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sql

sql

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Updated4/30/2026

PageRank0.0028

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50 linked nodes

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weaviate-client

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there are two non-obvious failure modes that catch you on the second/third table after the first one ships — instrumenting a previously un-scanned table for the privacy pipeline (sanitizeContent + recordPrivacyEvent + enqueuePrivacySweep + privacy_review_status).

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in which the query above is nested. Tension: why is it a must to alias in the second query and not the first ?

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You can choose the bootstrap user name when you create the cluster: — initdb -U fuzzy -E UTF8 --locale=en_US.utf8 /path/to/data/directory. Tension: But you can also rename the user any time later:. Outcome: ALTER ROLE postgres RENAME TO fuzzy;.

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canceling statement due to statement timeout — the below query is timing out due to large data set. Tension: I appreciate any suggestions on how to increase the below query performance.

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Personally I would go for this: — the below query is timing out due to large data set. Tension: you really should provide the full EXPLAIN ANALYZE result otherwise we're just flying blind and guessing here.

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Use UPDATE ... WHERE id IN (SELECT id FROM ... LIMIT $n) pattern. — The subquery selects the IDs with LIMIT, and the outer UPDATE targets only those rows. Tension: All filter predicates go in the inner SELECT. Outcome: Parameters are passed positionally ($1, $2, etc.) and the LIMIT parameter goes last.

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Check your `current_logfiles` file — to see what your db knows about where it's currently supposed to save logs. Outcome: select pg_read_file('current_logfiles');.

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you should give autovacuum enough memory — In addition, you should give autovacuum enough memory so that it runs as fast as it can. Tension: autovacuum may run for days and not complete in time. Outcome: by raising `maintenance_work_mem`.

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Use regex extraction and cast matched numbers to NUMERIC for ordering

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select string_to_array(code, ',') as array_codes, total from raw_data — where array_codes && array ['5', '7'];. Outcome: select sum(total) as sum_total.

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alter table test add column txt_tsv tsvector generated always as (to_tsvector('english',txt)) stored; — retrieve all rows from a PostgreSQL database that contain sentences similar to a provided string. Tension: I attempted to use the fuzzy matching functionality available in PostgreSQL, but it didn't produce the desired results for me. Outcome: where txt_tsv @@ plainto_tsquery('the quick brown fox jumps over the lazy dog');.

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Do you want ILIKE searches? If so, then you should do that. — for searches less than 3 characters. Tension: Maybe you should use `%` but with a different threshold. Outcome: Maybe you should use `%` but with a different threshold.

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First create a new table, to store some metadata — execute a function of operation to stamp access statistics before and after the business workflow. Tension: maybe is not has graphical representation with table visualizer and start or stop capabilities. Outcome: it does the job.

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That can be solved using a regular expression like: — select substring(ID, '(^[A-Z]{2}\.[A-Z]{1}(\.UP|\.DOWN)*)') from table1;. Outcome: select substring(ID, '(^[A-Z]{2}\.[A-Z]{1}(\.UP|\.DOWN)*)') from table1;.

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I want to retain the discrete event scoring, but also rank the racer in the series. Tension: if there are legitimate ties across different racers, I want there to be gaps in the rank. Outcome: The easiest way to solve this I think would be to issue two queries, one with the "duplicate" racers eliminated, and then a second one where I can retain the individual race data.

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start_hour2 and end_hour2 could be null as well. Tension: How do I check if the timestamp is not between (start_hour1 and end_hour1) and (start_hour2 and end_hour2). Outcome: or just pure and simple `BETWEEN`.

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