andygrove commented on code in PR #2538:
URL: https://github.com/apache/datafusion-comet/pull/2538#discussion_r2428961400
##########
docs/source/user-guide/latest/tuning.md:
##########
@@ -114,79 +88,36 @@ Workarounds for this problem include:
## Advanced Memory Tuning
-### Configuring Off-Heap Memory Pools
+### Configuring Comet Memory Pools
Comet implements multiple memory pool implementations. The type of pool can be
specified with `spark.comet.exec.memoryPool`.
-The valid pool types for off-heap mode are:
+The valid pool types are:
-- `fair_unified` (default when `spark.memory.offHeap.enabled=true` is set)
+- `fair_unified_global` (default when `spark.memory.offHeap.enabled=true` is
set)
+- `fair_unified`
- `greedy_unified`
-Both of these pools share off-heap memory between Spark and Comet. This
approach is referred to as
+All of these pools share off-heap memory between Spark and Comet. This
approach is referred to as
unified memory management. The size of the pool is specified by
`spark.memory.offHeap.size`.
-The `greedy_unified` pool type implements a greedy first-come first-serve
limit. This pool works well for queries that do not
-need to spill or have a single spillable operator.
+Comet's memory accounting isn't 100% accurate and this can result in Comet
using more memory than it reserves,
+leading to out-of-memory exceptions. To work around this issue, it is possible
to
+set `spark.comet.exec.memoryPool.fraction` to a value less than `1.0` to
restrict the amount of memory that can be
+reserved by Comet.
-The `fair_unified` pool type prevents operators from using more than an even
fraction of the available memory
+The `fair_unified` pool types prevents operators from using more than an even
fraction of the available memory
(i.e. `pool_size / num_reservations`). This pool works best when you know
beforehand
the query has multiple operators that will likely all need to spill. Sometimes
it will cause spills even
when there is sufficient memory in order to leave enough memory for other
operators.
-### Configuring On-Heap Memory Pools
-
-```{warning}
-Support for on-heap memory pools is deprecated and will be removed from a
future release.
-```
-
-When running in on-heap mode, Comet will use its own dedicated memory pools
that are not shared with Spark.
+`fair_unified_global` allows any task to use the full off-heap memory pool.
Review Comment:
Thanks. I have updated this.
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