In addition to reading this guide, we recommend you run the Elasticsearch Health Check-Up. It will detect issues and improve your Elasticsearch performance by analyzing your shard sizes, threadpools, memory, snapshots, disk watermarks and more.The Elasticsearch Check-Up is free and requires no installation.
Before you begin reading this guide, we recommend you try running the Elasticsearch Error Check-Up which analyzes 2 JSON files to detect many configuration errors.
To easily resolve issues in your deployment, try AutoOps for Elasticsearch. It diagnoses problems by analyzing hundreds of metrics collected by a lightweight agent and offers guidance for resolving them.
Elasticsearch Monitoring Metrics: Key Metrics for Optimal Performance
Monitoring Elasticsearch is crucial to ensure optimal performance and maintain the health of your cluster. In this article, we will discuss the key Elasticsearch monitoring metrics that you should keep an eye on to ensure the smooth operation of your cluster. These metrics will help you identify potential issues and take appropriate actions to prevent or resolve them.
1. Cluster Health Metrics
Cluster health metrics provide an overview of the overall health of your Elasticsearch cluster. Some important cluster health metrics include:
- Cluster status: Indicates the health of the cluster as green, yellow, or red. Green means all primary and replica shards are allocated, yellow means all primary shards are allocated but some replica shards are not, and red means at least one primary shard is not allocated.
- Active shards: The number of active primary and replica shards in the cluster.
- Unassigned shards: The number of shards that are not allocated to any node.
- Initializing shards: The number of shards that are currently being initialized.
- Relocating shards: The number of shards that are currently being moved from one node to another.
2. Node-Level Metrics
Node-level metrics provide information about the performance and resource usage of individual nodes in the cluster. Some important node-level metrics include:
- JVM heap usage: The amount of JVM heap memory used by Elasticsearch. High JVM heap usage can lead to performance issues and garbage collection (GC) overhead.
- CPU usage: The percentage of CPU used by Elasticsearch. High CPU usage can indicate that the node is under heavy load and may require additional resources or optimization.
- Disk usage: The amount of disk space used by Elasticsearch. High disk usage can lead to slow query performance and increased risk of data loss.
- Load average: The average system load on the node. A high load average can indicate that the node is under heavy load and may require additional resources or optimization.
3. Index-Level Metrics
Index-level metrics provide information about the performance and resource usage of individual indices in the cluster. Some important index-level metrics include:
- Indexing rate: The rate at which documents are being indexed. A low indexing rate can indicate performance issues or bottlenecks in the indexing process.
- Search rate: The rate at which search queries are being executed. A low search rate can indicate performance issues or bottlenecks in the search process.
- Merge rate: The rate at which segments are being merged. High merge rates can indicate that the index is under heavy load and may require optimization.
- Refresh rate: The rate at which the index is being refreshed. High refresh rates can indicate that the index is under heavy load and may require optimization.
4. Query Performance Metrics
Query performance metrics provide information about the performance of individual search queries. Some important query performance metrics include:
- Query latency: The time it takes to execute a search query. High query latency can indicate performance issues or bottlenecks in the search process.
- Fetch latency: The time it takes to fetch the results of a search query. High fetch latency can indicate performance issues or bottlenecks in the search process.
- Query cache hit rate: The percentage of search queries that are served from the query cache. A low query cache hit rate can indicate that the cache is not being utilized effectively and may require optimization.
Monitoring these key Elasticsearch metrics will help you maintain the health and performance of your cluster. Regularly reviewing these metrics and taking appropriate actions based on the insights they provide can prevent potential issues and ensure the smooth operation of your Elasticsearch cluster.
You may be thinking that monitoring all those metrics is a lot of work, but luckily there are plenty of monitoring solutions that can help you with that. If you are wondering which tool would suit you better when it comes to monitoring your cluster, take a look at this guide and start a free trial of Opster AutoOps.
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