10 Elasticsearch Index Creation Best Practices
Creating an index in Elasticsearch is easy, but there are a few best practices to keep in mind to ensure optimal performance and reliability.
Elasticsearch is a powerful search engine that can be used to store and search large amounts of data. However, in order to get the most out of Elasticsearch, it is important to understand how to create indexes correctly.
In this article, we will discuss 10 best practices for creating Elasticsearch indexes. We will cover topics such as index naming conventions, sharding, and replication. By following these best practices, you can ensure that your Elasticsearch indexes are optimized for performance and scalability.
1. Use a dedicated cluster for Elasticsearch
A dedicated cluster for Elasticsearch ensures that the resources allocated to your index are not shared with other applications or services. This helps ensure that your index is always running optimally and that it has access to all of the necessary resources. Additionally, a dedicated cluster allows you to easily scale up or down as needed without impacting other services. Finally, having a dedicated cluster makes it easier to monitor performance and troubleshoot any issues that may arise.
2. Create an index per day, or per week
Creating an index per day or week allows you to easily manage and monitor your data. It also makes it easier to delete old indices that are no longer needed, as well as optimize the performance of your cluster by allocating resources more efficiently. Additionally, creating an index per day or week helps ensure that your data is backed up in case of a disaster. Finally, having multiple indices can help with troubleshooting issues since you can isolate problems to specific indices.
3. Set the number of shards and replicas to 1
Shards are the building blocks of an elasticsearch index, and each shard is a separate Lucene instance. By setting the number of shards to 1, you ensure that all documents in your index will be stored on one shard, which makes it easier to manage and query.
Replicas provide redundancy for your data by creating copies of your primary shard. Setting the number of replicas to 1 ensures that there is only one copy of your data, reducing storage costs and improving performance.
4. Enable shard allocation awareness
Shard allocation awareness allows you to control which nodes in your cluster will host shards for a given index. This is important because it ensures that the data for an index is spread across multiple nodes, providing redundancy and improving performance. It also helps prevent overloading any single node with too much data, which can cause problems like slow query times or even outages.
Enabling shard allocation awareness requires setting up a custom routing policy, but it’s worth the effort as it can help ensure that your elasticsearch indices are properly distributed and balanced across your cluster.
5. Disable dynamic scripting
Dynamic scripting allows users to execute arbitrary code on the server, which can be used for malicious purposes. It also increases the risk of security vulnerabilities and performance issues due to inefficient scripts.
To disable dynamic scripting, you need to add a setting in your elasticsearch configuration file:
script.engine.disabled: true
This will prevent any dynamic scripting from being executed on the server. Additionally, it’s important to keep an eye out for any new settings that may enable dynamic scripting, as they could potentially open up your system to attack.
6. Configure the bulk request size
When creating an index, elasticsearch needs to process a large amount of data. If the bulk request size is too small, it will take longer for the index to be created as elasticsearch has to make multiple requests in order to process all the data. On the other hand, if the bulk request size is too large, it can cause performance issues and even timeouts.
Therefore, it’s important to configure the bulk request size so that it’s neither too small nor too large. The optimal size depends on your specific use case, but generally speaking, it should be between 5MB and 15MB.
7. Tune your JVM heap size
Elasticsearch is a memory-intensive application, and the JVM heap size determines how much memory it can use. If your heap size is too small, elasticsearch won’t be able to store enough data in memory, which will lead to slower performance. On the other hand, if your heap size is too large, you’ll waste resources that could be used for other tasks.
To determine the optimal heap size for your environment, start by setting the minimum and maximum sizes to the same value. Then, gradually increase the size until you reach an acceptable level of performance. You should also monitor your system’s CPU and memory usage while adjusting the heap size to ensure that you’re not overloading the system.
8. Monitor your indices
Monitoring your indices allows you to identify any issues that may arise with the index, such as slow performance or incorrect data. This can help you quickly address any problems and ensure that your search engine is running optimally. Additionally, monitoring your indices will allow you to track usage patterns and make adjustments accordingly. For example, if you notice a particular query is taking longer than usual to execute, you can adjust the index settings to improve its performance.
9. Don’t forget about backups
Elasticsearch is a distributed system, which means that data can be stored in multiple nodes. If one of those nodes fails or becomes corrupted, you could lose all your data if you don’t have backups.
To ensure that your data is safe and secure, it’s important to create regular backups of your elasticsearch indices. This way, if something does happen to the cluster, you’ll still have access to your data. You should also consider setting up automated backup processes so that you don’t have to manually back up your indices every time.
10. Delete old data
Elasticsearch stores data in indices, and over time these indices can become bloated with old or irrelevant data. This can lead to slower search performance, as well as increased storage costs.
To avoid this issue, it’s important to regularly delete old data from your elasticsearch indices. You can do this manually by deleting individual documents, or you can use a tool like Curator to automate the process. Additionally, you should consider setting up an index lifecycle policy that will automatically delete indices after they reach a certain age. By following these best practices, you’ll ensure that your elasticsearch indices remain lean and efficient.