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ManageEngine recognized in the 2023 Gartner® Magic Quadrant™ for Application Performance Monitoring and Observability. Read the report

Apache Spark Monitoring

Apache Spark Monitoring

Apache Spark is an open source big data processing framework built for speed, with built-in modules for streaming, SQL, machine learning and graph processing. Apache Spark has an advanced DAG execution engine that supports acyclic data flow and in-memory computing. Spark runs on Hadoop, Mesos, standalone, or in the cloud. It can access diverse data sources including HDFS, Cassandra, HBase, and S3.

There are many components coming together to make a spark application work. If you're planning to deploy Spark in your production environment, Applications Manager can make sure you can monitor the different components, understand performance parameters, get alerted when things go wrong and know how to troubleshoot issues.

Gain visibility into the performance of Spark

Automatically discover the entire service topology of your data pipeline and applications. Perform in real-time, full cluster and node management, and monitoring of Spark application execution with workflow visualization. Visualize, in the standalone mode, the master and the workers running on individual nodes and executor processes, for every application that is created in the cluster. Get up-to-the-second insight into cluster runtime metrics, individual nodes and configurations.

Apache Spark real-time data

Track resource utilization

Manage resources so that your Spark applications run optimally. When adding new jobs, operations teams must balance available resources with business priorities. Stay on top of your cluster health with fine-grained statistics of performance like from Disk I/O to Memory usage metrics; and node health (in real-time) with CPU usage for all the nodes, followed by JVM heap occupancy.

Apache Spark Memory Utilization

Get insight into Spark Cores and Applications

Gain insights into your Spark production application metrics; organize and segment your Spark applications based on user defined data; and sort through applications based on state (active, waiting, completed) and run duration. When a job fails, the cause is typically a lack of cores. Spark node/worker monitoring provides metrics including the number of free and uses cores so users can perform resource allocation based on cores.

Apache Spark Application Details

Understand performance of RDDs and Counters

Get performance metrics including stored RDDs (Resilient Distributed Datasets) for the given application, storage status and memory usage of a given RDD, and all the Spark counters for each of your Spark executions. Get deep insights into file level cache hits and parallel listing jobs for potential performance optimizations.

Apache Spark RDD Details


Fix Performance Problems Faster

Get instant notifications when there are performance issues with the components of Apache Spark components. Become aware of performance bottlenecks and find out which application is causing the excessive load. Take quick remedial actions before your end users experience issues.

What our customers say

Dec 15, 2021
All in one monitoring solution!!

The tool offers complete and unified visibility into our environment and helps us identify and resolve potential performance issues quickly.

- CloudOps Manager
Industry:TelecommunicationCompany Size: 30B+ USD

KFin Technologies reduces MTTR by 90% using Applications Manager

Industry: Financial services

KFintech, a financial services industry, having access to a surplus amount of data, was pertinent for to ensure that the performance of its databases was on point. With Applications Manager, KFintech was able to gain end-to-end insight into essential transactions, identify slow-performing queries, eliminate recurring performance issues, and ensure uninterrupted service delivery.

  • Gartner Magic Quadrant
  • Gartner peer insights
  • Gartner peer insights
  • Gartner peer insights
  • Gartner peer insights

You'll be in great company