Elasticsearch Understanding Elasticsearch Slop and Its Usage

By Opster Team

Updated: Nov 14, 2023

| 3 min read


What is slop in Elasticsearch?

Elasticsearch slop is a parameter used in phrase queries to allow for a certain degree of flexibility in matching documents. It is particularly useful when searching for phrases where the exact order of terms might not be crucial, or when dealing with user-generated content where typos and variations in word order are common.

In this article, we will explore the concept of slop, its usage, and how to optimize its value for better search results.

How Elasticsearch Slop Works

In a phrase query, Elasticsearch looks for documents containing the exact sequence of terms specified in the query. However, this can be too restrictive in some cases, as it might not account for slight variations in the order of terms or the presence of additional terms between the queried terms. This is where the slop parameter comes into play.

The slop parameter allows you to specify the number of positions by which the terms in the query can be transposed to match a document. A slop value of 0 (default) means that the terms must appear in the exact order specified in the query. A higher slop value allows for more flexibility in the order of terms and the presence of additional terms between the queried terms.

For example, consider the following documents:

  1. “The quick brown fox jumps over the lazy dog.”
  2. “The quick brown dog and fox jumps over the lazy cat.”
  3. “The quick brown dog jumps over the lazy fox.”

If we perform a phrase query for “quick brown fox” with a slop of 0, only the first document will be returned as a match. However, if we increase the slop value to 2, the second document will also be considered a match, as the terms “quick,” “brown,” and “fox” can be transposed by two positions to match the document. Similarly, with a slop value of 5, the third document will also be considered a match.

Using Slop in Elasticsearch Queries

To use the slop parameter in Elasticsearch, you need to include it in a `match_phrase` query. Here’s an example of how to do this using the Elasticsearch Query DSL:

  "query": {
    "match_phrase": {
      "field_name": {
        "query": "quick brown fox",
        "slop": 2

In this example, we are searching for the phrase “quick brown fox” in the field “field_name” with a slop value of 2. This will return documents that contain the terms “quick,” “brown,” and “fox” in close proximity, allowing for up to two transpositions or additional terms between the queried terms.

Optimizing Slop Value for Better Search Results

Choosing the right slop value is crucial for achieving a balance between precision and recall in your search results. A low slop value might result in too few matches, as it requires the terms to appear in a very specific order. On the other hand, a high slop value might return too many irrelevant matches, as it allows for more flexibility in the order of terms and the presence of additional terms between the queried terms.

To optimize the slop value for your use case, consider the following factors:

  1. Nature of the content: If the content you are searching is well-structured and has a consistent order of terms, a lower slop value might be sufficient. However, if the content is user-generated or has a high degree of variation in word order, a higher slop value might be necessary.
  2. User expectations: Consider the expectations of your users when searching for phrases. If they expect to find exact matches, a lower slop value might be appropriate. However, if they are more interested in finding relevant content regardless of the exact order of terms, a higher slop value might be more suitable.
  3. Performance: Higher slop values can result in slower query performance, as Elasticsearch needs to evaluate more potential matches. Therefore, it’s essential to strike a balance between search quality and performance when choosing a slop value.


In conclusion, Elasticsearch slop is a powerful parameter that allows you to fine-tune phrase queries for better search results. By understanding how slop works and optimizing its value based on your content and user expectations, you can improve the relevance and precision of your search results while maintaining acceptable query performance.