10 Elasticsearch Coordinating Node Best Practices

Elasticsearch is a powerful search engine that can be used to store and search large amounts of data. It is composed of several nodes, each of which has a specific role. One of the most important nodes is the coordinating node, which is responsible for managing the cluster and routing requests to the appropriate nodes.

In this article, we will discuss 10 best practices for configuring and managing the coordinating node in an Elasticsearch cluster. We will cover topics such as node selection, memory allocation, and shard allocation. By following these best practices, you can ensure that your Elasticsearch cluster is running optimally and efficiently.

1. Use a dedicated coordinating node

A coordinating node is responsible for managing the cluster state and routing requests to the appropriate nodes. If you use a single node as both a data node and a coordinating node, it can become overloaded with too many tasks. This can lead to poor performance and even outages.

By using a dedicated coordinating node, you ensure that your data nodes are not overburdened by coordinating tasks. This will help keep your Elasticsearch cluster running smoothly and efficiently.

2. Don’t use the same node for both ingest and search

When ingesting data, the node needs to be able to handle a large amount of requests and process them quickly. On the other hand, when searching for data, the node needs to be able to respond quickly with accurate results. If you use the same node for both tasks, it can lead to performance issues as the node is trying to do two things at once.

By using separate nodes for ingest and search, you can ensure that each task has its own dedicated resources and will not interfere with one another. This will help improve overall performance and reliability of your elasticsearch cluster.

3. Disable _all field in mappings

The _all field is a special field that contains the text of all other fields in the document. This means that when you search for something, it will look through the entire document and return any matches.

The problem with this is that it can be very resource intensive, especially if your documents are large or contain many fields. By disabling the _all field, you can reduce the amount of resources needed to perform searches, which can improve performance significantly. Additionally, since the _all field isn’t being used, there’s no need to index it, so you can save disk space as well.

4. Enable dynamic scripting

Dynamic scripting allows you to write custom scripts that can be used to modify the data stored in an elasticsearch cluster. This is especially useful when dealing with large datasets, as it allows for more efficient and accurate manipulation of the data.

Dynamic scripting also helps improve performance by allowing users to create custom scripts that are optimized for their specific use case. By enabling dynamic scripting, users can take advantage of the full power of elasticsearch without having to worry about writing complex queries or manually manipulating data.

5. Set up shard allocation awareness

Shard allocation awareness allows the coordinating node to be aware of which nodes are hosting shards for a given index. This helps ensure that requests from the coordinating node are routed to the correct nodes, and it also prevents unnecessary network traffic by avoiding sending requests to nodes that don’t have the data needed.

Setting up shard allocation awareness is relatively simple. All you need to do is add an attribute called “awareness” to your cluster settings. You can specify which attributes should be used to determine shard allocation (e.g., zone, rack, etc.), and then Elasticsearch will automatically route requests accordingly.

6. Configure thread pools

Thread pools are used to control the number of concurrent requests that can be handled by a node. If you don’t configure thread pools, then your node may become overwhelmed with too many requests and start to experience performance issues.

By configuring thread pools, you can ensure that each request is given enough resources to complete its task without overloading the system. This will help keep your cluster running smoothly and efficiently. Additionally, it’s important to monitor the thread pool usage in order to identify any potential bottlenecks or areas where additional tuning may be necessary.

7. Tune JVM heap size

The JVM heap size is the amount of memory allocated to the Java Virtual Machine (JVM) for running elasticsearch. If the heap size is too small, it can cause performance issues and even outages due to garbage collection pauses. On the other hand, if the heap size is too large, it can lead to wasted resources and higher costs.

To determine the optimal heap size for your environment, you should monitor the JVM metrics such as GC pause time, CPU utilization, and memory usage. Once you have a good understanding of these metrics, you can adjust the heap size accordingly. It’s also important to note that the heap size should be adjusted based on the number of shards in your cluster.

8. Monitor your cluster

Monitoring your cluster allows you to identify any potential issues before they become a problem. It also helps you understand how the nodes are performing and if there is anything that needs to be adjusted or optimized.

You can monitor your cluster using tools such as Kibana, which provides visualizations of your data and performance metrics. You can also use Elasticsearch APIs to query for information about the health of your cluster. Additionally, you should set up alerts so that you are notified when something goes wrong. This way, you can take action quickly and prevent any major problems from occurring.

9. Avoid using the _update_by_query API

The _update_by_query API is a powerful tool that can be used to update multiple documents in an index at once. However, it can also cause significant performance issues if not used correctly.

The main issue with the _update_by_query API is that it can cause too much load on the coordinating node. This is because the API sends requests to all of the data nodes in the cluster and then waits for them to respond before returning the results. If there are too many requests sent at once, the coordinating node can become overloaded and slow down or even crash.

To avoid this problem, it’s best to use other methods such as bulk updates or scripts when updating multiple documents in an index. These methods will help reduce the load on the coordinating node and ensure better performance.

10. Keep an eye on disk usage

Coordinating nodes are responsible for managing the cluster state and routing requests to the appropriate data nodes. As such, they need to have enough disk space available to store the cluster state information as well as any other metadata associated with the cluster. If a coordinating node runs out of disk space, it can cause serious performance issues or even lead to cluster instability.

To prevent this from happening, make sure you monitor your disk usage regularly and take action if necessary. You may want to consider adding additional storage capacity or deleting unnecessary files to free up some space. Additionally, you should also keep an eye on the size of your indices and delete old ones that are no longer needed.