520ec0ff9d
This pull request enables Unsafe mode by default in Spark SQL. In order to do this, we had to fix a number of small issues: **List of fixed blockers**: - [x] Make some default buffer sizes configurable so that HiveCompatibilitySuite can run properly (#7741). - [x] Memory leak on grouped aggregation of empty input (fixed by #7560 to fix this) - [x] Update planner to also check whether codegen is enabled before planning unsafe operators. - [x] Investigate failing HiveThriftBinaryServerSuite test. This turns out to be caused by a ClassCastException that occurs when Exchange tries to apply an interpreted RowOrdering to an UnsafeRow when range partitioning an RDD. This could be fixed by #7408, but a shorter-term fix is to just skip the Unsafe exchange path when RangePartitioner is used. - [x] Memory leak exceptions masking exceptions that actually caused tasks to fail (will be fixed by #7603). - [x] ~~https://issues.apache.org/jira/browse/SPARK-9162, to implement code generation for ScalaUDF. This is necessary for `UDFSuite` to pass. For now, I've just ignored this test in order to try to find other problems while we wait for a fix.~~ This is no longer necessary as of #7682. - [x] Memory leaks from Limit after UnsafeExternalSort cause the memory leak detector to fail tests. This is a huge problem in the HiveCompatibilitySuite (fixed by f4ac642a4e5b2a7931c5e04e086bb10e263b1db6). - [x] Tests in `AggregationQuerySuite` are failing due to NaN-handling issues in UnsafeRow, which were fixed in #7736. - [x] `org.apache.spark.sql.ColumnExpressionSuite.rand` needs to be updated so that the planner check also matches `TungstenProject`. - [x] After having lowered the buffer sizes to 4MB so that most of HiveCompatibilitySuite runs: - [x] Wrong answer in `join_1to1` (fixed by #7680) - [x] Wrong answer in `join_nulls` (fixed by #7680) - [x] Managed memory OOM / leak in `lateral_view` - [x] Seems to hang indefinitely in `partcols1`. This might be a deadlock in script transformation or a bug in error-handling code? The hang was fixed by #7710. - [x] Error while freeing memory in `partcols1`: will be fixed by #7734. - [x] After fixing the `partcols1` hang, it appears that a number of later tests have issues as well. - [x] Fix thread-safety bug in codegen fallback expression evaluation (#7759). Author: Josh Rosen <joshrosen@databricks.com> Closes #7564 from JoshRosen/unsafe-by-default and squashes the following commits: 83c0c56 [Josh Rosen] Merge remote-tracking branch 'origin/master' into unsafe-by-default f4cc859 [Josh Rosen] Merge remote-tracking branch 'origin/master' into unsafe-by-default 963f567 [Josh Rosen] Reduce buffer size for R tests d6986de [Josh Rosen] Lower page size in PySpark tests 013b9da [Josh Rosen] Also match TungstenProject in checkNumProjects 5d0b2d3 [Josh Rosen] Add task completion callback to avoid leak in limit after sort ea250da [Josh Rosen] Disable unsafe Exchange path when RangePartitioning is used 715517b [Josh Rosen] Enable Unsafe by default |
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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 | ||
CONTRIBUTING.md | ||
LICENSE | ||
make-distribution.sh | ||
NOTICE | ||
pom.xml | ||
pylintrc | ||
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 DataFrames, 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:
build/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 tests for a module, or individual 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.