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GHSA-g3rq-g295-4j3m","Warn: Project is vulnerable to: GHSA-h5c8-rqwp-cp95","Warn: Project is vulnerable to: GHSA-h75v-3vvj-5mfj","Warn: Project is vulnerable to: PYSEC-2019-220 / GHSA-hj2j-77xm-mc5v","Warn: Project is vulnerable to: GHSA-q2x7-8rv6-6q7h","Warn: Project is vulnerable to: PYSEC-2024-241 / GHSA-3v79-q7ph-j75h","Warn: Project is vulnerable to: GHSA-43c4-9qgj-x742","Warn: Project is vulnerable to: GHSA-49m6-vrr9-2cqm","Warn: Project is vulnerable to: GHSA-4qq5-mxxx-m6gg","Warn: Project is vulnerable to: PYSEC-2025-17 / GHSA-4rj2-9gcx-5qhx","Warn: Project is vulnerable to: GHSA-4rqf-8pfm-p36r","Warn: Project is vulnerable to: PYSEC-2023-253 / GHSA-554w-xh4j-8w64","Warn: Project is vulnerable to: GHSA-59v3-898r-qwhj","Warn: Project is vulnerable to: GHSA-5mvj-wmgj-7q8c","Warn: Project is vulnerable to: GHSA-5p3h-7fwh-92rc","Warn: Project is vulnerable to: PYSEC-2024-239 / GHSA-5q6c-ffvg-xcm9","Warn: Project is vulnerable to: PYSEC-2023-252 / GHSA-5r3q-93q3-f978","Warn: Project 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GHSA-j62r-wxqq-f3gf","Warn: Project is vulnerable to: GHSA-j8mg-pqc5-x9gj","Warn: Project is vulnerable to: GHSA-m49c-5c52-6696","Warn: Project is vulnerable to: GHSA-p4jx-q62p-x5jr","Warn: Project is vulnerable to: GHSA-pqcv-qw2r-r859","Warn: Project is vulnerable to: GHSA-q3gw-8236-5jw4","Warn: Project is vulnerable to: GHSA-qg8p-32gr-gh6x","Warn: Project is vulnerable to: PYSEC-2024-224 / GHSA-qpgc-w4mg-6v92","Warn: Project is vulnerable to: PYSEC-2023-309 / GHSA-v945-r3rc-6fjm","Warn: Project is vulnerable to: PYSEC-2023-260 / GHSA-vwhf-3v6x-wff8","Warn: Project is vulnerable to: GHSA-wf7f-8fxf-xfxc","Warn: Project is vulnerable to: GHSA-wqxf-447m-6f5f","Warn: Project is vulnerable to: GHSA-wv8q-4f85-2p8p","Warn: Project is vulnerable to: PYSEC-2025-52 / GHSA-wxj7-3fx5-pp9m","Warn: Project is vulnerable to: GHSA-x38x-g6gr-jqff","Warn: Project is vulnerable to: PYSEC-2024-244","Warn: Project is vulnerable to: GHSA-fpfv-jqm9-f5jm","Warn: Project is vulnerable to: PYSEC-2017-48 / GHSA-chqf-hx79-gxc6","Warn: Project is vulnerable to: GHSA-8qvm-5x2c-j2w7","Warn: Project is vulnerable to: PYSEC-2020-92 / GHSA-hj5v-574p-mj7c","Warn: Project is vulnerable to: PYSEC-2022-42969","Warn: Project is vulnerable to: PYSEC-2024-161","Warn: Project is vulnerable to: PYSEC-2021-142 / GHSA-8q59-q68h-6hv4","Warn: Project is vulnerable to: PYSEC-2018-49 / GHSA-rprw-h62v-c2w7","Warn: Project is vulnerable to: PYSEC-2020-107 / GHSA-jjw5-xxj6-pcv5","Warn: Project is vulnerable to: PYSEC-2024-110 / GHSA-jw8x-6495-233v","Warn: Project is vulnerable to: PYSEC-2020-108","Warn: Project is vulnerable to: PYSEC-2019-156 / GHSA-xp76-357g-9wqq","Warn: Project is vulnerable to: PYSEC-2023-102","Warn: Project is vulnerable to: PYSEC-2023-114"],"documentation":{"short":"Determines if the project has open, known unfixed 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Apache Spark\n\nSpark is a unified analytics engine for large-scale data processing. It provides\nhigh-level APIs in Scala, Java, Python, and R (Deprecated), and an optimized engine that\nsupports general computation graphs for data analysis. It also supports a\nrich set of higher-level tools including Spark SQL for SQL and DataFrames,\npandas API on Spark for pandas workloads, MLlib for machine learning, GraphX for graph processing,\nand Structured Streaming for stream processing.\n\n- Official version: \u003chttps://spark.apache.org/\u003e\n- Development version: \u003chttps://apache.github.io/spark/\u003e\n\n[![GitHub Actions Build](https://github.com/apache/spark/actions/workflows/build_main.yml/badge.svg)](https://github.com/apache/spark/actions/workflows/build_main.yml)\n[![PySpark Coverage](https://codecov.io/gh/apache/spark/branch/master/graph/badge.svg)](https://codecov.io/gh/apache/spark)\n[![PyPI Downloads](https://static.pepy.tech/personalized-badge/pyspark?period=month\u0026units=international_system\u0026left_color=black\u0026right_color=orange\u0026left_text=PyPI%20downloads)](https://pypi.org/project/pyspark/)\n\n\n## Online Documentation\n\nYou can find the latest Spark documentation, including a programming\nguide, on the [project web page](https://spark.apache.org/documentation.html).\nThis README file only contains basic setup instructions.\n\n## Building Spark\n\nSpark is built using [Apache Maven](https://maven.apache.org/).\nTo build Spark and its example programs, run:\n\n```bash\n./build/mvn -DskipTests clean package\n```\n\n(You do not need to do this if you downloaded a pre-built package.)\n\nMore detailed documentation is available from the project site, at\n[\"Building Spark\"](https://spark.apache.org/docs/latest/building-spark.html).\n\nFor general development tips, including info on developing Spark using an IDE, see [\"Useful Developer Tools\"](https://spark.apache.org/developer-tools.html).\n\n## Interactive Scala Shell\n\nThe easiest way to start using Spark is through the Scala shell:\n\n```bash\n./bin/spark-shell\n```\n\nTry the following command, which should return 1,000,000,000:\n\n```scala\nscala\u003e spark.range(1000 * 1000 * 1000).count()\n```\n\n## Interactive Python Shell\n\nAlternatively, if you prefer Python, you can use the Python shell:\n\n```bash\n./bin/pyspark\n```\n\nAnd run the following command, which should also return 1,000,000,000:\n\n```python\n\u003e\u003e\u003e spark.range(1000 * 1000 * 1000).count()\n```\n\n## Example Programs\n\nSpark also comes with several sample programs in the `examples` directory.\nTo run one of them, use `./bin/run-example \u003cclass\u003e [params]`. For example:\n\n```bash\n./bin/run-example SparkPi\n```\n\nwill run the Pi example locally.\n\nYou can set the MASTER environment variable when running examples to submit\nexamples to a cluster. This can be spark:// URL,\n\"yarn\" to run on YARN, and \"local\" to run\nlocally with one thread, or \"local[N]\" to run locally with N threads. You\ncan also use an abbreviated class name if the class is in the `examples`\npackage. For instance:\n\n```bash\nMASTER=spark://host:7077 ./bin/run-example SparkPi\n```\n\nMany of the example programs print usage help if no params are given.\n\n## Running Tests\n\nTesting first requires [building Spark](#building-spark). Once Spark is built, tests\ncan be run using:\n\n```bash\n./dev/run-tests\n```\n\nPlease see the guidance on how to\n[run tests for a module, or individual tests](https://spark.apache.org/developer-tools.html#individual-tests).\n\nThere is also a Kubernetes integration test, see resource-managers/kubernetes/integration-tests/README.md\n\n## A Note About Hadoop Versions\n\nSpark uses the Hadoop core library to talk to HDFS and other Hadoop-supported\nstorage systems. Because the protocols have changed in different versions of\nHadoop, you must build Spark against the same version that your cluster runs.\n\nPlease refer to the build documentation at\n[\"Specifying the Hadoop Version and Enabling YARN\"](https://spark.apache.org/docs/latest/building-spark.html#specifying-the-hadoop-version-and-enabling-yarn)\nfor detailed guidance on building for a particular distribution of Hadoop, including\nbuilding for particular Hive and Hive Thriftserver distributions.\n\n## Configuration\n\nPlease refer to the [Configuration Guide](https://spark.apache.org/docs/latest/configuration.html)\nin the online documentation for an overview on how to configure Spark.\n\n## Contributing\n\nPlease review the [Contribution to Spark guide](https://spark.apache.org/contributing.html)\nfor information on how to get started contributing to the project.\n","funding_links":[],"categories":["Scala","HarmonyOS","Table of Contents","Distributed Computing","Data Processing","Basic Components","机器学习框架","Data Management \u0026 Processing","分布式计算","Java","Distributed Computing Libraries","Data Lake Engines","数据库管理系统","Data Stream Processing","⚙️ Data Engineering","Curated List","系统设计","大数据","[](https://github.com/josephmisiti/awesome-machine-learning/blob/master/README.md#java)Java"],"sub_categories":["Windows Manager","Streaming Engine","Monitoring","Vectors","Computation","Database \u0026 Cloud Management","Row-based storage","网络服务_其他","Tools","Data Tools","[Tools](#tools-1)","Speech Recognition","大数据","Big Data","Drone Frames"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fapache%2Fspark","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fapache%2Fspark","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fapache%2Fspark/lists"}