[jira] [Assigned] (SPARK-40281) Memory Profiler on Executors
[ https://issues.apache.org/jira/browse/SPARK-40281?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Hyukjin Kwon reassigned SPARK-40281: Assignee: Xinrong Meng > Memory Profiler on Executors > > > Key: SPARK-40281 > URL: https://issues.apache.org/jira/browse/SPARK-40281 > Project: Spark > Issue Type: New Feature > Components: PySpark >Affects Versions: 3.4.0 >Reporter: Xinrong Meng >Assignee: Xinrong Meng >Priority: Major > > The ticket proposes to implement PySpark memory profiling on executors. See > more > [design|https://docs.google.com/document/d/e/2PACX-1vR2K4TdrM1eAjNDC1bsflCNRH67UWLoC-lCv6TSUVXD91Ruksm99pYTnCeIm7Ui3RgrrRNcQU_D8-oh/pub]. > There are many factors in a PySpark program’s performance. Memory, as one of > the key factors of a program’s performance, had been missing in PySpark > profiling. A PySpark program on the Spark driver can be profiled with [Memory > Profiler|https://www.google.com/url?q=https://pypi.org/project/memory-profiler/&sa=D&source=editors&ust=1668027860192689&usg=AOvVaw1t4LRcObEGuhaTr5oHEUwU] > as a normal Python process, but there was not an easy way to profile memory > on Spark executors. > PySpark UDFs, one of the most popular Python APIs, enable users to run custom > code on top of the Apache Spark™ engine. However, it is difficult to optimize > UDFs without understanding memory consumption. > The ticket proposes to introduce the PySpark memory profiler, which profiles > memory on executors. It provides information about total memory usage and > pinpoints which lines of code in a UDF attribute to the most memory usage. > That will help optimize PySpark UDFs and reduce the likelihood of > out-of-memory errors. -- This message was sent by Atlassian Jira (v8.20.10#820010) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Assigned] (SPARK-40281) Memory Profiler on Executors
[ https://issues.apache.org/jira/browse/SPARK-40281?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Apache Spark reassigned SPARK-40281: Assignee: (was: Apache Spark) > Memory Profiler on Executors > > > Key: SPARK-40281 > URL: https://issues.apache.org/jira/browse/SPARK-40281 > Project: Spark > Issue Type: New Feature > Components: PySpark >Affects Versions: 3.4.0 >Reporter: Xinrong Meng >Priority: Major > > Profiling is critical to performance engineering. Memory consumption is a key > indicator of how efficient a PySpark program is. There is an existing effort > on memory profiling of Python programs, Memory Profiler > ([https://pypi.org/project/memory-profiler/).|https://pypi.org/project/memory-profiler/] > PySpark applications run as independent sets of processes on a cluster, > coordinated by the SparkContext object in the driver program. On the driver > side, PySpark is a regular Python process, thus, we can profile it as a > normal Python program using Memory Profiler. > However, on the executors side, we are missing such memory profiler. Since > executors are distributed on different nodes in the cluster, we need to > aggregate profiles. Furthermore, Python worker processes are spawned per > executor for the Python/Pandas UDF execution, which makes the memory > profiling more intricate. > The ticket proposes to implement a Memory Profiler on Executors. -- This message was sent by Atlassian Jira (v8.20.10#820010) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Assigned] (SPARK-40281) Memory Profiler on Executors
[ https://issues.apache.org/jira/browse/SPARK-40281?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Apache Spark reassigned SPARK-40281: Assignee: Apache Spark > Memory Profiler on Executors > > > Key: SPARK-40281 > URL: https://issues.apache.org/jira/browse/SPARK-40281 > Project: Spark > Issue Type: New Feature > Components: PySpark >Affects Versions: 3.4.0 >Reporter: Xinrong Meng >Assignee: Apache Spark >Priority: Major > > Profiling is critical to performance engineering. Memory consumption is a key > indicator of how efficient a PySpark program is. There is an existing effort > on memory profiling of Python programs, Memory Profiler > ([https://pypi.org/project/memory-profiler/).|https://pypi.org/project/memory-profiler/] > PySpark applications run as independent sets of processes on a cluster, > coordinated by the SparkContext object in the driver program. On the driver > side, PySpark is a regular Python process, thus, we can profile it as a > normal Python program using Memory Profiler. > However, on the executors side, we are missing such memory profiler. Since > executors are distributed on different nodes in the cluster, we need to > aggregate profiles. Furthermore, Python worker processes are spawned per > executor for the Python/Pandas UDF execution, which makes the memory > profiling more intricate. > The ticket proposes to implement a Memory Profiler on Executors. -- This message was sent by Atlassian Jira (v8.20.10#820010) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Assigned] (SPARK-40281) Memory Profiler on Executors
[ https://issues.apache.org/jira/browse/SPARK-40281?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Xinrong Meng reassigned SPARK-40281: Assignee: (was: Xinrong Meng) > Memory Profiler on Executors > > > Key: SPARK-40281 > URL: https://issues.apache.org/jira/browse/SPARK-40281 > Project: Spark > Issue Type: Umbrella > Components: PySpark >Affects Versions: 3.4.0 >Reporter: Xinrong Meng >Priority: Major > > Profiling is critical to performance engineering. Memory consumption is a key > indicator of how efficient a PySpark program is. There is an existing effort > on memory profiling of Python progrms, Memory Profiler > ([https://pypi.org/project/memory-profiler/).|https://pypi.org/project/memory-profiler/] > PySpark applications run as independent sets of processes on a cluster, > coordinated by the SparkContext object in the driver program. On the driver > side, PySpark is a regular Python process, thus, we can profile it as a > normal Python program using Memory Profiler. > However, on the executors side, we are missing such memory profiler. Since > executors are distributed on different nodes in the cluster, we need to need > to aggregate profiles. Furthermore, Python worker processes are spawned per > executor for the Python/Pandas UDF execution, which makes the memory > profiling more intricate. > The umbrella proposes to implement a Memory Profiler on Executors. -- This message was sent by Atlassian Jira (v8.20.10#820010) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Assigned] (SPARK-40281) Memory Profiler on Executors
[ https://issues.apache.org/jira/browse/SPARK-40281?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Xinrong Meng reassigned SPARK-40281: Assignee: Xinrong Meng > Memory Profiler on Executors > > > Key: SPARK-40281 > URL: https://issues.apache.org/jira/browse/SPARK-40281 > Project: Spark > Issue Type: Umbrella > Components: PySpark >Affects Versions: 3.4.0 >Reporter: Xinrong Meng >Assignee: Xinrong Meng >Priority: Major > > Profiling is critical to performance engineering. Memory consumption is a key > indicator of how efficient a PySpark program is. There is an existing effort > on memory profiling of Python progrms, Memory Profiler > ([https://pypi.org/project/memory-profiler/).|https://pypi.org/project/memory-profiler/] > PySpark applications run as independent sets of processes on a cluster, > coordinated by the SparkContext object in the driver program. On the driver > side, PySpark is a regular Python process, thus, we can profile it as a > normal Python program using Memory Profiler. > However, on the executors side, we are missing such memory profiler. Since > executors are distributed on different nodes in the cluster, we need to need > to aggregate profiles. Furthermore, Python worker processes are spawned per > executor for the Python/Pandas UDF execution, which makes the memory > profiling more intricate. > The umbrella proposes to implement a Memory Profiler on Executors. -- This message was sent by Atlassian Jira (v8.20.10#820010) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
