andygrove commented on code in PR #1525: URL: https://github.com/apache/datafusion-comet/pull/1525#discussion_r1995643547
########## docs/source/user-guide/tuning.md: ########## @@ -17,18 +17,94 @@ specific language governing permissions and limitations under the License. --> -# Tuning Guide +# Comet Tuning Guide Comet provides some tuning options to help you get the best performance from your queries. ## Memory Tuning -### Unified Memory Management with Off-Heap Memory +It is necessary to specify how much memory Comet can use in addition to memory already allocated to Spark. In some +cases, it may be possible to reduce the amount of memory allocated to Spark so that overall memory allocation is +the same or lower than the original configuration. In other cases, enabling Comet may require allocating more memory +than before. See the [Determining How Much Memory to Allocate] section for more details. -The recommended way to share memory between Spark and Comet is to set `spark.memory.offHeap.enabled=true`. This allows -Comet to share an off-heap memory pool with Spark. The size of the pool is specified by `spark.memory.offHeap.size`. For more details about Spark off-heap memory mode, please refer to Spark documentation: https://spark.apache.org/docs/latest/configuration.html. +[Determining How Much Memory to Allocate]: #determining-how-much-memory-to-allocate -The type of pool can be specified with `spark.comet.exec.memoryPool`. +Comet supports Spark's on-heap (the default) and off-heap mode for allocating memory. However, we strongly recommend +using off-heap mode. Comet has some limitations when running in on-heap mode, such as requiring more memory overall, +and requiring shuffle memory to be separately configured. + +### Configuring Comet Memory in Off-Heap Mode + +The recommended way to allocate memory for Comet is to set `spark.memory.offHeap.enabled=true`. This allows +Comet to share an off-heap memory pool with Spark, reducing the overall memory overhead. The size of the pool is +specified by `spark.memory.offHeap.size`. For more details about Spark off-heap memory mode, please refer to +Spark documentation: https://spark.apache.org/docs/latest/configuration.html. + +### Configuring Comet Memory in On-Heap Mode + +When running in on-heap mode, Comet memory can be allocated by setting `spark.comet.memoryOverhead`. If this setting +is not provided, it will be calculated by multiplying the current Spark executor memory by +`spark.comet.memory.overhead.factor` (default value is `0.2`) which may or may not result in enough memory for +Comet to operate. It is not recommended to rely on this behavior. It is better to specify `spark.comet.memoryOverhead` +explicitly. + +Comet supports native shuffle and columnar shuffle (these terms are explained in the [shuffle] section below). +In on-heap mode, columnar shuffle memory must be separately allocated using `spark.comet.columnar.shuffle.memorySize`. +If this setting is not provided, it will be calculated by multiplying `spark.comet.memoryOverhead` by +`spark.comet.columnar.shuffle.memory.factor` (default value is `1.0`). If a shuffle exceeds this amount of memory +then the query will fail. + +[shuffle]: #shuffle + +### Determining How Much Memory to Allocate + +Generally, increasing the amount of memory allocated to Comet will improve query performance by reducing the +amount of time spent spilling to disk, especially for aggregate, join, and shuffle operations. Allocating insufficient +memory can result in out-of-memory errors. This is no different from allocating memory in Spark and the amount of +memory will vary for different workloads, so some experimentation will be required. + +Here is a real-world example, based on running benchmarks derived from TPC-H. + +**TODO: this section is a work-in-progress** + +The following table shows performance of Spark compared to Comet in both Off-Heap and On-Heap modes. The table shows +total query time for TPC-H @ 100GB. Smaller is better. + +| Total Executor Memory (GB) | Spark | Comet Off-Heap | Comet On-Heap | +| -------------------------- | ----- | -------------- | ------------- | +| 1 | OOM | OOM | OOM | +| 2 | OOM | OOM | OOM | +| 3 | 744 | OOM | OOM | +| 4 | 739 | OOM | OOM | +| 5 | 681 | 342 | OOM | +| 6 | 665 | | 344 | +| 7 | 657 | | 340 | +| 8 | 632 | 295 | 334 | +| 9 | 623 | | | +| 10 | 622 | | | Review Comment: This is with 8 cores single executor - I have removed this table because it was overkill and have now just summarized the key findings -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: github-unsubscr...@datafusion.apache.org For queries about this service, please contact Infrastructure at: us...@infra.apache.org --------------------------------------------------------------------- To unsubscribe, e-mail: github-unsubscr...@datafusion.apache.org For additional commands, e-mail: github-h...@datafusion.apache.org
