and then analyzing the results to identify trends or patterns
SELECT
    genre,
    CONCAT('Q', CEIL(MONTH(releasedate)/3)) AS release_quarter, -- Determine the release quarter
    YEAR(releasedate) AS year,
    AVG(hoursviewed) AS average_hours_viewed,
    AVG(rating) AS average_rating,
    COUNT(*) AS number_of_titles
FROM
    student.netflix2023_genre_exploded
GROUP BY
    genre,
    YEAR(releasedate),
    CONCAT('Q', CEIL(MONTH(releasedate)/3))
ORDER BY
    genre,
    year,
    release_quarter
INFO  : Query ID = hive_20240327071300_385eaf27-9a18-4f92-92cb-baf3bc3f9f53
INFO  : Total jobs = 1
INFO  : Launching Job 1 out of 1
INFO  : Starting task [Stage-1:MAPRED] in serial mode
INFO  : Session is already open
INFO  : Dag name: -- "How does the release t...release_quarter(Stage-1)
INFO  : Status: Running (Executing on YARN cluster with App id application_1634527506680_1335)

INFO  : Map 1: 0(+1)/1	Reducer 2: 0/1	Reducer 3: 0/1	
INFO  : Map 1: 1/1	Reducer 2: 0(+1)/1	Reducer 3: 0/1	
INFO  : Map 1: 1/1	Reducer 2: 1/1	Reducer 3: 1/1	
INFO  : Completed executing command(queryId=hive_20240327071300_385eaf27-9a18-4f92-92cb-baf3bc3f9f53); Time taken: 1.334 seconds
INFO  : OK
                                                                    genre release_quarter year average_hours_viewed average_rating number_of_titles
                                                                    genre release_quarter year average_hours_viewed average_rating number_of_titles
                                                                  1 SciFiQ12015530000071
                                                                  2 SciFiQ3201527000007.852
                                                                  3 SciFiQ4201519000007.83
                                                                  4 SciFiQ12016833333.33333333346.23
                                                                  5 SciFiQ2201657000006.152
                                                                  6 SciFiQ3201612000005.51
                                                                  7 SciFiQ42016164800005.9599999999999995
                                                                  8 SciFiQ1201783625006.3258
                                                                  9 SciFiQ220171271428.57142857145.89999999999999957
                                                                  10 SciFiQ3201714500007.0754
                                                                  11 SciFiQ4201740000006.0333333333333339
                                                                  12 SciFiQ120182218181.81818181846.86363636363636311
                                                                  13 SciFiQ220186792307.6923076927.1384615384615413
                                                                  14 SciFiQ320184809090.9090909096.60909090909090911
                                                                  15 SciFiQ420183784210.52631578976.69473684210526319
                                                                  16 SciFiQ120195026315.78947368456.357894736842105519
                                                                  17 SciFiQ220192042857.1428571436.6142857142857147
                                                                  18 SciFiQ320195854545.4545454547.0090909090909111
                                                                  19 SciFiQ420196892857.1428571436.45714285714285714
                                                                  20 SciFiQ120205441176.4705882356.505882352941176417
                                                                  21 SciFiQ2202036000006.49285714285714314
                                                                  22 SciFiQ320207893333.3333333336.226666666666667515
                                                                  23 SciFiQ420206846666.6666666677.04666666666666815
                                                                  24 SciFiQ120212108333.33333333356.93333333333333312
                                                                  25 SciFiQ2202161000005.936363636363636511
                                                                  26 SciFiQ3202186950006.03520
                                                                  27 SciFiQ420216923529.4117647065.88235294117647117
                                                                  28 SciFiQ1202211333333.3333333346.683333333333334518
                                                                  29 SciFiQ22022233187506.9437516
                                                                  30 SciFiQ320227157142.8571428576.64761904761904621
                                                                  31 SciFiQ4202214361904.7619047616.74761904761904721
                                                                  32 SciFiQ12023449250006.58333333333333312
                                                                  33 SciFiQ2202324081818.1818181846.09090909090909111
                                                                  34ActionQ22010123000006.252
                                                                  35ActionQ3201068000006.61
                                                                  36ActionQ1201175000007.31
                                                                  37ActionQ32011947000006.31
                                                                  38ActionQ2201366000004.41
                                                                  39ActionQ320139000006.61
                                                                  40ActionQ4201316000007.64
                                                                  41ActionQ1201423750006.84
                                                                  42ActionQ32014221750007.5254
                                                                  43ActionQ42014303000006.73
                                                                  44ActionQ12015181250006.1250000000000018
                                                                  45ActionQ2201565000007.4666666666666679
                                                                  46ActionQ3201549000006.71666666666666812
                                                                  47ActionQ4201525400006.66000000000000110
                                                                  48ActionQ120161458333.33333333336.57499999999999912
                                                                  49ActionQ220165994117.6470588236.80588235294117617
                                                                  50ActionQ3201611783333.3333333347.100000000000000524
                                                                  51ActionQ420168034782.6086956526.70869565217391423
                                                                  52ActionQ1201730500006.77647058823529334
                                                                  53ActionQ220177284615.3846153856.28076923076922926
                                                                  54ActionQ3201789718756.18750000000000132
                                                                  55ActionQ420174507142.8571428576.50178571428571556
                                                                  56ActionQ120184036538.46153846156.56730769230769252
                                                                  57ActionQ220184051162.79069767436.588372093023254543
                                                                  58ActionQ320185067241.3793103456.78965517241379458
                                                                  59ActionQ420185751851.8518518526.34320987654321281
                                                                  60ActionQ1201962600006.52399999999999850
                                                                  61ActionQ2201937800006.57999999999999855
                                                                  62ActionQ320197024390.2439024396.75243902439024282
                                                                  63ActionQ420196334020.6185567016.65876288659793897
                                                                  64ActionQ1202070000006.6186046511627986
                                                                  65ActionQ220207906756.7567567566.80405405405405574
                                                                  66ActionQ320204888235.2941176476.656862745098038102
                                                                  67ActionQ420207512820.5128205136.80769230769230778
                                                                  68ActionQ1202110567142.8571428566.392857142857141570
                                                                  69ActionQ220216670666.6666666676.568000000000000575
                                                                  70ActionQ320216666666.6666666676.58888888888888781
                                                                  71ActionQ420218727272.7272727276.480909090909091110
                                                                  72ActionQ120227216304.3478260876.7619565217391392
                                                                  73ActionQ220228744954.1284403676.5880733944954155109
                                                                  74ActionQ320229747252.7472527476.84945054945054991
                                                                  75ActionQ4202221915441.176470596.474264705882357136
                                                                  76ActionQ1202359413888.888888896.86805555555555472
                                                                  77ActionQ2202330069696.969696976.48181818181818466
                                                                  78AdventureQ22010176000007.252
                                                                  79AdventureQ3201072000006.52499999999999954
                                                                  80AdventureQ1201175000007.31
                                                                  81AdventureQ32013128000005.6999999999999992
                                                                  82AdventureQ42013230000071
                                                                  83AdventureQ120143133333.33333333356.8333333333333333
                                                                  84AdventureQ220145000008.51
                                                                  85AdventureQ3201439000006.9666666666666683
                                                                  86AdventureQ42014237000006.652
                                                                  87AdventureQ1201520971428.571428576.4428571428571437
                                                                  88AdventureQ220159466666.6666666666.6222222222222229
                                                                  89AdventureQ3201545500006.55714285714285814
                                                                  90AdventureQ4201510409090.9090909086.74545454545454611
                                                                  91AdventureQ1201665000006.3857142857142867
                                                                  92AdventureQ220168441666.6666666666.82499999999999912
                                                                  93AdventureQ3201697600007.17000000000000220
                                                                  94AdventureQ420165203846.1538461546.19999999999999926
                                                                  95AdventureQ120173903571.42857142866.58928571428571428
                                                                  96AdventureQ2201751600006.20999999999999930
                                                                  97AdventureQ3201786500006.43235294117647134
                                                                  98AdventureQ420175108510.6382978736.57446808510638547
                                                                  99AdventureQ120182832727.2727272736.45272727272727155
                                                                  100AdventureQ220185697826.08695652156.52173913043478246
                                                                  Snippet icon

                                                                  My Snippet
                                                                  8.70s
                                                                  Database
                                                                  Type

                                                                  -- "What are the emerging trends in viewer interests based on the popularity of certain keywords within show descriptions over time?"
                                                                  -- Note: Similarly, this is only an attempt at an initial exploration
                                                                  SELECT
                                                                  YEAR(releasedate) AS year,
                                                                  keyword,
                                                                  COUNT(*) AS keyword_count,
                                                                  AVG(rating) AS average_rating,
                                                                  SUM(hoursviewed) AS total_hours_viewed
                                                                  FROM
                                                                  (
                                                                  SELECT
                                                                  releasedate,
                                                                  rating,
                                                                  hoursviewed,
                                                                  lower(word) AS keyword
                                                                  FROM
                                                                  student.netflix2023
                                                                  LATERAL VIEW
                                                                  explode(split(description, ' ')) wordsTable AS word
                                                                  ) AS exploded_keywords
                                                                  WHERE
                                                                  keyword IN ('space', 'alien', 'future')
                                                                  GROUP BY
                                                                  YEAR(releasedate),
                                                                  keyword
                                                                  XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
                                                                    × 
                                                                    INFO  : Compiling command(queryId=hive_20240327071309_58c63f40-31bb-4f5b-b6a8-50e62d9632e3): SELECT
    YEAR(releasedate) AS year,
    keyword,
    COUNT(*) AS keyword_count,
    AVG(rating) AS average_rating,
    SUM(hoursviewed) AS total_hours_viewed
FROM
    (
        SELECT
            releasedate,
            rating,
            hoursviewed,
            lower(word) AS keyword
        FROM
            student.netflix2023
        LATERAL VIEW
            explode(split(description, ' ')) wordsTable AS word
    ) AS exploded_keywords
WHERE
    keyword IN ('space', 'alien', 'future')
GROUP BY
    YEAR(releasedate),
    keyword
ORDER BY
    year ASC,
    keyword_count DESC
INFO  : Semantic Analysis Completed
INFO  : Returning Hive schema: Schema(fieldSchemas:[FieldSchema(name:year, type:int, comment:null), FieldSchema(name:keyword, type:string, comment:null), FieldSchema(name:keyword_count, type:bigint, comment:null), FieldSchema(name:average_rating, type:double, comment:null), FieldSchema(name:total_hours_viewed, type:bigint, comment:null)], properties:null)
INFO  : Completed compiling command(queryId=hive_20240327071309_58c63f40-31bb-4f5b-b6a8-50e62d9632e3); Time taken: 0.022 seconds
INFO  : Concurrency mode is disabled, not creating a lock manager
INFO  : Executing command(queryId=hive_20240327071309_58c63f40-31bb-4f5b-b6a8-50e62d9632e3): SELECT
    YEAR(releasedate) AS year,
    keyword,
    COUNT(*) AS keyword_count,
    AVG(rating) AS average_rating,
    SUM(hoursviewed) AS total_hours_viewed
FROM
    (
        SELECT
            releasedate,
            rating,
            hoursviewed,
            lower(word) AS keyword
        FROM
            student.netflix2023
        LATERAL VIEW
            explode(split(description, ' ')) wordsTable AS word
    ) AS exploded_keywords
WHERE
    keyword IN ('space', 'alien', 'future')
GROUP BY
    YEAR(releasedate),
    keyword
ORDER BY
    year ASC,
    keyword_count DESC
INFO  : Query ID = hive_20240327071309_58c63f40-31bb-4f5b-b6a8-50e62d9632e3
INFO  : Total jobs = 1
INFO  : Launching Job 1 out of 1
INFO  : Starting task [Stage-1:MAPRED] in serial mode
INFO  : Session is already open
INFO  : Dag name: SELECT
    YEAR(releasedate) AS year,...DESC(Stage-1)
INFO  : Status: Running (Executing on YARN cluster with App id application_1634527506680_1335)

INFO  : Map 1: 0/1	Reducer 2: 0/1	Reducer 3: 0/1
INFO  : Map 1: 0(+1)/1	Reducer 2: 0/1	Reducer 3: 0/1
INFO  : Map 1: 1/1	Reducer 2: 0(+1)/1	Reducer 3: 0/1	
INFO  : Map 1: 1/1	Reducer 2: 1/1	Reducer 3: 0(+1)/1
INFO  : Map 1: 1/1	Reducer 2: 1/1	Reducer 3: 1/1	
INFO  : Completed executing command(queryId=hive_20240327071309_58c63f40-31bb-4f5b-b6a8-50e62d9632e3); Time taken: 5.942 seconds
INFO  : OK
                                                                      year keyword keyword_count average_rating total_hours_viewed
                                                                      year keyword keyword_count average_rating total_hours_viewed
                                                                    12011space16.24800000
                                                                    22013space15.72400000
                                                                    32014future27.55000000000000113600000
                                                                    42015future46.0530100000
                                                                    52015alien15.98800000
                                                                    62015space16.244000000
                                                                    72016future76.52857142857142970000000
                                                                    82016alien66.93333333333333417300000
                                                                    92016space26.5500000000000011500000
                                                                    102017alien146.735714285714287121900000
                                                                    112017future86.012520500000
                                                                    122017space37.03333333333333442400000
                                                                    132018alien226.527272727272727115400000
                                                                    142018future156.50666666666666745500000
                                                                    152018space27.14999999999999956400000
                                                                    162019alien207.075123000000
                                                                    172019future186.82222222222222287600000
                                                                    182019space106.92999999999999822000000
                                                                    192020alien226.65909090909090974500000
                                                                    202020future196.105263157894737127200000
                                                                    212020space107.22000000000000150600000
                                                                    222021alien196.94736842105263103600000
                                                                    232021future96.00000000000000121400000
                                                                    242021space65.38333333333333346200000
                                                                    252022alien176.223529411764706262900000
                                                                    262022future166.537500000000001182800000
                                                                    272022space97.2111111111111174500000
                                                                    282023alien146.5928571428571431524200000
                                                                    292023future115.781818181818182563100000
                                                                    302023space14.465200000

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