Fastest JSON Libraries in Rust and Java | Generated by AI
Fastest JSON Libraries in Rust and Java
To compare the fastest JSON libraries in Rust and Java, we need to look at performance for parsing (deserialization) and serialization, as these are the core operations. Performance is typically measured in throughput (e.g., GB/s) or time for standard datasets like twitter.json (medium-sized object array), canada.json (large array of numbers), and citm_catalog.json (nested objects). These datasets are common in JSON benchmarks.
Fastest in Rust: sonic-rs
- Overview: sonic-rs is a SIMD-accelerated library from CloudWeGo, designed for high-speed JSON handling in Rust. It parses directly to Rust structs without intermediate steps (like the “tape” in simd-json), making it faster for deserialization. It supports both structured (to structs) and untyped (to DOM-like) parsing.
- Key Performance Highlights (from benchmarks on Intel Xeon Platinum 8260 @ 2.40GHz):
- Deserialization to struct (time in ms, lower is better):
- twitter.json: ~0.7 ms
- canada.json: ~3.8 ms
- citm_catalog.json: ~1.2 ms
- This makes sonic-rs 1.5-2x faster than simd-json (another top Rust lib) for deserialization, and 3-4x faster than serde_json (the standard).
- Serialization: Comparable or slightly faster than simd-json, e.g., ~0.4 ms for twitter.json.
- Throughput: Often exceeds 2-4 GB/s for large inputs, thanks to SIMD optimizations for strings, numbers, and whitespace.
- Deserialization to struct (time in ms, lower is better):
- Strengths: Zero-copy where possible, low memory use, safe (checked) and unsafe (unchecked) modes for extra speed.
- Weaknesses: Newer lib, less mature ecosystem than serde_json.
Fastest in Java: DSL-JSON or simdjson-java (tied, depending on use case)
- Overview:
- DSL-JSON uses compile-time code generation (via annotations like @CompiledJson) to avoid reflection and minimize GC, making it exceptionally fast for deserialization in high-load scenarios.
- simdjson-java is a Java port of the simdjson C++ library, using SIMD for gigabyte-per-second parsing. It’s especially strong for large inputs but has limitations like partial Unicode support in early versions.
- Key Performance Highlights:
- DSL-JSON: 3-5x faster than Jackson for deserialization in tight loops (e.g., medium objects ~500 bytes). Specific dataset numbers are scarce, but it’s claimed to be on par with binary codecs like Protobuf. In general benchmarks, it outperforms Jackson by 3x+ in serialization and parsing.
- simdjson-java: ~1450 ops/sec on Intel Core i5-4590 for typical operations, 3x faster than Jackson, Jsoniter, and Fastjson2. For large inputs, it approaches 1-3 GB/s, similar to its C++ counterpart. In comparisons, it’s 3x faster than Jsoniter for parsing.
- Jsoniter (honorable mention): 2-6x faster than Jackson, with decode speeds like 3.22x Jackson for integers and 2.91x for object lists (throughput ratios in JMH benchmarks).
- For context, Jackson (popular but not fastest) handles standard datasets in 2-3x the time of these leaders.
- Strengths: DSL-JSON for low-GC, high-throughput apps; simdjson-java for raw speed on large data. Both handle JVM constraints well.
- Weaknesses: DSL-JSON requires annotations for max speed; simdjson-java has feature gaps (e.g., full float parsing in older versions).
Direct Comparison: Rust vs Java
- Performance Gap: Rust’s sonic-rs is generally 2-5x faster than Java’s top libs for similar tasks. For example:
- In a real-world AWS Lambda benchmark processing 1GB of JSON logs (streaming + parsing), Rust with simd-json took ~2 seconds (0.5 GB/s), while Java with Jsoniter took 8-10 seconds (0.1 GB/s). Using simdjson-java could close the gap to ~3 seconds (0.3 GB/s), but Rust still wins due to native compilation, no GC pauses, and better SIMD utilization.
- On standard datasets, sonic-rs deserializes canada.json in ~4 ms, while Java libs like Jsoniter or DSL-JSON take 10-20 ms in comparable setups (indirect, as direct head-to-head is rare).
- Serialization is closer, with Rust sometimes only 1.5-2x faster.
- Why Rust is Faster: Native code, no JVM overhead, zero-cost abstractions, and better control over memory. Java libs are limited by GC and bytecode, though optimizations like compile-time generation (DSL-JSON) help.
- Other Factors:
- Features: Rust libs integrate well with serde ecosystem for flexibility. Java libs like Jackson have more plugins for complex schemas.
- Use Cases: Rust for ultra-high-performance (e.g., servers, CLI tools); Java for enterprise with easier integration.
- Caveats: Benchmarks vary by hardware, dataset, and JVM version (e.g., Java 21+ improves GC). Rust’s safety checks can add overhead if not using unchecked modes.
If you have a specific dataset or use case (e.g., serialization only), I can dig deeper.
References
- sonic-rs GitHub (Rust benchmarks) (https://github.com/cloudwego/sonic-rs)
- simdjson GitHub (base for Rust and Java ports) (https://github.com/simdjson/simdjson)
- Serverless Speed: Rust vs. Go, Java, and Python in AWS Lambda (benchmark) (https://scanner.dev/blog/serverless-speed-rust-vs-go-java-and-python-in-aws-lambda-functions)
- The Lightning Fast JSON Parser Library For Java (simdjson-java) (https://www.i-programmer.info/news/80-java/16553-the-lightning-fast-json-parser-library-for-java.html)
- Jsoniter Benchmark (Java comparisons) (https://jsoniter.com/benchmark.html)
- DSL-JSON vs Jackson (Java deserialization) (https://medium.com/@vishalpriyadarshi/dsl-json-vs-jackson-who-deserializes-faster-in-java-7f0ee164606a)
- Optimizing JSON Performance in Java - 2025 Edition (https://medium.com/towardsdev/optimizing-json-performance-in-java-what-actually-works-2025-edition-4f124e24d786)