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diff --git a/docs/monitoring.md b/docs/monitoring.md new file mode 100644 index 0000000000..5f456b999b --- /dev/null +++ b/docs/monitoring.md @@ -0,0 +1,70 @@ +--- +layout: global +title: Monitoring and Instrumentation +--- + +There are several ways to monitor Spark applications. + +# Web Interfaces + +Every SparkContext launches a web UI, by default on port 4040, that +displays useful information about the application. This includes: + +* A list of scheduler stages and tasks +* A summary of RDD sizes and memory usage +* Information about the running executors +* Environmental information. + +You can access this interface by simply opening `http://<driver-node>:4040` in a web browser. +If multiple SparkContexts are running on the same host, they will bind to succesive ports +beginning with 4040 (4041, 4042, etc). + +Spark's Standlone Mode cluster manager also has its own +[web UI](spark-standalone.html#monitoring-and-logging). + +Note that in both of these UIs, the tables are sortable by clicking their headers, +making it easy to identify slow tasks, data skew, etc. + +# Metrics + +Spark has a configurable metrics system based on the +[Coda Hale Metrics Library](http://metrics.codahale.com/). +This allows users to report Spark metrics to a variety of sinks including HTTP, JMX, and CSV +files. The metrics system is configured via a configuration file that Spark expects to be present +at `$SPARK_HOME/conf/metrics.conf`. A custom file location can be specified via the +`spark.metrics.conf` Java system property. Spark's metrics are decoupled into different +_instances_ corresponding to Spark components. Within each instance, you can configure a +set of sinks to which metrics are reported. The following instances are currently supported: + +* `master`: The Spark standalone master process. +* `applications`: A component within the master which reports on various applications. +* `worker`: A Spark standalone worker process. +* `executor`: A Spark executor. +* `driver`: The Spark driver process (the process in which your SparkContext is created). + +Each instance can report to zero or more _sinks_. Sinks are contained in the +`org.apache.spark.metrics.sink` package: + +* `ConsoleSink`: Logs metrics information to the console. +* `CSVSink`: Exports metrics data to CSV files at regular intervals. +* `GangliaSink`: Sends metrics to a Ganglia node or multicast group. +* `JmxSink`: Registers metrics for viewing in a JXM console. +* `MetricsServlet`: Adds a servlet within the existing Spark UI to serve metrics data as JSON data. + +The syntax of the metrics configuration file is defined in an example configuration file, +`$SPARK_HOME/conf/metrics.conf.template`. + +# Advanced Instrumentation + +Several external tools can be used to help profile the performance of Spark jobs: + +* Cluster-wide monitoring tools, such as [Ganglia](http://ganglia.sourceforge.net/), can provide +insight into overall cluster utilization and resource bottlenecks. For instance, a Ganglia +dashboard can quickly reveal whether a particular workload is disk bound, network bound, or +CPU bound. +* OS profiling tools such as [dstat](http://dag.wieers.com/home-made/dstat/), +[iostat](http://linux.die.net/man/1/iostat), and [iotop](http://linux.die.net/man/1/iotop) +can provide fine-grained profiling on individual nodes. +* JVM utilities such as `jstack` for providing stack traces, `jmap` for creating heap-dumps, +`jstat` for reporting time-series statistics and `jconsole` for visually exploring various JVM +properties are useful for those comfortable with JVM internals. |