SnappyData : java.lang.OutOfMemoryError:超过GC开销限制

Question

我在S3上有1.2GB的orc数据，并且我试图用相同的方法执行以下操作：

1)缓存snappy集群snappydata 0.9上的数据

2)在缓存的数据集上执行groupby查询

3)与Spark2.0.0的性能比较。

我使用的是64 GB/8核心计算机，Snappy集群的配置如下所示：

$ cat locators
localhost

$cat leads
localhost -heap-size=4096m -spark.executor.cores=1

$cat servers
localhost -heap-size=6144m
localhost -heap-size=6144m
localhost -heap-size=6144m
localhost -heap-size=6144m
localhost -heap-size=6144m
localhost -heap-size=6144m

现在，我编写了一个小的python脚本，缓存来自S3的orc数据并按查询运行一个简单的组，如下所示：

from pyspark.sql.snappy import SnappyContext
from pyspark import SparkContext,SparkConf
conf = SparkConf().setAppName('snappy_sample')
sc = SparkContext(conf=conf)
sqlContext = SnappyContext(sc)

sqlContext.sql("CREATE EXTERNAL TABLE if not exists my_schema.my_table using orc options(path 's3a://access_key:secret_key@bucket_name/path')")
sqlContext.cacheTable("my_schema.my_table")

out = sqlContext.sql("select *  from my_schema.my_table where (WeekId = '1') order by sum_viewcount desc limit 25")
out.collect()

上面的脚本使用以下命令执行：

spark-submit --master local[*] snappy_sample.py

我得到了以下错误：

17/10/04 02:50:32 WARN memory.MemoryStore: Not enough space to cache rdd_2_5 in memory! (computed 21.2 MB so far)
17/10/04 02:50:32 WARN memory.MemoryStore: Not enough space to cache rdd_2_0 in memory! (computed 21.2 MB so far)
17/10/04 02:50:32 WARN storage.BlockManager: Persisting block rdd_2_5 to disk instead.
17/10/04 02:50:32 WARN storage.BlockManager: Persisting block rdd_2_0 to disk instead.
17/10/04 02:50:47 WARN storage.BlockManager: Putting block rdd_2_2 failed due to an exception
17/10/04 02:50:47 WARN storage.BlockManager: Block rdd_2_2 could not be removed as it was not found on disk or in memory
17/10/04 02:50:47 ERROR executor.Executor: Exception in task 2.0 in stage 0.0 (TID 2)
java.lang.OutOfMemoryError: GC overhead limit exceeded


at java.nio.HeapByteBuffer.<init>(HeapByteBuffer.java:57)
    at java.nio.ByteBuffer.allocate(ByteBuffer.java:335)
    at org.apache.spark.sql.execution.columnar.compression.CompressibleColumnBuilder$class.build(CompressibleColumnBuilder.scala:96)
    at org.apache.spark.sql.execution.columnar.NativeColumnBuilder.build(ColumnBuilder.scala:97)
    at org.apache.spark.sql.execution.columnar.InMemoryRelation$$anonfun$1$$anon$1$$anonfun$next$2.apply(InMemoryRelation.scala:135)
    at org.apache.spark.sql.execution.columnar.InMemoryRelation$$anonfun$1$$anon$1$$anonfun$next$2.apply(InMemoryRelation.scala:134)
    at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
    at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
    at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
    at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:186)
    at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
    at scala.collection.mutable.ArrayOps$ofRef.map(ArrayOps.scala:186)
    at org.apache.spark.sql.execution.columnar.InMemoryRelation$$anonfun$1$$anon$1.next(InMemoryRelation.scala:134)
    at org.apache.spark.sql.execution.columnar.InMemoryRelation$$anonfun$1$$anon$1.next(InMemoryRelation.scala:98)
    at org.apache.spark.storage.memory.MemoryStore.putIteratorAsValues(MemoryStore.scala:232)
    at org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:935)
    at org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:926)
    at org.apache.spark.storage.BlockManager.doPut(BlockManager.scala:866)
    at org.apache.spark.storage.BlockManager.doPutIterator(BlockManager.scala:926)
    at org.apache.spark.storage.BlockManager.getOrElseUpdate(BlockManager.scala:670)
    at org.apache.spark.rdd.RDD.getOrCompute(RDD.scala:331)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:282)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:320)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:284)
    at org.apache.spark.sql.execution.WholeStageCodegenRDD.compute(WholeStageCodegenExec.scala:496)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:320)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:284)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:320)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:284)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
17/10/04 02:50:47 ERROR util.SparkUncaughtExceptionHandler: Uncaught exception in thread Thread[Executor task launch worker-2,5,main]
java.lang.OutOfMemoryError: GC overhead limit exceeded
    at java.nio.HeapByteBuffer.<init>(HeapByteBuffer.java:57)
    at java.nio.ByteBuffer.allocate(ByteBuffer.java:335)
    at org.apache.spark.sql.execution.columnar.compression.CompressibleColumnBuilder$class.build(CompressibleColumnBuilder.scala:96)
    at org.apache.spark.sql.execution.columnar.NativeColumnBuilder.build(ColumnBuilder.scala:97)
    at org.apache.spark.sql.execution.columnar.InMemoryRelation$$anonfun$1$$anon$1$$anonfun$next$2.apply(InMemoryRelation.scala:135)
    at org.apache.spark.sql.execution.columnar.InMemoryRelation$$anonfun$1$$anon$1$$anonfun$next$2.apply(InMemoryRelation.scala:134)
    at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
    at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
    at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
    at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:186)
    at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
    at scala.collection.mutable.ArrayOps$ofRef.map(ArrayOps.scala:186)
    at org.apache.spark.sql.execution.columnar.InMemoryRelation$$anonfun$1$$anon$1.next(InMemoryRelation.scala:134)
    at org.apache.spark.sql.execution.columnar.InMemoryRelation$$anonfun$1$$anon$1.next(InMemoryRelation.scala:98)
    at org.apache.spark.storage.memory.MemoryStore.putIteratorAsValues(MemoryStore.scala:232)
    at org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:935)
    at org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:926)
    at org.apache.spark.storage.BlockManager.doPut(BlockManager.scala:866)
    at org.apache.spark.storage.BlockManager.doPutIterator(BlockManager.scala:926)
    at org.apache.spark.storage.BlockManager.getOrElseUpdate(BlockManager.scala:670)
    at org.apache.spark.rdd.RDD.getOrCompute(RDD.scala:331)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:282)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:320)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:284)
    at org.apache.spark.sql.execution.WholeStageCodegenRDD.compute(WholeStageCodegenExec.scala:496)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:320)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:284)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:320)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:284)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
17/10/04 02:50:48 INFO snappystore: VM is exiting - shutting down distributed system

除了上述错误外，如何检查数据是否已缓存在snappy集群中？

Answer 1

1)首先，它看起来不像是用python脚本连接到SnappyData集群，而是在本地模式下运行它。在这种情况下，python脚本启动的JVM在OOM中失败了。在“SnappyData”模式下使用python到智能连接器集群时：

spark-submit --master local[*] --conf snappydata.connection=locator:1527 snappy_sample.py

上面的主机:端口是运行节俭服务器的定位器主机和端口(默认情况下是1527)。

2)第二，您所举的示例将只是使用Spark缓存。如果要使用SnappyData，请将其加载到列表中：

from pyspark.sql.snappy import SnappySession
from pyspark import SparkContext,SparkConf
conf = SparkConf().setAppName('snappy_sample')
sc = SparkContext(conf=conf)
session = SnappySession(sc)

session.sql("CREATE EXTERNAL TABLE if not exists my_table using orc options(path 's3a://access_key:secret_key@bucket_name/path')")
session.table("my_table").write.format("column").saveAsTable("my_column_table")

out = session.sql("select *  from my_column_table where (WeekId = '1') order by sum_viewcount desc limit 25")
out.collect()

还要注意"SnappySession“的使用，而不是上下文的使用，因为Spark2.0.x就不再推荐它了。当与火花缓存进行比较时，您可以在单独的脚本中使用"cacheTable“，并在上游火花上运行。请注意，"cacheTable“将懒洋洋地进行缓存，这意味着第一个查询将执行实际的缓存，因此第一个查询的运行将非常缓慢，但是后续的缓存应该会更快。

3)更新到有许多改进的1.0版本，而不是使用0.9。在启动集群之前，还需要将hadoop-aws-2.7.3和aws-java-sdk-1.7.4添加到conf/leads和conf/server(或放入产品的jars目录)中的"-classpath“中。