Elasticsearch Elasticsearch Filter Aggregation: Advanced Usage and Optimization Techniques

By Opster Team

Updated: Jul 23, 2023

| 2 min read

Introduction

Filter aggregation is a powerful technique in Elasticsearch that allows you to narrow down the scope of your aggregations by applying specific filters to the data. This can be particularly useful when you want to analyze a subset of your data or when you need to perform complex aggregations based on multiple criteria. In this article, we will explore advanced usage and optimization techniques for Elasticsearch filter aggregation. If you want to learn about how to improve your Elasticsearch aggregation performance, check out this guide.  

1. Combining Multiple Filters

In some cases, you may want to apply multiple filters to your aggregation. You can achieve this by using the `bool` filter, which allows you to combine multiple filters using `filter`, `must`, `should`, and `must_not` clauses. Here’s an example:

GET /_search
{
  "aggs": {
    "filtered_aggregation": {
      "filter": {
        "bool": {
          "filter": [
            {"term": {"field1": "value1"}},
            {"range": {"field2": {"gte": 10, "lte": 100}}}
          ],
          "must_not": [
            {"term": {"field3": "value3"}}
          ]
        }
      },
      "aggs": {
        "my_aggregation": {
          "terms": {"field": "field4"}
        }
      }
    }
  }
}

In this example, the filter aggregation will only include documents that match both the `term` and `range` filters in the `filter` clause and do not match the `term` filter in the `must_not` clause.

2. Nested Filter Aggregations

You can also nest filter aggregations to create more complex queries. This can be useful when you want to apply different filters to different levels of your aggregation hierarchy. Here’s an example:

GET /_search
{
  "aggs": {
    "level1_aggregation": {
      "terms": {"field": "field1"},
      "aggs": {
        "level2_filtered_aggregation": {
          "filter": {"term": {"field2": "value2"}},
          "aggs": {
            "level2_aggregation": {
              "terms": {"field": "field3"}
            }
          }
        }
      }
    }
  }
}

In this example, the `level2_filtered_aggregation` is nested within the `level1_aggregation`. This means that the filter will only be applied to the documents that are already included in the `level1_aggregation`.

3. Optimizing Filter Aggregations

Filter aggregations can sometimes be resource-intensive, especially when dealing with large datasets. Here are some optimization techniques to improve the performance of your filter aggregations:

• Use the `filter` context: When using filters within aggregations, make sure to use the `filter` context instead of the `query` context. This can improve performance by skipping the scoring phase, as filters do not require scores.

• Use the `post_filter` parameter: If you want to apply a filter to the search results after the aggregations have been calculated, you can use the `post_filter` parameter. This can improve performance by reducing the number of documents that need to be filtered.

• Cache filter results: If you have filters that are used frequently and do not change often, consider caching the filter results using the `request_cache=true` query string parameter. This can improve performance by reusing the cached filter results for subsequent requests. This is usually not necessary as the shard request cache is enabled by default when creating new indexes.

Here’s an example that demonstrates some of these optimization techniques:

GET /_search?request_cache=true
{
  "size": 0,
  "aggs": {
    "filtered_aggregation": {
      "filter": {
        "bool": {
          "must": [
            {"term": {"field1": "value1"}},
            {"range": {"field2": {"gte": 10, "lte": 100}}}
          ]
        }
      },
      "aggs": {
        "my_aggregation": {
          "terms": {"field": "field3", "size": 10}
        }
      }
    }
  },
  "post_filter": {
    "term": {"field4": "value4"}
  }}

In this example, we use the `filter` context, the `post_filter` parameter, and the `request_cache` query string parameter to optimize the performance of the filter aggregation.

Conclusion

In conclusion, Elasticsearch filter aggregation is a powerful technique that allows you to narrow down the scope of your aggregations and perform complex queries. By combining multiple filters, nesting filter aggregations, and applying optimization techniques, you can create efficient and flexible aggregations that meet your specific requirements.