Query Types in Elasticsearch

All query types in elastic search

Full-Text Search Queries

Match Query: Searches text fields for matches on a given query string. It's the most common type and handles full-text search, including analyzing the query string.
Phrase Query: Looks for a specific sequence of terms in a specified order. Useful for searching exact phrases.
Query String Query: Supports a compact, expressive syntax for specifying complex search criteria, including boolean logic, wildcards, fuzzy matches, and more.
Multi-Match Query: Similar to the match query but allows for searching across multiple fields.
Simple Query String Query: A simpler, more robust version of the Query String Query.
More Like This Query: Finds documents that are "like" a given document or a specified text.

Non-Full-Text Search Queries

Term Query: Searches for the exact term in the field specified. It does not analyze the query string.
Range Query: Finds documents where the specified field falls within a specified range. Useful for numerical, date, and other range-based queries.
Boolean Query: Combines multiple queries using boolean logic. Includes must (AND), must_not (NOT), should (OR), and filter clauses. A logical query that combines other queries (which can be full-text or non-full-text).
Prefix Query: Finds documents containing terms that start with a specified prefix.
Wildcard Query: Supports searching with wildcard characters (* for zero or more characters, ? for a single character).
Fuzzy Query: Returns documents that contain terms similar to the search term, allowing for typos and misspellings.
Regexp Query: Enables searching with regular expressions.
Geo Queries: Used for geospatial search, such as finding documents within a certain distance from a geographic point.
Script Query: Allows custom scripts to specify custom query logic.
Nested Query: Used for searching nested objects (documents within documents).

Some query use examples

In a platform like Twitter, which involves searching through vast amounts of text-based data (tweets, user names, hashtags, etc.), various types of Elasticsearch queries would be employed to cater to different search functionalities. Here are some hypothetical examples of how Twitter might structure its queries:

Basic Text Search:
- Objective: A user wants to find tweets containing a specific word or phrase.
- Elasticsearch Query:
```
{
  "query": {
    "match": {
      "tweet_text": "Elasticsearch"
    }
  }
}
```
- Explanation: A match query is used to search the tweet_text field for the term "Elasticsearch."
Hashtag Search:
- Objective: Searching for tweets containing a specific hashtag.
- Elasticsearch Query:
```
{
  "query": {
    "term": {
      "hashtags": "#Elasticsearch"
    }
  }
}
```
- Explanation: A term query is ideal for exact matches, like searching for a specific hashtag.

Combining Text and User Search:

Objective: Finding tweets by a specific user containing certain words.

Elasticsearch Query:

{
  "query": {
    "bool": {
      "must": [
        { "match": { "tweet_text": "database" } },
        { "term": { "user_name": "tech_guru" } }
      ]
    }
  }
}

Explanation: A bool query combines a match query for text and a term query for the user's handle.

Searching with Date Ranges:

Objective: Finding tweets within a specific date range.

Elasticsearch Query:

{
  "query": {
    "range": {
      "post_date": {
        "gte": "2024-01-01",
        "lte": "2024-01-31"
      }
    }
  }
}

Explanation: A range query is used to find tweets posted in January 2024.

Complex Query for Trend Analysis:

Objective: Finding popular tweets about a topic within a date range.

Elasticsearch Query:

{
  "query": {
    "bool": {
      "must": [
        { "match": { "tweet_text": "new product" } },
        { "range": { "post_date": { "gte": "2024-03-01", "lte": "2024-03-18" } } }
      ],
      "filter": [
        { "term": { "is_retweet": false } },
        { "range": { "likes": { "gte": 100 } } }
      ]
    }
  }
}

Explanation: This uses a bool query to find original (non-retweeted) tweets about a "new product" from a specific date range that have received a certain number of likes, indicating popularity.

These examples demonstrate how Elasticsearch's diverse query capabilities can be utilized in a social media context like Twitter to perform efficient and accurate text searches, ranging from simple keyword searches to more complex queries involving multiple fields and criteria.