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02-22-2024
10:51 PM
1 Kudo
Spark and Java versions Supportability Matrix 1. Introduction: Apache Spark is a powerful open-source distributed computing system widely used for big data processing and analytics. However, choosing the right Java version for your Spark application is crucial for optimal performance, security, and compatibility. This article dives deep into the officially supported Java versions for Spark, along with helpful advice on choosing the right one for your project. 2. Matrix Table Spark Version Supported Java Version(s) Java 8 Java 11 Java 17 Java 21 Deprecated Java Version(s) 3.5.1 Java 8*/11/17 Yes Yes Yes No Java 8 prior to version 8u371 support is deprecated 3.5.0 Java 8*/11/17 Yes Yes Yes No Java 8 prior to version 8u371 support is deprecated 3.4.2 Java 8*/11/17 Yes Yes Yes No Java 8 prior to version 8u362 support is deprecated 3.4.1 Java 8*/11/17 Yes Yes Yes No Java 8 prior to version 8u362 support is deprecated 3.4.0 Java 8*/11/17 Yes Yes Yes No Java 8 prior to version 8u362 support is deprecated 3.3.3 Java 8*/11/17 Yes Yes Yes No Java 8 prior to version 8u201 support is deprecated 3.3.2 Java 8*/11/17 Yes Yes Yes No Java 8 prior to version 8u201 support is deprecated 3.3.1 Java 8*/11/17 Yes Yes Yes No Java 8 prior to version 8u201 support is deprecated 3.3.0 Java 8*/11/17^ Yes Yes No No Java 8 prior to version 8u201 support is deprecated 3.2.4 Java 8*/11 Yes Yes No No Java 8 prior to version 8u201 support is deprecated 3.2.3 Java 8*/11 Yes Yes No No Java 8 prior to version 8u201 support is deprecated 3.2.2 Java 8*/11 Yes Yes No No Java 8 prior to version 8u201 support is deprecated 3.2.1 Java 8*/11 Yes Yes No No Java 8 prior to version 8u201 support is deprecated 3.2.0 Java 8*/11 Yes Yes No No Java 8 prior to version 8u201 support is deprecated 3.1.3 Java 8*/11 Yes Yes No No Java 8 prior to version 8u92 support is deprecated 3.1.2 Java 8*/11 Yes Yes No No Java 8 prior to version 8u92 support is deprecated 3.1.1 Java 8*/11 Yes Yes No No Java 8 prior to version 8u92 support is deprecated 3.0.3 Java 8*/11 Yes Yes No No Java 8 prior to version 8u92 support is deprecated 3.0.2 Java 8*/11 Yes Yes No No Java 8 prior to version 8u92 support is deprecated 3.0.1 Java 8*/11 Yes Yes No No Java 8 prior to version 8u92 support is deprecated 3.0.0 Java 8*/11 Yes Yes No No Java 8 prior to version 8u92 support is deprecated 2.4.8 Java 8* Yes No No No 2.4.7 Java 8* Yes No No No 2.4.6 Java 8* Yes No No No 2.4.5 Java 8* Yes No No No 2.4.4 Java 8* Yes No No No 2.4.3 Java 8* Yes No No No 2.4.2 Java 8* Yes No No No 2.4.1 Java 8* Yes No No No 2.4.0 Java 8* Yes No No No * means Cloudera recommended Java version.
^ means Upstream Spark is supported. Note: According to the Cloudera documentation, Spark 3.3.0 only supports Java 8 and 11. However, the official Spark documentation lists Java 8, 11, and 17 as compatible versions. References: Apache Spark - A Unified engine for large-scale data analytics CDS 3.3 Powered by Apache Spark Requirements SPARK-24417 Support Matrix Cloudera 3. Problems Arising from Unsupported Spark & Java Versions Utilizing incompatible or unsupported versions of Spark and Java can introduce various challenges and impediments in the operation of your Spark environment. Performance Degradation: The usage of an incompatible Java version could lead to performance degradation or inefficiencies. This is attributable to the inability to leverage the latest optimizations and features provided by newer Java releases, resulting in the suboptimal performance of Spark jobs. Compatibility Issues: Spark's functionality may be compromised or rendered unstable when interfacing with specific versions of Java. This can manifest as unexpected errors or failures during runtime, hindering the smooth execution of Spark applications. Feature Limitations: Newer iterations of Spark may rely on features or enhancements exclusive to certain Java versions. Employing outdated or unsupported Java versions may curtail your ability to exploit these advanced features, constraining the capabilities and functionalities of your Spark applications. 4. End of Life (EOL) dates for Java versions: Java Version EOL Date 1 Java 8 31, December 2020 (Public Updates), Still supported with Long Term Support (LTS) until December 2030. 2 Java 11 30, September 2023 (Public Updates), Still supported with Long Term Support (LTS) until January 2032. 3 Java 17 30, September 2026 (Public Updates), Long Term Support (LTS) until September 2029. 4 Java 21 30, September 2028 (Public Updates), Long Term Support (LTS) until September 2031. Reference(s): Oracle Java SE Support Roadmap Java version history 5. JDK & Scala compatibility Minimum Scala versions: JDK Version Scala 3 Scala 2.13 Scala 2.12 Scala 2.11 22 (ea) 3.3.2 2.13.12 2.12.19 21 (LTS) 3.3.1 2.13.11 2.12.18 20 3.3.0 2.13.11 2.12.18 19 3.2.0 2.13.9 2.12.16 18 3.1.3 2.13.7 2.12.15 17 (LTS) 3.0.0 2.13.6 2.12.15 11 (LTS) 3.0.0 2.13.0 2.12.4 2.11.12 8 (LTS) 3.0.0 2.13.0 2.12.0 2.11.0 `*` = forthcoming; support available in nightly builds Thank you for taking the time to read this article. We hope you found it informative and helpful in enhancing your understanding of the topic. If you have any questions or feedback, please feel free to reach out to me. Remember, your support motivates us to continue creating valuable content. If this article helped you, please consider giving it a like and providing a kudos. We appreciate your support!
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02-05-2024
08:59 PM
If above answers are helped you, please accept as Solution. It will helpful for others.
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02-05-2024
08:58 PM
Hi @sonnh The way Spark and Hive handle reading and writing data back to the same table differs. Spark typically clears the target path before writing new data, while Hive writes to a temporary directory first and then replaces the target path with the result data upon task completion. When working with specific file formats like ORC or Parquet and interacting with Hive metastore, consider adjusting these Spark settings as needed: --conf spark.sql.hive.convertMetastoreParquet=false --conf spark.sql.hive.convertMetastoreOrc=false Reference: https://community.cloudera.com/t5/Support-Questions/Insert-overwrite-with-in-the-same-table-in-spark/m-p/242780 https://www.baifachuan.com/posts/da7bb348.html
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02-04-2024
08:05 PM
1 Kudo
Hi @Meepoljd Please let me know still you need any help on this issue. If any of the above solutions is helped then mark Accept as Solution.
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02-04-2024
08:11 AM
Hi @zhuw.bigdata To locate Spark logs, follow these steps: Access the Spark UI: Open the Spark UI in your web browser. Identify Nodes: Navigate to the Executors tab to view information about the driver and executor nodes involved in the Spark application. Determine Log Directory: Within the Spark UI, find the Hadoop settings section and locate the value of the yarn.nodemanager.log-dirs property. This specifies the base directory for Spark logs on the cluster. Access Log Location: Using a terminal or SSH, log in to the relevant node (driver or executor) where the logs you need are located. Navigate to Application Log Directory: Within the yarn.nodemanager.log-dirs directory, access the subdirectory for the specific application using the pattern application_${appid}, where ${appid} is the unique application ID of the Spark job. Find Container Logs: Within the application directory, locate the individual container log directories named container_{$contid}, where ${contid} is the container ID. Review Log Files: Each container directory contains the following log files generated by that container: stderr: Standard error output stdin: Standard input (if applicable) syslog: System-level logs
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02-04-2024
08:02 AM
Hi @zenaskun001 Could you please provide more details to check your issue. Check the following things: 1. By default SparkInterpreter will be installed. Check in your case $ZEPPELIN_HOME/interpreters location Spark Interpreter is installed or not. 2. After proper installation, you need to restart the Zeppelin and its related Components like Spark. 3. After restarting the Zeppelin service, try to login and check the Interpreter is installed or not. 4. As a last step, you need to check the Zeppelin logs (/var/log/zeppelin) path.
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02-04-2024
07:58 AM
Hi @sonnh Generally it is not advisable to read and write the same table at a time. It can result in anything between data corruption and complete data loss in case of failure. As a temporary solution, First create a temporary view by reading the table data and later you can use that data and finally save the data to destination table. Reference: https://stackoverflow.com/questions/38746773/read-from-a-hive-table-and-write-back-to-it-using-spark-sql https://issues.apache.org/jira/browse/SPARK-27030
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02-04-2024
07:46 AM
1 Kudo
Hi @Taries I hope you are doing good. Do you need any further help on this issue. If above solutions is helped in your case please accept the Solution. It will help for others.
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02-04-2024
07:44 AM
Hi @yagoaparecidoti Unfortunately Cloudera will not support installing/using the open source Spark because of some customisations needs to be done at Cloudera end support other component integrations.
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02-04-2024
07:40 AM
Hi @Kunank_kesar Could you please check the following things to resolve the above issue: 1. Have you closed the spark session properly. For better practise close the spark session if it is not closed. 2. Have you checked the application code by adding some loggers what driver is doing with out stopping the application. 3. As a last step, go to driver machine and collect the thread dumps and see is there any operation it is doing internally.
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