[jira] [Updated] (SPARK-25091) UNCACHE TABLE, CLEAR CACHE, rdd.unpersist() does not clean up executor memory
[ https://issues.apache.org/jira/browse/SPARK-25091?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Chao Fang updated SPARK-25091: -- Attachment: 4.png > UNCACHE TABLE, CLEAR CACHE, rdd.unpersist() does not clean up executor memory > - > > Key: SPARK-25091 > URL: https://issues.apache.org/jira/browse/SPARK-25091 > Project: Spark > Issue Type: Bug > Components: SQL >Affects Versions: 2.3.1 >Reporter: Yunling Cai >Priority: Critical > Attachments: 0.png, 1.png, 2.png, 3.png, 4.png > > > UNCACHE TABLE and CLEAR CACHE does not clean up executor memory. > Through Spark UI, although in Storage, we see the cached table removed. In > Executor, the executors continue to hold the RDD and the memory is not > cleared. This results in huge waste in executor memory usage. As we call > CACHE TABLE, we run into issues where the cached tables are spilled to disk > instead of reclaiming the memory storage. > Steps to reproduce: > CACHE TABLE test.test_cache; > UNCACHE TABLE test.test_cache; > == Storage shows table is not cached; Executor shows the executor storage > memory does not change == > CACHE TABLE test.test_cache; > CLEAR CACHE; > == Storage shows table is not cached; Executor shows the executor storage > memory does not change == > Similar behavior when using pyspark df.unpersist(). -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-25091) UNCACHE TABLE, CLEAR CACHE, rdd.unpersist() does not clean up executor memory
[ https://issues.apache.org/jira/browse/SPARK-25091?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Chao Fang updated SPARK-25091: -- Attachment: 3.png 2.png 1.png 0.png > UNCACHE TABLE, CLEAR CACHE, rdd.unpersist() does not clean up executor memory > - > > Key: SPARK-25091 > URL: https://issues.apache.org/jira/browse/SPARK-25091 > Project: Spark > Issue Type: Bug > Components: SQL >Affects Versions: 2.3.1 >Reporter: Yunling Cai >Priority: Critical > Attachments: 0.png, 1.png, 2.png, 3.png > > > UNCACHE TABLE and CLEAR CACHE does not clean up executor memory. > Through Spark UI, although in Storage, we see the cached table removed. In > Executor, the executors continue to hold the RDD and the memory is not > cleared. This results in huge waste in executor memory usage. As we call > CACHE TABLE, we run into issues where the cached tables are spilled to disk > instead of reclaiming the memory storage. > Steps to reproduce: > CACHE TABLE test.test_cache; > UNCACHE TABLE test.test_cache; > == Storage shows table is not cached; Executor shows the executor storage > memory does not change == > CACHE TABLE test.test_cache; > CLEAR CACHE; > == Storage shows table is not cached; Executor shows the executor storage > memory does not change == > Similar behavior when using pyspark df.unpersist(). -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-25091) UNCACHE TABLE, CLEAR CACHE, rdd.unpersist() does not clean up executor memory
[ https://issues.apache.org/jira/browse/SPARK-25091?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Yunling Cai updated SPARK-25091: Summary: UNCACHE TABLE, CLEAR CACHE, rdd.unpersist() does not clean up executor memory (was: Spark Thrift Server: UNCACHE TABLE and CLEAR CACHE does not clean up executor memory) > UNCACHE TABLE, CLEAR CACHE, rdd.unpersist() does not clean up executor memory > - > > Key: SPARK-25091 > URL: https://issues.apache.org/jira/browse/SPARK-25091 > Project: Spark > Issue Type: Bug > Components: SQL >Affects Versions: 2.3.1 >Reporter: Yunling Cai >Priority: Critical > > UNCACHE TABLE and CLEAR CACHE does not clean up executor memory. > Through Spark UI, although in Storage, we see the cached table removed. In > Executor, the executors continue to hold the RDD and the memory is not > cleared. This results in huge waste in executor memory usage. As we call > CACHE TABLE, we run into issues where the cached tables are spilled to disk > instead of reclaiming the memory storage. > Steps to reproduce: > CACHE TABLE test.test_cache; > UNCACHE TABLE test.test_cache; > == Storage shows table is not cached; Executor shows the executor storage > memory does not change == > CACHE TABLE test.test_cache; > CLEAR CACHE; > == Storage shows table is not cached; Executor shows the executor storage > memory does not change == > Similar behavior when using pyspark df.unpersist(). -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
