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Datadog | The Monitor blog

Introducing our open source AI-native SAST Instrument and monitor Boomi integration flows with OpenTelemetry and Datadog Not all index scans are equal: How we cut query latency by over 99% Platform engineering metrics: What to measure and what to ignore Integrate Recorded Future threat intelligence with Datadog Cloud SIEM CI/CD security: threat modeling using a MITRE-style threat matrix CI/CD security: How to secure your GitHub ecosystem Ingress NGINX is EOL: A practical guide for migrating to Kubernetes Gateway API Operating agentic AI with Amazon Bedrock AgentCore and Datadog LLM Observability: Lessons from NTT DATA Introducing the Datadog Code Security MCP Capture and analyze custom heatmaps in Session Replay Understand session replays faster with AI summaries and smart chapters Monitor ClickHouse query performance with Datadog Database Monitoring How we designed empathetic alert sounds for on-call engineers Search and act across Datadog to resolve issues faster with Bits Assistant Measure the business impact of every product change with Datadog Experiments Analyzing round trip query latency Configuring JavaScript caches for better performance Introducing Bits AI Dev Agent for Code Security Datadog achieves ISO 42001 certification for responsible AI Monitor Nutanix clusters, hosts, and VMs with Datadog Monitor Juniper Mist in Datadog A new Host Map for modern infrastructure Annotate traces to improve LLM quality with Datadog LLM Observability What’s new in Cloud SIEM: AI-powered investigations, enhanced threat intelligence, and scalable security operations Explore Kubernetes with native OpenTelemetry data Monitor Oracle Fusion Cloud Applications with Datadog Announcing the Datadog Terraform provider v4.0.0 Scaling Kubernetes workloads on custom metrics How to design cloud environments for AI-powered threat analysis Monitor Aruba Central in Datadog How we centralize and remediate risks with Datadog Case Management Accelerate incident response with Datadog and ServiceNow Monitor your application and network load balancer logs Understanding Karpenter architecture for Kubernetes autoscaling Tools for collecting metrics and logs from Karpenter Monitor Karpenter with Datadog What your product data is actually saying Key metrics for monitoring Karpenter Securing Datadog’s platform in the AI age: The role of observability data Four ways engineering teams use the Datadog MCP Server to power AI agents Approaching your observability migration with the right mindset Meet the new Bits AI SRE: Deeper reasoning, twice as fast Key learnings from the 2026 State of DevSecOps study Use plain English to query your multi-cloud infrastructure in Resource Catalog Simplifying troubleshooting across the user journey with Datadog Synthetic Monitoring Protect your OCI resources with Datadog Cloud Security This Month in Datadog - 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Monitor Apache Ignite with Datadog
Mallory Mooney · 2020-07-06 · via Datadog | The Monitor blog
Mallory Mooney

Mallory Mooney

Apache Ignite is a computing platform for storing and processing large datasets in memory. Ignite can leverage hardware RAM as both a caching and storage layer to serve as a distributed, in-memory database or data grid. This allows Ignite to ingest and process complex datasets—such as those from real-time machine learning and analytics systems—in parallel and at faster speeds than traditional databases supported by only disk storage.

Because industries rely on Ignite to accelerate processing for vital real-time data, getting visibility into the performance of Apache Ignite clusters and nodes is necessary to ensure they can support mission-critical applications. To help you monitor the health and performance of your Ignite architecture, we are excited to announce Datadog’s Apache Ignite integration, which collects node and cluster-wide memory, cache, and storage metrics and logs.

The integration is included with version 7+ of the Datadog Agent. Once you deploy and configure the Agent, you can start collecting and visualizing metrics from your Ignite nodes on an out-of-the-box dashboard. This means you can easily view key data on cache, memory, thread, page, and job activity, giving you an at-a-glance understanding of Ignite throughput and performance.

Apache Ignite integration dashboard

Monitor Ignite and JVM

As part of its architecture, Ignite uses Java virtual machines (JVM) to run server and client nodes. Server nodes are the base units for compute tasks or data storage and can be grouped together into clusters to pool resources for an application. Applications connect to a cluster via a client node. Datadog’s new integration helps you monitor the state of your Ignite clusters and individual nodes, as well as the performance of all running JVMs.

As with other Java-based platforms like Tomcat or Cassandra that expose JMX metrics, the Datadog Agent connects to Ignite’s JMX server to capture performance data from both Ignite and the underlying JVMs. This gives you a comprehensive view of Ignite’s performance and the flexibility to collect only the metrics that are most important to you.

Understand the state of your nodes

When a node goes offline, you will want to know about it as soon as possible to minimize the possibility of your application becoming unresponsive. The Agent includes a built-in health check for Ignite, which you can use to create an alert and notify you when a node goes offline.

Known when an Apache Ignite node goes offline

If a node goes offline or is kicked out of a cluster, you can use Datadog’s out-of-the-box JVM metrics dashboard to investigate. For example, a node’s health can be affected by increases in old generation size for the garbage collector, which is an indicator that an application is creating too many long-lived objects for the node to support. This causes the garbage collector to pause more frequently while it removes the old objects.

Monitor JVM metrics for Apache Ignite

You can also review garbage collection (GC) logs in the Log Explorer to get more detailed information about GC activity. If you are seeing an increase in logs for GC pauses, for example, you may need to tune garbage collection.

Track memory usage across every node

Ignite nodes can also quickly run out of memory if they are not configured with enough heap space to support an application. When this happens, you may see OutOfMemoryError exceptions in the logs, and the application may become unresponsive.

Datadog’s built-in Ignite dashboard enables you to monitor memory usage across your Ignite nodes so you can respond accordingly. You can also create alerts to automatically notify you when a node is seeing a significant increase in used heap memory.

An alert for Apache Ignite nodes

Increases in heap space usage are often a result of inefficient SQL queries that return large result sets. With Datadog APM, you can trace your Java application to find exactly where the bottlenecks are so you can optimize the queries that are consuming the most memory by splitting them up into smaller ones or using lazy queries.

Datadog + Ignite

With Datadog’s new integration, you can collect Ignite metrics and logs to get deeper insights into the health of your in-memory cache, data grid, or database. And Datadog supports a wide variety of environments—from on-premise hardware to Kubernetes clusters in the cloud—so you can always track the performance of your Ignite infrastructure, regardless of where it is deployed. Sign up for a free trial to get started.