advancedxy commented on code in PR #83:
URL:
https://github.com/apache/arrow-datafusion-comet/pull/83#discussion_r1502504983
##########
core/src/execution/jni_api.rs:
##########
@@ -103,6 +103,7 @@ pub unsafe extern "system" fn
Java_org_apache_comet_Native_createPlan(
iterators: jobjectArray,
serialized_query: jbyteArray,
metrics_node: JObject,
+ task_memory_manager_obj: JObject,
Review Comment:
how about rename this to `comet_task_memory_manager_obj`?
When I first read the code, I thought it was the Spark's TaskMemoryManager
object. However it's comet's `CometTaskMemoryManager`. It would be clear to
call it `comet_task_memory_manager_obj`
Other occurrence could be renamed too.
##########
spark/src/main/java/org/apache/spark/CometTaskMemoryManager.java:
##########
@@ -0,0 +1,66 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark;
+
+import java.io.IOException;
+
+import org.apache.spark.memory.MemoryConsumer;
+import org.apache.spark.memory.MemoryMode;
+import org.apache.spark.memory.TaskMemoryManager;
+
+/**
+ * A adapter class that is used by Comet native to acquire & release memory
through Spark's unified
+ * memory manager. This assumes Spark's off-heap memory mode is enabled.
+ */
+public class CometTaskMemoryManager {
+ private final TaskMemoryManager internal;
+ private final NativeMemoryConsumer nativeMemoryConsumer;
+
+ public CometTaskMemoryManager() {
+ this.internal = TaskContext$.MODULE$.get().taskMemoryManager();
+ this.nativeMemoryConsumer = new NativeMemoryConsumer();
+ }
+
+ // Called by Comet native through JNI.
+ // Returns the actual amount of memory (in bytes) granted.
+ public long acquireMemory(long size) {
+ return internal.acquireExecutionMemory(size, nativeMemoryConsumer);
+ }
+
+ // Called by Comet native through JNI
+ public void releaseMemory(long size) {
+ internal.releaseExecutionMemory(size, nativeMemoryConsumer);
+ }
+
+ /**
+ * A dummy memory consumer that does nothing when spilling. At the moment,
Comet native doesn't
+ * share the same API as Spark and cannot trigger spill when acquire memory.
Therefore, when
+ * acquiring memory from native or JVM, spilling can only be triggered from
JVM operators.
+ */
+ private class NativeMemoryConsumer extends MemoryConsumer {
Review Comment:
The consumer's toString might be used when the debugging log is turned on.
It would be great that we can override this class to provide `toString`
method and also add a unique flag/id to identify the corresponding consumer for
the native plan/execution.
##########
core/src/execution/jni_api.rs:
##########
@@ -186,18 +189,29 @@ fn prepare_datafusion_session_context(
let mut rt_config =
RuntimeConfig::new().with_disk_manager(DiskManagerConfig::NewOs);
- // Set up memory limit if specified
- if conf.contains_key("memory_limit") {
- let memory_limit = conf.get("memory_limit").unwrap().parse::<usize>()?;
-
- let memory_fraction = conf
- .get("memory_fraction")
- .ok_or(CometError::Internal(
- "Config 'memory_fraction' is not specified from Comet JVM
side".to_string(),
- ))?
- .parse::<f64>()?;
-
- rt_config = rt_config.with_memory_limit(memory_limit, memory_fraction);
+ let use_unified_memory_manager = conf
+ .get("use_unified_memory_manager")
+ .ok_or(CometError::Internal(
+ "Config 'use_unified_memory_manager' is not specified from Comet
JVM side".to_string(),
+ ))?
Review Comment:
I believe a more permissive way is to treat **unsetting**
`use_unified_memory_manager` as false?
##########
spark/src/test/scala/org/apache/spark/sql/CometTestBase.scala:
##########
@@ -65,28 +64,18 @@ abstract class CometTestBase
val conf = new SparkConf()
conf.set("spark.hadoop.fs.file.impl", classOf[DebugFilesystem].getName)
conf.set(SQLConf.SHUFFLE_PARTITIONS, 10) // reduce parallelism in tests
- conf.set("spark.shuffle.manager", shuffleManager)
+ conf.set(SQLConf.ANSI_ENABLED.key, "false")
+ conf.set(SHUFFLE_MANAGER, shuffleManager)
+ conf.set(MEMORY_OFFHEAP_ENABLED.key, "true")
Review Comment:
Are we still going to test the default memory pool implementation in
DataFusion?
Seems like all the test code path are the unified memory manager now.
##########
spark/src/main/scala/org/apache/comet/CometExecIterator.scala:
##########
@@ -54,7 +54,13 @@ class CometExecIterator(
}.toArray
private val plan = {
val configs = createNativeConf
- nativeLib.createPlan(id, configs, cometBatchIterators, protobufQueryPlan,
nativeMetrics)
+ nativeLib.createPlan(
+ id,
+ configs,
+ cometBatchIterators,
+ protobufQueryPlan,
+ nativeMetrics,
+ new CometTaskMemoryManager)
Review Comment:
I'm referring this. I think we can pass `id` to `CometTaskMemoryManager` and
use that for identity mark.
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