tweets
1 row where quoted_status = 1128284234928418816, retweet_count = 0 and retweeted = 0 sorted by lang
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created_at (date) 1 ✖
id | user | created_at | full_text | retweeted_status | quoted_status | place | source | truncated | display_text_range | in_reply_to_status_id | in_reply_to_user_id | in_reply_to_screen_name | geo | coordinates | contributors | is_quote_status | retweet_count | favorite_count | favorited | retweeted | possibly_sensitive | lang ▼ | scopes |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1430608769319198720 | swyx 33521530 | 2021-08-25T19:11:24+00:00 | @mwarger @FernandoTheRojo @jamonholmgren yup! gqlless was inspired by babel blade. i think the static analysis approach could probably be built into relay/apollo if people cared enough, but ultimately my attention shifted to GraphQL schema creation rather than consumption https://twitter.com/graphqlasia/status/1128284234928418816 | 1128284234928418816 1128284234928418816 | Twitter Web App 1f89d6a41b1505a3071169f8d0d028ba9ad6f952 | 0 | [41, 297] | 1430602199801999364 | 15886909 | mwarger | 1 | 0 | 2 | 0 | 0 | 0 | en |
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CREATE TABLE [tweets] ( [id] INTEGER PRIMARY KEY, [user] INTEGER REFERENCES [users]([id]), [created_at] TEXT, [full_text] TEXT, [retweeted_status] INTEGER, [quoted_status] INTEGER, [place] TEXT REFERENCES [places]([id]), [source] TEXT REFERENCES [sources]([id]), [truncated] INTEGER, [display_text_range] TEXT, [in_reply_to_status_id] INTEGER, [in_reply_to_user_id] INTEGER, [in_reply_to_screen_name] TEXT, [geo] TEXT, [coordinates] TEXT, [contributors] TEXT, [is_quote_status] INTEGER, [retweet_count] INTEGER, [favorite_count] INTEGER, [favorited] INTEGER, [retweeted] INTEGER, [possibly_sensitive] INTEGER, [lang] TEXT, [scopes] TEXT, FOREIGN KEY([retweeted_status]) REFERENCES [tweets]([id]), FOREIGN KEY([quoted_status]) REFERENCES [tweets]([id]) ); CREATE INDEX [idx_tweets_source] ON [tweets] ([source]);