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hive-json | rough prototype of a tool for discovering Apache Hive

 by   hortonworks Java Version: Current License: No License

 by   hortonworks Java Version: Current License: No License

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kandi X-RAY | hive-json Summary

hive-json is a Java library typically used in Big Data, React applications. hive-json has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.
Hive JSON Schema Finder ===. This project is a rough prototype that I’ve written to analyze large collections of JSON documents and discover their Apache Hive schema. I’ve used it to anaylyze the githubarchive.org’s log data. To build the project, use Maven (3.0.x) from http://maven.apache.org/. I’ve uploaded the discovered schema for githubarchive.org to https://gist.github.com/omalley/5125691.
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Quality
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Security
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kandi-support Support

  • hive-json has a low active ecosystem.
  • It has 41 star(s) with 32 fork(s). There are 15 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 2 open issues and 4 have been closed. On average issues are closed in 150 days. There are 1 open pull requests and 0 closed requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of hive-json is current.
hive-json Support
Best in #Java
Average in #Java
hive-json Support
Best in #Java
Average in #Java

quality kandi Quality

  • hive-json has 0 bugs and 0 code smells.
hive-json Quality
Best in #Java
Average in #Java
hive-json Quality
Best in #Java
Average in #Java

securitySecurity

  • hive-json has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • hive-json code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
hive-json Security
Best in #Java
Average in #Java
hive-json Security
Best in #Java
Average in #Java

license License

  • hive-json does not have a standard license declared.
  • Check the repository for any license declaration and review the terms closely.
  • Without a license, all rights are reserved, and you cannot use the library in your applications.
hive-json License
Best in #Java
Average in #Java
hive-json License
Best in #Java
Average in #Java

buildReuse

  • hive-json releases are not available. You will need to build from source code and install.
  • Build file is available. You can build the component from source.
  • hive-json saves you 477 person hours of effort in developing the same functionality from scratch.
  • It has 1123 lines of code, 73 functions and 12 files.
  • It has medium code complexity. Code complexity directly impacts maintainability of the code.
hive-json Reuse
Best in #Java
Average in #Java
hive-json Reuse
Best in #Java
Average in #Java
Top functions reviewed by kandi - BETA

kandi has reviewed hive-json and discovered the below as its top functions. This is intended to give you an instant insight into hive-json implemented functionality, and help decide if they suit your requirements.

  • Pick the type from the JSON .
    • Print the type
      • Main method for testing
        • reduce a JSON object
          • Returns a string representation of this object .
            • Merge other .
              • Compares this object to another .
                • Adds a type to the union .
                  • Adds a field .
                    • Prints a formatted summary of the stream

                      Get all kandi verified functions for this library.

                      Get all kandi verified functions for this library.

                      hive-json Key Features

                      A rough prototype of a tool for discovering Apache Hive schemas from JSON documents.

                      Community Discussions

                      Trending Discussions on hive-json
                      • Spark History Server very slow when driver running on master node
                      • java.lang.ClassNotFoundException: com.mysql.jdbc.Driver in Jupyter Notebook on Amazon EMR
                      Trending Discussions on hive-json

                      QUESTION

                      Spark History Server very slow when driver running on master node

                      Asked 2020-Jul-06 at 21:21

                      I'm using Spark 2.4.5 running on AWS EMR 5.30.0 with r5.4xlarge instances (16 vCore, 128 GiB memory, EBS only storage, EBS Storage:256 GiB) : 1 master, 1 core and 30 task.

                      I launched Spark Thrift Server on the master node and it's the only job that is running on the cluster

                      sudo /usr/lib/spark/sbin/start-thriftserver.sh --conf spark.blacklist.enabled=true --conf spark.blacklist.stage.maxFailedExecutorsPerNode=4 --conf spark.blacklist.task.maxTaskAttemptsPerNode=3 --conf spark.driver.cores=12 --conf spark.driver.maxResultSize=10g --conf spark.driver.memory=86000M --conf spark.driver.memoryOverhead=10240 --conf spark.kryoserializer.buffer.max=768m --conf spark.rpc.askTimeout=700 --conf spark.sql.broadcastTimeout=800 --conf spark.sql.sources.partitionOverwriteMode=dynamic --conf spark.task.maxFailures=20
                      

                      Then I launch SQL queries on it with JDBC but when heavy queries are running, the UI gets really slow. I thought it would be fine if I put spark.driver.cores=12 (there are 16 in the master node) and spark.driver.memory=86000M (there are 128GB of memory) to leave some margin for the master node to be able to run other processes like the history server but it is still slow.

                      So I guess there are other settings that I can edit to make the UI works fine but I'm not sure what.

                      Those are the settings from spark-defaults.conf in the cluster FYI:

                      spark.driver.extraClassPath      /usr/lib/hadoop-lzo/lib/*:/usr/lib/hadoop/hadoop-aws.jar:/usr/share/aws/aws-java-sdk/*:/usr/share/aws/emr/emrfs/conf:/usr/share/aws/emr/emrfs/lib/*:/usr/share/aws/emr/emrfs/auxlib/*:/usr/share/aws/emr/goodies/lib/emr-spark-goodies.jar:/usr/share/aws/emr/security/conf:/usr/share/aws/emr/security/lib/*:/usr/share/aws/hmclient/lib/aws-glue-datacatalog-spark-client.jar:/usr/share/java/Hive-JSON-Serde/hive-openx-serde.jar:/usr/share/aws/sagemaker-spark-sdk/lib/sagemaker-spark-sdk.jar:/usr/share/aws/emr/s3select/lib/emr-s3-select-spark-connector.jar
                      spark.driver.extraLibraryPath    /usr/lib/hadoop/lib/native:/usr/lib/hadoop-lzo/lib/native
                      spark.executor.extraClassPath    /usr/lib/hadoop-lzo/lib/*:/usr/lib/hadoop/hadoop-aws.jar:/usr/share/aws/aws-java-sdk/*:/usr/share/aws/emr/emrfs/conf:/usr/share/aws/emr/emrfs/lib/*:/usr/share/aws/emr/emrfs/auxlib/*:/usr/share/aws/emr/goodies/lib/emr-spark-goodies.jar:/usr/share/aws/emr/security/conf:/usr/share/aws/emr/security/lib/*:/usr/share/aws/hmclient/lib/aws-glue-datacatalog-spark-client.jar:/usr/share/java/Hive-JSON-Serde/hive-openx-serde.jar:/usr/share/aws/sagemaker-spark-sdk/lib/sagemaker-spark-sdk.jar:/usr/share/aws/emr/s3select/lib/emr-s3-select-spark-connector.jar
                      spark.executor.extraLibraryPath  /usr/lib/hadoop/lib/native:/usr/lib/hadoop-lzo/lib/native
                      spark.eventLog.enabled           true
                      spark.eventLog.dir               hdfs:///var/log/spark/apps
                      spark.history.fs.logDirectory    hdfs:///var/log/spark/apps
                      spark.sql.warehouse.dir          hdfs:///user/spark/warehouse
                      spark.sql.hive.metastore.sharedPrefixes com.amazonaws.services.dynamodbv2
                      spark.yarn.historyServer.address <xxxxx>:18080
                      spark.history.ui.port            18080
                      spark.shuffle.service.enabled    true
                      spark.yarn.dist.files            /etc/spark/conf/hive-site.xml
                      spark.driver.extraJavaOptions    -XX:+UseConcMarkSweepGC -XX:CMSInitiatingOccupancyFraction=70 -XX:MaxHeapFreeRatio=70 -XX:+CMSClassUnloadingEnabled -XX:OnOutOfMemoryError='kill -9 %p'
                      spark.dynamicAllocation.enabled  true
                      spark.blacklist.decommissioning.enabled true
                      spark.blacklist.decommissioning.timeout 1h
                      spark.resourceManager.cleanupExpiredHost true
                      spark.stage.attempt.ignoreOnDecommissionFetchFailure true
                      spark.decommissioning.timeout.threshold 20
                      spark.executor.extraJavaOptions  -verbose:gc -XX:+PrintGCDetails -XX:+PrintGCDateStamps -XX:+UseConcMarkSweepGC -XX:CMSInitiatingOccupancyFraction=70 -XX:MaxHeapFreeRatio=70 -XX:+CMSClassUnloadingEnabled -XX:OnOutOfMemoryError='kill -9 %p'
                      spark.hadoop.yarn.timeline-service.enabled false
                      spark.yarn.appMasterEnv.SPARK_PUBLIC_DNS $(hostname -f)
                      spark.files.fetchFailure.unRegisterOutputOnHost true
                      spark.hadoop.mapreduce.fileoutputcommitter.algorithm.version.emr_internal_use_only.EmrFileSystem 2
                      spark.hadoop.mapreduce.fileoutputcommitter.cleanup-failures.ignored.emr_internal_use_only.EmrFileSystem true
                      spark.hadoop.fs.s3.getObject.initialSocketTimeoutMilliseconds 2000
                      spark.sql.parquet.output.committer.class com.amazon.emr.committer.EmrOptimizedSparkSqlParquetOutputCommitter
                      spark.sql.parquet.fs.optimized.committer.optimization-enabled true
                      spark.sql.emr.internal.extensions com.amazonaws.emr.spark.EmrSparkSessionExtensions
                      spark.sql.sources.partitionOverwriteMode dynamic
                      spark.executor.instances         1
                      spark.executor.cores             16
                      spark.driver.memory              2048M
                      spark.executor.memory            109498M
                      spark.default.parallelism        32
                      spark.emr.maximizeResourceAllocation true```
                      

                      ANSWER

                      Answered 2020-Jul-06 at 21:21

                      The problem was having only 1 core instance as the logs were saved in HDFS so this instance became a bottleneck. I added another core instance and it's going much better now.

                      Another solution could be to save the logs to S3/S3A instead of HDFS, changing those parameters in spark-defaults.conf (make sure they are changed in the UI config too) but it might require adding some JAR files to work.

                      spark.eventLog.dir               hdfs:///var/log/spark/apps
                      spark.history.fs.logDirectory    hdfs:///var/log/spark/apps
                      

                      Source https://stackoverflow.com/questions/62521705

                      Community Discussions, Code Snippets contain sources that include Stack Exchange Network

                      Vulnerabilities

                      No vulnerabilities reported

                      Install hive-json

                      You can download it from GitHub.
                      You can use hive-json like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the hive-json component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .

                      Support

                      For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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