The Ultimate JSON Library: JSON.simple vs GSON vs Jackson vs JSONP

The Ultimate JSON Library: JSON.simple vs. GSON vs. Jackson vs. JSONP | Harness Blog

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Benchmark tests revealed GSON as the fastest JSON library for both big and small files in 2021, significantly outperforming Jackson, JSONP, and JSON.simple, with Java 11 and updated libraries showing overall performance improvements.

JSON is the accepted standard these days for transmitting data between servers and web applications. We often don’t think about the JSON libraries we use, but there are some differences between them.

JSON is often used to transport and parse big files. This is a scenario that is common in data processing applications running in Hadoop or Spark clusters. Given the size of these files, you can be looking at significant differences in parsing speed between libraries.

Small files come up all the time as incoming requests at high throughput, and parsing them happens quickly, so the differences in performance may not seem to be a big deal at first. But the differences add up, as often you need to parse lots of small files in rapid succession during times of heavy traffic. Microservices and distributed architectures often use JSON for transporting these kinds of files, as it is the de facto format for web APIs.

Not all JSON libraries perform the same. Picking the right one for your environment can be critical.  We ran a benchmark test to see how fast four of the most popular JSON libraries for Java parse different sizes of files. This benchmark can help you decide.

The JSON Libraries

JSON.simple vs GSON vs Jackson vs JSONP For the benchmark tests, we looked at four major JSON libraries for Java: JSON.simple, GSON, Jackson, and JSONP. All of these libraries are popularly used for JSON processing in a Java environment, and were chosen according to their popularity in Github projects. Here are the ones we tested:

The Benchmark: 2017

We ran a benchmark test on the libraries for both big files and small files. The requirements (and therefore performance) for handling different file sizes are different, as are the environments in which the need to parse these files arise.

The benchmark tested two key scenarios: parsing speed for big files (190 MB) and parsing speed for small files (1 KB). The big files were taken from GitHub. The small files were randomly generated from here the JSON Generator.

For both big and small files, we ran each file 10 times per library. Given the size of the big file, we did 10 iterations per run for each library. Each small file was iterated 10,000 times per run for each library. For the small files test, we didn’t retain the files in memory between iterations and the test was run on a c3.large instance on AWS.

The results for the big file are shown in full below, but I’ve further averaged the results for the small files in the interest of space. To view the extended results, go here. If you want to view the source code for the small files or the libraries, go here.

Big File Results: 2017

Big differences here! Depending on the run, Jackson or JSON.simple traded fastest times, with Jackson edging out JSON.simple in aggregate. Looking at the average result across all the test runs, Jackson and JSON.simple come out well ahead on the big file, with JSONP a distant third and GSON far in last place.

Let’s put that in percentage terms. Jackson is the winner in average time across all the runs. Looking at the numbers from two different angles, here are the percentage results:

Those are big differences between the library speeds!

Takeaway: It was a photo finish, but Jackson is your winning library. JSON.simple is a nose behind and the other two are in the rearview mirror.

Small Files Results: 2017

The table above shows the average of 10 runs for each file, and the total average at the bottom. The tally for fastest library on number of files won is:

Looking at the average result for all the test runs across all the files, GSON is the winner here, with JSON.simple and JSONP taking a distinct second and third place, respectively.

Jackson came in 2nd to last. So despite not being the fastest on any single file, JSON.simple is the second fastest in aggregate. And despite being the fastest on a handful of files, JSONP is well in third place in aggregate.

Jackson is very consistent across all the files, while the other three libraries are occasionally much faster than Jackson, but on some files end up running at about the same speed or even slightly slower.

Let’s put the numbers in percentage terms, again looking at the numbers from two different angles:

Compared to the big file tests, these are smaller differences, but still quite noticeable.

Takeaway: Bad luck for JSON.simple, as it again loses a close race, but GSON is your winner. JSONP is a clear third and Jackson brings up the rear.

Conclusion: 2017

Parsing speed isn’t the only consideration when choosing a JSON library, but it is an important one. Upon running this benchmark test, what we found was that there is no one library that blows the others away on parsing speed across all file sizes and all runs. The libraries that performed best for big files suffered for small files and vice versa.

Choosing which library to use on the merit of parsing speed comes down to your environment then.

As far as parsing speed goes, JSONP doesn’t have much to recommend for it in any scenario. It performs poorly for both big and small files compared to other available options. Fortunately, Java 9 is reportedly getting native JSON implementation, which one would imagine is going to be an improvement over the reference implementation.

If you’re concerned about parsing speed for your JSON library, choose Jackson for big files, GSON for small files, and JSON.simple for handling both.

The Benchmark: 2021

We ran the same benchmarks again but are now using the latest Libraries and ran the tests on Java 11.

The numbers look quite different from when we ran these benchmarks previously. In general the performance increased drastically.

Big File Results: 2021

Last benchmarks Jackson was the winner, but this time GSON is by far the fastest, with JSONP being a close second and then followed by Jackson and then JSON.simple last.

And again looking at the numbers from two different angles, here are the percentage results:

GSON is a clear winner.

Small Files Result: 2021

In the small File Results Gson is still the winner. Clearly beating the other 3 contenders.

Here are the results:

  1. Gson
  2. Jsonp
  3. Json.simple
  4. Jackson

Gson is processing those files faster by almost 200+ ms before the next candidate.

Conclusion: 2021

The obvious elephant in the room is that java and the json libraries got faster. Like way faster then back when the first benchmarks were run.

It is also obvious that GSON stepped up big and won both benchmarks for Big and small files. In both cases very clearly.

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5 Best Practices for Testing in Production with Feature Flags

Managing a Monolith Breakup – Stateful Services

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Understanding canary releases and feature flags in software delivery

Four Shades of Progressive Delivery

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4 Myths, 4 Realities About Experimentation

Elixir SDK Is Available for Feature Management & Experimentation

Improve pipeline visibility with Harness notifications for Datadog

Enhancing control of feature rollouts for enterprise software

Speed Up Your Gradle Builds with Harness CI: Faster Builds, Better Efficiency

OpenID Connect (OIDC): A Smarter Way to Secure Pipeline Deployments

Testing application resilience under load with Grafana K6

GitHub Actions Supply Chain Attack: tj-actions/changed-files - Impact Assessment and Mitigation Guidance

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Harness Cloud: The Ultimate Managed Build Infrastructure for Fast, Secure CI

Azure Functions Deployment Made Easy with Harness

8 Essential Questions to Boost Developer Productivity

A Simpler Way to Handle Post-Production Rollbacks

Elite Engineering Teams Don’t Guess—They Prove Impact

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Decoupling Deploy from Release: An Essential Foundation

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Split’s Simple How-To Guide for A/B Testing

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Feature Management Architecture & Security

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Testing AI Models Using Split

Database Migrations with Feature Flags

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When to Use a Holdback Pattern

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How to Reduce Code Cycle Time with Feature Flags

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Serverless Applications Powered by Split Feature Flags

Overcoming Experimentation Obstacles In B2B

The benefits of streaming architecture for feature flags

Split Embraces OpenFeature

Where Developers Spend Time (vs. Where They Should Be)

How Top Engineering Teams Use Software Engineering Insights (SEI) to Drive Measurable Business Impact

Fidelity's OpenTofu Migration: A DevOps Success Story Worth Studying

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Harness STO + Checkmarx One: Orchestrating Security Scanning in your CI Pipeline

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Database DevOps: Lessons Learned from Manual Migration Hell

Harness AI Test Automation: End-to-End, AI-Powered Testing for Faster, Smarter DevOps

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Harness launches MCP tools to enhance its AI powered Chaos Engineering Capabilities

Flexible Governance: Solving the "All or Nothing" Problem in Pipeline Templates

Harness AI Unveils Advanced DevOps Automation: Smarter Pipelines, Faster Delivery, and Enterprise-Ready Compliance

How Harness is Using AI to Simplify Chaos Engineering Adoption

State vs Script Migrations in Modern Database DevOps

Improving Liquibase Developer Experience with Harness Database DevOps Automated Change Generation

Confidently Ship Reusable OpenTofu and Terraform Modules

Top Open Source Software Deployment Tools in 2025

Rego 101: Policy Driven DevOps

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Modernize your Jenkins pipelines to a highly secure, AI DevOps platform with patented technology from Harness.

Trunk vs Feature vs Environment: Tales from Database Deployment Hell

Resilience Testing using Harness

AI-Powered Resilience Testing with Harness MCP Server and Windsurf

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AI-Powered Chaos Engineering with Harness MCP Server and Cursor

Harness GitOps: Scaling Argo CD with Enterprise-Grade Control

How Harness addresses the challenges of AI in software delivery

Enhance CI/CD efficiency with Amazon Kiro and Harness integration

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Infrastructure as Code Management: Terragrunt & Multi-IaC

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Mini Shai-Hulud Explained: How the TanStack and RubyGems Supply Chain Attacks Worked

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The NoSQL Storm - Stop fighting the MongoDB

Bring Your Playwright Suite to Harness: No Rewrites, No Infrastructure, AI-Powered Triage Built In

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Introducing AI DLC Insights to Prove the ROI of Your AI Engineering Investment

Harness Launches Two Products to Give Enterprise Teams Full Visibility into ROI of AI Spend

Anthropic’s Mythos, Glasswing, and how the industry must move forward

Feature Flag Tools Compared: 10 Best Platforms for Safer Releases

BigQuery CI/CD and Database DevOps with Harness

Get Ship Done: Everything We Shipped in May 2026

Shai-Hulud Miasma: Inside the Compromise of Red Hat’s Packages

Mainframe DevOps: Modern CI/CD for Big Iron

Announcing OPA Policy Evaluation on Your Own Infrastructure

The Future of IaC: Continuous Governance Through a Control Plane

With AI, The Proof Is in Production

Azure Deployment Strategies & CI/CD Best Practices

From Commit to Approval, Without Leaving VS Code

Real-Time CPU and Memory Insights for Harness CI Cloud Builds

Beyond Static Thresholds: Why Harness AI Verification and Rollback Outshines Argo CD Analysis Templates

Ship From Where You Build: Harness Delivery Intelligence, Now Inside Antigravity

AI Coding Security Risks Demand Dependency Firewalls

When metrics scream, your flags hit mute

Prepare for the EU AI Act with Harness AI Security

Get Ship Done: Everything We Shipped in June 2026

AI Is Writing More Code Than Ever. Your Release Process Hasn't Kept Up.

Poisoning The Pipeline: How The Mastra AI Ecosystem Was Poisoned At The Registry Level

Compliance Without Complexity: Introducing Harness Rego Policy Packs

Announcing the Harness CLI: Built for Humans and Agents

How to Build Runbooks That Work — and Automate Them with Harness AI SRE

DevOps Solutions: How to Pick the Right Stack for Your Team

How to Troubleshoot and Debug Ansible Playbooks: A Complete Guide

DevOps Platform Explained: Why Unified Wins Over Siloed Tools

DevOps Tools List: The Only Stack You Need in 2026

Zero-Downtime Database Migrations: Patterns for Safe Schema Evolution

Infrastructure as Code Isn't Enough: Why Database Delivery Must Evolve

Securing the Agent DLC

Introducing Harness Agent DLC: Extending your SDLC to AI Agents

Runtime Control for AI Agents. Introducing Harness AI Configs.

Ship AI Agents You Can Trust

Introducing Harness AgentTrace: An Observability and Guardrail Framework for AI Agents

Introducing AI Agent Deployment in Harness Continuous Delivery

Organizing and governing AI Assets

Software Release Management: A Practical Guide for Engineering Teams

DevOps Toolchain Explained: How to Build One That Actually Scales

Feature Flag Security in your CI/CD Pipeline

DevOps Technologies in 2026: What's Changed and What Actually Matters

Staying in Control: Auditing and Reporting with Harness Artifact Registry

What Is Web App and API Protection (WAAP)?

A Step-by-Step Guide to Feature Flag Implementation in CI/CD Pipelines

Engineer Cloud Cost Awareness: Why It Fails & Fixes

Strategic Cloud Cost Management: Evolution Guide

Boost Developer Productivity: 8 Key Questions

You're Not Overspending, You're Under-Saving: A New FinOps Paradigm

Cloud Cost Optimization Strategy: Fix Your Approach

Install Terraform: Secure & Scalable IaC Setup Guide

Your Production System Is Now in Your Pocket

Updating Reference Data with Rollbacks Using Harness Database DevOps

Control Runtime Behavior with Config Management

Get Ship Done: Everything We Shipped in July 2026

Why Cloud Cost Visibility at Scale Fails (And How to Fix It)

How to automate artifact cleanup in Harness Artifact Registry without breaking production

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