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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| .. | ||
| pkg | ||
| .gitignore | ||
| create-docs.sh | ||
| DOCUMENTATION.md | ||
| install-dev.bat | ||
| install-dev.sh | ||
| log4j.properties | ||
| README.md | ||
| run-tests.sh | ||
| WINDOWS.md | ||
R on Spark
SparkR is an R package that provides a light-weight frontend to use Spark from R.
SparkR development
Build Spark
Build Spark with Maven and include the -Psparkr profile to build the R package. For example to use the default Hadoop versions you can run
build/mvn -DskipTests -Psparkr package
Running sparkR
You can start using SparkR by launching the SparkR shell with
./bin/sparkR
The sparkR script automatically creates a SparkContext with Spark by default in
local mode. To specify the Spark master of a cluster for the automatically created
SparkContext, you can run
./bin/sparkR --master "local[2]"
To set other options like driver memory, executor memory etc. you can pass in the spark-submit arguments to ./bin/sparkR
Using SparkR from RStudio
If you wish to use SparkR from RStudio or other R frontends you will need to set some environment variables which point SparkR to your Spark installation. For example
# Set this to where Spark is installed
Sys.setenv(SPARK_HOME="/Users/shivaram/spark")
# This line loads SparkR from the installed directory
.libPaths(c(file.path(Sys.getenv("SPARK_HOME"), "R", "lib"), .libPaths()))
library(SparkR)
sc <- sparkR.init(master="local")
Making changes to SparkR
The instructions for making contributions to Spark also apply to SparkR.
If you only make R file changes (i.e. no Scala changes) then you can just re-install the R package using R/install-dev.sh and test your changes.
Once you have made your changes, please include unit tests for them and run existing unit tests using the run-tests.sh script as described below.
Generating documentation
The SparkR documentation (Rd files and HTML files) are not a part of the source repository. To generate them you can run the script R/create-docs.sh. This script uses devtools and knitr to generate the docs and these packages need to be installed on the machine before using the script.
Examples, Unit tests
SparkR comes with several sample programs in the examples/src/main/r directory.
To run one of them, use ./bin/sparkR <filename> <args>. For example:
./bin/sparkR examples/src/main/r/dataframe.R
You can also run the unit-tests for SparkR by running (you need to install the testthat package first):
R -e 'install.packages("testthat", repos="http://cran.us.r-project.org")'
./R/run-tests.sh
Running on YARN
The ./bin/spark-submit and ./bin/sparkR can also be used to submit jobs to YARN clusters. You will need to set YARN conf dir before doing so. For example on CDH you can run
export YARN_CONF_DIR=/etc/hadoop/conf
./bin/spark-submit --master yarn examples/src/main/r/dataframe.R