I forgot to mention. I am using spark-1.5.1-bin-hadoop2.6 From: Andrew Davidson <a...@santacruzintegration.com> Date: Tuesday, November 17, 2015 at 2:26 PM To: "user @spark" <user@spark.apache.org> Subject: Re: WARN LoadSnappy: Snappy native library not loaded
> FYI > > After 17 min. only 26112/228155 have succeeded > > This seems very slow > > Kind regards > > Andy > > > > From: Andrew Davidson <a...@santacruzintegration.com> > Date: Tuesday, November 17, 2015 at 2:22 PM > To: "user @spark" <user@spark.apache.org> > Subject: WARN LoadSnappy: Snappy native library not loaded > > >> I started a spark POC. I created a ec2 cluster on AWS using spark-c2. I >> have 3 slaves. In general I am running into trouble even with small work >> loads. I am using IPython notebooks running on my spark cluster. >> Everything is painfully slow. I am using the standAlone cluster manager. >> I noticed that I am getting the following warning on my driver console. >> Any idea what the problem might be? >> >> >> >> 15/11/17 22:01:59 WARN MetricsSystem: Using default name DAGScheduler for >> source because spark.app.id is not set. >> 15/11/17 22:03:05 WARN NativeCodeLoader: Unable to load native-hadoop >> library for your platform... using builtin-java classes where applicable >> 15/11/17 22:03:05 WARN LoadSnappy: Snappy native library not loaded >> >> >> >> Here is an overview of my POS app. I have a file on hdfs containing about >> 5000 twitter status strings. >> >> tweetStrings = sc.textFile(dataURL) >> >> jTweets = (tweetStrings.map(lambda x: json.loads(x)).take(10)) >> >> >> Generated the following error ³error occurred while calling >> o78.partitions.: java.lang.OutOfMemoryError: GC overhead limit exceeded² >> >> Any idea what we need to do to improve new spark user¹s out of the box >> experience? >> >> Kind regards >> >> Andy >> >> export PYSPARK_PYTHON=python3.4 >> export PYSPARK_DRIVER_PYTHON=python3.4 >> export IPYTHON_OPTS="notebook --no-browser --port=7000 --log-level=WARN" >> >> MASTER_URL=spark://ec2-55-218-207-122.us-west-1.compute.amazonaws.com:7077 >> >> >> numCores=2 >> $SPARK_ROOT/bin/pyspark --master $MASTER_URL --total-executor-cores >> $numCores $*
