AWS redshift documentation change
Summary
Expanded explanation of query compilation process, detailing code composition optimization and compilation caching mechanisms.
Security assessment
The changes describe performance optimizations in query execution (composition technique, compilation caching) without any mention of security vulnerabilities, access controls, or data protection. Focus is purely on query speed and compilation efficiency.
Diff
diff --git a/redshift/latest/dg/c-query-performance.md b/redshift/latest/dg/c-query-performance.md index fd1595658..437241c00 100644 --- a//redshift/latest/dg/c-query-performance.md +++ b//redshift/latest/dg/c-query-performance.md @@ -25 +25 @@ A number of factors can affect query performance. The following aspects of your - * Code compilation – Amazon Redshift generates and compiles code for each query execution plan. + * Code compilation – Amazon Redshift generates and compiles optimized code for each query execution plan. The compiled code runs faster because it eliminates the overhead of using an interpreter. To minimize latency for new queries while preserving the performance benefits of compiled code, Amazon Redshift uses a technique called composition. Composition generates a lightweight arrangement of pre-existing logic to process new queries immediately, while simultaneously compiling highly optimized, query-specific code in the background. This removes compilation from the critical path of query execution, so new queries start faster and deliver performance consistent with subsequent runs. @@ -27,3 +27 @@ A number of factors can affect query performance. The following aspects of your -The compiled code runs faster because it removes the overhead of using an interpreter. You generally have some overhead cost the first time code is generated and compiled. As a result, the performance of a query the first time you run it can be misleading. The overhead cost might be especially noticeable when you run one-off queries. Run the query a second time to determine its typical performance. Amazon Redshift uses a serverless compilation service to scale query compilations beyond the compute resources of an Amazon Redshift cluster. The compiled code segments are cached locally on the cluster and in a virtually unlimited cache. This cache persists after cluster reboots. Subsequent executions of the same query run faster because they can skip the compilation phase. - -The cache is not compatible across Amazon Redshift versions, so the compilation cache is flushed and the code is recompiled when queries run after a version upgrade. If your queries have strict SLAs, we recommend you pre-run query segments that scan data from cluster tables. This lets Amazon Redshift cache the base table data, reducing the planning time for queries after a version upgrade. By using a scalable compilation service, Amazon Redshift can compile code in parallel to provide consistently fast performance. The magnitude of workload speed-up depends on the complexity and concurrency of queries. +Amazon Redshift also uses a serverless compilation service to scale query compilations beyond the compute resources of an Amazon Redshift cluster. Compiled code segments are cached both locally on the cluster and in a virtually unlimited remote cache that persists after cluster reboots. Subsequent executions of the same query run faster because they can skip the compilation phase. By using a scalable compilation service, Amazon Redshift compiles code in parallel to provide consistently fast performance.