<p>log/slog, Zap, Zerolog, Logrus, Apex/log, Go-kit Log, Phuslu/log, Charmbracelet/log, Google/logger, and Gookit/slog are the 10 best Golang logging libraries in 2026.<br></p>

Top 10 Best Golang Logging Libraries for Developers in 2026

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10 Best Logging Libraries for Golang Developers in 2026

<p>Virtuoso, Testim, Mabl, Functionize, AcceIQ, testRigor, LambdaTest AI Agents, Testsigma, Diffblue, etc, are leading AI agents in software testing recently.</p>

Top 12 Leading AI Agents in Software Testing For Businesses

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Top 12 Best AI Agents in Software Testing For Businesses

<p dir="ltr" style="text-align: left;"><a href="https://reliasoftware.com/blog/what-is-agentic-ai">AI agent</a> is a system that can observe what is happening, make decisions, and take actions on its own instead of following fixed instructions. Because of this, AI agents work well in software testing, where applications change often and need flexible, adaptive testing. They help developers and businesses test faster, cover more real user scenarios, and reduce the time spent writing and maintaining test scripts.</p>
<p dir="ltr" style="text-align: left;">In this article, you&rsquo;ll learn how AI agents in software testing work, the roles they play in modern QA, and best practices for using them effectively. Reading this guide will help you understand when AI agents make sense for your <a href="https://reliasoftware.com/blog/software-testing-life-cycle">software testing process</a> and how they can improve software quality and delivery speed.</p>
<p dir="ltr" style="text-align: left;"><em>&gt;&gt; Read more:&nbsp;</em></p>
<ul>
<li dir="ltr" style="font-style: italic;" role="presentation"><em>Top 6 Open-source <a href="https://reliasoftware.com/blog/ai-agent-frameworks">AI Agent Frameworks</a></em></li>
<li dir="ltr" style="font-style: italic;" role="presentation"><em>7 Best <a href="https://reliasoftware.com/blog/golang-ai-agent-frameworks">Golang AI Agent Frameworks</a> You Should Know</em></li>
</ul>
<h2 dir="ltr" style="text-align: left;">What are AI Agents in Software Testing?</h2>
<p dir="ltr" style="text-align: left;">AI agents in software testing are systems that can automatically determine what to test and execute those tests within an application or software without fixed testing scripts. They act as real testers who analyze the app, create and run test cases, and improve their actions based on past results. They often use Natural Language Processing (NLP) and generative AI to understand text, user flows, and create new test scenarios.</p>
<p dir="ltr" style="text-align: left;"><a href="https://reliasoftware.com/blog/top-automation-testing-tools">Traditional automation testing tools</a> depend on fixed scripts that often break when the app changes, such as when a button moves or a field name is updated. Meanwhile, AI agents use patterns, text, and visual clues to understand what they are testing, so they can still continue their work if the layout changes. This helps QA teams test faster, discover new issues, and keep up with apps that change frequently.</p>
<p dir="ltr" style="text-align: left;"><em>&gt;&gt; Read more: What is <a href="https://reliasoftware.com/blog/automation-testing-functions-tools-cost-and-benefits">Automation Testing</a> in Software Testing?</em></p>
<h2 dir="ltr" style="text-align: left;">Roles of AI Agent in Software Testing</h2>
<h3 dir="ltr" style="text-align: left;">Automated <a href="https://reliasoftware.com/blog/test-case-design-techniques">Test Case Generation</a></h3>
<p dir="ltr" style="text-align: left;">AI agents can create test cases by using the app like a real user. They click through screens, fill in forms, and try different actions to see how the system works, then turn those actions into test cases. This helps QA teams cover more user flows and edge cases without writing every test manually, and also keeps tests easier to update when the app changes.</p>
<h3 dir="ltr" style="text-align: left;">Intelligent Test Execution</h3>
<p dir="ltr" style="text-align: left;">AI agents execute tests more smartly and efficiently than traditional automation tools. They can run tests 24/7, in parallel, and at any time without manual triggers. Based on recent code changes, past failures, or risk levels, the agent can decide which tests should run first to ensure critical areas are validated early.</p>
<p dir="ltr" style="text-align: left;">This approach helps QA teams shorten test cycles and get faster feedback from each build. Instead of running every test blindly, AI agents focus execution on the most important scenarios, improving reliability while saving time and resources.</p>
<h3 dir="ltr" style="text-align: left;">Adaptive Test Scripts</h3>
<p dir="ltr" style="text-align: left;">Adaptive test scripts, also known as self-healing test scripts, allow AI agents to keep tests working even when the application changes. When a button moves, a field is renamed, or the UI structure is updated, the agent can detect these changes using visual and text clues and automatically adjust the test to match the new structure.&nbsp;</p>
<p dir="ltr" style="text-align: left;">Unlike traditional scripts that break easily, self-healing scripts reduce maintenance effort in fast-moving, agile environments. This helps QA teams spend less time fixing broken tests and more time focusing on improving overall software quality.</p>
<p dir="ltr" style="text-align: left;"><em>&gt;&gt; Read more: How to Write A Powerful <a href="https://reliasoftware.com/blog/test-plan-in-software-testing">Test Plan in Software Testing</a>?</em></p>
<h3 dir="ltr" style="text-align: left;">Shift-Left Testing</h3>
<p dir="ltr" style="text-align: left;">Shift-left testing is a <a href="https://reliasoftware.com/blog/levels-of-testing-in-software-testing">testing approach</a> where quality checks are done earlier in <a href="https://reliasoftware.com/blog/software-development-life-cycle-sdlc">the software development process</a> instead of waiting until the end. By getting involved from the start, AI agents can review APIs, requirements, or early builds to create and run tests before the full user interface is ready. This helps teams find problems sooner, reduce rework, and keep the product more stable as it grows.</p>
<p dir="ltr" style="text-align: left;"><em>&gt;&gt; Read more:</em></p>
<ul>
<li dir="ltr" role="presentation"><em>What is <a href="https://reliasoftware.com/blog/what-is-unit-testing">Unit Testing</a>? Definition, Benefits &amp; Process</em></li>
<li dir="ltr" role="presentation"><em>What is&nbsp;<a href="https://reliasoftware.com/blog/integration-testing">Integration Testing</a>? Example, Approaches, Process</em></li>
<li><em>Mastering <a href="https://reliasoftware.com/blog/functional-testing">Functional Testing</a> for Delivering Bug-Free Software</em></li>
</ul>
<h3 dir="ltr" style="text-align: left;">Visual Testing</h3>
<p dir="ltr" style="text-align: left;">Visual testing checks how an application looks on the screen to make sure the layout, text, and buttons appear correctly. Instead of comparing pixels, AI agents review the screen in a more human way, allowing them to spot issues such as missing content or broken layouts even when small design changes occur.</p>
<p dir="ltr" style="text-align: left;">AI agents can also compare screenshots across different devices, screen sizes, and resolutions to ensure a consistent user experience. This helps QA teams quickly detect UI differences and visual problems without being distracted by minor or expected design updates.</p>
<h3 dir="ltr" style="text-align: left;">Self-Learning and Predictive Analysis</h3>
<p dir="ltr" style="text-align: left;">AI agents improve as they run more tests and collect more results. They learn which parts of the app tend to break and which ones usually work fine, then use that knowledge to decide what to test next. Based on past results, they can focus on risky areas, remove redundant tests, and spot problems earlier before they affect users.</p>
<h3 dir="ltr" style="text-align: left;">Supporting Performance &amp; <a href="https://reliasoftware.com/blog/security-testing-in-software-testing">Security Testing</a></h3>
<p dir="ltr" style="text-align: left;">AI agents can support performance and security testing by running tests under heavy load and risky conditions. They generate different types of traffic, try unusual inputs, and monitor how the application responds. This helps teams find performance issues and security weaknesses that normal tests might not catch.</p>
<p dir="ltr" style="text-align: left;"><em>&gt;&gt; Read more:&nbsp;</em></p>
<ul>
<li dir="ltr" style="font-style: italic;" role="presentation"><em>Top 7 <a href="https://reliasoftware.com/blog/web-app-security-testing-tools">Web App Security Testing Tools</a> For Developers</em></li>
<li dir="ltr" style="font-style: italic;" role="presentation"><em>7 Leading <a href="https://reliasoftware.com/blog/performance-testing-tools">Performance Testing Tools</a> for Developers</em></li>
</ul>
<figure class="image align-center"><img title="Roles of AI Agent in Software Testing" src="https://api.reliasoftware.com/uploads/roles_of_ai_agents_in_software_testing_7bd5b6ebe1.webp" alt="roles of ai agents in software testing" width="1000" height="600">
<figcaption>Roles of AI Agent in Software Testing</figcaption>
</figure>
<h2 dir="ltr" style="text-align: left;">Types of AI Agents in Software Testing</h2>
<h3 dir="ltr" style="text-align: left;">Simple Reflex Agents</h3>
<p dir="ltr" style="text-align: left;">Simple reflex agents act based on fixed rules by checking what is happening on the screen and responding with a set action, such as confirming a button is visible or an error message appears. These agents are useful for basic checks, but they do not remember past actions or learn from experience.</p>
<h3 dir="ltr" style="text-align: left;">Model-Based Reflex Agents</h3>
<p dir="ltr" style="text-align: left;">Model-based reflex agents keep track of both current inputs and past experiences, allowing them to handle more complex scenarios. They use this information to understand how pages and actions are connected, which helps them test complete user journeys such as moving from login to checkout.</p>
<h3 dir="ltr" style="text-align: left;">Goal-Based Agents</h3>
<p dir="ltr" style="text-align: left;">Goal-based agents work toward a specific target, such as completing a signup, placing an order, or reaching a certain page. They choose actions based on what will move them closer to that goal, which makes them useful for testing full user flows and making sure important tasks in the app work from start to finish.</p>
<h3 dir="ltr" style="text-align: left;">Utility-Based Agents</h3>
<p dir="ltr" style="text-align: left;">Utility-based agents choose actions based on how useful each option is. They compare different paths in the application and pick the one that gives the best result, such as higher test coverage or lower risk. This helps them focus on the most valuable areas to test instead of just following one fixed route.</p>
<h3 dir="ltr" style="text-align: left;">Learning Agents</h3>
<p dir="ltr" style="text-align: left;">Learning agents are AI agents that improve by learning from past tests. They track what fails and what stays stable, then adjust how they test based on that experience. Over time, this helps them make better testing decisions and work more effectively.</p>
<figure class="image align-center"><img title="Types of AI Agents in Software Testing" src="https://api.reliasoftware.com/uploads/types_of_ai_agents_in_software_testing_a85f664df6.webp" alt="types of ai agents in software testing" width="1000" height="600">
<figcaption>Types of AI Agents in Software Testing</figcaption>
</figure>
<h2 dir="ltr" style="text-align: left;">Advantages Of AI Agents in Software Testing</h2>
<ul>
<li dir="ltr" role="presentation"><strong>Faster release cycles</strong>: AI agents automate and adapt test execution, helping teams shorten testing phases and deliver software more quickly.</li>
<li dir="ltr" role="presentation"><strong>Broader coverage</strong>: It can explore a wider range of scenarios and edge cases that human testers might miss.</li>
<li dir="ltr" role="presentation"><strong>Improved accuracy</strong>: Consistently follow testing rules and analyze results carefully, helping reduce human errors and false test failures.</li>
<li dir="ltr" role="presentation"><strong>Lower costs</strong>: Automating repetitive tasks and finding bugs earlier in the process leads to significant cost savings.</li>
<li dir="ltr" role="presentation"><strong>Better use of QA resources</strong>: AI agents allow QA teams avoid wasted time and effort on repetitive work and fixing tests, and more time on quality planning and confident releases.</li>
</ul>
<h2 dir="ltr" style="text-align: left;">Best Practices for Implementing AI Agents in Software Testing</h2>
<p dir="ltr" style="text-align: left;"><strong>Start Small First</strong></p>
<p dir="ltr" style="text-align: left;">You should start by using AI agents on a small set of tests or in test environments instead of applying them to the whole app at once. This helps teams see how the agents work, manage risks, and avoid issues in production. Once the results are reliable, the agents can be expanded to more parts of the application.</p>
<p dir="ltr" style="text-align: left;"><strong>Prioritize Data Quality</strong></p>
<p dir="ltr" style="text-align: left;">AI agents depend on data to work, so data quality is very important. Teams should ensure to use clean, realistic test data that reflects real user behavior and common scenariosto train the agents effectively from day one. Good data helps AI agents make better testing decisions, find real issues, and produce more reliable results from the start.</p>
<p dir="ltr" style="text-align: left;"><strong>Set Clear Boundaries and Rules</strong></p>
<p dir="ltr" style="text-align: left;">Teams should clearly specify which parts of the application the AI agent can access, what actions it is allowed to perform, and when it should stop testing. These boundaries help keep testing safe and aligned with business goals.</p>
<p dir="ltr" style="text-align: left;"><strong>Monitor Agent Behavior</strong></p>
<p dir="ltr" style="text-align: left;">It is important for teams to monitor both test results and how the AI agent behaves during testing, including its actions and decisions. Regular review and adjustment help ensure the agent stays focused on high-risk areas and continues to deliver reliable value as the application changes.</p>
<p dir="ltr" style="text-align: left;"><strong>Combine AI Agents with Human Oversight</strong></p>
<p dir="ltr" style="text-align: left;">AI agents are effective at running and exploring tests, but human review is still essential. QA teams should review critical test flows and important findings to ensure results make sense and meet quality expectations.</p>
<p dir="ltr" style="text-align: left;"><strong>Choose the Right Tools</strong></p>
<p dir="ltr" style="text-align: left;">Consider using platforms with pre-built AI agents, such as those available from LambdaTest or ACCELQ, rather than building from scratch. These tools help teams get started faster, reduce setup effort, and fit more easily into existing testing workflows, allowing QA teams to focus on improving test quality instead of building and maintaining AI systems.</p>
<h2 dir="ltr" style="text-align: left;">Challenges of AI Agents in Software Testing</h2>
<ul>
<li dir="ltr" role="presentation"><strong>Dependence on data quality</strong>: Inaccurate or insufficient data can limit the effectiveness of AI-driven testing decisions.</li>
<li dir="ltr" role="presentation"><strong>Reduced predictability</strong>: Non-deterministic behavior may make test outcomes harder to reproduce and audit.</li>
<li dir="ltr" role="presentation"><strong>Limited transparency</strong>: Understanding how an AI agent reached a decision can be challenging, which may affect trust and compliance.</li>
<li dir="ltr" role="presentation"><strong>Governance and security concerns</strong>: Enterprises must ensure proper controls to prevent unintended actions, data leakage, or compliance issues.</li>
</ul>
<h2 dir="ltr" style="text-align: left;">FAQs</h2>
<p dir="ltr" style="text-align: left;"><strong>1. Can AI agents replace human QA testers?</strong></p>
<p dir="ltr" style="text-align: left;">No. AI agents help QA teams but do not replace people. They handle repetitive and large-scale testing, while humans are still needed for test planning, business logic checks, and decision-making.</p>
<p dir="ltr" style="text-align: left;"><em>&gt;&gt; Read more: <a href="https://reliasoftware.com/blog/will-ai-replace-software-engineers">Will AI Replace Software Engineers Altogether</a>?</em></p>
<p dir="ltr" style="text-align: left;"><strong>2. How do teams check if AI agent results are reliable?</strong></p>
<p dir="ltr" style="text-align: left;">Teams review test logs, compare results across different runs, and manually check important test flows. Clear rules and human review are often used for critical areas.</p>
<p dir="ltr" style="text-align: left;"><strong>3. Do AI agents work with <a href="https://reliasoftware.com/blog/5-best-practices-for-enhancing-ci-cd-pipeline-security">CI/CD pipelines</a>?</strong></p>
<p dir="ltr" style="text-align: left;">Yes. AI agents can be added to CI/CD pipelines to run tests on new builds, focus on risky changes, and give faster feedback during development.</p>
<p dir="ltr" style="text-align: left;"><strong>4. Are AI agents safe to use in enterprise or regulated environments?</strong></p>
<p dir="ltr" style="text-align: left;">They can be, if proper controls are in place. Many enterprises use clear rules, audit logs, and human oversight to meet security and compliance needs.</p>
<p dir="ltr" style="text-align: left;"><strong>5. What is the difference between using AI to help testing and testing an AI agent?</strong></p>
<p dir="ltr" style="text-align: left;">Using AI to help with testing means using AI features to test a normal application. Testing an AI agent means checking how the agent itself makes decisions, learns, and reacts to different situations over time.</p>
<p dir="ltr" style="text-align: left;"><strong>6. What is the difference between <a href="https://reliasoftware.com/blog/ai-in-software-testing">AI in software testing</a> and AI agents in software testing?</strong></p>
<p dir="ltr" style="text-align: left;">AI in software testing means using AI to assist certain testing tasks while people still control the overall process. AI agents in software testing go a step further by acting like testers themselves, deciding what to test, taking actions on their own, and learning from past test runs.</p>
<h2 dir="ltr" style="text-align: left;">Conclusion</h2>
<p dir="ltr" style="text-align: left;">AI agents in software testing are becoming an important part of modern QA as applications grow more complex and change more quickly. They help development teams conduct tests more efficiently, reduce maintenance effort, and improve overall test coverage.</p>
<p dir="ltr" style="text-align: left;">Using AI agents in software testing is not about replacing human testers, but about supporting developers and businesses with smarter and more flexible testing approaches. When applied with clear rules, oversight, and best practices, AI agents can significantly improve software quality and help teams deliver reliable products faster.</p>
<p style="text-align: right;"><em>&gt;&gt;&gt; Follow and Contact <a href="https://reliasoftware.com/">Relia Software</a> for more information!</em></p>

<p>AI agents in software testing are systems that can automatically determine what to test and execute those tests inside an application without any test scripts.</p>

AI Agents in Software Testing: Roles, Types & Best Practices

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A Practical Guide to AI Agents in Software Testing

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12 Popular MERN Stack Project Ideas for Businesses

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12 Common MERN Stack Project Ideas for Businesses

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Top 9 Trusted Laravel Development Companies in Vietnam

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<p>Relia Software, Saigon Technology, KMS Technology, Kyanon Digital, Orient Software, PowerGate Software, etc, are 10 best MERN stack development companies in Vietnam.</p>

Top 10 MERN Stack Development Companies in Vietnam

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<p>HIPAA-compliant app development means building mobile/web apps that protect Protected Health Information (PHI) by strictly adhering to safeguards set by the HIPAA.</p>

A Complete Guide to HIPAA Compliant App Development in 2026

A Complete Guide to HIPAA Compliant App Development

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<p>If your applications rely on querying complex, nested data, especially JSON arrays or multi-valued columns, mastering multi-valued indexes is essential for maximizing database performance and scalability. This post delivers a no-nonsense, clear, and detailed guide to what multi-valued indexes are, why they matter, how to implement them well, and how to avoid common pitfalls. It includes specific, real-world examples and best practices backed by current standards and benchmarks.</p>
<p>&gt;&gt;&nbsp;<em>Read more: Mastering 6 Different <a href="https://reliasoftware.com/blog/types-of-indexes-in-sql">Types of Indexes in SQL with Examples</a></em></p>
<h2>What Are Multi-Valued Indexes?</h2>
<p>At their core, multi-valued indexes break down a database column that stores arrays or JSON lists so that each individual element inside that array gets its own index entry. Unlike standard indexes which create one entry per row, multi-valued indexes create multiple entries per row, one for each element. This results in a one-to-many mapping from index entries to rows.</p>
<p>Why? Because without it, filtering against array elements inside columns requires expensive full table scans or runtime JSON parsing, killing query speed.</p>
<p><strong>Concrete Example:</strong></p>
<p>Imagine a <code>users</code> table with a JSON column <code>favorite_tags</code> holding a list of tags per user:</p>
<pre class="language-json"><code>{
  "favorite_tags": ["golang", "cloud", "docker"]
}</code></pre>
<p>A multi-valued index creates three separate index entries for this single row, one for each tag. This lets the database jump directly to all users with the tag "docker," for example, instead of scanning every row.</p>
<h2>Single-Valued Index vs. Multi-Valued Index: A Quick Comparison</h2>
<table style="border-collapse: collapse; border-color: rgb(0, 0, 0); border-style: solid; width: 92.8624%;" border="1" data-path-to-node="3">
<thead>
<tr>
<td style="border-color: rgb(0, 0, 0); width: 15.5556%;"><strong>Feature</strong></td>
<td style="border-color: rgb(0, 0, 0); width: 40.3175%;"><strong>Standard Index (Single-Value)</strong></td>
<td style="border-color: rgb(0, 0, 0); width: 44.127%;"><strong>Multi-Valued Index</strong></td>
</tr>
</thead>
<tbody>
<tr>
<td style="border-color: rgb(0, 0, 0); width: 15.5556%;"><strong data-path-to-node="3,1,0,0" data-index-in-node="0">Data Relationship</strong></td>
<td style="border-color: rgb(0, 0, 0); width: 40.3175%;"><strong data-path-to-node="3,1,1,0" data-index-in-node="0">1:1 Mapping</strong> &mdash; One index entry points to exactly one row.</td>
<td style="border-color: rgb(0, 0, 0); width: 44.127%;"><strong data-path-to-node="3,1,2,0" data-index-in-node="0">N:1 Mapping</strong> &mdash; Multiple index entries can point back to the same row.</td>
</tr>
<tr>
<td style="border-color: rgb(0, 0, 0); width: 15.5556%;"><strong data-path-to-node="3,2,0,0" data-index-in-node="0">Typical Data Type</strong></td>
<td style="border-color: rgb(0, 0, 0); width: 40.3175%;">Scalar values (INT, VARCHAR, DATE, etc.).</td>
<td style="border-color: rgb(0, 0, 0); width: 44.127%;">Collection types (JSON Arrays, Sets, nested lists).</td>
</tr>
<tr>
<td style="border-color: rgb(0, 0, 0); width: 15.5556%;"><span data-path-to-node="3,3,0,0"><strong data-path-to-node="3,3,0,0" data-index-in-node="0">Primary Use Case</strong></span></td>
<td style="border-color: rgb(0, 0, 0); width: 40.3175%;"><span data-path-to-node="3,3,1,0">Finding a specific user by <code data-path-to-node="3,3,1,0" data-index-in-node="27">email</code> or <code data-path-to-node="3,3,1,0" data-index-in-node="36">id</code>.</span></td>
<td style="border-color: rgb(0, 0, 0); width: 44.127%;"><span data-path-to-node="3,3,2,0">Finding all products that contain the tag <code data-path-to-node="3,3,2,0" data-index-in-node="42">"sale"</code> in a tags array.</span></td>
</tr>
<tr>
<td style="border-color: rgb(0, 0, 0); width: 15.5556%;"><strong data-path-to-node="3,4,0,0" data-index-in-node="0">Query Operators</strong></td>
<td style="border-color: rgb(0, 0, 0); width: 40.3175%;"><code data-path-to-node="3,4,1,0" data-index-in-node="0">=</code>, <code data-path-to-node="3,4,1,0" data-index-in-node="3">&gt;</code>, <code data-path-to-node="3,4,1,0" data-index-in-node="6">LIKE</code>, <code data-path-to-node="3,4,1,0" data-index-in-node="12">BETWEEN</code>.</td>
<td style="border-color: rgb(0, 0, 0); width: 44.127%;"><code data-path-to-node="3,4,2,0" data-index-in-node="0">MEMBER OF()</code>, <code data-path-to-node="3,4,2,0" data-index-in-node="13">JSON_CONTAINS()</code>, <code data-path-to-node="3,4,2,0" data-index-in-node="30">JSON_OVERLAPS()</code>.</td>
</tr>
<tr>
<td style="border-color: rgb(0, 0, 0); width: 15.5556%;"><strong data-path-to-node="3,5,0,0" data-index-in-node="0">Storage Impact</strong></td>
<td style="border-color: rgb(0, 0, 0); width: 40.3175%;"><strong data-path-to-node="3,5,1,0" data-index-in-node="0">Linear Growth.</strong> Directly proportional to the number of rows.</td>
<td style="border-color: rgb(0, 0, 0); width: 44.127%;"><strong data-path-to-node="3,5,2,0" data-index-in-node="0">Exponential Potential.</strong> Grows based on (Number of Rows <span class="math-inline" data-math="\times" data-index-in-node="54">$\times$</span> Average Array Length).</td>
</tr>
<tr>
<td style="border-color: rgb(0, 0, 0); width: 15.5556%;"><span data-path-to-node="3,6,0,0"><strong data-path-to-node="3,6,0,0" data-index-in-node="0">Write Overhead</strong></span></td>
<td style="border-color: rgb(0, 0, 0); width: 40.3175%;"><span data-path-to-node="3,6,1,0">Low. One index update per <code data-path-to-node="3,6,1,0" data-index-in-node="26">INSERT</code>.</span></td>
<td style="border-color: rgb(0, 0, 0); width: 44.127%;"><span data-path-to-node="3,6,2,0">High. If an array has 20 items, one <code data-path-to-node="3,6,2,0" data-index-in-node="36">INSERT</code> triggers 20 index updates.</span></td>
</tr>
<tr>
<td style="border-color: rgb(0, 0, 0); width: 15.5556%;"><span data-path-to-node="3,7,0,0"><strong data-path-to-node="3,7,0,0" data-index-in-node="0">Search Logic</strong></span></td>
<td style="border-color: rgb(0, 0, 0); width: 40.3175%;"><span data-path-to-node="3,7,1,0">Jumps to a specific leaf node and stops (or scans a range).</span></td>
<td style="border-color: rgb(0, 0, 0); width: 44.127%;"><span data-path-to-node="3,7,2,0">Scans multiple leaf nodes that may all reference the same record.</span></td>
</tr>
</tbody>
</table>
<h2>How Multi-Valued Indexes Work?</h2>
<h3 data-start="546" data-end="587">Step 1: Definition</h3>
<p data-start="588" data-end="817">First, you create a multi-valued index on a column that stores <strong data-start="651" data-end="683">arrays or complex data types</strong>, such as a JSON array. This tells the database that the values inside the array should be indexed individually, not as a single blob.</p>
<p data-start="819" data-end="931">At this point, nothing changes about how your data is stored in the table. Only the index behavior is different.</p>
<blockquote data-path-to-node="6">
<p data-path-to-node="6,0"><strong data-path-to-node="6,0" data-index-in-node="0">Key Concept:</strong> You aren't indexing the column itself; you are indexing the <em data-path-to-node="6,0" data-index-in-node="73">result</em> of a function that extracts an array from that column.</p>
</blockquote>
<h3 data-start="938" data-end="987">Step 2: <strong class="Yjhzub" data-processed="true">Indexing Process</strong></h3>
<p data-path-to-node="9">Consider a row where <code data-path-to-node="9" data-index-in-node="21">user_id = 101</code> and the tags are <code data-path-to-node="9" data-index-in-node="52">["golang", "cloud", "docker"]</code>. Instead of one entry, the database generates three distinct index keys:</p>
<ol start="1" data-path-to-node="10">
<li>
<p data-path-to-node="10,0,0"><strong data-path-to-node="10,0,0" data-index-in-node="0">Key:</strong> <code data-path-to-node="10,0,0" data-index-in-node="5">golang</code> &rarr; <strong data-path-to-node="10,0,0" data-index-in-node="14">Pointer:</strong> <code data-path-to-node="10,0,0" data-index-in-node="23">Row ID 101</code></p>
</li>
<li>
<p data-path-to-node="10,1,0"><strong data-path-to-node="10,1,0" data-index-in-node="0">Key:</strong> <code data-path-to-node="10,1,0" data-index-in-node="5">cloud</code> &rarr; <strong data-path-to-node="10,1,0" data-index-in-node="14">Pointer:</strong> <code data-path-to-node="10,1,0" data-index-in-node="23">Row ID 101</code></p>
</li>
<li>
<p data-path-to-node="10,2,0"><strong data-path-to-node="10,2,0" data-index-in-node="0">Key:</strong> <code data-path-to-node="10,2,0" data-index-in-node="5">docker</code> &rarr; <strong data-path-to-node="10,2,0" data-index-in-node="14">Pointer:</strong> <code data-path-to-node="10,2,0" data-index-in-node="23">Row ID 101</code></p>
</li>
</ol>
<h3>Step 3: Querying</h3>
<p data-path-to-node="12">When a user executes a query to find everyone interested in "docker": <code data-path-to-node="12" data-index-in-node="70">SELECT * FROM users WHERE JSON_CONTAINS(tags, '"docker"');</code></p>
<p data-path-to-node="13">The database bypasses the table entirely at first. It goes straight to the index, finds the "docker" leaf node, and immediately retrieves a list of all Row IDs associated with that tag. It doesn't matter if "docker" was the first item in the array or the fiftieth, the lookup time is nearly identical.</p>
<h3 data-path-to-node="13">Step 4: <span class="Yjhzub">Performance</span></h3>
<p data-path-to-node="17">With the multi-valued index, the database performs a Direct Seek. Benchmarks in modern environments like MySQL 8.0 or Oracle 23c show that for a table with 1 million rows, a multi-valued index can reduce query time from seconds to milliseconds.</p>
<figure class="image align-center"><img title="multi-valued-indexes-diagram.webp" src="https://api.reliasoftware.com/uploads/multi_valued_indexes_diagram_7d54518d56.webp" alt="How Multi-Valued Indexes Work?" width="657" height="394">
<figcaption>
<p>How Multi-Valued Indexes Work?</p>
</figcaption>
</figure>
<h2>Key Benefits of Multi-Valued Indexes</h2>
<ul>
<li><strong>Faster Queries:</strong> Benchmarks show query times drop by 5x to 50x when filtering on multi-valued columns using multi-valued indexes.</li>
<li><strong>Efficient Many-to-Many Relationships:</strong> Perfect for tagging, categorization, preference lists, and other array-driven data models.</li>
<li><strong>Reduced CPU and IO Usage:</strong> No need to parse JSON data on the fly or scan large tables.</li>
<li><strong>Better Resource Allocation:</strong> Save server costs by lowering query execution times.</li>
<li><strong>Supports Complex Queries:</strong> You can combine multi-valued indexes with other index types for compound filtering.</li>
</ul>
<p>But be mindful: multi-valued indexes increase storage use and write overhead because they store multiple entries per row, potentially increasing latency on inserts, updates, and deletes.</p>
<h2>Multi-Valued Indexes Example in Practice</h2>
<h3>MySQL Multi-Valued Indexes</h3>
<p>Suppose this table:</p>
<pre class="language-sql"><code>CREATE TABLE user_favorites (
  user_id INT PRIMARY KEY,
  tags JSON
);
INSERT INTO user_favorites VALUES
  (1, '["golang", "cloud", "docker"]'),
  (2, '["java", "cloud"]'),
  (3, '["python", "docker"]');
</code></pre>
<p><strong>Step 1: Create a Multi-Valued Index on&nbsp;<code>tags</code></strong></p>
<pre class="language-sql"><code>CREATE INDEX idx_tags ON user_favorites
((CAST(JSON_TABLE(tags, '$[*]' COLUMNS(tag VARCHAR(50) PATH '$')) AS CHAR(50) ARRAY)));
</code></pre>
<p>This tells MySQL to create index entries for each string inside <code>tags</code> arrays.</p>
<p><strong>Step 2: Query Efficiently with the Index</strong></p>
<pre class="language-sql"><code>SELECT user_id FROM user_favorites
WHERE JSON_CONTAINS(tags, '"docker"');
</code></pre>
<p>Thanks to the index, MySQL performs an index seek instead of a full table scan.</p>
<h3>Multi-Valued Indexing in Oracle</h3>
<p>Oracle uses function-based indexes with multi-value JSON support:</p>
<pre class="language-sql"><code>CREATE INDEX idx_multi_valued_tags ON user_favorites(
  CAST(JSON_TABLE(tags, '$[*]' COLUMNS(tag VARCHAR2(50) PATH '$')) AS VARCHAR2(50))
);
</code></pre>
<p>You combine this with <code>JSON_EXISTS</code> or <code>JSON_TABLE</code> predicates for fast filtering.</p>
<h3>Composite Multi-Valued Indexes</h3>
<p>Multi-valued indexes can be part of composite indexes combining multiple columns. For instance, index on both user region and JSON tags for geo-filtered tag searches:</p>
<pre class="language-sql"><code>CREATE INDEX idx_region_tags ON user_activity(region,
  (CAST(JSON_TABLE(tags, '$[*]' COLUMNS(tag VARCHAR(50) PATH '$')) AS CHAR(50) ARRAY))
);
</code></pre>
<p>This accelerates queries like:</p>
<pre class="language-sql"><code>SELECT * FROM user_activity
WHERE region = 'US' AND JSON_CONTAINS(tags, '"golang"');
</code></pre>
<h2>Best Practices for Using Multi-Valued Indexes Right</h2>
<p>Multi-valued indexes have nuances. Here&rsquo;s what you must do to avoid common traps:</p>
<h3>Target Only Frequently Queried Columns</h3>
<p>Index only the JSON arrays or collection elements that your queries filter, join, or search on regularly. Indexing rarely used fields increases index size and write cost with little benefit.</p>
<h3>Confirm Your Database&rsquo;s Multi-Valued Index Support</h3>
<p>Support and syntax vary by database engine:</p>
<ul>
<li><strong>MySQL 8.0+:</strong> Supports native multi-valued JSON indexing with JSON_TABLE and functional indexes.</li>
<li><strong>Oracle:</strong> Offers function-based multivalue indexes on JSON.</li>
<li><strong>TiDB:</strong> Supports expression-based multi-valued JSON indexes.</li>
<li><strong>Others:</strong> Some NoSQL or NewSQL platforms have unique capabilities.</li>
</ul>
<p>Always check engine-specific limits and behaviors before designing indexes.</p>
<h3>Expect Higher Write Costs</h3>
<p>Each array element becomes a separate index entry during writes, increasing maintenance work for the database. In write-heavy workloads, this can slow bulk inserts and updates by 30&ndash;50%, so indexing coverage should be chosen carefully. Using batch writes and scheduling heavy updates during off-peak windows helps reduce the impact.</p>
<h3>Validate Query Plans</h3>
<p>Always verify that queries actually use the multi-valued index:</p>
<ul>
<li>Run <code data-start="1975" data-end="1984">EXPLAIN</code> (or equivalent) on critical queries.</li>
<li>Watch for fallback to full table scans or JSON parsing.</li>
<li>Re-test plans as data volume and query patterns change.</li>
</ul>
<h3 data-start="1320" data-end="1361">Watch Cardinality and Index Growth</h3>
<p data-start="1362" data-end="1400">Multi-valued indexes can grow quickly:</p>
<ul>
<li data-start="1404" data-end="1433">Monitor index size regularly.</li>
<li>Be cautious with low-cardinality arrays (for example, boolean flags or very small value sets), as they often provide limited filtering power.</li>
<li>Avoid indexing unbounded or user-generated lists without limits.</li>
</ul>
<h3>Benchmark Everything</h3>
<p>Don&rsquo;t guess. Measure query latency, CPU usage, and I/O before and after adding multi-valued indexes using realistic data and traffic patterns.</p>
<h3>Combine Index Types Strategically</h3>
<p>Multi-valued indexes work best when paired with other indexed columns. Use composite indexes combining multi-valued and conventional indexed columns to support multi-condition filters.</p>
<h3>Use Uniqueness Constraints Carefully</h3>
<p>Unique multi-valued indexes enforce uniqueness across all array elements, but allow duplicates within the same record. This can be useful but also surprising, so apply constraints intentionally.</p>
<h3>Design Arrays for Index Efficiency</h3>
<p>Keep arrays flat and well-structured when possible. Deep nesting, very large arrays, or poorly normalized JSON structures can reduce index efficiency and inflate storage.</p>
<h2>Emerging Trends and Future Directions</h2>
<ul>
<li>Expanding support for indexing nested JSON paths with JSON path expressions.</li>
<li>Combining multi-valued, bitmap, and inverted indexes for hybrid solutions.</li>
<li>Cloud services embedding AI-based index optimizers recommending multi-valued indexes dynamically.</li>
<li>Increasing adoption in distributed SQL/<a href="https://reliasoftware.com/blog/popular-nosql-databases">NoSQL platforms</a> enabling scalable JSON querying.</li>
</ul>
<p><em>&gt;&gt;&nbsp;You can consider: </em></p>
<ul>
<li><em><a href="https://reliasoftware.com/blog/difference-between-sql-and-nosql">SQL vs NoSQL Databases</a>: What's the Difference &amp; When to Use?</em></li>
<li><em>Mastering <a href="https://reliasoftware.com/blog/index-in-sql">Index in SQL to Improve Database Performance</a></em></li>
</ul>
<h2>Wrapping Up</h2>
<p>Multi-valued indexes are essential in 2026 for applications querying array or JSON data. They unlock massive performance gains but at the cost of higher storage and write overhead. Use them strategically for columns you query often by elements, benchmark rigorously, and combine with other index types for best results.</p>
<p>They&rsquo;re no longer experimental, they&rsquo;re a proven feature transforming how we handle complex nested data at scale. Master multi-valued indexes now to future-proof your database skills.</p>
<p style="text-align: right;"><em>&gt;&gt;&gt; Follow and Contact&nbsp;</em><a href="https://reliasoftware.com/"><em>Relia Software</em></a><em> for more information!</em></p>

<p>Multi-valued indexes break down a database column that stores arrays or JSON lists so that each individual element inside that array gets its own index entry.</p>

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