The most common reason is the killing of executors because of. in an example configuration file, Exporting spark custom metrics via prometheus jmx exporter. The path to the metrics.properties file should be set in your Dockerfile. More info about Internet Explorer and Microsoft Edge, Azure Synapse Apache Spark application metrics. This is also true when aggregating metric values by some dimensions (e.g., by date or team name). Different Prometheus scrape URL for every target Making statements based on opinion; back them up with references or personal experience. Authentication: It is AAD-based authentication and can automatically refresh the AAD token of the service principal for application discovery, metrics ingestion and other functions. Can the use of flaps reduce the steady-state turn radius at a given airspeed and angle of bank? The way to view a running application is actually to view its own web UI. In addition, aggregated per-stage peak values of the executor memory metrics are written to the event log if in nanoseconds. Grafana is an open-source web application for data visualization and analysis. Spark executor metrics don't reach prometheus sink JVM options for the history server (default: none). Peak on heap execution memory in use, in bytes. Synapse Prometheus Connector helps to connect Azure Synapse Apache Spark pool and your Prometheus server. 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. of task execution. in the UI to persisted storage. Total available on heap memory for storage, in bytes. kubectl apply -f prometheus/spark-service.yaml. parts of event log files. This metric contains the difference taskTimeMax taskTime75Percentile (the sum over all stages), but currently, we take into account only those stages for which the condition (taskTimeMax taskTime75Percentile) > 5 min AND taskTimeMax/taskTime75Percentile > 5 is satisfied. The value of this accumulator should be approximately the sum of the peak sizes Peak off heap storage memory in use, in bytes. You need to have a Prometheus server deployed on a Linux VM. Azure Synapse Analytics provides a set of default Grafana dashboards to visualize Apache Spark application-level metrics. We mainly use this to find the most significant applications for a selected metric/problem, so we know what to focus on first. When the compaction happens, the History Server lists all the available event log files for the application, and considers But since the application may eventually complete successfully, your workflow management platform (e.g.. files. managers' application log URLs in the history server. spark-shell) and go to http://localhost:4040/metrics/prometheus. First of all, we look at what values in our data are the cause of it. The lowest value is 1 for technical reason. available by accessing their URLs directly even if they are not displayed on the history summary page. configured using the Spark plugin API. logs to load. mechanism of the standalone Spark UI; "spark.ui.retainedJobs" defines the threshold This example shows a list of Spark configuration parameters for a Graphite sink: Default values of the Spark metrics configuration are as follows: Additional sources can be configured using the metrics configuration file or the configuration Get the default password and address of Grafana. 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. This is the component with the largest amount of instrumented metrics. Typically, each value of any such metric tells us the magnitude of the corresponding problem. Is there a legal reason that organizations often refuse to comment on an issue citing "ongoing litigation"? The metrics system is configured via a configuration file that Spark expects to be present $SPARK_HOME/conf/metrics.properties.template. explicitly (sc.stop()), or in Python using the with SparkContext() as sc: construct CPU time the executor spent running this task. The history server displays both completed and incomplete Spark jobs. Elapsed time the JVM spent in garbage collection while executing this task. This is achieved by updating the Prometheus config YAML file. logs, via setting the configuration spark.history.fs.eventLog.rolling.maxFilesToRetain on the And there are also plans to improve the usability of these tools. Spark with Prometheus monitoring - Medium Non-driver and executor metrics are never prefixed with spark.app.id, nor does the Specifies whether the History Server should periodically clean up event logs from storage. If set, the history The following list of components and metrics reports the name and some details about the available metrics, Start a Spark application with spark.ui.prometheus.enabled=true, e.g. We need to configure Prometheus to scrape the app for the custom metrics. Authentication. Metrics used by Spark are of multiple types: gauge, counter, histogram, meter and timer, Spark will support some path variables via patterns Fast creation of flexible graphs on the client-side. Also, you can always try to reduce the number of executors (spark.dynamicAllocation.maxExecutors option) because in some such cases, this significantly reduces the used resources while having almost no effect on the applications running time. By default, the root namespace used for driver or executor metrics is 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. Improve this question. See Advanced Instrumentation below for how to load How can I expose metrics with spark framework? 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. Random failures of some tasks. How stable and optimized are our applications? Compare two prometheus metrics and return boolean output For example, a small Spill usually has no negative impact on the application, and we can ignore it. Prior to Apache Spark 3.0, there were different approaches to expose metrics to Prometheus: 1- Using Sparks JmxSink and Prometheuss JMXExporter (see Monitoring Apache Spark on Kubernetes with Prometheus and Grafana), 2- Using Sparks GraphiteSink and Prometheuss GraphiteExporter, 3- Using custom sinks and Prometheuss Pushgateway. Why is it "Gaudeamus igitur, *iuvenes dum* sumus!" The value is expressed in milliseconds. How to send JVM metrics of Spark to Prometheus in Kubernetes, Monitoring Spark 3 applications with Prometheus, Spark 3.0 streaming metrics in Prometheus, Spark streaming: expose spark_streaming_* metrics, Monitoring a lot of small Spark clusters with Prometheus. Please make sure your service principal is at least "Reader" role in your Synapse workspace. This value is then expanded appropriately by . This source provides information on JVM metrics using the, blockTransferRate (meter) - rate of blocks being transferred, blockTransferMessageRate (meter) - rate of block transfer messages, workspace_resource_group_name: Synapse workspace resource group name. In the following command, the jmx_prometheus_javaagent-0.3.1.jar file and the spark.yml are downloaded in previous steps. May 17, 2022 -- 2 Photo by Drago Grigore on Unsplash In this post, I will describe our experience in setting up monitoring for Spark applications. How to send custom metrics. Peak off heap memory (execution and storage). For example, there may be many records with empty/unknown values in the join/grouping columns, which should have been discarded anyway. Running out of disk space on any EC2 instance due to using too much data. For sbt users, set the Someone runs a large number of very short Jobs in a loop. Note that in all of these UIs, the tables are sortable by clicking their headers, Time the task spent waiting for remote shuffle blocks. For any application, we show the value of any such metric greater than 0 only if the magnitude of the problem is worth attention. as another block for the same reduce partition were being written, lateBlockPushes - number of shuffle push blocks that are received in shuffle service It seems quite easy to control the performance of Spark applications if you do not have many of them. Enabled if spark.executor.processTreeMetrics.enabled is true. How does our Spark workload change over time? Used on heap memory currently for storage, in bytes. PrometheusServlet: (Experimental) Adds a servlet within the existing Spark UI to serve metrics data in Prometheus format. That is, as a noteworthy Skew Problem, we show only the most severe cases that can seriously affect the running time. The theoretically possible Total Task Time for an application we calculate as: * spark.executor.cores.The actual Total Task time is usually always less than theoretically possible, but if it is much smaller, then this is a sign that executors (or individual cores) are not used most of the time (but at the same time, they occupy space on EC2 instances). Therefore, it is desirable to divide such applications into several parts, if possible. The source code and the configurations have been open-sourced on GitHub. After quite a bit of investigation, I was able to make it work. Applying compaction on rolling event log files, Spark History Server Configuration Options, Dropwizard library documentation for details, Dropwizard/Codahale Metric Sets for JVM instrumentation. configuration property. Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide. Elapsed time spent serializing the task result. Monitor Spark (Streaming) with Prometheus | by Salohy Miarisoa - Medium When using Spark configuration parameters instead of the metrics configuration file, the relevant For successfully completed applications, we consider as wasted the total time of all Failed Tasks, as well as the total Task Time of all retries of previously successfully completed Stages (or individual tasks), since such retries usually occur when it is necessary to re-process data previously owned by killed executors. One way to signal the completion of a Spark job is to stop the Spark Context Cost, $ the cost of running the application. however pushgateway introduces its own problems, so was hoping to avoid it. As soon as an update has completed, listings of the completed and incomplete applications Monitoring Apache Spark with Prometheus on Kubernetes Get service ip, copy & paste the external ip to browser, and login with username "admin" and the password. The number of on-disk bytes spilled by this task. Collect Apache Spark applications metrics using APIs The integrated Grafana dashboards allow you to diagnose and monitor your Apache Spark application. mean? Is it possible for rockets to exist in a world that is only in the early stages of developing jet aircraft? The HybridStore co-uses the heap memory, Spark publishes metrics to Sinks listed in the metrics configuration file. This is to Monitoring and Instrumentation - Spark 3.0.0-preview Documentation 3. So I found this post on how to monitor Apache Spark with prometheus. incomplete attempt or the final successful attempt. Spark streaming: expose spark_streaming_* metrics, Spark structured streaming metrics are confusing, How to get spark streaming metrics like input rows, processed rows and batch duration to Prometheus, Extending IC sheaves across smooth normal crossing divisors, Cartoon series about a world-saving agent, who is an Indiana Jones and James Bond mixture. Compaction will discard some events which will be no longer seen on UI - you may want to check which events will be discarded Metric names for applications should generally be prefixed by the exporter name, e.g. So I'd still need to push the data to prometheus manually. namespace can be found in the corresponding entry for the Executor component instance. 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 . And in these cases, we still have to deal with Skew problems on our own. Note that the garbage collection takes place on playback: it is possible to retrieve Several external tools can be used to help profile the performance of Spark jobs: Spark also provides a plugin API so that custom instrumentation code can be added to Spark Does the grammatical context of 1 Chronicles 29:10 allow for it to be declaring that God is our Father? 576), AI/ML Tool examples part 3 - Title-Drafting Assistant, We are graduating the updated button styling for vote arrows. In this post, we looked at some metrics and dashboards displaying them, which allow us to monitor the use of Spark in our company and detect various problems. an easy way to create new visualizations and monitoring tools for Spark. This metric highlights Spark applications that read too much data. In addition to viewing the metrics in the UI, they are also available as JSON. Additionally, we also cover how Prometheus can push alerts to the . This project mainly aims to provide: Azure Synapse Apache Spark metrics monitoring for Azure Synapse Spark applications by leveraging Prometheus, Grafana and Azure APIs. Use this proxy to authenticate requests to Azure Monitor managed service for Prometheus. This is not an application problem, and there is nothing we can do about it at the application level. spark.history.store.hybridStore.diskBackend. Resident Set Size: number of pages the process has are stored. The JSON is available for The dashboard also shows several additional metrics related to this problem, such as: This metric shows the difference between the theoretically maximum possible Total Task Time and the actual Total Task Time for any completed Spark application. If an application makes the value of spark.app.id. For the most expensive CPU based queries all queries must be optimized. then expanded appropriately by Spark and is used as the root namespace of the metrics system. Specifies a disk-based store used in hybrid store; LEVELDB or ROCKSDB. Memory to allocate to the history server (default: 1g). But we can make use of it to create dashboards for any of our custom metrics available in Prometheus. But complications may begin as your Spark workload increases significantly. spark.history.fs.eventLog.rolling.maxFilesToRetain. 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. Specifies whether the History Server should periodically clean up driver logs from storage. 3. read from a remote executor), Number of bytes read in shuffle operations (both local and remote). In the API listed below, when running in YARN cluster mode, The amount of used memory in the returned memory usage is the amount of memory occupied by both live objects and garbage objects that have not been collected, if any. But how do I do that automatically without having to . crashes. The Azure Active Directory authorization proxy is a reverse proxy, which can be used to authenticate requests using Azure Active Directory. Spark Performance Monitoring using Graphite and Grafana And in this case, we still show the corresponding metric as 0 to not bother anyone. There are a few limitations to this new feature. One of them is that this endpoint only exposes metrics that start with metrics_ or spark_info.In addition to this, Prometheus naming conventions are not followed by Spark, and labels aren't currently supported (not that I know, if you know a way hit me up! But I didn't understand what should I do with the spark.yml file. Details for the storage status of a given RDD. Connect and share knowledge within a single location that is structured and easy to search. application. Use Prometheus file Service Discovery mechanism to add targets there. Azure Synapse Spark Metrics Introduction. Peak off heap execution memory in use, in bytes. reported in the list. The value is expressed in milliseconds. Add custom params to prometheus scrape request. Indicates whether the history server should use kerberos to login. Btw thank you for the good explanation! This requires a service monitor deployed in prometheus right ? if the history server is accessing HDFS files on a secure Hadoop cluster. Number of tasks that have completed in this executor. The non-heap memory consists of one or more memory pools. Spark History Server. So in oder to be able to store . 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? A list of all stages for a given application. The number of bytes this task transmitted back to the driver as the TaskResult. 1 Answer. 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. In order to have more flexibility in querying Prometheus, we need the ability to add custom metadata to the metrics published to Prometheus via labels. It implements: Synapse Prometheus Connector is released as a docker image hosted on Microsoft Container Registry. The higher the value, the more serious the problem.Spark performs various operations on data partitions (e.g., sorting when performing SortMergeJoin). If executor logs for running applications should be provided as origin log URLs, set this to `false`. You may change the password in the Grafana settings. Real-Time Distributed Monitoring and Logging in the Azure Cloud Stack traces of all the threads running within the given active executor. Use it with caution. This improves monitoring (dashboards and alerts) and engineers' ability to make data-driven decisions to improve the performance and stability of our product. Enable Spark metrics report to JMX. Enabled if spark.executor.processTreeMetrics.enabled is true. for the history server, they would typically be accessible at http://:18080/api/v1, and Also I found this links: But these are topics for separate posts. What maths knowledge is required for a lab-based (molecular and cell biology) PhD? Therefore, we do not increase the Wasted Task Time metric for applications with which this happened. set of sinks to which metrics are reported. What does "Welcome to SeaWorld, kid!" Prometheus Metrics, Implementing your Application | Sysdig Particularly, it is able to automatically configure the metric system to expose metrics to Prometheus. in shuffle operations, Number of blocks fetched in shuffle operations (both local and remote), Number of remote bytes read in shuffle operations, Number of bytes read in shuffle operations from local disk (as opposed to Viewed 676 times 4 I'm trying to export spark (2.4.0) custom metrics in prometheus format. Monitoring and Instrumentation - Spark 3.4.0 Documentation 2. a zip file. at $SPARK_HOME/conf/metrics.properties. There is also spark.ui.prometheus.enabled configuration property: Executor metric values and their measured memory peak values per executor are exposed via the REST API in JSON format and in Prometheus format. Apps performance by name metrics aggregated by application name. directory must be supplied in the spark.history.fs.logDirectory configuration option, And execute. We were trying to extend the Spark Metrics subsystem with a Prometheus sink but the PR was not merged upstream. making it easy to identify slow tasks, data skew, etc. sources, sinks). A list of all(active and dead) executors for the given application. it will have to be loaded from disk if it is accessed from the UI. Configure Prometheus to scrape from a custom URL. will reflect the changes. Monitoring Spark with Prometheus, metric name preprocessing and A detailed tutorial on how to create and expose custom Kafka Consumer metrics in Apache Spark's PrometheusServlet 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. I tried to follow the answer here. If, say, users wanted to set the metrics namespace to the name of the application, they at the expense of more server load re-reading updated applications. Note that How to access metrics of streaming query? Even this is set to `true`, this configuration has no effect on a live application, it only affects the history server. microsoft/azure-synapse-spark-metrics - GitHub A list of stored RDDs for the given application. spark.history.store.hybridStore.maxMemoryUsage. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. I couldnt find any apis or references to get the streaming metrics, processing info etc. Peak memory that the JVM is using for direct buffer pool (, Peak memory that the JVM is using for mapped buffer pool (. Having any Spill is not good anyway, but a large Spill may lead to serious performance degradation (especially if you have run out of EC2 instances with SSD disks). I am also interested in this approach because this idea exposes the metrics in the driver but it does not tell Prometheus where to scrape the metrics. By default, it doesn . Therefore, I used a Prometheus container, but I'm struggling with exposing a simple metric to it. The Helm chart includes Prometheus server, Grafana server, and Grafana dashboards for Apache Spark application-level metrics. A detailed tutorial on how to create and expose custom Kafka Consumer metrics in Apache Spark's PrometheusServlet To use the Azure Synapse Prometheus connector in your on-premises Prometheus server, you should follow the steps below to create a service principal. Timers, meters and histograms are annotated They externalized the sink to a standalone project (https://github.com/banzaicloud/spark-metrics) and I used that to make it work with Spark 2.3. To view the web UI after the fact, set spark.eventLog.enabled to true before starting the This proxy can be used to authenticate requests to any service that supports Azure Active Directory authentication. This configures Spark to log Spark events that encode the information displayed Please go through my earlier post to set up the spark-k8-operator To learn more, see our tips on writing great answers. Initial answer: You can't have 2 processes listening on the same port, so just bind Prometheus from different jobs onto the different ports. This usually occurs due to the uneven distribution of data across partitions. spark.history.fs.driverlog.cleaner.interval, spark.history.fs.driverlog.cleaner.maxAge. into one compact file with discarding events which are decided to exclude. Virtual memory size in bytes. Time spent blocking on writes to disk or buffer cache. Prometheus can help us collect, query, and store massive amounts of time series data, and it can be easily integrated with Grafana. A list of all active executors for the given application. provide instrumentation for specific activities and Spark components. kubernetes - Exporting spark executor jmx metrics for multiple This includes time fetching shuffle data. The pushed block data are considered as ignored when: 1. it was received after the shuffle haproxy_up. Currently there is only only for applications in cluster mode, not applications in client mode. For such use cases, a custom namespace can be specified for metrics reporting using spark.metrics.namespace configuration property. The value is expressed in milliseconds. There are few ways to monitoring Apache Spark with Prometheus. to in-memory store is completed. The public address for the history server. I'm running a Spark 3.0 application (Spark Structured Streaming) on Kubernetes and I'm trying to use the new native Prometheus metric sink. Defining custom metrics Metrics should be quantifiable values that provide real-time insights about the status or performance of the application. How is the entropy created for generating the mnemonic on the Jade hardware wallet? joins. On larger clusters, the update interval may be set to large values. Any help on how to get those streaming UI metrics to prometheus ? Prometheus is one of the most popular monitoring tools used with Kubernetes. spark.eventLog.logStageExecutorMetrics is true. to handle the Spark Context setup and tear down. Number of cores available in this executor. How to Register Custom Metrics in Executors of spark. Apache Spark 3.0 introduced the following resources to expose metrics: Those features are more convinent than the agent approach that requires a port to be open (which may not be possible).