b55e4b9a52
tdas zsxwing this is the new PR for Spark-8080 I have merged https://github.com/apache/spark/pull/6659 Also to mention , for MEMORY_ONLY settings , when Block is not able to unrollSafely to memory if enough space is not there, BlockManager won't try to put the block and ReceivedBlockHandler will throw SparkException as it could not find the block id in PutResult. Thus number of records in block won't be counted if Block failed to unroll in memory. Which is fine. For MEMORY_DISK settings , if BlockManager not able to unroll block to memory, block will still get deseralized to Disk. Same for WAL based store. So for those cases ( storage level = memory + disk ) number of records will be counted even though the block not able to unroll to memory. thus I added the isFullyConsumed in the CountingIterator but have not used it as such case will never happen that block not fully consumed and ReceivedBlockHandler still get the block ID. I have added few test cases to cover those block unrolling scenarios also. Author: Dibyendu Bhattacharya <dibyendu.bhattacharya1@pearson.com> Author: U-PEROOT\UBHATD1 <UBHATD1@PIN-L-PI046.PEROOT.com> Closes #6707 from dibbhatt/master and squashes the following commits: f6cb6b5 [Dibyendu Bhattacharya] [SPARK-8080][STREAMING] Receiver.store with Iterator does not give correct count at Spark UI f37cfd8 [Dibyendu Bhattacharya] [SPARK-8080][STREAMING] Receiver.store with Iterator does not give correct count at Spark UI 5a8344a [Dibyendu Bhattacharya] [SPARK-8080][STREAMING] Receiver.store with Iterator does not give correct count at Spark UI Count ByteBufferBlock as 1 count fceac72 [Dibyendu Bhattacharya] [SPARK-8080][STREAMING] Receiver.store with Iterator does not give correct count at Spark UI 0153e7e [Dibyendu Bhattacharya] [SPARK-8080][STREAMING] Receiver.store with Iterator does not give correct count at Spark UI Fixed comments given by @zsxwing 4c5931d [Dibyendu Bhattacharya] [SPARK-8080][STREAMING] Receiver.store with Iterator does not give correct count at Spark UI 01e6dc8 [U-PEROOT\UBHATD1] A |
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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.