Performance testing recommendations that will ensure Elasticsearch is fully optimized and operating as expected.
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Elasticsearch Monitoring: What do you need to know?
An introduction to Elasticsearch
Elasticsearch is a popular enterprise search engine driving some of the fastest searches on the web. It has a variety of notable features such as real-time analytics, scalable search solutions, and multi-tenancy support that are used to store and analyze large amounts of data. For IT professionals, Elasticsearch can be utilized to power real-time searches that support all your data discovery applications. As the adoption of Elasticsearch continues to grow, it is vital to be able to monitor your Elasticsearch environment so you can utilize data in a way that is actionable and provides insight.
The components of Elasticsearch
Elasticsearch is deployed as a cluster of nodes, with the most common being master-eligible, data and client nodes. Using various APIs allows you to manage/monitor at the cluster level and configure them based on the specifics of your environment. A primary benefit of Elasticsearch is that it’s easy to scale; adding more nodes automatically distributes the workload and as your cluster scales, performing tasks through APIs becomes even more significant.
Important Elasticsearch performance metrics
Elasticsearch APIs efficiently capture performance metrics at any time and while Elasticsearch’s endless offering of metrics prove to be useful, it can be difficult to pinpoint the ones that are the most relevant to your monitoring efforts. Here a few of the most important performance metrics to focus on when looking for greater visibility into Elasticsearch:
- Cluster health: Cluster health is the most standard Elasticsearch metric. Cluster health outlines your running nodes and the status of shards allocated to the nodes. During system upgrades or updates, it is helpful to know how long a cluster needs to distribute the shards
- Search performance: Search requests are common in Elasticsearch and have a direct impact on the user experience in regards to simple and analytical queries. Query load, query latency and fetch latency are particularly important to watch when overseeing search performance
- Node performance: CPU, memory usage, and disk I/O are the primary system metrics for each Elasticsearch node that exists within your environment
- Indexing performance: Measuring your index rate can provide direct insight into performance and indicate issues with data sources or refresh/merge times. Indexing latency and flush latency should be top of mind when new information is added to an index or existing information is updated or deleted
There are a wide range of software’s available that offer monitoring for Elasticsearch. When looking for the solution that best fits your needs, be sure to select a tool that monitors all Elasticsearch nodes in one convenient place, measures memory usage over time, and visualizes cluster behavior so you can easily spot issues/trends occurring within your Elasticsearch environment.
Opsview’s Elasticsearch Opspack ensures that search remains lightning fast for your users and maintains optimal performance for all your Elasticsearch nodes.
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