Planet MySQL HA Blog

This blog post explains the in-memory data cluster reloading improvements of MySQL HeatWave version 9.4.2

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An overview of Library Feature in the MySQL HeatWave managed cloud service and elsewhere.

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When Oracle announced the deprecation of MySQL Enterprise Monitor (MEM), many organizations running MySQL Enterprise Server suddenly faced a gap in their operations stack. MEM had long been part of the Enterprise subscription, providing monitoring and limited automation. Now, DBAs and DevOps teams must look elsewhere for visibility and control. The good news? Moving away […]

The post From MEM to Automation: Managing MySQL Enterprise with ClusterControl appeared first on Severalnines.

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Bridging natural language processing with semi-structured data brings both opportunity and complexity.

MySQL HeatWave GenAI’s NL2SQL feature shows how natural language can simplify data interaction — even for JSON documents. Yet, because JSON embeds both data and metadata within a single column, LLMs may struggle without explicit schema cues.

By creating well-structured views that reveal JSON’s internal organization, you can transform unstructured data into a relational format the model understands — improving both query accuracy and overall usability.

This approach…

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Discover how to tame big tables using LIMIT and OFFSET in MySQL! This post makes pagination easy for newcomers, using friendly library metaphors, step-by-step SQL examples, and practical tips to help you view your data one page at a time.

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This blog post introduces the MySQL Autopilot Indexing feature.

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We are excited to announce the release of Natural Language to SQL (NL2SQL) capability for MySQL (with the AI option) and MySQL HeatWave cloud service. Natural language interface is essential for modern data platforms, allowing users to explore information more quickly.

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This article showcases the power of combining the LangChain framework with an LLM hosted on MySQL HeatWave GenAI. Our goal is to create an agent that can answer the question, "What is the current weather in Fahrenheit?". To do this, the agent must first fetch the weather (which is in Celsius) and then convert it to Fahrenheit, a classic multi-step task. This agent will demonstrate how a Large Language Model (LLM) can use tools and a logical sequence of steps to solve a problem.

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This post will guide you through building a simple RAG workflow using the popular LangChain framework, powered entirely by the in-database AI capabilities of MySQL HeatWave GenAI. We will ingest a public blog post, index it in the HeatWave Vector Store, and use a HeatWave-hosted LLM to answer questions based on its content following the LangChain guide to creating vector stores and using them for RAG.

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Ready to grow your querying skills? This post introduces curious newcomers to the WHERE clause in MySQL, showing how simple conditions, clever operators, and cheerful library metaphors can help you filter data like a pro. Find the exact records you need using approachable SQL examples—no dusty catalogs required!

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