how do u want to process 1T of data when you set your executor memory to be 2g? look at spark ui, metrics of tasks...if any look at spark logs on executor machine under work dir(unless you configured log4j)
I think your executors are thrashing or spilling to disk. check memory metrics/swapping On 11 August 2015 at 23:19, java8964 <java8...@hotmail.com> wrote: > Currently we have a IBM BigInsight cluster with 1 namenode + 1 JobTracker > + 42 data/task nodes, which runs with BigInsight V3.0.0.2, corresponding > with Hadoop 2.2.0 with MR1. > > Since IBM BigInsight doesn't come with Spark, so we build Spark 1.2.2 with > Hadoop 2.2.0 + Hive 0.12 by ourselves, and deploy it on the same cluster. > > The IBM Biginsight comes with IBM jdk 1.7, but during our experience on > stage environment, we found out Spark works better with Oracle JVM. So we > run spark under Oracle JDK 1.7.0_79. > > Now on production, we are facing a issue we never faced, nor can reproduce > on our staging cluster. > > We are using Spark Standalone cluster, and here is the basic > configurations: > > more spark-env.sh > export JAVA_HOME=/opt/java > export PATH=$JAVA_HOME/bin:$PATH > export HADOOP_CONF_DIR=/opt/ibm/biginsights/hadoop-conf/ > export > SPARK_CLASSPATH=/opt/ibm/biginsights/IHC/lib/ibm-compression.jar:/opt/ibm/biginsights/hive/lib > /db2jcc4-10.6.jar > export > SPARK_LOCAL_DIRS=/data1/spark/local,/data2/spark/local,/data3/spark/local > export SPARK_MASTER_WEBUI_PORT=8081 > export SPARK_MASTER_IP=host1 > export SPARK_MASTER_OPTS="-Dspark.deploy.defaultCores=42" > export SPARK_WORKER_MEMORY=24g > export SPARK_WORKER_CORES=6 > export SPARK_WORKER_DIR=/tmp/spark/work > export SPARK_DRIVER_MEMORY=2g > export SPARK_EXECUTOR_MEMORY=2g > > more spark-defaults.conf > spark.master spark://host1:7077 > spark.eventLog.enabled true > spark.eventLog.dir hdfs://host1:9000/spark/eventLog > spark.serializer org.apache.spark.serializer.KryoSerializer > spark.executor.extraJavaOptions -verbose:gc -XX:+PrintGCDetails > -XX:+PrintGCTimeStamps > > We are using AVRO file format a lot, and we have these 2 datasets, one is > about 96G, and the other one is a little over 1T. Since we are using AVRO, > so we also built spark-avro of commit " > a788c9fce51b0ec1bb4ce88dc65c1d55aaa675b8 > <https://github.com/databricks/spark-avro/tree/a788c9fce51b0ec1bb4ce88dc65c1d55aaa675b8>", > which is the latest version supporting Spark 1.2.x. > > Here is the problem we are facing on our production cluster, even the > following simple spark-shell commands will hang in our production cluster: > > import org.apache.spark.sql.SQLContext > val sqlContext = new org.apache.spark.sql.hive.HiveContext(sc) > import com.databricks.spark.avro._ > val bigData = sqlContext.avroFile("hdfs://namenode:9000/bigData/") > bigData.registerTempTable("bigData") > bigData.count() > > From the console, we saw following: > [Stage 0:> > (44 + 42) / 7800] > > no update for more than 30 minutes and longer. > > The big dataset with 1T should generate 7800 HDFS block, but Spark's HDFS > client looks like having problem to read them. Since we are running Spark > on the data nodes, all the Spark tasks are running as "NODE_LOCAL" on > locality level. > > If I go to the data/task node which Spark tasks hang, and use the JStack > to dump the thread, I got the following on the top: > > 015-08-11 15:38:38 > Full thread dump Java HotSpot(TM) 64-Bit Server VM (24.79-b02 mixed mode): > > "Attach Listener" daemon prio=10 tid=0x00007f0660589000 nid=0x1584d > waiting on condition [0x0000000000000000] > java.lang.Thread.State: RUNNABLE > > "org.apache.hadoop.hdfs.PeerCache@4a88ec00" daemon prio=10 > tid=0x00007f06508b7800 nid=0x13302 waiting on condition [0x00007f060be94000] > java.lang.Thread.State: TIMED_WAITING (sleeping) > at java.lang.Thread.sleep(Native Method) > at org.apache.hadoop.hdfs.PeerCache.run(PeerCache.java:252) > at org.apache.hadoop.hdfs.PeerCache.access$000(PeerCache.java:39) > at org.apache.hadoop.hdfs.PeerCache$1.run(PeerCache.java:135) > at java.lang.Thread.run(Thread.java:745) > > "shuffle-client-1" daemon prio=10 tid=0x00007f0650687000 nid=0x132fc > runnable [0x00007f060d198000] > java.lang.Thread.State: RUNNABLE > at sun.nio.ch.EPollArrayWrapper.epollWait(Native Method) > at sun.nio.ch.EPollArrayWrapper.poll(EPollArrayWrapper.java:269) > at sun.nio.ch.EPollSelectorImpl.doSelect(EPollSelectorImpl.java:79) > at sun.nio.ch.SelectorImpl.lockAndDoSelect(SelectorImpl.java:87) > - locked <0x000000067bf47710> (a > io.netty.channel.nio.SelectedSelectionKeySet) > - locked <0x000000067bf374e8> (a > java.util.Collections$UnmodifiableSet) > - locked <0x000000067bf373d0> (a sun.nio.ch.EPollSelectorImpl) > at sun.nio.ch.SelectorImpl.select(SelectorImpl.java:98) > at io.netty.channel.nio.NioEventLoop.select(NioEventLoop.java:622) > at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:310) > at > io.netty.util.concurrent.SingleThreadEventExecutor$2.run(SingleThreadEventExecutor.java:116) > at java.lang.Thread.run(Thread.java:745) > > Meantime, I can confirm our Hadoop/HDFS cluster works fine, as the > MapReduce jobs also run without any problem, and "Hadoop fs" command works > fine in the BigInsight. > > I attached the jstack output with this email, but I don't know what could > be the root reason. > The same Spark shell command works fine, if I point to the small dataset, > instead of big dataset. The small dataset will have around 800 HDFS blocks, > and Spark finishes without any problem. > > Here are some facts I know: > > 1) Since the BigInsight is running on IBM JDK, so I make the Spark run > under the same JDK, same problem for BigData set. > 2) I even changed "--total-executor-cores" to 42, which will make each > executor runs with one core (as we have 42 Spark workers), to avoid any > multithreads, but still no luck. > 3) This problem of scanning 1T data hanging is NOT 100% for sure > happening. Sometime I didn't see it, but more than 50% I will see it if I > try. > 4) We never met this issue on our stage cluster, but it has only (1 > namenode + 1 jobtracker + 3 data/task nodes), and the same dataset is only > 160G on it. > 5) While the Spark java processing hanging, I didn't see any exception or > issue on the HDFS data node log. > > Does anyone have any clue about this? > > Thanks > > Yong > > > > > --------------------------------------------------------------------- > To unsubscribe, e-mail: user-unsubscr...@spark.apache.org > For additional commands, e-mail: user-h...@spark.apache.org >
