Introduction
Elasticsearch is a widely used search and analytics engine that is known for its scalability, performance, and ease of use. One of the critical aspects of managing an Elasticsearch cluster is ensuring efficient storage utilization.
In this article, we will discuss advanced techniques and best practices for optimizing Elasticsearch storage. If you want to learn how to calculate the storage size of specific fields in an Elasticsearch Index, check out this guide.
Advanced techniques and best practices for optimizing Elasticsearch storage
1. Use Appropriate Data Types
Choosing the right data types for your fields can significantly impact storage efficiency. For example, using the ‘keyword’ data type for short, unique strings is more storage-efficient than using the ‘text’ data type. Similarly, using integer or long data types for numeric fields can save storage space compared to using double or float data types.
2. Enable Best Compression
Elasticsearch allows you to configure the compression algorithm used for storing data. By default, it uses the ‘LZ4’ compression algorithm, which provides a good balance between compression ratio and performance. However, if storage efficiency is a priority, you can enable the ‘DEFLATE’ compression algorithm, which provides better compression at the cost of slightly lower performance. To enable DEFLATE compression, add the following setting to your index settings:
{
"settings": {
"index": {
"codec": "best_compression"
}
}
}3. Optimize Mapping and Index Settings
Properly configuring your index settings and mapping can help reduce storage usage. Some of the key settings to consider include:
- Disable `_source` field: If you don’t need to retrieve the original JSON document, you can disable the `_source` field to save storage space. However, be aware that this will limit some Elasticsearch features, such as reindexing and update operations.
- Disable unused features: If you don’t use certain features, such as norms, doc_values, or term vectors, you can disable them in your mapping to save storage space.
- Use custom analyzers: By default, Elasticsearch uses the ‘standard’ analyzer for text fields, which may not be the most storage-efficient option. You can create custom analyzers tailored to your data to reduce storage usage.
4. Use Time-Based Indices and Index Lifecycle Management (ILM)
For time-series data, using time-based indices can help optimize storage usage. By creating a new index for each time period (e.g., daily, weekly, or monthly), you can manage the lifecycle of your indices more efficiently. You can use Elasticsearch’s Index Lifecycle Management (ILM) feature to automate the process of creating, managing, and deleting indices based on your retention policies.
5. Optimize Shard Allocation
Elasticsearch stores data in shards, which are distributed across the nodes in your cluster. Properly configuring shard allocation can help optimize storage usage and improve performance. Some of the key shard allocation settings to consider include:
- Set the appropriate number of primary shards: The number of primary shards determines the maximum amount of data that can be stored in an index. Choose a value
- Set the appropriate number of replica shards: The number of replica shards determines the redundancy of your data as well as the search capacity of your cluster but also contributes to increased storage space.
6. Upgrade to newer versions
Each Elasticsearch release comes with a load of improvements as they continuously strive to optimize the way the data is stored and retrieved. Look out for the release blog articles as these optimizations are often announced when new releases come out.
Conclusion
In conclusion, optimizing Elasticsearch storage is crucial for maintaining a healthy and efficient cluster. By following the best practices and advanced techniques discussed in this article, you can ensure that your Elasticsearch cluster remains performant and cost-effective.
