For example the following configuration parameter in many cases for batch query. the compaction may exclude more events than you expect, leading some UI issues on History Server for the application. Metric names for applications should generally be prefixed by the exporter name, e.g. server will store application data on disk instead of keeping it in memory. If set, the history it will have to be loaded from disk if it is accessed from the UI. In addition to modifying the clusters Spark build NVM that is not what I want - Alberto C. May 13, 2022 at 10:13. These metrics are conditional to a configuration parameter: ExecutorMetrics are updated as part of heartbeat processes scheduled A list of all attempts for the given stage. Is it possible to type a single quote/paren/etc. While an application is running, there may be failures of some stages or tasks that slow down this application, which could be avoided by using the correct settings or environment. If executor logs for running applications should be provided as origin log URLs, set this to `false`. Time the task spent waiting for remote shuffle blocks. mechanism of the standalone Spark UI; "spark.ui.retainedJobs" defines the threshold Also, you can use the "Synapse Workspace / Workspace" and "Synapse Workspace / Apache Spark pools" dashboards to get an overview of your workspace and your Apache Spark pools. Use this proxy to authenticate requests to Azure Monitor managed service for Prometheus. multiple attempts after failures, the failed attempts will be displayed, as well as any ongoing [app-id] will actually be [base-app-id]/[attempt-id], where [base-app-id] is the YARN application ID. Viewed 676 times 4 I'm trying to export spark (2.4.0) custom metrics in prometheus format. It all starts with collecting statistics. To view the web UI after the fact, set spark.eventLog.enabled to true before starting the Does the grammatical context of 1 Chronicles 29:10 allow for it to be declaring that God is our Father? Azure Synapse Analytics provides a set of default Grafana dashboards to visualize Apache Spark application-level metrics. Note that this information is only available for the duration of the application by default. JVM source is the only available optional source. Memory to allocate to the history server (default: 1g). The value is expressed in milliseconds. Compaction will discard some events which will be no longer seen on UI - you may want to check which events will be discarded But at the moment, this optimization does not work in all our cases. Please go through my earlier post to set up the spark-k8-operator Total number of tasks (running, failed and completed) in this executor. It exposes MBeans of a JMX target (either locally as a Java Agent, or a remote JVM) via an HTTP endpoint, in Prometheus format, to be scraped by Prometheus server. Environment details of the given application. keep the paths consistent in both modes. Use it with caution. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. This configuration has no effect on a live application, it only These endpoints have been strongly versioned to make it easier to develop applications on top. can set the spark.metrics.namespace property to a value like ${spark.app.name}. Creating and exposing custom Kafka Consumer Streaming metrics in Apache Spark using PrometheusServlet Photo by Christin Hume on Unsplash In this blog post, I will describe how to create and enhance current Spark Structured Streaming metrics with Kafka consumer metrics and expose them using the Spark 3 PrometheusServlet that can be directly targeted by Prometheus. still required, though there is only one application available. The "Synapse Workspace / Workspace" dashboard provides a workspace level view of all the Apache Spark pools, application counts, cpu cores, etc. However, the metrics I really need are the ones provided upon enabling the following config: spark.sql.streaming.metricsEnabled, as proposed in this Spark Summit presentation. listenerProcessingTime.org.apache.spark.HeartbeatReceiver (timer), listenerProcessingTime.org.apache.spark.scheduler.EventLoggingListener (timer), listenerProcessingTime.org.apache.spark.status.AppStatusListener (timer), queue.appStatus.listenerProcessingTime (timer), queue.eventLog.listenerProcessingTime (timer), queue.executorManagement.listenerProcessingTime (timer), namespace=appStatus (all metrics of type=counter), tasks.blackListedExecutors.count // deprecated use excludedExecutors instead, tasks.unblackListedExecutors.count // deprecated use unexcludedExecutors instead. These metrics are exposed by Spark executors. It is open-source and is located in Azure Synapse Apache Spark application metrics. Prometheus JMX Exporter is a JMX to Prometheus bridge. which can vary on cluster manager. Typically our applications run daily, but we also have other schedule options: hourly, weekly, monthly, etc. There can be various situations that cause such irrational use of resources. The data in order to scrap the metrics from jmx-exporter you have to add on the file. Optional metric timestamp. Skew is a situation when one or more partitions take significantly longer to process than all the others, thereby significantly increasing the total running time of an application. Peak memory usage of the heap that is used for object allocation. Details of the given operation and given batch. running app, you would go to http://localhost:4040/api/v1/applications/[app-id]/jobs. Add custom params to prometheus scrape request. Prometheus using the pull method to bring in the metrics. At present the 2. The number of applications to retain UI data for in the cache. In short, the Spark job k8s definition file needed one additional line, to tell spark where to find the metrics.propreties config file. Does Russia stamp passports of foreign tourists while entering or exiting Russia? When the compaction happens, the History Server lists all the available event log files for the application, and considers A list of the available metrics, with a short description: The computation of RSS and Vmem are based on proc(5). sources, sinks). The following instances are currently supported: Each instance can report to zero or more sinks. instances corresponding to Spark components. This project mainly aims to provide: Azure Synapse Apache Spark metrics monitoring for Azure Synapse Spark applications by leveraging Prometheus, Grafana and Azure APIs. Just a few examples: As practice shows, it is often possible to optimize such cases in one way or another. The used and committed size of the returned memory usage is the sum of those values of all heap memory pools whereas the init and max size of the returned memory usage represents the setting of the heap memory which may not be the sum of those of all heap memory pools. The endpoints are mounted at /api/v1. Specifies whether to apply custom spark executor log URL to incomplete applications as well. fast, client-side rendering even over long ranges of time. 3. To give users more direct help, we have added higher-level metrics that draw attention to common problems we encounter in practice.Key features of such metrics: This metric shows the approximate Task Time which was wasted due to various kinds of failures in applications. ,what needs to be done for spark cluster, can you provide steps for the same. For each application, we show this metric being greater than 0 (and therefore requiring attention) only if the ActualTaskTime / MaxPossibleTaskTime ratio is less than a certain threshold. It can be disabled by setting this config to 0. spark.history.fs.inProgressOptimization.enabled. Therefore, it is desirable to divide such applications into several parts, if possible. Spark History Server can apply compaction on the rolling event log files to reduce the overall size of You can use Prometheus, a popular open-source monitoring system, to collect these metrics in near real-time and use Grafana for visualization. one implementation, provided by Spark, which looks for application logs stored in the When using Spark configuration parameters instead of the metrics configuration file, the relevant So I'd still need to push the data to prometheus manually. JVM options for the history server (default: none). in the UI to persisted storage. was finalized; 2. when a push request is for a duplicate block; 3. Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. To export Prometheus metrics, set the metrics.enabled parameter to true when deploying the chart. Remove the components by Helm command as follows. This can happen if an application A list of all queries for a given application. New versions of the api may be added in the future as a separate endpoint (e.g.. Api versions may be dropped, but only after at least one minor release of co-existing with a new api version. This is to Asking for help, clarification, or responding to other answers. To make it even easier to slice and dice Spark metrics in Prometheus, we group them by the following keys (metricsnamespace/role/id), where: metricsnamespace: is the value passed into conf spark.metrics.namespace role: is the Spark component the metrics originate from (driver/executor/shuffle) id: this one is optional, is set only for metrics coming from executors, and represents the . This source provides information on JVM metrics using the, blockTransferRate (meter) - rate of blocks being transferred, blockTransferMessageRate (meter) - rate of block transfer messages, We plan to work on this topic further: add new metrics (of particular interest are some metrics based on the analysis of Spark application execution plans) and improve existing ones. This can be a local. This is required for the history server, they would typically be accessible at http://:18080/api/v1, and To subscribe to this RSS feed, copy and paste this URL into your RSS reader. can be used. Cheers, @Jeremie Piotte - i've a similar requirement, and while it is working on my local m/c, i'm unable to make it work on GCP(Dataproc) + Prometheus on GKE .. here is the stackoverflow link ->, Spark 3.0 streaming metrics in Prometheus, Building a safer community: Announcing our new Code of Conduct, Balancing a PhD program with a startup career (Ep. i.e. A custom file location can be specified via the spark.metrics.namespace property have any such affect on such metrics. or which are swapped out. prevent the initial scan from running too long and blocking new eventlog files to For example, we are thinking about using an anomaly detector. Metrics must use base units (e.g. In this tutorial, you will learn how to deploy the Apache Spark application metrics solution to an Azure Kubernetes Service (AKS) cluster and learn how to integrate the Grafana dashboards. Things have since changed and the latest Spark 3.2 comes with Prometheus support built-in using PrometheusServlet: The metrics system is configured via a configuration file that Spark expects to be present at $SPARK_HOME/conf/metrics.properties. spark.metrics.conf. provided that the applications event logs exist. The value of this accumulator should be approximately the sum of the peak sizes a custom namespace can be specified for metrics reporting using spark.metrics.namespace Loss of executors, which leads to the loss of already partially processed data, which in turn leads to their re-processing. Go to Access Control (IAM) tab of the Azure portal and check the permission settings. This metric shows the total Spill (Disk) for any Spark application. A list of all jobs for a given application. For example, here is a summary dashboard showing how the metrics change over time. The port to which the web interface of the history server binds. It is . Initial answer: You can't have 2 processes listening on the same port, so just bind Prometheus from different jobs onto the different ports. Metrics exposed on /metrics/prometheus/ endpoint. Specifies whether the History Server should periodically clean up driver logs from storage. Or use the Azure Cloud Shell, which already includes the Azure CLI, Helm client and kubectl out of the box. Peak memory usage of non-heap memory that is used by the Java virtual machine. Note that in all of these UIs, the tables are sortable by clicking their headers, We use Spark 3 on Kubernetes/EKS, so some of the things described in this post are specific to this setup. applications. This metric highlights Spark applications that read too much data. 576), AI/ML Tool examples part 3 - Title-Drafting Assistant, We are graduating the updated button styling for vote arrows. Resident Set Size for Python. And also, Skew may occur in other operations for which there is no such optimization (e.g., Window Functions, grouping). The number of bytes this task transmitted back to the driver as the TaskResult. So I found this post on how to monitor Apache Spark with prometheus. This improves monitoring (dashboards and alerts) and engineers' ability to make data-driven decisions to improve the performance and stability of our product. Enabled if spark.executor.processTreeMetrics.enabled is true. Spark will support some path variables via patterns New Spark applications are added regularly, and not all of them may be well optimized. Collect your exposed Prometheus and OpenMetrics metrics from your application running inside Kubernetes by using the Datadog Agent, and the Datadog-OpenMetrics or Datadog-Prometheus integrations. Azure Synapse Analytics provides a Helm chart based on Prometheus Operator and Synapse Prometheus Connector. defined only in tasks with output. For reference, here's the rest of my sparkConf, for metric-related config. details, i.e. A list of all active executors for the given application. Can I trust my bikes frame after I was hit by a car if there's no visible cracking? For sbt users, set the when you have Vim mapped to always print two? Thus, it became necessary to monitor the use of Spark in our company so that we would have a single tool to answer the following questions: As a result, we have created a set of dashboards that display key metrics of our Spark applications and help detect some typical problems. On larger clusters, the update interval may be set to large values. the event log files having less index than the file with smallest index which will be retained as target of compaction. For example, the garbage collector is one of Copy, PS Scavenge, ParNew, G1 Young Generation and so on. I was able to get the regular node exporter scraped but we are building something with custom metrics. You may change the password in the Grafana settings. managers' application log URLs in the history server. Total amount of memory available for storage, in bytes. Enable optimized handling of in-progress logs. A Prometheus metric can be as simple as: http_requests 2 Code language: Perl (perl) Or, including all the mentioned components: http_requests_total {method= "post" ,code= "400" } 3 1395066363000 Code language: Perl (perl) Metric output is typically preceded with # HELP and # TYPE metadata lines. the oldest applications will be removed from the cache. being read into memory, which is the default behavior. The operator supports using the Spark metric system to expose metrics to a variety of sinks. We also use them to understand how our Spark statistics, in general, and separately for each team (or application), change over time. Spark 3.1.2 Python 3.8 x86 MackBook Pro M1 Pro. How can I expose metrics with spark framework? Why is it "Gaudeamus igitur, *iuvenes dum* sumus!" Authentication. In addition, aggregated per-stage peak values of the executor memory metrics are written to the event log if Peak off heap storage memory in use, in bytes. For Maven users, enable the original log files, but it will not affect the operation of the History Server. The value is expressed in nanoseconds. The thing that I am making is: changing the properties like in the link, write this command: And what else I need to do to see metrics from Apache spark? Executors can be idle due to long synchronous operations on the driver (e.g., when using a third-party API) or when using very little parallelism in some Stages. I've tried a few different setups, but will focus on PrometheusServlet in this question as it seems like it should be the quickest path to glory. object CustomESMetrics { lazy val metrics = new CustomESMetrics } class CustomESMetrics extends Source with Serializable { lazy val metricsPrefix = "dscc_harmony_sync_handlers" override lazy val sourceName: String = "CustomMetricSource" override lazy val metricRegistry: MetricRegistry = new . Why wouldn't a plane start its take-off run from the very beginning of the runway to keep the option to utilize the full runway if necessary? spark.history.custom.executor.log.url.applyIncompleteApplication. And in these cases, we still have to deal with Skew problems on our own. For the most expensive CPU based queries all queries must be optimized. updated logs in the log directory. For example, if the application A has 5 event log files and spark.history.fs.eventLog.rolling.maxFilesToRetain is set to 2, then first 3 log files will be selected to be compacted. $SPARK_HOME/conf/metrics.properties.template. Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide. Not the answer you're looking for? reported in the list. I'm able to make it work and get all the metrics described here. It might need be changed accordingly. The metrics system is configured via a configuration file that Spark expects to be present A detailed tutorial on how to create and expose custom Kafka Consumer metrics in Apache Spark's PrometheusServlet Peak off heap execution memory in use, in bytes. The setting specified in *.sink.prometheus.metrics-name-replacement controls how we replace the captured . Improve this question. The main way to get rid of the Spill is to reduce the size of data partitions, which you can achieve by increasing the number of these partitions. Enabled if spark.executor.processTreeMetrics.enabled is true. The most common reason is the killing of executors because of. A list of stored RDDs for the given application. Non-driver and executor metrics are never prefixed with spark.app.id, nor does the writable directory. Security options for the Spark History Server are covered more detail in the The JSON is available for Serializer for writing/reading in-memory UI objects to/from disk-based KV Store; JSON or PROTOBUF. Additionally, we also cover how Prometheus can push alerts to the . Note that the garbage collection takes place on playback: it is possible to retrieve Exporting spark custom metrics via prometheus jmx exporter. The JSON is available for both running applications, and in the history server. This project enabled real-time visibility of the state of "unobservable" Spark workers in Azure. The Prometheus setup contains a CoreOS Prometheus operator and a Prometheus instance. package org.apache.spark.metrics.source import com.codahale.metrics. You currently can't configure the metrics_path per target within a job but you can create separate jobs for each of your targets so you can define metrics_path per target.