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How to Convert SQL Queries to Elasticsearch DSL

Learn how to translate complex SQL queries into `Elasticsearch DSL` for efficient querying and data retrieval. Discover step-by-step solutions that simplify your transition from SQL to Elasticsearch. --- This video is based on the question https://stackoverflow.com/q/71219536/ asked by the user 'a.hermes' ( https://stackoverflow.com/u/18277260/ ) and on the answer https://stackoverflow.com/a/71231319/ provided by the user 'ESCoder' ( https://stackoverflow.com/u/10348758/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions. Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: How convert needed SQL query to Elastic DSL Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l... The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license. If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com. --- How to Convert SQL Queries to Elasticsearch DSL In the world of data and analytics, transitioning from SQL databases to search engines like Elasticsearch can be challenging, especially for those accustomed to traditional SQL queries. If you’ve found yourself needing to convert a SQL query into Elasticsearch DSL (Domain Specific Language) but didn’t know where to begin, you’re not alone! In this post, we will explore how to efficiently convert a specific SQL query into its Elasticsearch counterpart. The Problem: Understanding the SQL Query Consider the following SQL query that retrieves records from an index called my-index based on specific conditions: [[See Video to Reveal this Text or Code Snippet]] Components of the SQL Query: Selecting Records: The SQL statement selects all fields from my-index. Conditions: It filters results where field1 and field2 match specific values. Text Search: It looks for a substring within any of the 19 fields in the index. This structure is straightforward for SQL, but how do we translate this into something that Elasticsearch can understand? The Solution: Creating an Elasticsearch Query To convert the SQL query to Elasticsearch DSL, we will use a combination of bool, terms, and multi_match queries. Below is the equivalent Elasticsearch query that achieves the same results: [[See Video to Reveal this Text or Code Snippet]] Breakdown of the Elasticsearch Query: Query Structure: The outermost object is the query, which contains a bool query allowing us to combine multiple conditions. Under bool, we use the must clause to ensure all specified conditions are met. Filtering Fields: Field1: The first terms query checks if field1 matches either value1 or value2. Field2: The second terms query checks if field2 matches any of the values in the list (valueX, valueY, valueZ). Text Matching: The multi_match query allows searching across multiple fields with the specified query text. This is crucial when dealing with text-based searches across multiple fields. Conclusion By converting SQL queries into Elasticsearch DSL, data retrieval can be vastly optimized, taking advantage of Elasticsearch's powerful search capabilities. This allows for better performance and scalability when working with large datasets. The provided structure helps you align your SQL logic with Elasticsearch’s querying mechanisms, paving the way for more efficient data operations. With this process, you'll be well-equipped to handle similar conversions in your data projects. Remember, practice makes perfect; the more you familiarize yourself with Elasticsearch DSL, the easier it will become to translate your SQL queries!