QIAN-ME/bird_schema_data huggingface dataset:
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 music_platform_2
【Schema】
# Table: podcasts
[
(podcast_id:TEXT, Primary Key, Examples: [a00018b54eb342567c94dacfb2a3e504]),
(itunes_id:INTEGER, Examples: [1313466221, 158973461, 139076942]),
(slug:TEXT, Examples: [scaling-global]),
(itunes_url:TEXT),
(title:TEXT, Examples: [Scaling Global])
]
# Table: runs
[
(run_at:TEXT, Examples: [2021-05-10 02:53:00, 2021-06-06 21:34:36, 2021-07-02 18:04:55]),
(max_rowid:INTEGER, Examples: [3266481, 3300773, 3329699]),
(reviews_added:INTEGER, Examples: [1215223, 13139, 11561])
]
# Table: categories
[
(podcast_id:TEXT, Primary Key, Examples: [a00018b54eb342567c94dacfb2a3e504]),
(category:TEXT, Primary Key, Examples: [arts, arts-performing-arts, music])
]
# Table: reviews
[
(podcast_id:TEXT, Examples: [c61aa81c9b929a66f0c1db6cbe5d8548]),
(title:TEXT, Examples: [really interesting!]),
(content:TEXT),
(rating:INTEGER, Examples: [5, 1, 3]),
(author_id:TEXT, Examples: [F7E5A318989779D, F6BF5472689BD12, 1AB95B8E6E1309E]),
(created_at:TEXT, Examples: [2018-04-24T12:05:16-07:00])
]
【Foreign keys】
categories.podcast_id=podcasts.podcast_id
reviews.podcast_id=podcasts.podcast_id

    【Question】
    How many reviews does 'LifeAfter/The Message' have which were rated below 3? LifeAfter/The Message' is the title of podcast; rated below 3 refers to rating < 3

    【Evidence】
    LifeAfter/The Message' is the title of podcast; rated below 3 refers to rating < 3

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT COUNT(T2.rating) FROM podcasts AS T1 INNER JOIN reviews AS T2 ON T2.podcast_id = T1.podcast_id WHERE T1.title = 'LifeAfter/The Message' AND T2.rating <= 3
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 music_platform_2
【Schema】
# Table: podcasts
[
(podcast_id:TEXT, Primary Key, Examples: [a00018b54eb342567c94dacfb2a3e504]),
(itunes_id:INTEGER, Examples: [1313466221, 158973461, 139076942]),
(slug:TEXT, Examples: [scaling-global]),
(itunes_url:TEXT),
(title:TEXT, Examples: [Scaling Global])
]
# Table: runs
[
(run_at:TEXT, Examples: [2021-05-10 02:53:00, 2021-06-06 21:34:36, 2021-07-02 18:04:55]),
(max_rowid:INTEGER, Examples: [3266481, 3300773, 3329699]),
(reviews_added:INTEGER, Examples: [1215223, 13139, 11561])
]
# Table: categories
[
(podcast_id:TEXT, Primary Key, Examples: [a00018b54eb342567c94dacfb2a3e504]),
(category:TEXT, Primary Key, Examples: [arts, arts-performing-arts, music])
]
# Table: reviews
[
(podcast_id:TEXT, Examples: [c61aa81c9b929a66f0c1db6cbe5d8548]),
(title:TEXT, Examples: [really interesting!]),
(content:TEXT),
(rating:INTEGER, Examples: [5, 1, 3]),
(author_id:TEXT, Examples: [F7E5A318989779D, F6BF5472689BD12, 1AB95B8E6E1309E]),
(created_at:TEXT, Examples: [2018-04-24T12:05:16-07:00])
]
【Foreign keys】
categories.podcast_id=podcasts.podcast_id
reviews.podcast_id=podcasts.podcast_id

    【Question】
    The 'More Stupider: A 90-Day Fiance Podcast' belongs to which category and what is the average rating of the podcast? More Stupider: A 90-Day Fiance Podcast' is the title of podcast; average rating = Divide (Sum(rating), Count(rating))

    【Evidence】
    More Stupider: A 90-Day Fiance Podcast' is the title of podcast; average rating = Divide (Sum(rating), Count(rating))

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT AVG(T3.rating) FROM categories AS T1 INNER JOIN podcasts AS T2 ON T2.podcast_id = T1.podcast_id INNER JOIN reviews AS T3 ON T3.podcast_id = T2.podcast_id WHERE T2.title = 'More Stupider: A 90-Day Fiance Podcast'
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 music_platform_2
【Schema】
# Table: podcasts
[
(podcast_id:TEXT, Primary Key, Examples: [a00018b54eb342567c94dacfb2a3e504]),
(itunes_id:INTEGER, Examples: [1313466221, 158973461, 139076942]),
(slug:TEXT, Examples: [scaling-global]),
(itunes_url:TEXT),
(title:TEXT, Examples: [Scaling Global])
]
# Table: runs
[
(run_at:TEXT, Examples: [2021-05-10 02:53:00, 2021-06-06 21:34:36, 2021-07-02 18:04:55]),
(max_rowid:INTEGER, Examples: [3266481, 3300773, 3329699]),
(reviews_added:INTEGER, Examples: [1215223, 13139, 11561])
]
# Table: categories
[
(podcast_id:TEXT, Primary Key, Examples: [a00018b54eb342567c94dacfb2a3e504]),
(category:TEXT, Primary Key, Examples: [arts, arts-performing-arts, music])
]
# Table: reviews
[
(podcast_id:TEXT, Examples: [c61aa81c9b929a66f0c1db6cbe5d8548]),
(title:TEXT, Examples: [really interesting!]),
(content:TEXT),
(rating:INTEGER, Examples: [5, 1, 3]),
(author_id:TEXT, Examples: [F7E5A318989779D, F6BF5472689BD12, 1AB95B8E6E1309E]),
(created_at:TEXT, Examples: [2018-04-24T12:05:16-07:00])
]
【Foreign keys】
categories.podcast_id=podcasts.podcast_id
reviews.podcast_id=podcasts.podcast_id

    【Question】
    Of the arts-books and arts-design categories, which one has more podcasts and what is the numerical difference between them? arts-books' and 'arts-design' are category; numerical difference = Subtract(Count(podcast_id(category = 'arts-books')), Count(podcast_id(category = 'arts-design'))); one has much more podcast refers to Max(Count(podcast_id))

    【Evidence】
    arts-books' and 'arts-design' are category; numerical difference = Subtract(Count(podcast_id(category = 'arts-books')), Count(podcast_id(category = 'arts-design'))); one has much more podcast refers to Max(Count(podcast_id))

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT ( SELECT category FROM categories WHERE category = 'arts-books' OR category = 'arts-design' GROUP BY category ORDER BY COUNT(podcast_id) DESC LIMIT 1 ) "has more podcasts" , ( SELECT SUM(CASE WHEN category = 'arts-books' THEN 1 ELSE 0 END) - SUM(CASE WHEN category = 'arts-design' THEN 1 ELSE 0 END) FROM categories ) "differenct BETWEEN arts-books and arts-design"
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 music_platform_2
【Schema】
# Table: podcasts
[
(podcast_id:TEXT, Primary Key, Examples: [a00018b54eb342567c94dacfb2a3e504]),
(itunes_id:INTEGER, Examples: [1313466221, 158973461, 139076942]),
(slug:TEXT, Examples: [scaling-global]),
(itunes_url:TEXT),
(title:TEXT, Examples: [Scaling Global])
]
# Table: runs
[
(run_at:TEXT, Examples: [2021-05-10 02:53:00, 2021-06-06 21:34:36, 2021-07-02 18:04:55]),
(max_rowid:INTEGER, Examples: [3266481, 3300773, 3329699]),
(reviews_added:INTEGER, Examples: [1215223, 13139, 11561])
]
# Table: categories
[
(podcast_id:TEXT, Primary Key, Examples: [a00018b54eb342567c94dacfb2a3e504]),
(category:TEXT, Primary Key, Examples: [arts, arts-performing-arts, music])
]
# Table: reviews
[
(podcast_id:TEXT, Examples: [c61aa81c9b929a66f0c1db6cbe5d8548]),
(title:TEXT, Examples: [really interesting!]),
(content:TEXT),
(rating:INTEGER, Examples: [5, 1, 3]),
(author_id:TEXT, Examples: [F7E5A318989779D, F6BF5472689BD12, 1AB95B8E6E1309E]),
(created_at:TEXT, Examples: [2018-04-24T12:05:16-07:00])
]
【Foreign keys】
categories.podcast_id=podcasts.podcast_id
reviews.podcast_id=podcasts.podcast_id

    【Question】
    How many total reviews runned at in June 2022 were added to the podcasts? run at in June 2022 refers to run_at BETWEEN '2022-06-01 00:00:00' and '2022-06-30 23:59:59'; reviews refers to review_added

    【Evidence】
    run at in June 2022 refers to run_at BETWEEN '2022-06-01 00:00:00' and '2022-06-30 23:59:59'; reviews refers to review_added

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT SUM(reviews_added) FROM runs WHERE run_at LIKE '2022-06-%'
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 music_platform_2
【Schema】
# Table: podcasts
[
(podcast_id:TEXT, Primary Key, Examples: [a00018b54eb342567c94dacfb2a3e504]),
(itunes_id:INTEGER, Examples: [1313466221, 158973461, 139076942]),
(slug:TEXT, Examples: [scaling-global]),
(itunes_url:TEXT),
(title:TEXT, Examples: [Scaling Global])
]
# Table: runs
[
(run_at:TEXT, Examples: [2021-05-10 02:53:00, 2021-06-06 21:34:36, 2021-07-02 18:04:55]),
(max_rowid:INTEGER, Examples: [3266481, 3300773, 3329699]),
(reviews_added:INTEGER, Examples: [1215223, 13139, 11561])
]
# Table: categories
[
(podcast_id:TEXT, Primary Key, Examples: [a00018b54eb342567c94dacfb2a3e504]),
(category:TEXT, Primary Key, Examples: [arts, arts-performing-arts, music])
]
# Table: reviews
[
(podcast_id:TEXT, Examples: [c61aa81c9b929a66f0c1db6cbe5d8548]),
(title:TEXT, Examples: [really interesting!]),
(content:TEXT),
(rating:INTEGER, Examples: [5, 1, 3]),
(author_id:TEXT, Examples: [F7E5A318989779D, F6BF5472689BD12, 1AB95B8E6E1309E]),
(created_at:TEXT, Examples: [2018-04-24T12:05:16-07:00])
]
【Foreign keys】
categories.podcast_id=podcasts.podcast_id
reviews.podcast_id=podcasts.podcast_id

    【Question】
    How many podcast reviews with a rating of 3 were created during the first quarter of 2015? rating of 3 refers to rating = 3; created during the first quarter of 2015 refers to created_at BETWEEN'2015-01-01T00:00:00-07:00' and '2015-03-31T23:59:59-07:00'

    【Evidence】
    rating of 3 refers to rating = 3; created during the first quarter of 2015 refers to created_at BETWEEN'2015-01-01T00:00:00-07:00' and '2015-03-31T23:59:59-07:00'

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT COUNT(podcast_id) FROM reviews WHERE rating = 3 AND created_at BETWEEN '2015-01-01T00:00:00-07:00' AND '2015-03-31T23:59:59-07:00'
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 music_platform_2
【Schema】
# Table: podcasts
[
(podcast_id:TEXT, Primary Key, Examples: [a00018b54eb342567c94dacfb2a3e504]),
(itunes_id:INTEGER, Examples: [1313466221, 158973461, 139076942]),
(slug:TEXT, Examples: [scaling-global]),
(itunes_url:TEXT),
(title:TEXT, Examples: [Scaling Global])
]
# Table: runs
[
(run_at:TEXT, Examples: [2021-05-10 02:53:00, 2021-06-06 21:34:36, 2021-07-02 18:04:55]),
(max_rowid:INTEGER, Examples: [3266481, 3300773, 3329699]),
(reviews_added:INTEGER, Examples: [1215223, 13139, 11561])
]
# Table: categories
[
(podcast_id:TEXT, Primary Key, Examples: [a00018b54eb342567c94dacfb2a3e504]),
(category:TEXT, Primary Key, Examples: [arts, arts-performing-arts, music])
]
# Table: reviews
[
(podcast_id:TEXT, Examples: [c61aa81c9b929a66f0c1db6cbe5d8548]),
(title:TEXT, Examples: [really interesting!]),
(content:TEXT),
(rating:INTEGER, Examples: [5, 1, 3]),
(author_id:TEXT, Examples: [F7E5A318989779D, F6BF5472689BD12, 1AB95B8E6E1309E]),
(created_at:TEXT, Examples: [2018-04-24T12:05:16-07:00])
]
【Foreign keys】
categories.podcast_id=podcasts.podcast_id
reviews.podcast_id=podcasts.podcast_id

    【Question】
    Calculate the percentage of podcasts in the fiction-science-fiction category. percentage =   Divide (Count(podcast_id(category = 'fiction-science-fiction')), Count(podcast_id)) * 100

    【Evidence】
    percentage =   Divide (Count(podcast_id(category = 'fiction-science-fiction')), Count(podcast_id)) * 100

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT CAST(SUM(CASE WHEN category = 'fiction-science-fiction' THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(podcast_id) OR '%' "percentage" FROM categories
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 music_platform_2
【Schema】
# Table: podcasts
[
(podcast_id:TEXT, Primary Key, Examples: [a00018b54eb342567c94dacfb2a3e504]),
(itunes_id:INTEGER, Examples: [1313466221, 158973461, 139076942]),
(slug:TEXT, Examples: [scaling-global]),
(itunes_url:TEXT),
(title:TEXT, Examples: [Scaling Global])
]
# Table: runs
[
(run_at:TEXT, Examples: [2021-05-10 02:53:00, 2021-06-06 21:34:36, 2021-07-02 18:04:55]),
(max_rowid:INTEGER, Examples: [3266481, 3300773, 3329699]),
(reviews_added:INTEGER, Examples: [1215223, 13139, 11561])
]
# Table: categories
[
(podcast_id:TEXT, Primary Key, Examples: [a00018b54eb342567c94dacfb2a3e504]),
(category:TEXT, Primary Key, Examples: [arts, arts-performing-arts, music])
]
# Table: reviews
[
(podcast_id:TEXT, Examples: [c61aa81c9b929a66f0c1db6cbe5d8548]),
(title:TEXT, Examples: [really interesting!]),
(content:TEXT),
(rating:INTEGER, Examples: [5, 1, 3]),
(author_id:TEXT, Examples: [F7E5A318989779D, F6BF5472689BD12, 1AB95B8E6E1309E]),
(created_at:TEXT, Examples: [2018-04-24T12:05:16-07:00])
]
【Foreign keys】
categories.podcast_id=podcasts.podcast_id
reviews.podcast_id=podcasts.podcast_id

    【Question】
    What is the average rating of all the podcasts with reviews created in 2019? created in 2019 refers to created_at BETWEEN '2019-01-01T00:00:00' and '2019-12-31T23:59:59'; average rating  = Divide (Sum(rating), Count(podcast_id))

    【Evidence】
    created in 2019 refers to created_at BETWEEN '2019-01-01T00:00:00' and '2019-12-31T23:59:59'; average rating  = Divide (Sum(rating), Count(podcast_id))

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT AVG(rating) FROM reviews WHERE created_at BETWEEN '2019-01-01T00:00:00-07:00' AND '2019-12-31T23:59:59-07:00'
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 music_platform_2
【Schema】
# Table: podcasts
[
(podcast_id:TEXT, Primary Key, Examples: [a00018b54eb342567c94dacfb2a3e504]),
(itunes_id:INTEGER, Examples: [1313466221, 158973461, 139076942]),
(slug:TEXT, Examples: [scaling-global]),
(itunes_url:TEXT),
(title:TEXT, Examples: [Scaling Global])
]
# Table: runs
[
(run_at:TEXT, Examples: [2021-05-10 02:53:00, 2021-06-06 21:34:36, 2021-07-02 18:04:55]),
(max_rowid:INTEGER, Examples: [3266481, 3300773, 3329699]),
(reviews_added:INTEGER, Examples: [1215223, 13139, 11561])
]
# Table: categories
[
(podcast_id:TEXT, Primary Key, Examples: [a00018b54eb342567c94dacfb2a3e504]),
(category:TEXT, Primary Key, Examples: [arts, arts-performing-arts, music])
]
# Table: reviews
[
(podcast_id:TEXT, Examples: [c61aa81c9b929a66f0c1db6cbe5d8548]),
(title:TEXT, Examples: [really interesting!]),
(content:TEXT),
(rating:INTEGER, Examples: [5, 1, 3]),
(author_id:TEXT, Examples: [F7E5A318989779D, F6BF5472689BD12, 1AB95B8E6E1309E]),
(created_at:TEXT, Examples: [2018-04-24T12:05:16-07:00])
]
【Foreign keys】
categories.podcast_id=podcasts.podcast_id
reviews.podcast_id=podcasts.podcast_id

    【Question】
    What is the percentage of reviews added each year of the total reviews added? review added each year refers to runs_at like '2021%' and runs_at like '2022%'; percentage for 2021 = Divide (Sum(reviews_added(runs_at like '2021%)), Sum(reviews_added)) * 100; percentage of 2022 = Divide (Sum(reviews_added(runs_at like '2022%')), Sum(reviews_added)) * 100

    【Evidence】
    review added each year refers to runs_at like '2021%' and runs_at like '2022%'; percentage for 2021 = Divide (Sum(reviews_added(runs_at like '2021%)), Sum(reviews_added)) * 100; percentage of 2022 = Divide (Sum(reviews_added(runs_at like '2022%')), Sum(reviews_added)) * 100

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT CAST((SUM(CASE WHEN run_at LIKE '2022-%' THEN reviews_added ELSE 0 END) - SUM(CASE WHEN run_at LIKE '2021-%' THEN reviews_added ELSE 0 END)) AS REAL) * 100 / SUM(reviews_added) OR '%' "percentage" FROM runs
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 music_platform_2
【Schema】
# Table: podcasts
[
(podcast_id:TEXT, Primary Key, Examples: [a00018b54eb342567c94dacfb2a3e504]),
(itunes_id:INTEGER, Examples: [1313466221, 158973461, 139076942]),
(slug:TEXT, Examples: [scaling-global]),
(itunes_url:TEXT),
(title:TEXT, Examples: [Scaling Global])
]
# Table: runs
[
(run_at:TEXT, Examples: [2021-05-10 02:53:00, 2021-06-06 21:34:36, 2021-07-02 18:04:55]),
(max_rowid:INTEGER, Examples: [3266481, 3300773, 3329699]),
(reviews_added:INTEGER, Examples: [1215223, 13139, 11561])
]
# Table: categories
[
(podcast_id:TEXT, Primary Key, Examples: [a00018b54eb342567c94dacfb2a3e504]),
(category:TEXT, Primary Key, Examples: [arts, arts-performing-arts, music])
]
# Table: reviews
[
(podcast_id:TEXT, Examples: [c61aa81c9b929a66f0c1db6cbe5d8548]),
(title:TEXT, Examples: [really interesting!]),
(content:TEXT),
(rating:INTEGER, Examples: [5, 1, 3]),
(author_id:TEXT, Examples: [F7E5A318989779D, F6BF5472689BD12, 1AB95B8E6E1309E]),
(created_at:TEXT, Examples: [2018-04-24T12:05:16-07:00])
]
【Foreign keys】
categories.podcast_id=podcasts.podcast_id
reviews.podcast_id=podcasts.podcast_id

    【Question】
    Indicates the title of all podcasts in the fiction category.

    【Evidence】
    

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT T2.title FROM categories AS T1 INNER JOIN podcasts AS T2 ON T2.podcast_id = T1.podcast_id WHERE T1.category = 'fiction'
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 music_platform_2
【Schema】
# Table: podcasts
[
(podcast_id:TEXT, Primary Key, Examples: [a00018b54eb342567c94dacfb2a3e504]),
(itunes_id:INTEGER, Examples: [1313466221, 158973461, 139076942]),
(slug:TEXT, Examples: [scaling-global]),
(itunes_url:TEXT),
(title:TEXT, Examples: [Scaling Global])
]
# Table: runs
[
(run_at:TEXT, Examples: [2021-05-10 02:53:00, 2021-06-06 21:34:36, 2021-07-02 18:04:55]),
(max_rowid:INTEGER, Examples: [3266481, 3300773, 3329699]),
(reviews_added:INTEGER, Examples: [1215223, 13139, 11561])
]
# Table: categories
[
(podcast_id:TEXT, Primary Key, Examples: [a00018b54eb342567c94dacfb2a3e504]),
(category:TEXT, Primary Key, Examples: [arts, arts-performing-arts, music])
]
# Table: reviews
[
(podcast_id:TEXT, Examples: [c61aa81c9b929a66f0c1db6cbe5d8548]),
(title:TEXT, Examples: [really interesting!]),
(content:TEXT),
(rating:INTEGER, Examples: [5, 1, 3]),
(author_id:TEXT, Examples: [F7E5A318989779D, F6BF5472689BD12, 1AB95B8E6E1309E]),
(created_at:TEXT, Examples: [2018-04-24T12:05:16-07:00])
]
【Foreign keys】
categories.podcast_id=podcasts.podcast_id
reviews.podcast_id=podcasts.podcast_id

    【Question】
    What is the rating and category of the podcast entitled Sitcomadon? entitled refers to title; 'Sitcomadon' is the title of podcast

    【Evidence】
    entitled refers to title; 'Sitcomadon' is the title of podcast

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT DISTINCT T3.rating, T1.category FROM categories AS T1 INNER JOIN podcasts AS T2 ON T2.podcast_id = T1.podcast_id INNER JOIN reviews AS T3 ON T3.podcast_id = T2.podcast_id WHERE T2.title = 'Sitcomadon'
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 music_platform_2
【Schema】
# Table: podcasts
[
(podcast_id:TEXT, Primary Key, Examples: [a00018b54eb342567c94dacfb2a3e504]),
(itunes_id:INTEGER, Examples: [1313466221, 158973461, 139076942]),
(slug:TEXT, Examples: [scaling-global]),
(itunes_url:TEXT),
(title:TEXT, Examples: [Scaling Global])
]
# Table: runs
[
(run_at:TEXT, Examples: [2021-05-10 02:53:00, 2021-06-06 21:34:36, 2021-07-02 18:04:55]),
(max_rowid:INTEGER, Examples: [3266481, 3300773, 3329699]),
(reviews_added:INTEGER, Examples: [1215223, 13139, 11561])
]
# Table: categories
[
(podcast_id:TEXT, Primary Key, Examples: [a00018b54eb342567c94dacfb2a3e504]),
(category:TEXT, Primary Key, Examples: [arts, arts-performing-arts, music])
]
# Table: reviews
[
(podcast_id:TEXT, Examples: [c61aa81c9b929a66f0c1db6cbe5d8548]),
(title:TEXT, Examples: [really interesting!]),
(content:TEXT),
(rating:INTEGER, Examples: [5, 1, 3]),
(author_id:TEXT, Examples: [F7E5A318989779D, F6BF5472689BD12, 1AB95B8E6E1309E]),
(created_at:TEXT, Examples: [2018-04-24T12:05:16-07:00])
]
【Foreign keys】
categories.podcast_id=podcasts.podcast_id
reviews.podcast_id=podcasts.podcast_id

    【Question】
    Indicate the id of the reviewer whose itunes id is 1516665400. "151665400" is itunes_id; id of reviewer refers to author_id

    【Evidence】
    "151665400" is itunes_id; id of reviewer refers to author_id

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT T2.author_id FROM podcasts AS T1 INNER JOIN reviews AS T2 ON T2.podcast_id = T1.podcast_id WHERE T1.itunes_id = 1516665400
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 music_platform_2
【Schema】
# Table: podcasts
[
(podcast_id:TEXT, Primary Key, Examples: [a00018b54eb342567c94dacfb2a3e504]),
(itunes_id:INTEGER, Examples: [1313466221, 158973461, 139076942]),
(slug:TEXT, Examples: [scaling-global]),
(itunes_url:TEXT),
(title:TEXT, Examples: [Scaling Global])
]
# Table: runs
[
(run_at:TEXT, Examples: [2021-05-10 02:53:00, 2021-06-06 21:34:36, 2021-07-02 18:04:55]),
(max_rowid:INTEGER, Examples: [3266481, 3300773, 3329699]),
(reviews_added:INTEGER, Examples: [1215223, 13139, 11561])
]
# Table: categories
[
(podcast_id:TEXT, Primary Key, Examples: [a00018b54eb342567c94dacfb2a3e504]),
(category:TEXT, Primary Key, Examples: [arts, arts-performing-arts, music])
]
# Table: reviews
[
(podcast_id:TEXT, Examples: [c61aa81c9b929a66f0c1db6cbe5d8548]),
(title:TEXT, Examples: [really interesting!]),
(content:TEXT),
(rating:INTEGER, Examples: [5, 1, 3]),
(author_id:TEXT, Examples: [F7E5A318989779D, F6BF5472689BD12, 1AB95B8E6E1309E]),
(created_at:TEXT, Examples: [2018-04-24T12:05:16-07:00])
]
【Foreign keys】
categories.podcast_id=podcasts.podcast_id
reviews.podcast_id=podcasts.podcast_id

    【Question】
    What are the titles of the podcasts whose reviews were created between 2018-08-22T11:53:16-07:00 and 2018-11-20T11:14:20-07:00? created between 2018-08-22T11:53:16-07:00 and 2018-11-20T11:14:20-07:00 refers to created at BETWEEN '2018-08-22T11:53:16-07:00' and '2018-11-20T11:14:20-07:00'

    【Evidence】
    created between 2018-08-22T11:53:16-07:00 and 2018-11-20T11:14:20-07:00 refers to created at BETWEEN '2018-08-22T11:53:16-07:00' and '2018-11-20T11:14:20-07:00'

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT DISTINCT T1.title FROM podcasts AS T1 INNER JOIN reviews AS T2 ON T2.podcast_id = T1.podcast_id WHERE T2.created_at BETWEEN '2018-08-22T11:53:16-07:00' AND '2018-11-20T11:14:20-07:00'