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SnappyData：java.lang.OutOfMemoryError：GC开销超过限制

2017-10-04 172 views
1

我对S3兽人数据的1.2GB，我试图做同样的下列内容：SnappyData：java.lang.OutOfMemoryError：GC开销超过限制

1）高速缓存活泼的群集上的数据[snappydata 0.9]

2）上的高速缓存的数据集

3）比较用火花的性能执行一个查询GROUPBY 2.0.0

我使用的是64 GB/8芯机和用于斯纳皮配置集群如下：

012现在 
$ 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

，我已经写了一个小python脚本，缓存从S3兽人数据并运行通过查询一个简单的基团，其是如下：

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:[email protected]_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()

上述脚本用执行下面的命令：

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

，我得到以下错误：

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

除了上面的错误，我该如何检查数据是否被缓存在快速集群中？

来源

2017-10-04 Harsh Bafna

回答

2

1）首先，它看起来不像是用python脚本连接到SnappyData集群，而是以本地模式运行它。在这种情况下，python脚本启动的JVM会与预期的OOM一起失败。当使用Python连接到SnappyData集群中的“smart connector”模式：

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

主机：端口以上是定位器主机和端口上节俭服务器正在运行（1527默认情况下）。其次，你有的例子只是使用Spark来缓存。如果你想使用SnappyData，负载成列的表：

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:[email protected]_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()

还要注意使用“SnappySession”，而不是背景，因为星火2.0.x的它被废弃与Spark缓存进行比较时，可以在单独的脚本中使用“cacheTable”并针对上游的Spark运行。请注意，“cacheTable”将缓慢执行缓存，这意味着第一个查询将执行实际的缓存，因此第一次查询运行将非常缓慢，但后续的应该更快。

3）更新到1.0版本，有很多改进，而不是使用0.9。在启动群集之前，您还需要将hadoop-aws-2.7.3和aws-java-sdk-1.7.4添加到conf/leads和conf/servers中的“-classpath”（或放入产品的jars目录中）。

来源

2017-10-04 11:44:52 Sumedh

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