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Subject [GitHub] [spark] dongjoon-hyun commented on issue #25686: [SPARK-28981][K8S] Missing library for reading/writing Snappy-compressed files
Date Thu, 05 Sep 2019 17:42:22 GMT
dongjoon-hyun commented on issue #25686: [SPARK-28981][K8S] Missing library for reading/writing
Snappy-compressed files
URL: https://github.com/apache/spark/pull/25686#issuecomment-528492922
 
 
   Thank you for your first contribution, @psschwei . As @srowen mentioned, this is resolved
at 2.4.4. Please download and build docker image from `Apache Spark 2.4.4`. I'll close this
PR.
   ```
   $ docker build -t spark:2.4.4 -f kubernetes/dockerfiles/spark/Dockerfile .
   $ docker run --rm -it spark:2.4.4 /opt/spark/bin/spark-shell
   ++ id -u
   + myuid=0
   ++ id -g
   + mygid=0
   + set +e
   ++ getent passwd 0
   + uidentry=root:x:0:0:root:/root:/bin/ash
   + set -e
   + '[' -z root:x:0:0:root:/root:/bin/ash ']'
   + SPARK_K8S_CMD=/opt/spark/bin/spark-shell
   + case "$SPARK_K8S_CMD" in
   + echo 'Non-spark-on-k8s command provided, proceeding in pass-through mode...'
   Non-spark-on-k8s command provided, proceeding in pass-through mode...
   + exec /sbin/tini -s -- /opt/spark/bin/spark-shell
   19/09/05 17:39:18 WARN NativeCodeLoader: Unable to load native-hadoop library for your
platform... using builtin-java classes where applicable
   Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
   Setting default log level to "WARN".
   To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
   Spark context Web UI available at http://454a817f8cee:4040
   Spark context available as 'sc' (master = local[*], app id = local-1567705163260).
   Spark session available as 'spark'.
   Welcome to
         ____              __
        / __/__  ___ _____/ /__
       _\ \/ _ \/ _ `/ __/  '_/
      /___/ .__/\_,_/_/ /_/\_\   version 2.4.4
         /_/
   
   Using Scala version 2.11.12 (OpenJDK 64-Bit Server VM, Java 1.8.0_212)
   Type in expressions to have them evaluated.
   Type :help for more information.
   
   scala> spark.range(10).write.parquet("/tmp/p")
   19/09/05 17:39:38 WARN MemoryManager: Total allocation exceeds 95.00% (906,992,014 bytes)
of heap memory
   Scaling row group sizes to 96.54% for 7 writers
   19/09/05 17:39:38 WARN MemoryManager: Total allocation exceeds 95.00% (906,992,014 bytes)
of heap memory
   Scaling row group sizes to 84.47% for 8 writers
   19/09/05 17:39:38 WARN MemoryManager: Total allocation exceeds 95.00% (906,992,014 bytes)
of heap memory
   Scaling row group sizes to 75.08% for 9 writers
   19/09/05 17:39:38 WARN MemoryManager: Total allocation exceeds 95.00% (906,992,014 bytes)
of heap memory
   Scaling row group sizes to 67.58% for 10 writers
   19/09/05 17:39:38 WARN MemoryManager: Total allocation exceeds 95.00% (906,992,014 bytes)
of heap memory
   Scaling row group sizes to 61.43% for 11 writers
   19/09/05 17:39:38 WARN MemoryManager: Total allocation exceeds 95.00% (906,992,014 bytes)
of heap memory
   Scaling row group sizes to 67.58% for 10 writers
   19/09/05 17:39:38 WARN MemoryManager: Total allocation exceeds 95.00% (906,992,014 bytes)
of heap memory
   Scaling row group sizes to 75.08% for 9 writers
   19/09/05 17:39:38 WARN MemoryManager: Total allocation exceeds 95.00% (906,992,014 bytes)
of heap memory
   Scaling row group sizes to 84.47% for 8 writers
   19/09/05 17:39:38 WARN MemoryManager: Total allocation exceeds 95.00% (906,992,014 bytes)
of heap memory
   Scaling row group sizes to 96.54% for 7 writers
   
   scala> spark.read.parquet("/tmp/p").count
   res1: Long = 10
   ```

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