Optimizing Sort by Elasticsearch for Faster Performance

To optimize sort by Elasticsearch, it's essential to understand how Elasticsearch handles sorting. Elasticsearch uses a technique called "sort aggregation" to sort search results.

The sort aggregation is a costly operation, which can significantly impact performance. This is because Elasticsearch needs to re-sort the results after filtering, which can lead to slower query performance.

A key factor in optimizing sort performance is the use of the "sort" parameter. This parameter allows you to specify the field to sort on and the order of the sort. By using the "sort" parameter, you can avoid the need for a separate sort aggregation.

Using the "sort" parameter can lead to significant performance improvements, especially for large datasets. For example, if you're sorting a field with a high cardinality, using the "sort" parameter can reduce the number of documents that need to be sorted.

Broaden your view: Elasticsearch Performance

Elasticsearch queries can be optimized with various techniques to improve performance.

Index sorting can greatly speed up sort queries on a particular field, making them super fast. This is achieved by sorting the whole index in that field, which allows Elasticsearch to simply take the first K documents and skip the rest.

A distance_feature query can be used to optimize numeric sort queries, resulting in great speedups of up to 35x gains. This is possible by substituting sort with a distance_feature query, which returns top K documents closest to a given origin.

Sorting with search_after is also a common use case, and Elasticsearch has optimized this by making comparators and collectors within Lucene skip non-competitive documents, leading to speedups of up to 10x. This was introduced in Elasticsearch 7.16.

Here's an interesting read: Elasticsearch Index Api

We've found a clever way to speed up sort queries on numeric or date fields by using a distance_feature query. This query returns top K documents closest to a given origin.

By using the minimum value for the field as the origin, we get top K documents sorted by ascending order, and using the maximum value as the origin yields top K documents in the descending order.

The distance_feature query efficiently skips non-competitive blocks of documents by relying on the properties of BKD trees used in Elasticsearch for indexing numeric and date fields.

A BKD index is divided into cells, each knowing its min and max values, allowing the distance_feature query to skip non-competitive cells of documents. This optimization works only if a numeric or date field is both indexed and has doc values.

We achieved great speedups, up to 35x gains on some datasets, by substituting sorting on doc values with a distance_feature query. This optimization was introduced in Elasticsearch 7.6 for date and long fields.

Related reading: Elasticsearch Date Range Query

Index sorting is a great solution for speeding up sort queries, but it can only be done in a single way. This means it's not useful for sort queries that use different sort criteria, such as descending vs. ascending, different fields, or different combinations.

To speed up sort queries on numeric or date fields, you can substitute sort with a distance_feature query. This query returns top K documents closest to a given origin, making it efficient for skipping non-competitive blocks of documents.

The distance_feature query relies on the properties of BKD trees, which are used in Elasticsearch for indexing numeric and date fields. This allows it to efficiently skip non-competitive cells of documents.

By substituting sorting on doc values with a distance_feature query, you can achieve great speedups, with some datasets showing up to 35x gains.

Search_after is another common use case for sort queries, but it required a different solution. Instead of rewriting sort queries in Elasticsearch, the comparators and collectors within Lucene were modified to skip non-competitive documents.

This solution was introduced in Elasticsearch 7.16 and immediately showed great speedups, with some nightly performance benchmarks seeing up to 10x gains.

Sorting across multiple segments can also be optimized by starting processing with the segment that is the best candidate for containing documents with top K values. This is especially useful for time series indices, where the latest events are the most interesting.

To achieve this, segments can be sorted on the timestamp field descending, allowing for faster skipping of other segments that only contain worse values.

In addition, a new merge policy was introduced to ensure that merged segments have a balanced arrangement of documents, making it easier to find the most recent documents.

Finally, sorting across multiple shards can be optimized by pre-sorting shards based on the max/min value of the primary sort field. This allows for a shortcut in the query phase, skipping shards that should not contain competitive hits.

You might like: Elasticsearch Shard

Using keyword fields can be a game-changer for sorting search results. You can define a field as a keyword type in your index mapping, which allows it to maintain the original order of terms.

Keyword fields are not analyzed, making them more suitable for sorting purposes. This is because analyzed fields are broken down into individual terms, losing their original order in the process.

See what others are reading: Elasticsearch Fields

To use keyword fields for sorting, you can define a field as a keyword type in your index mapping. For example, you can sort your search results by the “title” field.

Keyword fields are particularly useful for sorting search results by text fields. They provide a more accurate and reliable way to sort results, especially when compared to analyzed fields.

Elasticsearch has several techniques to optimize sort queries, including index sorting, which can speed up sort queries by up to 35x on some datasets.

Index sorting is a great solution, but it's only useful for sorting by a single field in a single way. If you need to sort by different fields or criteria, other approaches are needed.

One such approach is using the distance_feature query, which can efficiently skip non-competitive blocks of documents and achieve great speedups, even up to 35x gains.

This query works by examining cells' min and max values in the BKD tree, which is used for indexing numeric and date fields, and skipping cells that are not competitive.

Broaden your view: Elasticsearch Index Format

Sorting by text fields in Elasticsearch can be problematic because text fields are analyzed, meaning they are broken down into individual tokens. This process makes it difficult to sort the data in a meaningful way, as the tokens are not stored in their original order.

Sorting by text fields can be resource-intensive, leading to slower query performance. In fact, this can be a major challenge, especially when dealing with large datasets.

The analyzed nature of text fields is a significant hurdle in sorting data. It's not just a matter of rearranging the tokens, but also understanding the context in which they were analyzed.

Sorting by text fields can be a major bottleneck in Elasticsearch queries. It's essential to find ways to optimize this process to improve query efficiency.

Readers also liked: Elasticsearch Search Text

You can use multi-fields to index a single field in multiple ways, allowing for more efficient sorting and searching. This is particularly useful when you want to perform a full-text search on one aspect of a field and sort the results based on another aspect.

Additional reading: Elastic Search by Field

For instance, you can index a field as both a text and keyword type, enabling you to perform a full-text search on the "title" field and sort the results using the "title.keyword" field.

Using multi-fields can significantly improve query efficiency by providing more flexibility in how you index and search your data.

Expand your knowledge: Elasticsearch _template

Implementing Custom Analyzers for Performance can greatly improve the efficiency of your queries. By creating a custom analyzer, you can tailor your search results to fit your specific needs.

For example, if you want to sort text fields in a case-insensitive manner, you can use the "lowercase" token filter. This allows you to sort your search results using a field like "title.lowercase", which will ignore case differences.

Custom analyzers can also help you achieve better sorting performance by allowing you to create a field that is optimized for sorting. This can be especially useful when you need to sort large datasets.

Sorting on relevancy score is the default behavior in Elasticsearch, and it's done in descending order by default if no sorting is specified in the query.

The relevancy score is stored in the _score field, and you can reverse the order by adding the _score field to specify the order.

To sort on a non-scoring document field, you can simply add the field to the query, as shown in the example where the data is sorted based on rating from highest to lowest.

Elasticsearch doesn't compute the score when you use a custom field for sorting, but you can ask it to do so by using the track_scores Boolean field.

Relevant search results are often determined by the search algorithm's ability to understand the intent behind a search query. This is why it's essential to use specific keywords that accurately reflect what you're looking for.

The search algorithm considers various factors, including the user's search history, location, and device type, to provide personalized results. This is why you might see different search results on your phone compared to your computer.

A study found that 75% of users don't click past the first page of search results, making it crucial to optimize your content for search engines. This is why meta descriptions and keywords are so important.

The average user spends only 10 seconds on a search engine results page (SERP) before clicking on a result, highlighting the need for quick and relevant information. This is why concise and informative content is vital.

A well-structured search results page can significantly impact user experience and search engine rankings. This is why it's essential to prioritize user experience and mobile-friendliness.

The relevancy score is a crucial factor in search results, and Elasticsearch makes it easy to sort documents based on it. By default, documents are sorted in descending order by score if no sorting is specified in the query.

To reverse the order and get the lower scored documents at the top, you simply need to add the _score field to specify the order. This is equivalent to issuing the sort block in a query.

On a similar theme: Elasticsearch Updating Documents

You can also sort on non-scoring document fields, like rating, by simply adding the field to the query. For example, sorting on rating from highest to lowest in descending order can be done by adding the rating field to the query.

If you sort on a non-scoring field, the score is set to null in the results. However, you can ask Elasticsearch to compute the score even if you move away from sorting on _score by using the track_scores Boolean field.

Sorting on multiple fields is also possible, and the sort order is important. For example, sorting on the rating and release_date fields will first sort the results in ascending order on the rating field, and then break the tie by sorting by release_date in ascending order.

Sorted queries can be slow, especially on big indices, because Elasticsearch has to examine and compare doc values for all documents matched by the filter.

Index sorting is a great solution to optimize sort queries on a particular field, as it allows Elasticsearch to simply take the first K documents without examining the rest.

Optimizing sort queries with search_after is also crucial, as it enables Elasticsearch to skip non-competitive documents and speed up the query process.

The introduction of sort optimization with search_after parameter in Elasticsearch 7.16 resulted in great speedups, up to 10x, on some nightly performance benchmarks.

To take advantage of these sort optimizations, you should disable track_total_hits or set it to a lesser number, as this will allow Elasticsearch to start sort optimization earlier and provide faster responses.

Recently, Kibana also started to send requests with track_total_hits disabled, which should make sort queries in Kibana faster as well.

Expand your knowledge: Kibana Elasticsearch

Elasticsearch's distributed search capabilities make it a powerful tool, but optimizing performance requires considering the distributed aspect. Searches can span hundreds of shards, especially when dealing with time series indices.

Pre-sorting shards based on the max/min value of the primary sort field can significantly improve performance. This approach allows Elasticsearch to start a distributed search with a set of shards that are the best candidates for containing top values.

Starting from Elasticsearch 7.6, shards are pre-sorted based on the max/min value of the primary sort field. This optimization enables Elasticsearch to start a distributed search with a set of shards that are the best candidates for containing top values.

Shard skipping can be particularly efficient when dealing with machine-generated time series indices. These indices often follow an index lifecycle policy, starting in performance-optimized hardware and ending in cost-optimized hardware before they are deleted.

In many cases, the shards on slower and more economic hardware can be skipped, making the use of searchable snapshots particularly efficient. This means users can send broad queries and enjoy query performance defined by their performance-optimized hardware.

You might like: Elasticsearch Indices

Sorted queries can be super fast if the index is sorted by a particular field, allowing you to simply take the first K documents without examining the rest.

This is achieved through index sorting, which sorts the whole index in a specific field, making it efficient to retrieve top K documents sorted by that field.

Index sorting is not useful for sort queries that use different sort criteria, such as descending vs. ascending, different fields, or different combinations from the ones defined in an index sorting definition.

Sorting with a search_after parameter is very common, and Elasticsearch 7.16 introduced a sort optimization that allows for great speedups, up to 10x on some nightly performance benchmarks.

This optimization skips non-competitive documents, allowing for faster sorting with search_after.

Sorting across multiple segments can be optimized by starting processing with the segment that is the best candidate for containing documents with top K values.

For time series indices, sorting segments on the @timestamp field descending can result in good speedups for sort queries on the timestamp field descending.

To take advantage of sort optimizations in Elasticsearch, you don't need to track the exact number of total hits for a request and the request shouldn't contain aggregations.

The default value of track_total_hits is set to 10,000, which means the sort optimization only starts to kick in once you collect 10,000 documents. This can lead to faster responses for you.

If you set track_total_hits to a lesser number or to "false", Elasticsearch starts sort optimization much earlier, resulting in even faster responses.