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Elasticsearch Multiple Indexes: Best Practices and Performance Optimization
Managing multiple indexes in Elasticsearch is a common requirement for many applications. This article will discuss best practices and performance optimization techniques when working with multiple indexes in Elasticsearch. We will cover topics such as index naming conventions, index aliasing, and index lifecycle management.
1. Index Naming Conventions
Adopting a consistent naming convention for your indexes is essential for easy management and maintenance. A good naming convention should include information about the index’s purpose, data type, and time frame. For example, you can use a pattern like “logs-appname-yyyy.MM.dd” for daily log indexes.
2. Index Aliasing
Index aliases are a powerful feature in Elasticsearch that allows you to create an alias for one or more indexes. This can simplify your application code and make it easier to switch between different indexes without changing the application code. Here’s how to create an alias:
POST /_aliases
{
"actions": [
{
"add": {
"index": "logs-appname-2022.01.01",
"alias": "logs-appname"
}
}
]
}Once you run the command above you’ll be able to access the documents indexed in the “logs-appname-2022.01.01” index by querying the “logs-appname” index. You could refactor your application to query the alias and then whenever needed you can change the alias so it points to a new daily index and in this case it wouldn’t be necessary to refactor your application, since it would be using the index alias.
Even better, you could create an alias that points to a collection of indices by using a wildcard (*):
POST /_aliases
{
"actions": [
{
"add": {
"index": "logs-appname-*",
"alias": "logs-appname"
}
}
]
}Now your application will have access to all existing logs indices and also the ones that will be created daily by simply using the alias “logs-appname”.
3. Use Index Templates
Index templates allow you to define settings, mappings, and aliases for new indexes that match a specified pattern. This can help ensure consistent configurations across multiple indexes. To create an index template, use the following API call:
PUT /_index_template/template_name
{
"index_patterns": ["logs-appname-*"],
"template": {
"settings": {
"number_of_shards": 3,
"number_of_replicas": 1
},
"mappings": {
"properties": {
"timestamp": {
"type": "date"
},
"message": {
"type": "text"
}
}
},
"aliases": {
"logs-appname": {}
}
}
}The index pattern we just created will be applied to all indices created with a name that matches the “logs-appname-*” pattern. Those indices will have the settings, mappings and aliases defined in the index pattern automatically applied to them.
4. Index Lifecycle Management (ILM)
ILM is a feature in Elasticsearch that allows you to automate the management of your indexes throughout their lifecycle, including rollover, shrink, force merge, and delete actions. This can help optimize storage and performance for your indexes. To create an ILM policy, use the following API call:
PUT /_ilm/policy/policy_name
{
"policy": {
"phases": {
"hot": {
"actions": {
"rollover": {
"max_size": "50gb",
"max_age": "30d"
}
}
},
"delete": {
"min_age": "90d",
"actions": {
"delete": {}
}
}
}
}
}The ILM policy created above executes the defined actions for each phase. For example, after the indices will be deleted after 90. With the command above we merely create a policy, in order to have it applied to your indices you will have to reference it in the “index.lifecycle.name” settings parameter. This is typically done in an index template, so it gets automatically applied to every new index created with the name pattern that matches the one defined in the index template.
5. Optimize Query Performance
When querying multiple indexes, it’s crucial to optimize your queries for better performance. Some tips for query optimization include:
- Use filter context for non-scoring queries to improve caching and performance.
- Limit the use of wildcard queries and regular expressions, as they can be resource-intensive.
- Use the “_source” parameter to return only the fields you need in the search results.
- Use the “_search_shards” API to identify the shards involved in a query and optimize your index settings accordingly.
6. Monitor and Maintain Index Health
Regularly monitoring your indexes’ health and performance is essential to ensure optimal operation. Some key metrics to monitor include:
- Index size and document count
- Query latency and throughput
- Shard allocation and rebalancing
- Disk usage and I/O
Use the Elasticsearch APIs, such as _cat/indices, _cat/shards, and _cat/allocation, to gather these metrics and make informed decisions about index management.
Conclusion
Managing multiple indexes in Elasticsearch requires careful planning and optimization. By following best practices such as consistent naming conventions, using index aliases and templates, implementing ILM policies, optimizing query performance, and monitoring index health, you can ensure efficient and effective management of your Elasticsearch indexes.
If you want to learn more about index templates take a look at this guide available on Opster’s website. You can also read more on aliases.
