| commit | 5643cfb71d343133a185aa257f137074f41abfb3 | [log] [tgz] |
|---|---|---|
| author | panbingkun <panbingkun@baidu.com> | Thu May 16 23:20:23 2024 -0700 |
| committer | Gengliang Wang <gengliang@apache.org> | Thu May 16 23:20:23 2024 -0700 |
| tree | 3590fe505c5be0cfb880a127399263912930b8b8 | |
| parent | e07f1af03edf20a633a6051117a894390b41c5f1 [diff] |
[SPARK-48303][CORE] Reorganize `LogKeys` ### What changes were proposed in this pull request? The pr aims to `reorganize` `LogKeys`, includes: - remove some unused `LogLeys` ACTUAL_BROADCAST_OUTPUT_STATUS_SIZE DEFAULT_COMPACTION_INTERVAL DRIVER_LIBRARY_PATH_KEY EXISTING_JARS EXPECTED_ANSWER FILTERS HAS_R_PACKAGE JAR_ENTRY LOG_KEY_FILE NUM_ADDED_MASTERS NUM_ADDED_WORKERS NUM_PARTITION_VALUES OUTPUT_LINE OUTPUT_LINE_NUMBER PARTITIONS_SIZE RULE_BATCH_NAME SERIALIZE_OUTPUT_LENGTH SHELL_COMMAND STREAM_SOURCE - merge `PARAMETER` into `PARAM` (because some are `full` spelled, and some are `abbreviations`, which are not unified) ESTIMATOR_PARAMETER_MAP -> ESTIMATOR_PARAM_MAP FUNCTION_PARAMETER -> FUNCTION_PARAM METHOD_PARAMETER_TYPES -> METHOD_PARAM_TYPES - merge `NUMBER` into `NUM` (abbreviations) MIN_VERSION_NUMBER -> MIN_VERSION_NUM RULE_NUMBER_OF_RUNS -> NUM_RULE_OF_RUNS VERSION_NUMBER -> VERSION_NUM - merge `TOTAL` into `NUM` TOTAL_RECORDS_READ -> NUM_RECORDS_READ TRAIN_WORD_COUNT -> NUM_TRAIN_WORD - `NUM` as prefix CHECKSUM_FILE_NUM -> NUM_CHECKSUM_FILE DATA_FILE_NUM -> NUM_DATA_FILE INDEX_FILE_NUM -> NUM_INDEX_FILE - COUNR -> NUM EXECUTOR_DESIRED_COUNT -> NUM_EXECUTOR_DESIRED EXECUTOR_LAUNCH_COUNT -> NUM_EXECUTOR_LAUNCH EXECUTOR_TARGET_COUNT -> NUM_EXECUTOR_TARGET KAFKA_PULLS_COUNT -> NUM_KAFKA_PULLS KAFKA_RECORDS_PULLED_COUNT -> NUM_KAFKA_RECORDS_PULLED MIN_FREQUENT_PATTERN_COUNT -> MIN_NUM_FREQUENT_PATTERN POD_COUNT -> NUM_POD POD_SHARED_SLOT_COUNT -> NUM_POD_SHARED_SLOT POD_TARGET_COUNT -> NUM_POD_TARGET RETRY_COUNT -> NUM_RETRY - fix some `typo` MALFORMATTED_STIRNG -> MALFORMATTED_STRING - other MAX_LOG_NUM_POLICY -> MAX_NUM_LOG_POLICY WEIGHTED_NUM -> NUM_WEIGHTED_EXAMPLES Changes in other code are additional changes caused by the above adjustments. ### Why are the changes needed? Let's make `LogKeys` easier to understand and more consistent. ### Does this PR introduce _any_ user-facing change? No. ### How was this patch tested? Pass GA. ### Was this patch authored or co-authored using generative AI tooling? No. Closes #46612 from panbingkun/reorganize_logkey. Authored-by: panbingkun <panbingkun@baidu.com> Signed-off-by: Gengliang Wang <gengliang@apache.org>
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, pandas API on Spark for pandas workloads, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.
You can find the latest Spark documentation, including a programming guide, on the project web page. This README file only contains basic setup instructions.
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”.
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()
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()
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 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.
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
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.
Please refer to the Configuration Guide in the online documentation for an overview on how to configure Spark.
Please review the Contribution to Spark guide for information on how to get started contributing to the project.
