What is New with Apache Spark Performance Monitoring in Spark 3.0
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The document outlines the new features and improvements in Apache Spark 3.0 related to performance monitoring, including enhanced memory monitoring, SQL metrics, and custom plugins for monitoring various data sources. It emphasizes the importance of memory allocation for performance stability and introduces tools and frameworks for troubleshooting and visualizing metrics. The document also discusses community efforts for further enhancing Spark monitoring capabilities and provides insights on upcoming enhancements in Spark 3.1.
![Improvements Expected in Spark 3.1 and WIP
▪ [SPARK-27142] New SQL REST API
▪ [SPARK-32119] Plugins can be distributed with –-jars and –packages on YARN,
this adds support for K8S and Standalone
▪ [SPARK-33088] Enhance Executor Plugin API to include callbacks on task start
and end events
▪ [SPARK-23431] Expose stage level peak executor metrics via REST API
▪ [SPARK-30985] Support propagating SPARK_CONF_DIR files to driver and
executor pods
▪ [SPARK-31711] Executor metrics in local mode
▪ WIP [SPARK-30306] Python execution time instrumentation
▪ WIP on Hadoop (targeting Hadoop 3.4)
▪ [HADOOP-16830] Add public IOStatistics API](https://image.slidesharecdn.com/118lucacanali-201129183621/85/What-is-New-with-Apache-Spark-Performance-Monitoring-in-Spark-3-0-38-320.jpg)
What is New with Apache Spark Performance Monitoring in Spark 3.0
- 1. What’s New with Spark Performance Monitoring in Apache Spark 3.0 Luca Canali Data Engineer, CERN
- 2. Agenda Intro and Motivations Spark Monitoring ▪ Recap of the available instrumentation Spark 3.0 Improvements ▪ Memory monitoring ▪ Plugins for custom monitoring ▪ Web UI: SQL tab, Streaming monitoring Outlook and Community
- 3. About Luca 3#UnifiedDataAnalytics #SparkAISummit • Data Engineer at CERN – Hadoop and Spark service, database services – 20+ years with databases and data engineering – Performance engineering • Blog, tools, contributions to Apache Spark – @LucaCanaliDB – http://cern.ch/canali
- 4. Data at the Large Hadron Collider: Exabyte Scale LHC experiments raw data: >300 PB Computing jobs on the WLCG Grid: using ~1M cores 4
- 5. Apache Spark Clusters at CERN • Spark running on clusters: – YARN/Hadoop -> established – Spark on Kubernetes -> growing adoption Accelerator logging (part of LHC infrastructure) Hadoop - YARN - 30 nodes (Cores - 1200, Mem - 13 TB, Storage – 7.5 PB) General Purpose Hadoop - YARN, 39 nodes (Cores – 1.7k, Mem – 18 TB, Storage – 12 PB) Cloud containers Kubernetes on Openstack VMs, Cores - 270, Mem – 2 TB Storage: remote HDFS or custom storage (CERN EOS, for physics data, S3 on Ceph also available). Note: GPU resources available. 5
- 6. Analytics Platform Outlook HEP software Experiments storage HDFS Personal storage Integrating with existing infrastructure: • Software • Data Jupyter notebooks are a common interface for accessing data • CERN has established on- demand notebooks via “Swan” Other Cloud storage, S3 on Ceph GPUs
- 7. Monitoring, Measuring and Troubleshooting ▪ Collect and expose metrics (Spark, Cluster, OS) ▪ For troubleshooting and insights on performance Workload monitoring data + Spark architectural knowledge Application Info on application architecture Info on computing environment Agent produces: insights + actions
- 8. Monitoring Features in Apache Spark, a Recap
- 9. Web UI • First and main point of access to monitor Spark applications • Details on jobs, stages, tasks, SQL, streaming, etc • Default URL: http://driver_host:4040 • New doc in Spark 3.0: https://spark.apache.org/docs/latest/web- ui.html 9
- 10. Spark Metrics with the REST API … 10
- 11. Spark Listeners: @DeveloperApi • Custom class, extends SparkListener • Methods react on events to collect data, example: • Attach custom Listener class to Spark Session --conf spark.extraListeners=.. 11
- 13. Tooling: sparkMeasure 13#UnifiedDataAnalytics #SparkAISummit • bin/spark-shell --packages ch.cern.sparkmeasure:spark- measure_2.12:0.17 • bin/spark-shell --packages ch.cern.sparkmeasure:spark- measure_2.12:0.17 • val stageMetrics = ch.cern.sparkmeasure.StageMetrics(spark) • • bin/spark-shell --packages ch.cern.sparkmeasure:spark- measure_2.12:0.17 • val stageMetrics = ch.cern.sparkmeasure.StageMetrics(spark) • • val myQuery = "select count(*) from range(1000) cross join range(1000) cross join range(1000)" • stageMetrics.runAndMeasure(spark.sql(myQuery).show())
- 14. Task Metrics Measured with sparkMeasure 14#UnifiedDataAnalytics #SparkAISummit • Spark Context default degree of parallelism = 8 • Aggregated Spark stage metrics: • numStages => 3 • numTasks => 17 • elapsedTime => 13520 (14 s) • stageDuration => 13411 (13 s) • executorRunTime => 100020 (1.7 min) • executorCpuTime => 98899 (1.6 min) • executorDeserializeTime => 4358 (4 s) • executorDeserializeCpuTime => 1887 (2 s) • resultSerializationTime => 2 (2 ms) • jvmGCTime => 56 (56 ms) • shuffleFetchWaitTime => 0 (0 ms) • shuffleWriteTime => 11 (11 ms) • resultSize => 19955 (19.0 KB) • diskBytesSpilled => 0 (0 Bytes) • memoryBytesSpilled => 0 (0 Bytes) • peakExecutionMemory => 0 • recordsRead => 2000 • bytesRead => 0 (0 Bytes) • recordsWritten => 0 • bytesWritten => 0 (0 Bytes) • shuffleRecordsRead => 8 • shuffleTotalBlocksFetched => 8 • shuffleLocalBlocksFetched => 8 • shuffleRemoteBlocksFetched => 0 • shuffleTotalBytesRead => 472 (472 Bytes) • shuffleLocalBytesRead => 472 (472 Bytes) • shuffleRemoteBytesRead => 0 (0 Bytes) • shuffleRemoteBytesReadToDisk => 0 (0 Bytes) • shuffleBytesWritten => 472 (472 Bytes) • shuffleRecordsWritten => 8
- 15. Further Instrumentation in Spark: the Spark Metrics System ▪ Bullet 1 ▪ Sub-bullet ▪ Sub-bullet ▪ Bullet 2 ▪ Sub-bullet ▪ Sub-bullet ▪ Bullet 3 ▪ Sub-bullet ▪ Sub-bullet
- 16. Send Spark Metrics to a Sink 16#UnifiedDataAnalytics #SparkAISummit • $ SPARK_HOME/bin/spark-shell • --conf "spark.metrics.conf.driver.sink.graphite.class"="org.apache.spark.metrics.sink.GraphiteSink" • --conf "spark.metrics.conf.executor.sink.graphite.class"="org.apache.spark.metrics.sink.GraphiteSink" • --conf "spark.metrics.conf.*.sink.graphite.host"="graphiteEndPoint_influxDB_hostName>" • --conf "spark.metrics.conf.*.sink.graphite.port"=<graphite_listening_port> • --conf "spark.metrics.conf.*.sink.graphite.period"=10 • --conf "spark.metrics.conf.*.sink.graphite.unit"=seconds • --conf "spark.metrics.conf.*.sink.graphite.prefix"="lucatest" • --conf "spark.metrics.conf.*.source.jvm.class"="org.apache.spark.metrics.source.JvmSource" • Edit $SPARK_HOME/conf/metrics.properties • Alternative: use the config parameters spark.metrics.conf.*
- 17. Spark Grafana Dashboard • Visualize Spark metrics – In real time and historical data – Summaries and time series of key metrics • Data for drill-down and root-cause analysis 17#UnifiedDataAnalytics #SparkAISummit
- 18. What’s New in Apache Spark 3.0
- 19. Memory Usage Monitoring in Spark 3.0 ▪ The problem we want to solve ▪ Memory is key for the performance and stability of Spark jobs ▪ Java OOM (out of memory) errors can be hard to troubleshoot ▪ We want to allocate the needed memory, not overshooting/wasting ▪ New “Executor Metrics” in Spark 3.0 ▪ Measure details of memory usage by memory component ▪ Peak values measurements (you get OOM on peak allocation not average) ▪ Spark Metrics System: report memory metrics as time series
- 20. "peakMemoryMetrics" : { "JVMHeapMemory" : 29487812552, "JVMOffHeapMemory" : 149957200, "OnHeapExecutionMemory" : 12458956272, "OffHeapExecutionMemory" : 0, "OnHeapStorageMemory" : 83578970, "OffHeapStorageMemory" : 0, "OnHeapUnifiedMemory" : 12540212490, "OffHeapUnifiedMemory" : 0, "DirectPoolMemory" : 66809076, "MappedPoolMemory" : 0, "ProcessTreeJVMVMemory" : 38084534272, "ProcessTreeJVMRSSMemory" : 36998328320, "ProcessTreePythonVMemory" : 0, "ProcessTreePythonRSSMemory" : 0, "ProcessTreeOtherVMemory" : 0, "ProcessTreeOtherRSSMemory" : 0, "MinorGCCount" : 561, "MinorGCTime" : 49918, "MajorGCCount" : 0, "MajorGCTime" : 0 }, REST API: peak memory usage values per executor WebUI URL + /api/v1/applications/<application_id>/executors Extra tunables: spark.executor.processTreeMetrics.enabled=true spark.executor.metrics.pollingInterval=<time in ms> Executor Metrics, REST API
- 21. Spark Memory Allocations ▪ Bullet 1 ▪ Sub-bullet ▪ Sub-bullet ▪ Bullet 2 ▪ Sub-bullet ▪ Sub-bullet ▪ Bullet 3 ▪ Sub-bullet ▪ Sub-bullet
- 22. Memory Monitoring Graphs in the Dashboard Memory metrics as time series Dashboard: Visualize memory usage over time Details of on-heap, unified memory, etc Compare with other metrics Correlate with jobs/sql start time
- 23. Custom Monitoring with Spark Plugins ▪ Spark 3.0 Plugins ▪ Plugins are executed at the startup of executors and the driver ▪ Plugins allow to extend Spark metrics with custom code and instrumentation ▪ Examples of monitoring enhancements with plugins ▪ Cloud Storage monitoring (S3A, GS, WASBS, OCI, CERN’s ROOT, etc) ▪ Improved HDFS monitoring ▪ OS metrics: cgroup metrics for Spark on Kubernetes ▪ Custom application metrics
- 24. Plugins Extend Spark Metrics ▪ Custom metrics for instrumentation or debugging ▪ Metrics from libraries/packages on top of Apache Spark ▪ Spark Plugins provide: ▪ API for integrating custom instrumentation with the rest of the Spark monitoring ExecutorPackage Plugin Plugin registers extra metrics using package API Extra metrics join the Spark metrics stream JVM Metrics Sink
- 25. Spark Plugins API ▪ Code snippets Spark 3.0 Plugin API This is how you hook to the Spark Metrics instrumentation
- 26. Getting Started with Spark Plugins ▪ From https://github.com/cerndb/SparkPlugins ▪ Plugins for demo + plugins for Cloud I/O and OS monitoring ▪ RunOSCommandPlugin runs an OS command at executor startup ▪ DemoMetricsPlugin show how to integrate with Spark Metrics • bin/spark-shell –master yarn • --packages ch.cern.sparkmeasure:spark-plugins_2.12:0.1 • --conf spark.plugins=ch.cern.RunOSCommandPlugin, • ch.cern.DemoMetricsPlugin
- 27. Measuring OS and Container Metrics ▪ Example of how to measure OS metrics from cgroup instrumentation ▪ Useful for Spark on K8S ▪ Brings together OS metrics with other Spark-workload metrics ▪ By default Apache Spark instruments only CPU usage bin/spark-shell --master k8s://https://<K8S URL>:6443 --packages ch.cern.sparkmeasure:spark-plugins_2.12:0.1 --conf spark.kubernetes.container.image=<registry>/spark:v310-SNAP --conf spark.plugins=ch.cern.CgroupMetrics ...
- 28. Cgroups Metrics – Use for Spark on K8S ▪ Metrics implemented (gauges), with prefix ch.cern.CgroupMetrics: ▪ CPUTimeNanosec: CPU time used by the processes in the cgroup ▪ this includes CPU used in Python ▪ MemoryRss: number of bytes of anonymous and swap cache memory. ▪ MemorySwap: number of bytes of swap usage. ▪ MemoryCache: number of bytes of page cache memory. ▪ NetworkBytesIn: network traffic inbound. ▪ NetworkBytesOut: network traffic outbound.
- 29. • docker run --network=host -d lucacanali/spark-dashboard:v01 • bin/spark-shell --master k8s://https:... • --packages ch.cern.sparkmeasure:spark-plugins_2.12:0.1 • --conf spark.plugins=ch.cern.CgroupMetrics • --conf "spark.metrics.conf.*.sink.graphite.class"="org.apache.spark. metrics.sink.GraphiteSink" • --conf "spark.metrics.conf.*.sink.graphite.host"=`hostname` • --conf "spark.metrics.conf.*.sink.graphite.port"=2003 • --conf "spark.metrics.conf.*.sink.graphite.period"=10 • --conf "spark.metrics.conf.*.sink.graphite.unit"=seconds • --conf "spark.metrics.conf.*.sink.graphite.prefix"="luca" Example: Dashboard for Spark on K8S Network bytes (in) vs. time
- 30. • docker run --network=host -d lucacanali/spark-dashboard:v01 • bin/spark-shell --master k8s://https:... • --packages ch.cern.sparkmeasure:spark-plugins_2.12:0.1 • --conf spark.plugins=ch.cern.CgroupMetrics • --conf "spark.metrics.conf.*.sink.graphite.class"="org.apache.spark. metrics.sink.GraphiteSink" • --conf "spark.metrics.conf.*.sink.graphite.host"=`hostname` • --conf "spark.metrics.conf.*.sink.graphite.port"=2003 • --conf "spark.metrics.conf.*.sink.graphite.period"=10 • --conf "spark.metrics.conf.*.sink.graphite.unit"=seconds • --conf "spark.metrics.conf.*.sink.graphite.prefix"="luca" Example: Dashboard for Spark on K8S Network bytes (in) vs. time
- 31. Measuring S3A and other Cloud Filesystems ▪ Example of how to measure S3A throughput metrics ▪ Note: Apache Spark instruments only HDFS and local filesystem ▪ Plugins uses HDFS client API for Hadoop Compatible filesystems ▪ Metrics: bytesRead, bytesWritten, readOps, writeOps --conf spark.plugins=ch.cern.CloudFSMetrics --conf spark.cernSparkPlugin.cloudFsName=<name of the filesystem> (example: "s3a", "gs", "wasbs", "oci", "root", etc.) S3A bytes read vs. time
- 32. Measuring S3A and other Cloud Filesystems ▪ Example of how to measure S3A throughput metrics ▪ Note: Apache Spark instruments only HDFS and local filesystem ▪ Plugins uses HDFS client API for Hadoop Compatible filesystems ▪ Metrics: bytesRead, bytesWritten, readOps, writeOps --conf spark.plugins=ch.cern.CloudFSMetrics --conf spark.cernSparkPlugin.cloudFsName=<name of the filesystem> (example: "s3a", "gs", "wasbs", "oci", "root", etc.) S3A bytes read vs. time
- 33. Experimental: Measuring I/O Time ▪ Plugins used to measure I/O read time with HDFS and S3A ▪ Use for advanced troubleshooting ▪ Also example of plugins used to instrument custom libraries ▪ Plugin: --conf spark.plugins=ch.cern.experimental.S3ATimeInstrumentation ▪ Custom S3A jar with time instrumentation: https://github.com/LucaCanali/hadoop/tree/s3aAndHDFSTimeInstrumentation ▪ Metrics: S3AReadTimeMuSec, S3ASeekTimeMuSec, S3AGetObjectMetadataMuSec
- 34. SQL Monitoring Improvements in Spark 3.0 ▪ Improved SQL Metrics and SQL tab execution plan visualization ▪ Improved SQL metrics instrumentation ▪ SQL metrics documented at https://spark.apache.org/docs/latest/web- ui.html#sql-metrics ▪ Improved execution plan visualization and available metrics Improved metrics list for shuffle operations Added Codegen IdImproved stats visualization + info on max value
- 35. More Monitoring Improvements in Spark 3.0 ▪ Spark Streaming tab ▪ Metrics and graphs ▪ New Structured Streaming UI (SPARK-29543) ▪ Experimental support for Prometheus ▪ REST API: /metrics/executors/Prometheus conditional to spark.ui.prometheus.enabled=true ▪ Improved documentation ▪ New: Web UI doc https://spark.apache.org/docs/latest/web-ui.html ▪ Monitoring doc: https://spark.apache.org/docs/latest/monitoring.html
- 37. Community Effort What You Can Do To Improve Spark Monitoring ▪ (developers) Tooling: Improving Spark Metrics and instrumentation ▪ Sink for InfluxDB and Prometheus ▪ Further instrument Spark core and ecosystem (ex: I/O time, Python UDF) ▪ Develop and share plugins: instrument libraries, OS metrics (GPUs), etc ▪ (product experts) Methods: root-cause performance analysis ▪ Use metrics and graphs for troubleshooting and root-cause analysis ▪ Adopt tools and methods to your platform/users ▪ (innovators) Holy Grail of Monitoring: ▪ Building automated (AI) systems that can perform root-cause analysis and autotune data/database systems
- 38. Improvements Expected in Spark 3.1 and WIP ▪ [SPARK-27142] New SQL REST API ▪ [SPARK-32119] Plugins can be distributed with –-jars and –packages on YARN, this adds support for K8S and Standalone ▪ [SPARK-33088] Enhance Executor Plugin API to include callbacks on task start and end events ▪ [SPARK-23431] Expose stage level peak executor metrics via REST API ▪ [SPARK-30985] Support propagating SPARK_CONF_DIR files to driver and executor pods ▪ [SPARK-31711] Executor metrics in local mode ▪ WIP [SPARK-30306] Python execution time instrumentation ▪ WIP on Hadoop (targeting Hadoop 3.4) ▪ [HADOOP-16830] Add public IOStatistics API
- 39. Conclusions ▪ Monitoring and instrumentation improvements: ▪ One more reason to upgrade to Apache Spark 3.0 ▪ Memory monitoring with Executor Metrics in Spark 3.0 ▪ Help troubleshooting and preventing OOM ▪ Spark Plugin API ▪ Use to measure Cloud FileSystems I/O, OS and container metrics ▪ Use to build your custom application metrics ▪ Build and share! ▪ Web UI, streaming and Spark SQL monitoring also improved
- 40. Acknowledgements and Links • Thanks to colleagues at CERN, Hadoop and Spark service • Thanks to Apache Spark committers and community – For their help with JIRAs and PRs: SPARK-26928, SPARK-27189, SPARK- 28091, SPARK-29654, SPARK-30041, SPARK-30775 • Links: – Executor Metrics and memory monitoring: SPARK-23429 and SPARK-27189 – Spark Plugins: SPARK-29397, SPARK-28091 – https://db-blog.web.cern.ch/blog/luca-canali/2020-08-spark3-memory- monitoring – https://github.com/LucaCanali/sparkMeasure – https://github.com/cerndb/spark-dashboard – https://github.com/cerndb/SparkPlugins
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