spark-user mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From java8964 <>
Subject RE: Why I didn't see the benefits of using KryoSerializer
Date Thu, 19 Mar 2015 17:57:28 GMT
I read the Spark code a little bit, trying to understand my own question.
It looks like the different is really between org.apache.spark.serializer.JavaSerializer and
org.apache.spark.serializer.KryoSerializer, both having the method named writeObject.
In my test case, for each line of my text file, it is about 140 bytes of String. When either
JavaSerializer.writeObject(140 bytes of String) or KryoSerializer.writeObject(140 bytes of
String), I didn't see difference in the underline OutputStream space usage.
Does this mean that KryoSerializer really doesn't give us any benefit for String type? I understand
that for primitives types, it shouldn't have any benefits, but how about String type?
When we talk about lower the memory using KryoSerializer in spark, under what case it can
bring significant benefits? It is my first experience with the KryoSerializer, so maybe I
am total wrong about its usage.
Subject: Why I didn't see the benefits of using KryoSerializer
Date: Tue, 17 Mar 2015 12:01:35 -0400

Hi, I am new to Spark. I tried to understand the memory benefits of using KryoSerializer.
I have this one box standalone test environment, which is 24 cores with 24G memory. I installed
Hadoop 2.2 plus Spark 1.2.0.
I put one text file in the hdfs about 1.2G.  Here is the settings in the
export SPARK_MASTER_OPTS="-Dspark.deploy.defaultCores=4"export SPARK_WORKER_MEMORY=32gexport
First test case:val log=sc.textFile("hdfs://namenode:9000/test_1g/")log.persist(
The data is about 3M rows. For the first test case, from the storage in the web UI, I can
see "Size in Memory" is 1787M, and "Fraction Cached" is 70% with 7 cached partitions.This
matched with what I thought, and first count finished about 17s, and 2nd count finished about
2nd test case after restart the spark-shell:val log=sc.textFile("hdfs://namenode:9000/test_1g/")log.persist(
Now from the web UI, I can see "Size in Memory" is 1231M, and "Fraction Cached" is 100% with
10 cached partitions. It looks like caching the default "java serialized format" reduce the
memory usage, but coming with a cost that first count finished around 39s and 2nd count finished
around 9s. So the job runs slower, with less memory usage.
So far I can understand all what happened and the tradeoff.
Now the problem comes with when I tried to test with KryoSerializer
SPARK_JAVA_OPTS="-Dspark.serializer=org.apache.spark.serializer.KryoSerializer" /opt/spark/bin/spark-shellval
First, I saw that the new serializer setting passed in, as proven in the Spark Properties
of "Environment" shows "



". This is not there for first 2 test cases.But in the web UI of "Storage", the "Size in Memory"
is 1234M, with 100% "Fraction Cached" and 10 cached partitions. The first count took 46s and
2nd count took 23s.
I don't get much less memory size as I expected, but longer run time for both counts. Anything
I did wrong? Why the memory foot print of "MEMORY_ONLY_SER" for KryoSerializer still use the
same size as default Java serializer, with worse duration?
View raw message