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:
- Yidong Fang’s JSON.simple – JSON.simple is a Java toolkit for encoding and decoding JSON text. It’s meant to be a lightweight and simple library that still performs at a high level.
- Google’s GSON – GSON is a Java library that converts Java Objects into JSON and vice versa. It provides the added benefit of full support for Java Generics, and it doesn’t require you to annotate your classes. Not needing to add annotations makes for simpler implementation and can even be a requirement if you don’t have access to your source code.
- FasterXML’s Jackson Project – Jackson is a group of data processing tools highlighted by its streaming JSON parser and generator library. Designed for Java, it can also handle other non-JSON encodings. It’s the most popular JSON parser, according to our findings on Github usages.
- Oracle’s JSONP – JSONP (JSON Processing) is a Java API for JSON processing, namely around consuming and producing streaming JSON text. It’s the open source reference implementation of JSR353.
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:
- GSON – 14
- JSONP – 5
- Jackson – 1
- JSON.simple – 0
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.
- If you have an environment that deals often or primarily with big JSON files, then Jackson is your library of interest. GSON struggles the most with big files.
- If your environment primarily deals with lots of small JSON requests, such as in a micro services or distributed architecture setup, then GSON is your library of interest. Jackson struggles the most with small files.
- If you end up having to often deal with both types of files, JSON.simple came in a very close 2nd place in both tests, making it a good workhorse for a variable environment. Neither Jackson nor GSON perform as well across multiple file sizes.
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:
- Gson
- Jsonp
- Json.simple
- 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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Managing a Monolith Breakup – Stateful Services
Optimize Your DevOps With Feature Flag as a Service
Understanding canary releases and feature flags in software delivery
Four Shades of Progressive Delivery
Guide to managing your DIY feature flagging system effectively
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
Optimizing Bazel Projects with Harness CI Intelligence
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
Top Challenges in Database DevOps
Applying Feature Flag Context To Your OpenTelemetry Spans
Decoupling Deploy from Release: An Essential Foundation
Splitting a Monolith With Feature Flags
Split’s Simple How-To Guide for A/B Testing
Add Feature Flags to Your Angular App in 10 Minutes
Dynamic Configurations: Run more experiments without changing code
Automating Trunk-Based Development With CI/CD
7 Ways Feature Flags Improve Software Development
Why Would You Decouple Deployment from Release?
Implementing Production Testing with Feature Flag Management
10 Tools Every React Developer Needs
Build an API with Node.js, Express, and TypeScript
Why You Should Use Undefined Instead of Null in Javascript
Automating Environment-Specific Verification Queries with Liquibase and Harness Database DevOps
Feature Management Architecture & Security
Managing Feature Flag Retirement and Technical Debt
Experimentation in Split: Make Your Events Work for You!
Database Migrations with Feature Flags
How Feature Flags Can Help You Optimize Your Conversion Rates
When to Use a Holdback Pattern
7 Ways We Use Feature Flags Every Day at Split
How to Reduce Code Cycle Time with Feature Flags
How Feature Flags Can Improve Your Logging
Serverless Applications Powered by Split Feature Flags
Overcoming Experimentation Obstacles In B2B
The benefits of streaming architecture for feature flags
Where Developers Spend Time (vs. Where They Should Be)
Fidelity's OpenTofu Migration: A DevOps Success Story Worth Studying
Meet Harness' MCP Server: A Smarter DevOps Way
Harness STO + Checkmarx One: Orchestrating Security Scanning in your CI Pipeline
Getting Continuous Deployment Right: A Practical Guide
Database DevOps: Lessons Learned from Manual Migration Hell
Harness AI Test Automation: End-to-End, AI-Powered Testing for Faster, Smarter DevOps
How Git Strategy Can Break Your Database Pipeline
Harness launches MCP tools to enhance its AI powered Chaos Engineering Capabilities
Flexible Governance: Solving the "All or Nothing" Problem in Pipeline Templates
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
Transform database DevOps with AI-driven migration solutions
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
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How Harness addresses the challenges of AI in software delivery
Enhance CI/CD efficiency with Amazon Kiro and Harness integration
Harness Celebrates Hacktoberfest with LitmusChaos
Go Memory Leak: How One Line Drained Memory Across 1000+ Goroutines | Harness
Bridging the Gap Between Finance & Engineering: The Harness Playbook
DevOps enhances AI's role in improving software delivery
Harness Database DevOps vs Liquibase vs Flyway
Introducing AI-Powered Database Migration Authoring: The Last Mile of DevOps Just Got Smarter
Streamline feature management with Harness MCP and Claude Code
Automate CockroachDB Schema Changes with Harness Database DevOps
Validating chaos experiments with GCP Cloud Monitoring probes
You’re Late to the OpenTofu Party. Here’s Why That’s a Problem.
The AI Knowledge Agent: Making Internal Developer Portals Smarter
When Cloud Providers Have an Outage, Your Feature Flags Shouldn’t
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KubeCon NA 2025 Recap: The Dawn of the AI Native Era
Automating Chaos Engineering with Terraform
Harness in Seattle at PASS Data Community Summit 2025
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Recommended Experiments for Production Resilience in Harness Resilience Testing
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Database DevOps vs. Database Migration Systems and Why You Need Both
DBA vs Developer Dynamics: Bridging the Gap with Database DevOps
How Self-Service Workflows Transform Developer Productivity
Protect Against Critical Unauthenticated RCE in React & Next.js (CVE-2025-55182) with Traceable WAF
Harness Database DevOps Now Supports Google AlloyDB
How Enterprises Modernize and Migrate to the Cloud Safely with Harness Automation
CTO Predictions for 2026: Special ShipTalk Episode with Nick Durkin
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Harness AI December 2025 Updates: Ship Faster Without Sacrificing Control
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Terraform License Change: BSL vs Open Source Guide
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Harness AI January 2026 Updates: Human-Aware SRE and Smarter API and Application Security
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Move Harness Projects Between Orgs Without Downtime
NoSQL Change Control for Compliance
Agentic AI in DevOps: The Architect's Guide to Autonomous Infrastructure
Cloud Cost Optimization: Why Your Approach Is Broken
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Measuring Developer Productivity: Prove Impact
Database Governance with OPA in Harness DB DevOps
Harness Artifact Registry: Your Unified OCI-Compliant Gateway for Secure Artifact Management
What Is a Software Catalog? A Guide for Modern DevOps Teams
Argo CD Install: Helm-Based Setup for Enterprise DevOps Teams
API Failure: 7 Causes and How to Fix Them
From Artifact Storage to Supply Chain Control: Rethinking Artifact Management with Harness
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Resilience Testing Is Non-Negotiable in the Enterprise SDLC
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Intelligent Caching for CI/CD Build Optimization
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Flaky Tests: The Quiet Killer of Productivity in Your CI Pipeline
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The Multiverse of IT Storytelling
Argo CD Installation: Step-by-Step Guide for Enterprise DevOps Teams
How Harness AI Helps Scale Platform-Wide Support
LiteLLM Compromise: Securing AI Pipelines from PyPI Supply Chain Attacks
AI Deployment in Production: Orchestrate LLMs, RAG, Agents
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Harness Ships Five Capabilities to Power Confident Releases at AI Speed
Release Orchestration Bridges the AI Delivery Gap
Making Deploys Safe Shouldn’t be Hard
The McKinsey Incident Is a Warning Shot for AI-Native Design
Regression Testing in CI/CD: Deliver Faster Without the Fear
Why Warehouse Native Experimentation Matters for Platform Teams
AI Ships More Code. Harness FME Helps You Release It Safely
Terraform Vendor Lock-In: How to Escape It
The Dangerous Myth: “We Have SCA, So We’re Covered”
Shift-Left FinOps: Proactive Cloud Cost Control
Operationalizing Production Data Testing with Harness Database DevOps
Defeating Context Rot: Mastering the Flow of AI Sessions
Cost Awareness in CI/CD Pipelines: A FinOps Guide
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From Chaos to Confidence: Debunking the 3 Biggest Myths of Chaos Engineering
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Agentic Coding And The New Role Of Internal Developer Portals
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Ansible vs Terraform Explained: Key Differences for Modern Infrastructure Automation
AI for GitOps: Tame your Argo Sprawl
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Phil Christianson on Balancing Innovation and Reliability in Modern Product Teams
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Why Connected Platforms Will Power the Next Generation of AI in Engineering
Why DR Testing Can No Longer Be an Afterthought
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Site Reliability Engineering (SRE) 101: Everything You Need to Know
Cloud Cost Visibility at Scale: Why It Fails & How to Fix It
Women in Tech: Journeys, Grit, and the Future We’re Building
A/B Testing Tools: The CTO's Guide to Safe and Measurable Change
The Complete Guide to Feature Testing for Modern DevOps Teams
What is Terragrunt and how does it simplify Terraform Workflows?
An Introduction to Disaster Recovery Testing: What You Need to Know in 2026
Beyond the Big Bang: De-risking Cloud Migrations with Progressive Delivery
A/B Testing at Scale: Enable Safe Experimentation for Platform Teams
How to Install Terraform for Secure and Scalable Infrastructure Automation
Eliminate Manual Authentication Configuration for Fast & Effective API Security Scanning
Shift Left, Protect Right: How Harness + Wiz Close the AppSec Gap
Mean Time to Failure (MTTF): What It Is and Why It Matters for Platform Engineering
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Why GitOps for MongoDB Matters: A Case for Harness DB DevOps
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Infrastructure as Code Management: Terragrunt & Multi-IaC
Building for Resilience: An Engineering Guide to the Mythos Era
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Automated Release Management: From CABs to Continuous Delivery
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Why Artifact Repository Sprawl Slows Down Software Delivery
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The NoSQL Storm - Stop fighting the MongoDB
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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
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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
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
Introducing Harness Agent DLC: Extending your SDLC to AI Agents
Runtime Control for AI Agents. Introducing Harness AI Configs.
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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