Elasticsearch is an incredibly powerful distributed search and analytics engine that allows organizations to quickly index, search, and analyze large volumes of data. However, achieving optimal performance and efficiency requires a deep understanding of how to best index and query your data. With the release of Elasticsearch 8.17, new features and improvements offer more flexibility and control, but understanding best practices is crucial to harnessing the full potential of the system.
In this blog post, we’ll explore some of the best practices for indexing and querying data in Elasticsearch 8.17. These practices will help ensure better performance, maintainability, and scalability as your data grows.
Best Practices for Indexing Data in Elasticsearch 8.17
Efficient indexing is the foundation of any Elasticsearch-based system. Proper indexing ensures that queries run fast, data is organized efficiently, and resources like memory and disk space are used optimally. Here are some best practices to follow when indexing data.
1. Define Proper Mappings
Elasticsearch uses mappings to define how documents and fields should be stored and indexed. If mappings are not defined correctly, Elasticsearch may use default mappings that may not be ideal for your data.
Best Practice:
Example:
PUT /my_index
{
“mappings”: {
“properties”: {
“title”: { “type”: “text” },
“category”: { “type”: “keyword” },
“price”: { “type”: “float” },
“created_at”: { “type”: “date” }
}
}
}
2. Use Custom Index Settings
Custom index settings allow you to control aspects like the number of shards and replicas for your index. These settings can have a big impact on the performance and scalability of your Elasticsearch cluster.
Best Practice:
Example:
PUT /my_index
{
“settings”: {
“number_of_shards”: 3,
“number_of_replicas”: 1,
“refresh_interval”: “30s”
}
}
3. Avoid Over-Indexing Unnecessary Fields
Indexing too many fields can lead to excessive disk space usage and slower indexing times. Avoid indexing fields that you don’t need to search, aggregate, or filter on.
Best Practice:
Example:
PUT /my_index
{
“mappings”: {
“properties”: {
“non_searchable_field”: { “type”: “text”, “index”: false },
“user_id”: { “type”: “keyword” }
}
}
}
4. Use Index Lifecycle Management (ILM)
As data grows over time, you need to manage the lifecycle of your indices efficiently. Index Lifecycle Management (ILM) automates the process of rolling over, deleting, or archiving older indices based on defined policies.
Best Practice:
Example:
PUT /_ilm/policy/my_policy
{
“policy”: {
“phases”: {
“hot”: {
“actions”: {
“rollover”: { “max_age”: “7d”, “max_docs”: 1000000 }
}
},
“delete”: {
“min_age”: “30d”,
“actions”: { “delete”: {} }
}
}
}
}
Best Practices for Querying Data in Elasticsearch 8.17
Efficient querying ensures that your search and analytics operations are fast and scalable. Below are some best practices for writing efficient queries in Elasticsearch 8.17.
1. Use Filters for Exact Matches
Elasticsearch uses filters for exact matches, and these filters are faster than full-text search queries because they don’t score documents.
Best Practice:
Example:
GET /my_index/_search
{
“query”: {
“bool”: {
“filter”: [
{ “term”: { “category”: “electronics” } },
{ “range”: { “price”: { “gte”: 100, “lte”: 500 } } }
]
}
}
}
2. Use doc_values for Sorting and Aggregations
When sorting or aggregating data, Elasticsearch needs to access field values. By default, text fields do not have optimized access patterns for sorting or aggregations, but doc_values allow Elasticsearch to store field values in a columnar format, making these operations more efficient.
Best Practice:
Example:
PUT /my_index
{
“mappings”: {
“properties”: {
“price”: { “type”: “float”, “doc_values”: true },
“created_at”: { “type”: “date”, “doc_values”: true }
}
}
}
3. Avoid Wildcard Queries on Large Datasets
Wildcard queries (e.g., * or ? in patterns) can be very slow, especially on large datasets. Elasticsearch needs to evaluate every possible match, which can be expensive for large indices.
Best Practice:
Example:
GET /my_index/_search
{
“query”: {
“wildcard”: {
“title”: “*smartphone*”
}
}
}
4. Use Aggregations for Data Analysis
Aggregations are a powerful feature in Elasticsearch for summarizing and analyzing data. When querying large datasets, aggregations help you get insights from the data without retrieving all documents.
Best Practice:
Example:
GET /my_index/_search
{
“size”: 0,
“aggs”: {
“category_count”: {
“terms”: { “field”: “category.keyword” }
}
}
}
Conclusion
Elasticsearch 8.17 offers a wealth of new features and improvements that make it even more powerful for managing large datasets and performing complex search queries. By following the best practices for indexing and querying data, you can ensure that your Elasticsearch cluster is optimized for speed, efficiency, and scalability.
Whether you’re working with full-text search, log data, or complex analytics use cases, adhering to these best practices will help you get the most out of Elasticsearch 8.17, providing faster response times, lower resource consumption, and a more reliable search experience for your users. Happy querying!