Reword the highlight section - #10358
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@pnthao, @akuzm: please review this pull request.
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| **Release Highlights** | ||
| * **Chunk exclusion for DML operations** drastically improves the performance of `UPDATE` and `DELETE` statements on hypertables. By acquiring exclusive locks only on the specific chunks being modified rather than the entire hypertable, this enhancement eliminates massive lock contention and keeps high-concurrency workloads running smoothly without unnecessary slowdowns. | ||
| * Intelligent **row-by-row decompression** enables the query planner to decompress data row-by-row rather than in large batches when an operation prioritizes a fast initial response (such as queries with `LIMIT` clauses). This dramatically reduces memory overhead and query latency, ensuring lightning-fast performance when you only need to retrieve a small subset of records from your compressed hypertables. | ||
| * Chunk exclusion for DML operations improves the performance of `UPDATE` and `DELETE` statements on hypertables. By acquiring row exclusive locks only on the specific chunks being modified rather than the entire hypertable. This enhancement reduces lock contention in high-concurrency workloads involving DML, speeding up the planning of DML queries by using the optimized TimescaleDB chunk exclusion instead of the baseline Postgres constraint exclusion. |
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In this rewritten version it reads as if it's removes lock contention by speeding up the planning, but these are separate independent improvements from this change.
| * **Chunk exclusion for DML operations** drastically improves the performance of `UPDATE` and `DELETE` statements on hypertables. By acquiring exclusive locks only on the specific chunks being modified rather than the entire hypertable, this enhancement eliminates massive lock contention and keeps high-concurrency workloads running smoothly without unnecessary slowdowns. | ||
| * Intelligent **row-by-row decompression** enables the query planner to decompress data row-by-row rather than in large batches when an operation prioritizes a fast initial response (such as queries with `LIMIT` clauses). This dramatically reduces memory overhead and query latency, ensuring lightning-fast performance when you only need to retrieve a small subset of records from your compressed hypertables. | ||
| * Chunk exclusion for DML operations improves the performance of `UPDATE` and `DELETE` statements on hypertables. By acquiring row exclusive locks only on the specific chunks being modified rather than the entire hypertable. This enhancement reduces lock contention in high-concurrency workloads involving DML, speeding up the planning of DML queries by using the optimized TimescaleDB chunk exclusion instead of the baseline Postgres constraint exclusion. | ||
| * The query planner can now choose an additional algorithm for retrieving a small number of rows, for reading the columnstore data, optimizing the default behavior. This optimization improves some last-point queries on columnstore (think `ORDER BY time DESC LIMIT 1`) run several times faster than in previous releases. |
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This optimization improves some last-point queries on columnstore (think ORDER BY time DESC LIMIT 1) run several times faster than in previous releases.
This sentence looks just grammatically incorrect.
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Let's ask our technical writers to look at this to make sure it reads well. For the reference, my original suggestion was:
Chunk exclusion for DML operations improves the performance of UPDATE and DELETE statements on hypertables. By acquiring row exclusive locks only on the specific chunks being modified rather than the entire hypertable, this enhancement reduces lock contention in high-concurrency workloads involving DML. Moreover, it speeds up the planning of DML queries by using the optimized TimescaleDB chunk exclusion code instead of the baseline Postgres constraint exclusion.
The query planner can now choose an algorithm for reading the columnstore data, that is optimized for retrieving a small number of rows, in contrast to the baseline algorithm optimized for throughput. This makes some last-point queries on columnstore (think ORDER BY time DESC LIMIT 1) run several times faster than in previous releases.
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Pushed adjusted wording from @atovpeko |
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