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Graph/sql-shape-drift

AntiPattern

SQL Query Shape Drift

sql-shape-drift

Aggregations and set operations get reshaped by SQL clauses (ROLLUP, UNION, IN/EXISTS, window functions, LIMIT-in-subqueries), causing either missing subtotal/duplicate logic or unexpectedly heavy execution plans. The stakes are wrong results and slow or brittle migrations.

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Updated7/11/2026

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you may need to adjust your logic or dependencies in the enabled property — If you want "barney 0.5" to be logged every time useQuery is executed. Outcome: ensure it reflects the desired behavior.

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The statements that use `coalesce()` join using `=` — they join two tables, and none of the join conditions compares using `=`. Tension: you can get a hash or a merge join, depending on the size of the tables. Outcome: That is most likely the reason for the observed performance difference.

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Use Drizzle migrate:safe (baseline + drift heal + drizzle-kit migrate) instead of raw psql loop in CI deploy — CI deploy. Tension: instead of raw psql loop. Outcome: baseline + drift heal + drizzle-kit migrate.

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the existing validate.ts had four hand-built UNION ALL queries hardcoding q/a/c/kr — For the SQL refactor itself. Tension: each query has different per-table column projections. Outcome: The cleanest pattern.

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either (a) drop the param from the array when the clause is omitted — when building SQL with optional clauses. Tension: requires dynamic position recomputation, error-prone. Outcome: (b) is safer for most cases.

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having a Lambda function collect the values and run this query on a regular basis — refreshing it once or twice a day might be an acceptable compromise for now. Tension: Ideally in real time, but refreshing it once or twice a day might be an acceptable compromise for now. Outcome: For now I've implemented a manual update of the projections in a Lambda function.

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More complex, multi-statement queries, or things like `insert..on conflict` and `merge`.

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Drop ORDER BY when there's no LIMIT. Tension: Salience ordering only matters when you're picking the top-K. Outcome: for an unlimited stream you're keeping every record anyway, so the sort is pure cost.

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Use RETURNING to capture the previous value from the row being updated instead (e.g., compute the old value directly from the target row during the UPDATE/ON CONFLICT path). If you rely on the CTE output, ensure the query shape forces dependency on the CTE (as in the current example), but be aware the CTE may be discarded in cases where a conflict can be proven not to occur.

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You need dynamic SQL to do this properly. — in PostgreSQL, I am trying to have SQL which orders by multiple columns. Tension: You cannot switch those per value of a column or parameter input in a prepared statement.

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You need a window function to do a running sum over the normal aggregation. — select count(distinct mt.customer) as count. Outcome: sum(count(distinct mt.customer)) over (order by mt.enrollment_dt rows unbounded preceding) as running_count.

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Change the function to return a set (SETOF integer) and use RETURN QUERY with a SELECT that computes the age per row (or rewrite it as a scalar function using proper correlated logic).

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I am trying to create a generated always column that takes the Current Date, subtract the previously mentioned int column to get an Age (int). — I am trying to learn Postgresql (12) "on the job" and seem to be stuck on a rather simple issue. Tension: the logic is faulty in that the `asset_age` is calculated when the row is inserted in the table or its underlying columns updated so as time moves on your asset ages go out of sync. Outcome: You can most easily solve your issue by dropping column `asset_age` from your table and create a view that calculates the value whenever you need it:.

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Restructure the query or Grafana transformations so that each `kpi` is a separate series: either use a panel Transform like “Partition by values” on `kpi` and then map each resulting series to its own fields, or pivot the SQL result into separate columns per `kpi` (one series per column), ensuring Grafana receives distinct series rather than one combined dataset.

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Use a union of two event rows per order (createdat with val=0 and doneat with val=price when status='completed' else 0), then compute sum(val) over (partition by userid order by event time, keep desc) and filter to the created rows to produce the cumulative total at placement.

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Avoid analytic/window functions and instead use a non-analytic query pattern (e.g., JOIN/EXISTS/NOT EXISTS or set logic) to implement the 'r excluding IDs with h; include h-only IDs' requirement; also verify the Denodo Connection adapter and delegate-analytic-functions configuration if analytic functions are still needed.

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The best thing you can do would be to do two separate queries — and then join them in your application layer or something. Tension: you cannot achieve this. Outcome: join them in your application layer or something.

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Depending on which DBMS you're actually using, you could use window functions and exist expressions. — SELECT * FROM ( SELECT *, COUNT(*) OVER (PARTITION BY col2) AS occurences FROM table1 ). Tension: where col2 = t1.col2. Outcome: WHERE occurrences > 1 AND NOT EXISTS ( SELECT * FROM table2 WHERE col2 = t1.col2 ).

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Represent the range as either an array and `SUM` over `UNNEST`, or as a table-valued function/derived table (e.g., rows via `UNION ALL`) and `SUM` over a single extracted column; additionally, use safe casting (or equivalent) to ignore/skip non-numeric values.

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Define a composite type (e.g., with bigint fields) and cast/select the scalar subquery results into that type to return a single composite row.

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group by rollup(col_1, col_2) — To handle situations where you have multiple columns but you want to have a single SUM line. Tension: show Grand Total only and not Subtotals. Outcome: having grouping(col_1) = grouping(col_2) order by (col_1, col_2);.

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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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Or you can increment value in one query — for READ_COMMITTED. Tension: There are solutions for READ_COMMITTED:.

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if you use derived queries.

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the solution shown below — exceeding the SQL parameter limit. Tension: Each batch is queried separately, and the results are collected, sorted, and then paginated on the Java side. Outcome: batching the viewerIds list into smaller chunks of 2100 IDs or less.

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Use the JPQL/HQL variant that adds an OVER clause with partitioning (and ordering) so Hibernate accepts ARRAY_AGG, returning the projection via a constructor expression.

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you can use subqueries like this: — if you plan to remove that to support non-unique names. Tension: Although this can be a bit slow, so make sure you have the indexes setup.

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Drizzle supports this cleanly with `or()` + `isNull()` — Drizzle ORM. Tension: no raw SQL needed. Outcome: const rows = await db.select().from(questions).where( or( eq(questions.tenantId, tenantId), isNull(questions.tenantId) ) );.

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sample 500 real user queries and cluster them — Before deciding. Tension: If the top 20 clusters cover 80% of traffic and those clusters are mostly templated. Outcome: For a typical B2B SaaS support bot it's usually 60-40 RAG/fine-tuning territory.

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UNION ALL is the right call. — Unified activity feeds. Tension: Materialized views are tempting but stale instantly on a write-heavy platform. Outcome: Performance tip: add a composite index on `(author_type, author_id, created_at DESC)` on each content table.

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