d65f534c5a
### What changes were proposed in this pull request? With benchmark original, where the timestamp values are valid to the new parser the result is ```scala [info] Running benchmark: Read dates and timestamps [info] Running case: timestamp strings [info] Stopped after 3 iterations, 5781 ms [info] Running case: parse timestamps from Dataset[String] [info] Stopped after 3 iterations, 44764 ms [info] Running case: infer timestamps from Dataset[String] [info] Stopped after 3 iterations, 93764 ms [info] Running case: from_json(timestamp) [info] Stopped after 3 iterations, 59021 ms ``` When we modify the benchmark to ```scala def timestampStr: Dataset[String] = { spark.range(0, rowsNum, 1, 1).mapPartitions { iter => iter.map(i => s"""{"timestamp":"1970-01-01T01:02:03.${i % 100}"}""") }.select($"value".as("timestamp")).as[String] } readBench.addCase("timestamp strings", numIters) { _ => timestampStr.noop() } readBench.addCase("parse timestamps from Dataset[String]", numIters) { _ => spark.read.schema(tsSchema).json(timestampStr).noop() } readBench.addCase("infer timestamps from Dataset[String]", numIters) { _ => spark.read.json(timestampStr).noop() } ``` where the timestamp values are invalid for the new parser which causes a fallback to legacy parser(2.4). the result is ```scala [info] Running benchmark: Read dates and timestamps [info] Running case: timestamp strings [info] Stopped after 3 iterations, 5623 ms [info] Running case: parse timestamps from Dataset[String] [info] Stopped after 3 iterations, 506637 ms [info] Running case: infer timestamps from Dataset[String] [info] Stopped after 3 iterations, 509076 ms ``` About 10x perf-regression BUT if we modify the timestamp pattern to `....HH:mm:ss[.SSS][XXX]` which make all timestamp values valid for the new parser to prohibit fallback, the result is ```scala [info] Running benchmark: Read dates and timestamps [info] Running case: timestamp strings [info] Stopped after 3 iterations, 5623 ms [info] Running case: parse timestamps from Dataset[String] [info] Stopped after 3 iterations, 506637 ms [info] Running case: infer timestamps from Dataset[String] [info] Stopped after 3 iterations, 509076 ms ``` ### Why are the changes needed? Fix performance regression. ### Does this PR introduce any user-facing change? NO ### How was this patch tested? new tests added. Closes #28181 from yaooqinn/SPARK-31414. Authored-by: Kent Yao <yaooqinn@hotmail.com> Signed-off-by: Wenchen Fan <wenchen@databricks.com> |
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Apache Spark
Spark is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R, 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 Structured Streaming for stream processing.
Online Documentation
You can find the latest Spark documentation, including a programming guide, on the project web page. 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".
For general development tips, including info on developing Spark using an IDE, see "Useful Developer Tools".
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 1,000,000,000:
scala> spark.range(1000 * 1000 * 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 1,000,000,000:
>>> spark.range(1000 * 1000 * 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" 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.
There is also a Kubernetes integration test, see resource-managers/kubernetes/integration-tests/README.md
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 and Enabling YARN" for detailed guidance on building for a particular distribution of Hadoop, including building for particular Hive and Hive Thriftserver distributions.
Configuration
Please refer to the Configuration Guide in the online documentation for an overview on how to configure Spark.
Contributing
Please review the Contribution to Spark guide for information on how to get started contributing to the project.