Redis Introduces SQL Querying Capability via New Library
Redis has announced the release of new tools enabling developers to query Redis databases using standard SQL syntax, eliminating the need for Large Language Models (LLMs) for translation. Recognizing that data science teams are fluent in SQL, Redis introduced the 'sql-redis' library on PyPi and integrated the 'SQLQuery' class into its 'redisvl' Python client. This update allows users to leverage the speed and flexibility of Redis while utilizing familiar SQL commands. The underlying technology uses the 'sqlglot' parser to deterministically convert SQL statements into an Abstract Syntax Tree (AST), which is then validated against the index schema to generate appropriate Redis FT.SEARCH or FT.AGGREGATE commands. The system supports complex operations including aggregations, full-text search, geo queries, date functions, and asynchronous execution. With cached schema metadata, the translation overhead remains minimal, typically in the low milliseconds. This development aims to bridge the gap between traditional relational database querying habits and the high-performance NoSQL capabilities of Redis, making it more accessible for data scientists and AI agents.
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Redis Introduces SQL Querying Capability via New Library
Redis has announced the release of new tools enabling developers to query Redis databases using standard SQL syntax, eliminating the need for Large Language Models (LLMs) for translation. Recognizing that data science teams are fluent in SQL, Redis introduced the 'sql-redis' library on PyPi and integrated the 'SQLQuery' class into its 'redisvl' Python client. This update allows users to leverage the speed and flexibility of Redis while utilizing familiar SQL commands. The underlying technology uses the 'sqlglot' parser to deterministically convert SQL statements into an Abstract Syntax Tree (AST), which is then validated against the index schema to generate appropriate Redis FT.SEARCH or FT.AGGREGATE commands. The system supports complex operations including aggregations, full-text search, geo queries, date functions, and asynchronous execution. With cached schema metadata, the translation overhead remains minimal, typically in the low milliseconds. This development aims to bridge the gap between traditional relational database querying habits and the high-performance NoSQL capabilities of Redis, making it more accessible for data scientists and AI agents.
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