25b2f95fe3
In PySpark we get memory used before and after spill, then use the difference of these two value as memorySpilled, but if the before value is small than after value, then we will get a negative value, but this scenario 0 value may be more reasonable.
Below is the result in HistoryServer we have tested:
Index ID Attempt Status Locality Level Executor ID / Host Launch Time Duration GC Time Input Size / Records Write Time Shuffle Write Size / Records Shuffle Spill (Memory) Shuffle Spill (Disk) Errors
0 0 0 SUCCESS NODE_LOCAL 3 / vm119 2015/05/04 17:31:06 21 s 0.1 s 128.1 MB (hadoop) / 3237 70 ms 10.1 MB / 2529 0.0 B 5.7 MB
2 2 0 SUCCESS NODE_LOCAL 1 / vm118 2015/05/04 17:31:06 22 s 89 ms 128.1 MB (hadoop) / 3205 0.1 s 10.1 MB / 2529 -1048576.0 B 5.9 MB
1 1 0 SUCCESS NODE_LOCAL 2 / vm117 2015/05/04 17:31:06 22 s 0.1 s 128.1 MB (hadoop) / 3271 68 ms 10.1 MB / 2529 -1048576.0 B 5.6 MB
4 4 0 SUCCESS NODE_LOCAL 2 / vm117 2015/05/04 17:31:06 22 s 0.1 s 128.1 MB (hadoop) / 3192 51 ms 10.1 MB / 2529 -1048576.0 B 5.9 MB
3 3 0 SUCCESS NODE_LOCAL 3 / vm119 2015/05/04 17:31:06 22 s 0.1 s 128.1 MB (hadoop) / 3262 51 ms 10.1 MB / 2529 1024.0 KB 5.8 MB
5 5 0 SUCCESS NODE_LOCAL 1 / vm118 2015/05/04 17:31:06 22 s 89 ms 128.1 MB (hadoop) / 3256 93 ms 10.1 MB / 2529 -1048576.0 B 5.7 MB
/cc davies
Author: linweizhong <linweizhong@huawei.com>
Closes #5887 from Sephiroth-Lin/spark-7339 and squashes the following commits:
9186c81 [linweizhong] Use max function to get a nonnegative value
d41672b [linweizhong] Update MemoryBytesSpilled when memorySpilled > 0
(cherry picked from commit
|
||
---|---|---|
assembly | ||
bagel | ||
bin | ||
build | ||
conf | ||
core | ||
data/mllib | ||
dev | ||
docker | ||
docs | ||
ec2 | ||
examples | ||
external | ||
extras | ||
graphx | ||
launcher | ||
mllib | ||
network | ||
project | ||
python | ||
R | ||
repl | ||
sbin | ||
sbt | ||
sql | ||
streaming | ||
tools | ||
unsafe | ||
yarn | ||
.gitattributes | ||
.gitignore | ||
.rat-excludes | ||
CHANGES.txt | ||
CONTRIBUTING.md | ||
LICENSE | ||
make-distribution.sh | ||
NOTICE | ||
pom.xml | ||
README.md | ||
scalastyle-config.xml | ||
tox.ini |
Apache Spark
Spark is a fast and general cluster computing system for Big Data. It provides high-level APIs in Scala, Java, and Python, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and structured data processing, MLlib for machine learning, GraphX for graph processing, and Spark Streaming for stream processing.
Online Documentation
You can find the latest Spark documentation, including a programming guide, on the project web page and project wiki. This README file only contains basic setup instructions.
Building Spark
Spark is built using Apache Maven. To build Spark and its example programs, run:
mvn -DskipTests clean package
(You do not need to do this if you downloaded a pre-built package.) More detailed documentation is available from the project site, at "Building Spark".
Interactive Scala Shell
The easiest way to start using Spark is through the Scala shell:
./bin/spark-shell
Try the following command, which should return 1000:
scala> sc.parallelize(1 to 1000).count()
Interactive Python Shell
Alternatively, if you prefer Python, you can use the Python shell:
./bin/pyspark
And run the following command, which should also return 1000:
>>> sc.parallelize(range(1000)).count()
Example Programs
Spark also comes with several sample programs in the examples
directory.
To run one of them, use ./bin/run-example <class> [params]
. For example:
./bin/run-example SparkPi
will run the Pi example locally.
You can set the MASTER environment variable when running examples to submit
examples to a cluster. This can be a mesos:// or spark:// URL,
"yarn-cluster" or "yarn-client" to run on YARN, and "local" to run
locally with one thread, or "local[N]" to run locally with N threads. You
can also use an abbreviated class name if the class is in the examples
package. For instance:
MASTER=spark://host:7077 ./bin/run-example SparkPi
Many of the example programs print usage help if no params are given.
Running Tests
Testing first requires building Spark. Once Spark is built, tests can be run using:
./dev/run-tests
Please see the guidance on how to run all automated tests.
A Note About Hadoop Versions
Spark uses the Hadoop core library to talk to HDFS and other Hadoop-supported storage systems. Because the protocols have changed in different versions of Hadoop, you must build Spark against the same version that your cluster runs.
Please refer to the build documentation at "Specifying the Hadoop Version" for detailed guidance on building for a particular distribution of Hadoop, including building for particular Hive and Hive Thriftserver distributions. See also "Third Party Hadoop Distributions" for guidance on building a Spark application that works with a particular distribution.
Configuration
Please refer to the Configuration guide in the online documentation for an overview on how to configure Spark.