JSON Tools RS

A high-performance Rust library for advanced JSON manipulation with SIMD-accelerated parsing, providing unified flattening and unflattening operations through a clean builder pattern API. Ships with Rust and Python bindings.

Why JSON Tools RS?

JSON Tools RS is designed for developers who need to:

• Transform nested JSON into flat structures for databases, CSV exports, or analytics
• Clean and normalize JSON data from external APIs or user input
• Process large batches of JSON documents efficiently
• Maintain type safety with perfect roundtrip support (flatten → unflatten → original)
• Work with both Rust and Python using the same consistent API

Unlike simple JSON parsers, JSON Tools RS provides a complete toolkit for JSON transformation with production-ready performance and error handling.

Features

• 🚀 Unified API: Single JSONTools entry point for flattening, unflattening, or pass-through transforms (.normal())
• 🔧 Builder Pattern: Fluent, chainable API for easy configuration and method chaining
• ⚡ High Performance: SIMD-accelerated JSON parsing with FxHashMap, SmallVec stack allocation, and tiered caching
• 🚄 Parallel Processing: Built-in Rayon-based parallelism (persistent work-stealing pool) for faster batch operations and large nested structures
• 🎯 Complete Roundtrip: Flatten JSON and unflatten back to original structure with perfect fidelity
• 🧹 Comprehensive Filtering: Remove empty strings, nulls, empty objects, and empty arrays (works for both flatten and unflatten)
• 🔄 Advanced Replacements: Key/value replacements, literal (exact substring match) by default, or regex by wrapping the pattern in r'...'
• 🚫 Key/Value Exclusion: Drop entire keys (and their subtree) or key-value pairs by pattern match with .exclude_key()/.exclude_value()
• 🛡️ Collision Handling: Intelligent .handle_key_collision(true) to collect colliding values into arrays
• 📅 Date Normalization: Automatic detection and normalization of ISO-8601 dates to UTC
• 🔀 Automatic Type Conversion: Convert strings to numbers, booleans, and nulls with .auto_convert_types(true)
• 📦 Batch Processing: Process single JSON or batches; Python also supports dicts and lists of dicts
• 🐍 Python Bindings: Full Python support with perfect type preservation (input type = output type)
• 📊 DataFrame/Series Support: Native support for Pandas, Polars, PyArrow, and PySpark DataFrames and Series in Python

Table of Contents

• Why JSON Tools RS?
• Features
• Quick Start
  • Rust Examples
  • Python Examples
  • Runnable Examples
• Quick Reference
• Installation
• Architecture
• Performance
• Contributing
• License
• Changelog

Quick Start

Rust - Unified JSONTools API

The JSONTools struct provides a unified builder pattern API for all JSON manipulation operations. Simply call .flatten() or .unflatten() to set the operation mode, then chain configuration methods and call .execute().

Basic Flattening

use json_tools_rs::{JSONTools, JsonOutput};

let json = r#"{"user": {"name": "John", "profile": {"age": 30, "city": "NYC"}}}"#;
let result = JSONTools::new()
    .flatten()
    .execute(json)?;

if let JsonOutput::Single(flattened) = result {
    println!("{}", flattened);
}
// Output: {"user.name": "John", "user.profile.age": 30, "user.profile.city": "NYC"}

Advanced Flattening with Filtering

use json_tools_rs::{JSONTools, JsonOutput};

let json = r#"{"user": {"name": "John", "details": {"age": null, "city": ""}}}"#;
let result = JSONTools::new()
    .flatten()
    .separator("::")
    .lowercase_keys(true)
    .key_replacement("r'(User|Admin)_'", "")
    .value_replacement("@example.com", "@company.org")
    .remove_empty_strings(true)
    .remove_nulls(true)
    .remove_empty_objects(true)
    .remove_empty_arrays(true)
    .execute(json)?;

if let JsonOutput::Single(flattened) = result {
    println!("{}", flattened);
}
// Output: {"user::name": "John"}

Automatic Type Conversion

Convert string values to numbers, booleans, dates, and null automatically for data cleaning and normalization.

use json_tools_rs::{JSONTools, JsonOutput};

let json = r#"{
    "id": "123",
    "price": "$1,234.56",
    "discount": "15%",
    "active": "yes",
    "verified": "1",
    "created": "2024-01-15T10:30:00+05:00",
    "status": "N/A"
}"#;

let result = JSONTools::new()
    .flatten()
    .auto_convert_types(true)
    .execute(json)?;

if let JsonOutput::Single(flattened) = result {
    println!("{}", flattened);
}
// Output: {
//   "id": 123,
//   "price": 1234.56,
//   "discount": 15.0,
//   "active": true,
//   "verified": 1,
//   "created": "2024-01-15T05:30:00Z", // Normalized to UTC
//   "status": null
// }

Python - Unified JSONTools API

The Python bindings provide the same unified JSONTools API with perfect type matching: input type equals output type.

Basic Usage

import json_tools_rs as jt

# Basic flattening - dict input → dict output
result = jt.JSONTools().flatten().execute({"user": {"name": "John", "age": 30}})
print(result)  # {'user.name': 'John', 'user.age': 30}

# Basic unflattening - dict input → dict output
result = jt.JSONTools().unflatten().execute({"user.name": "John", "user.age": 30})
print(result)  # {'user': {'name': 'John', 'age': 30}}

Advanced Configuration & Parallelism

import json_tools_rs as jt

# Configure tools with parallel processing settings
tools = (jt.JSONTools()
    .flatten()
    .separator("::")
    .lowercase_keys(True)
    .remove_empty_strings(True)
    .parallel_threshold(50)       # Parallelize batches >= 50 items
    .num_threads(4)               # Use 4 threads
    .nested_parallel_threshold(200) # Parallelize large objects
)

# Process a batch of data
batch = [{"data": i} for i in range(100)]
results = tools.execute(batch)

DataFrame & Series Support

import json_tools_rs as jt
import pandas as pd

# Pandas DataFrame input → Pandas DataFrame output
df = pd.DataFrame([
    {"user": {"name": "Alice", "age": 30}},
    {"user": {"name": "Bob", "age": 25}},
])
result = jt.JSONTools().flatten().execute(df)
print(type(result))  # <class 'pandas.core.frame.DataFrame'>

# Also works with Polars, PyArrow Tables, and PySpark DataFrames
# Series input → Series output (Pandas, Polars, PyArrow)

# Or skip having a DataFrame at all -- normalise=True always returns a wide
# DataFrame regardless of input shape (dict, str, list), with target= picking
# the library (pandas/polars/pyarrow/pyspark) or auto-resolving if omitted.
df = jt.JSONTools().flatten().execute(
    {"user": {"name": "Alice", "age": 30}}, normalise=True
)

Runnable Examples

Every builder feature has a standalone, runnable example in both languages, plus curated multi-feature pipelines (not an exhaustive combinatorial sweep -- the builder has ~10 independent toggles -- but realistic groupings commonly used together, and one "kitchen sink" pipeline exercising nearly everything at once). Both language versions use matching inputs and produce matching output.

# Rust
cargo run --example feature_by_feature
cargo run --example feature_combinations

# Python
python3 python/examples/feature_by_feature.py
python3 python/examples/feature_combinations.py

There are also narrative walkthroughs for a quicker first read: examples/basic_usage.rs / examples/advance_usage.rs (Rust) and python/examples/examples.py (Python).

Quick Reference

Method Cheat Sheet

Automatic Type Conversion

When .auto_convert_types(true) is enabled, the library performs smart parsing on string values. For independent control over each category below (e.g. only converting numbers, or customizing date/null/boolean matching), use .convert_dates()/.convert_nulls()/.convert_booleans()/.convert_numbers() instead -- see Automatic Type Conversion for the full per-category reference across all three language bindings.

1. Date & Time (ISO-8601):

• Detects date strings to avoid converting them to numbers (e.g., "2024-01-01").
• Normalizes datetimes to UTC.
• Supports offsets (+05:00), Z suffix, and naive datetimes.

2. Numbers:

• Basic: "123" → 123, "45.67" → 45.67
• Separators: "1,234.56" (US), "1.234,56" (EU), "1 234.56" (Space)
• Currency: "$123", "€99", "£50", "¥1000", "R$50"
• Scientific: "1e5" → 100000
• Percentages: "50%" → 50.0, "12.5%" → 12.5
• Basis Points: "50bps" → 0.005, "100 bp" → 0.01
• Suffixes: "1K", "2.5M", "5B" (Thousand, Million, Billion)

3. Booleans:

• "true", "false", "yes", "no", "on", "off", "y", "n" (case-insensitive).
• Note: "1" and "0" are treated as numbers, not booleans.

4. Nulls:

• "null", "nil", "none", "N/A" (case-insensitive) → null.

Installation

Rust

cargo add json-tools-rs

Python

pip install json-tools-rs

Architecture

The codebase is organized into focused, single-responsibility modules:

src/
├── lib.rs            Facade: mod declarations + pub use re-exports
├── json_parser.rs    Conditional SIMD parser (sonic-rs on 64-bit, simd-json on 32-bit)
├── types.rs          Core types: JsonInput, JsonOutput
├── error.rs          Error types with codes E001-E008
├── config.rs         Configuration structs and operation modes
├── cache.rs          Tiered regex pattern caching (compile-time table, thread-local, global)
├── convert.rs        Type conversion: numbers, dates, booleans, nulls (SIMD-optimized)
├── transform.rs      Filtering, key/value replacements, collision handling
├── flatten.rs        Flattening algorithm with Rayon parallelism
├── unflatten.rs      Unflattening with SIMD separator detection
├── builder.rs        Public JSONTools builder API and execute() entry point
├── python.rs         Python bindings via PyO3
├── tests.rs          Unit tests
└── main.rs           CLI examples

The processing pipeline:

1. Parse -- SIMD-accelerated JSON parsing (json_parser)
2. Flatten/Unflatten -- Recursive traversal with CompactString/arena-backed key storage (flatten/unflatten)
3. Transform -- Lowercase, replacements (cached regex), collision handling (transform)
4. Filter -- Remove empty strings, nulls, empty objects/arrays (transform)
5. Convert -- Type conversion with first-byte discriminators (convert)
6. Serialize -- Output to JSON string or native Python types

Performance

Benchmark Results

Measured on Apple Silicon (M4) via cargo bench --bench stress_benchmarks, v0.9.5. Results may vary by platform and data shape.

Optimization Techniques

JSON Tools RS uses several techniques to achieve high performance:

• SIMD-JSON: Hardware-accelerated parsing via sonic-rs (64-bit) / simd-json (32-bit).
• SIMD Byte Search: memchr/memmem for SIMD-accelerated string operations and pattern matching.
• FxHashMap: Faster hashing for string keys via a hand-rolled FxHash-style hasher (src/fxhash.rs; no external hashing crate dependency).
• Tiered Caching: Three-level regex cache (compile-time pattern table → thread-local FxHashMap → global RwLock<FxHashMap>).
• SmallVec & Cow: Stack allocation for depth stacks and number buffers; zero-copy string handling.
• CompactString & Arena Keys: Object keys are inlined via CompactString (no heap allocation up to 24 bytes); flatten's slow path additionally uses a bumpalo arena for deep-nested keys, to minimize allocations in wide/deep JSON.
• First-Byte Discriminators: Rapid rejection of non-convertible strings during type conversion.
• Parallelism: Rayon's persistent work-stealing thread pool for batch processing and large nested structures (avoids per-call OS thread spawn cost).

CLI Demo

The crate includes an educational demo binary that showcases library features:

cargo run

This prints progressive examples covering basic flattening, unflattening, custom separators, filtering, replacements, collision handling, type conversion, and batch processing.

Contributing

See CONTRIBUTING.md for development setup, testing, benchmarking, and PR guidelines.

License

Dual-licensed under either MIT or Apache-2.0, at your option.

Changelog

v0.9.27 (Current)

• Performance: round 13 algorithmic audit of the DataFrame/normalise layer. Eliminated duplicate Arrow string-column extraction on the flat-DataFrame fast-path fallback (.flatten().execute(df) with an embedded-JSON string column used to extract every string column twice) -- verified via code tracing; no consistent end-to-end wall-clock signal, reported as a redundant-work fix rather than a speed claim. normalise()'s per-batch column-slot allocation reduced from n_keys separate heap allocations to one, for batches with mostly-disjoint keys -- confirmed ~30-35% faster median and eliminates the wide run-to-run variance the many-allocations version showed. Also a small free memoization fix in unflatten.rs.

See CHANGELOG.md for the full, itemized list.

v0.9.26

• Maintenance: round 11 of this project's ongoing performance-optimization effort researched and A/B tested several candidates (sonic-rs vs simd-json, simdutf8, mimalloc vs jemalloc, PyO3 free-threaded Python), but profiling confirmed the hot path is already at the ceiling reached by the prior 10 rounds -- nothing measurable to ship. Lifted two dependency pins left artificially stale by the 2026-07-30 MSRV bump to 1.85: sonic-rs 0.5.7 -> 0.5.8, indexmap <2.12 -> <2.15. Verified clean on stable and MSRV 1.85; no measurable latency change (neither dependency sits on this crate's hot path).

See CHANGELOG.md for the full, itemized list.

v0.9.25

• Concurrency: the flat-DataFrame fast path (.flatten().execute(df) on pandas/Polars/PyArrow, added in 0.9.24) now releases the GIL for its computation, matching every other execution path -- it was the one path that held the GIL for its entire duration, stalling other Python threads in the process for no reason during a large-DataFrame call. Not a latency change (confirmed no regression) -- other Python threads can now make progress while it runs. Measured via a background pure-Python counting thread run concurrently with execute(df) (ratio of concurrent to solo throughput, 20K x 20 DataFrame): Polars 0.21 -> 1.00, PyArrow 0.14 -> 0.97, pandas 0.73 -> 0.76 (smaller, bounded by pandas' own per-cell object construction).
• Fixed: pandas flat-DataFrame fast path -- a column with both a genuine null and a remove_empty_strings-filtered-to-empty cell now matches the slow path's reconstruction exactly (None vs pandas' own NaN-for-missing-key behavior), a narrow pre-existing 0.9.24 gap caught while implementing the fix above. Polars/PyArrow were never affected.

See CHANGELOG.md for the full, itemized list.

v0.9.24

• Performance: execute(df) on a pandas/Polars/PyArrow DataFrame with no nested columns now skips the JSON-text round trip entirely -- reads column values directly and applies the same per-cell transform logic natively instead of serialize/parse/deserialize/reconstruct. Measured ~90-99ms of old-path overhead for a 20K-row x 20-col DataFrame down to a fraction of that. Confirmed via interleaved A/B: Polars ~2.7-3.7x faster, PyArrow ~4.2-5.6x faster, pandas ~1.5-2.2x faster (up to ~345x for the pure column-rename case). Strict whole-DataFrame fallback to the existing pipeline for anything nested/uncertain -- no behavior change, differential-tested across 10-11 cases per backend. Also: unflatten() no longer re-checks a container's array-vs-object classification on every visit to an already-created node, found via this project's own tracked CI benchmark history -- ~9-10% faster.

See CHANGELOG.md for the full, itemized list.

v0.9.23

• Performance: follow-up zero-copy audit of convert.rs/flatten.rs/unflatten.rs/python.rs. auto_convert_types's converted-value chain switched from Cow<str> to a new ConvertedStr type backed by CompactString, so short converted values (bools, small numbers) stay on the stack instead of heap-allocating; flatten.rs's/unflatten.rs's collision-handling value storage got the same treatment. Measured ~11-13% faster for .flatten() with a key transform configured, ~3-9% faster for .normal() mode alone, both via interleaved A/B. A third change (Arrow JSON-string-column extraction in python.rs, same idea) showed no consistent signal under the same measurement and is kept as correct but not claimed as a win. No behavior changes.

See CHANGELOG.md for the full, itemized list.

v0.9.22

• Added: .always_array_keys([...]) -- flattened key names that must always render as a JSON array, even with only one value present, keeping a key's shape consistent across every document/row of a batch regardless of .handle_key_collision(). Also guarantees normalise() resolves that column to List<T> even when a particular batch has zero collisions for it.
• Performance: audited every clone/copy site in the codebase. PyJsonOutput.get_single()/get_multiple()/__str__() cloned a result before PyO3's own unavoidable copy at the FFI boundary -- fixed to build the Python object directly from the borrowed value. Measured ~25-27% faster via interleaved A/B, zero behavior change.

See CHANGELOG.md for the full, itemized list.

v0.9.21

• Performance: three rounds of profiling-driven fixes to hot allocation paths. Date/datetime normalization output no longer re-parses a chrono format string per value (~10.6% faster on date-heavy convert_dates(True) workloads); unflatten() reuses one path buffer across a document instead of allocating one per key (~3.8% faster on wide documents); the Arrow-native normalise() engine no longer double-parses list-valued columns (~5-6% faster on handle_key_collision(True)-heavy data) and no longer re-clones already-seen keys when unioning columns across rows (~13% faster at issue #31's scale, 754 rows x 4,042 columns); DataFrame extraction no longer allocates an owned String per JSON key when un-nesting or splicing embedded columns (~6-9% faster per call, ~8.9% faster end-to-end for embedded-JSON-string-column DataFrames). No behavior changes.

See CHANGELOG.md for the full, itemized list.

v0.9.20

• Changed (BREAKING): DataFrame column expansion no longer prefixes with the source column's name -- a column named payload holding {"user": {"name": "Alice"}} now expands to user.name, not payload.user.name. Genuine nesting within a column's content still prefixes normally; array-valued columns are unaffected.
• Changed (BREAKING): normalise=True/target=... reconstruction is now Arrow-native -- one real Arrow RecordBatch built directly in Rust, no new methods. handle_key_collision(True) list columns and recognized date/datetime columns (gated on .convert_dates()) now build as real, correctly-typed List<T>/Date32/Timestamp columns instead of being stringified. target="pandas" output uses Arrow-backed dtypes (a breaking dtype change); target="pandas"/"pyspark" now require pyarrow installed, target="polars" does not.
• Removed (BREAKING): the JVM/Java/Scala binding has been removed entirely -- the Rust core and Python bindings are unaffected; the published Maven Central artifact will not receive new versions. Databricks/Spark users should switch to the Python bindings wrapped in a pandas_udf.
• Performance: normalise=True/target=... reconstruction ~1-4% faster end-to-end; .convert_dates(True)'s own detection cost measured at ~0.1-4.5%, not charged when off.

See CHANGELOG.md for the full, itemized list.

v0.9.19

• Changed: MSRV raised from 1.80 to 1.85, required for current pyo3-arrow releases (see below). Affects every source (cargo add) consumer.
• Performance: execute() on a Polars DataFrame/PyArrow Table with an embedded JSON-string column is ~41-48% faster end-to-end -- detection/extraction now uses pyo3-arrow's zero-copy Arrow buffer access instead of round-tripping through the DataFrame's native JSON writer (escape, then immediately unescape). Column ordering is preserved exactly as before. Scoped to Polars/PyArrow; plain pandas and PySpark are unaffected.

v0.9.18

• Performance: execute() on a PyArrow Table/RecordBatch is ~2x faster -- extraction now bridges through pandas's native JSON writer (using types_mapper=pd.ArrowDtype to avoid a real integer-with-nulls-to-float corruption bug caught while building this fix) instead of to_pylist() + per-item conversion. splice_row's per-key escaping now reuses the crate's existing zero-allocation key writer instead of serde_json::to_string; a real cleanup, though measured end-to-end impact was within noise at realistic scale.

v0.9.17

• Performance: core flatten()/unflatten() collision-handling paths ~5-8% faster (removed a redundant second hashmap lookup per unique key); the remaining non-normalise DataFrame/Series reconstruction functions now share the PyOnceLock import caching added in 0.9.16; mimalloc's doc comment updated to real measured numbers (~14-28%, previously an unverified "~5-10%") plus new CI coverage.

v0.9.16

• Performance: execute(..., normalise=True, target=...) and PySpark execute(spark_df) are 13-18% faster for large/wide results (e.g. 754 rows x 4,042 columns). union_and_columnarize rewritten from an O(rows x columns) PyDict hash-lookup pattern to a single forward pass, plus PyOnceLock-cached pandas/polars/pyarrow/pyspark module imports across the reconstruction path. Verified via interleaved A/B against the real Python API.

v0.9.15

• Fix: execute(spark_df) no longer crashes when a .key_replacement()/.handle_key_collision(True) list column holds genuinely mixed element types (#33). The list-flavored twin of the 0.9.14 fix: a collision list is built from each colliding key's own independently-converted value, so a single row's collision could already mix kinds (e.g. [100, "abc"]); such columns now fall back to string elements, while uniformly-typed list columns (e.g. all int) correctly get a typed array instead of unnecessary stringification.

v0.9.14

• Fix: execute(spark_df) with auto_convert_types(True) no longer crashes on columns with genuinely mixed types (#32). A column that ends up holding both str and int values across rows (a natural consequence of per-value auto-conversion) previously broke Spark's Arrow bridge with PySparkTypeError; such columns now fall back to a uniform string column instead, while int/float-only mixes still promote correctly to double. Also hardens normalise(target=...) for all four DataFrame backends, which share the same column-unioning step.

v0.9.13

• Fix: execute(df) on a PySpark DataFrame now returns a real, distributed pyspark.sql.DataFrame, not a plain list[dict] (#31). Behavior change: code relying on the old list fallback needs updating. Polars input was independently confirmed to already work correctly.
• Performance: JSON-string-column auto-expansion (0.9.12) is ~40-43% faster for large embedded payloads, rewritten around serde_json::value::RawValue to avoid a redundant full-tree parse/reserialize per row, while keeping the same graceful per-row fallback behavior.

v0.9.12

• Fix: execute(df) in .flatten() mode now auto-expands DataFrame columns holding JSON strings, not just columns already typed as dicts/structs (#30). Behavior change: a DataFrame with a JSON-string column now produces more/differently-shaped output columns in flatten mode than before; .unflatten()/.normal() mode are unaffected.

v0.9.11

• Fix: execute(..., normalise=True, target="pyspark") could silently corrupt an all-None column on Spark's non-Arrow fallback path (taken automatically when pyarrow isn't installed, which pyspark does not depend on) -- a missing value could serialize as the literal string "<NA>" instead of a real null. Fixed by computing an explicit StructType schema from the data (instead of relying on Spark to infer it) and using plain Python None instead of pandas's nullable extension type, verified correct with and without pyarrow installed.

v0.9.10

• New: execute(input, normalise=True, target=None) (Python) -- always returns a wide DataFrame (one column per flattened key) regardless of input shape (str/dict/list/DataFrame/Series all supported), working natively across pandas, polars, pyarrow, and now genuinely PySpark (a real pyspark.sql.DataFrame, closing the previous list-of-dicts fallback for this path). See DataFrame & Series Support.
• New: JSONTools (Python) is now picklable (pickle.dumps/pickle.loads), including across a real process boundary (e.g. captured in a PySpark UDF/mapInPandas closure via cloudpickle) -- via __reduce__ plus a new to_config_json()/from_config_json() method pair. (#29)
• Fix: critical auto_convert_types panic on multi-byte UTF-8 content in specific positions (e.g. "5€ García", a "+1Á2" timezone offset) -- two fixed-byte-offset string slices assumed the offset was always a UTF-8 character boundary. Fixed with an is_char_boundary guard at each site; no behavior change for valid inputs. (#29)

See CHANGELOG.md for full details.

v0.9.8

• Changed: orjson is now a required Python dependency, used automatically for dict/DataFrame-row JSON (de)serialization -- pip install json-tools-rs is all that's needed. A per-call fallback to the standard library still covers inputs orjson can't handle (e.g. integers beyond 64-bit range).
• Performance: Python binding marshaling ~37% faster (dict calls) / ~39% faster (str calls) via a detection fast-path plus the orjson backend; JVM binding marshaling ~22-38% faster per call (UTF-8 byte[] across the JNI boundary instead of String); unflatten ~5-6% faster and roundtrip ~4-5% faster (corpus-tuned container capacity hints, single-lookup entry()); flatten ~13-16% faster across payload sizes (removed a double-scan in the core tape scanner).

See CHANGELOG.md for full details.

v0.9.7

• New: .exclude_key(pattern) (Rust/Python/JVM) -- drop any key, and its entire value/subtree, whose name contains pattern (literal by default, r'...' for regex). Matching a container key drops its entire subtree in O(1), without walking it. See Key Exclusion.
• New: .exclude_value(pattern) (Rust/Python/JVM) -- drop a key-value pair whose value contains pattern. Applies only to scalar leaf values; checked after .value_replacement()/.auto_convert_types() have run. See Value Exclusion.
• Fix: .remove_nulls() now runs consistently last across .flatten()/.unflatten()/.normal() mode -- previously .value_replacement() and .auto_convert_types() composed in different orders across the three engines, so a value that only became null after a replacement could slip past .remove_nulls() depending on mode.

See CHANGELOG.md for full details, including edge-case coverage across all three languages.

v0.9.6

• New: fine-grained, per-category control over automatic type conversion -- .convert_dates(), .convert_nulls(), .convert_booleans(), .convert_numbers() (Rust/Python/JVM) let each category be enabled/disabled independently, each also accepting real customization (date UTC-normalization toggles, extra null/boolean tokens, per-sub-format number toggles). .auto_convert_types(bool) is unchanged and still means "all four, default behavior." See Automatic Type Conversion.
• Breaking (pre-1.0): ProcessingConfig/FilteringConfig/CollisionConfig/ReplacementConfig are now #[non_exhaustive]; ProcessingConfig.auto_convert_types: bool removed in favor of ProcessingConfig.type_conversion: TypeConversionConfig. Only affects code constructing these via a bare struct literal or reading that field directly -- the JSONTools builder is unaffected.
• Performance: the existing hot-path type-conversion function is untouched by this change; the new per-category dispatch is selected once per execute() call, confirmed within ~1% of prior auto_convert_types cost (Criterion).

v0.9.5

• Documentation-wide accuracy sweep: every root-level doc, the full mdBook site, and the JVM Java source's own doc comments audited against actual source code and live runtime behavior (not just re-read) across four parallel passes. Corrected fabricated/stale internals (references to a phf key cache, rustc-hash, Arc<str> key dedup, and function names that no longer exist -- none of that is in the current codebase), stale benchmark numbers (some off by 3-14x), wrong error-handling semantics (e.g. .separator("") documented as panicking; it returns a config error), several broken guide examples, and stale "not yet published" claims for Maven Central/PyPI (both have been live for a while). Also fixed a real internal contradiction in the JVM Java source itself (FlattenUDF/BatchTransform javadoc claimed Lakeflow Pipeline support that Databricks doesn't actually allow) and added a missing JVM API reference page.
• New: runnable examples covering every builder feature individually, plus curated multi-feature pipelines, mirrored across all three language bindings with matching inputs/outputs -- see Runnable Examples below.
• Performance: regex pattern lookup for key_replacement/value_replacement no longer re-hashes and re-walks the cache on every key/value check (a thread-local "sticky" cache of recently-used patterns short-circuits the common case) -- regex scenarios 9-22% faster (Criterion). Consolidated two near-duplicate replacement-application code paths, which also fixed a missing SIMD fast-path for literal value replacement (~15-19% faster for that case).

See CHANGELOG.md for full details on all of the above.

v0.9.4

• Bug fix: auto_convert_types silently corrupted the trailing digits of large integer strings (17+ digits, e.g. Snowflake/Discord/database bigint IDs) by always round-tripping through f64, which only has ~15-17 significant decimal digits of exact precision. Now reuses already-canonical integer strings directly instead of reformatting through a float.
• Python bindings: dict/list[dict]/DataFrame/Series conversion switched from the pythonize crate's generic serde-based traversal to direct calls to Python's own json module. Benchmarked against the actual built extension: ~18% faster for a single nested dict, ~1.6x faster for a 200-row pandas DataFrame (the realistic cases this library exists for); flat/tiny dicts see a smaller, reported-honestly regression. Removes the pythonize dependency entirely.
• Performance: credit/debit currency suffix stripping ("100CR"/"100DR") in auto_convert_types no longer goes through std's generic string-pattern search machinery -- ~13-17% faster on currency-heavy conversion (Criterion). Literal (non-regex) key/value replacement now uses SIMD substring search -- ~2.6-4.8% faster. unflatten's internal object maps now start pre-sized instead of growing from empty -- ~7-9% faster combined. auto_convert_types's date detection hand-rolled instead of using chrono's generic parser -- ~25% faster on mixed real-dates/false-positive workloads. flatten's slow path (key transforms configured) now uses an arena allocator for deep-nested documents -- up to ~14% faster end-to-end.

See CHANGELOG.md for full details on all of the above, including the honest trade-offs.

v0.9.3

• Bug fix: flatten produced invalid JSON for any key containing an escaped character (\", \\, control chars) when no key transform was configured -- the default, most common usage.
• Bug fix: re-escaping corrupted multi-byte UTF-8 characters (e.g. café "quoted" became café \"quoted\") whenever a string needed escaping and also contained non-ASCII text -- affected key escaping under lowercase_keys/key_replacement/collision-handling, value escaping under value_replacement, and unflatten's key serialization.
• Performance: JSON object keys now use CompactString instead of String (inlines keys up to 24 bytes, no heap allocation) -- unflatten is ~19-22% faster (Criterion). Redundant separator re-scan eliminated in unflatten's tree-building. Regex pattern cache now evicts genuinely least-recently-used entries instead of arbitrary ones. Key/value re-escaping is ~17-22% faster as a side effect of the UTF-8 corruption fix above.

See CHANGELOG.md for full details on all of the above.

v0.9.2

(v0.9.1 was tagged a day earlier but only completed publishing to Maven Central -- a crates.io/PyPI release pipeline bug caused those two to fail before any upload. Fixed and re-cut as v0.9.2 across all three registries; no code changes beyond the release pipeline fix itself.)

• JVM (Java) bindings (BREAKING for key_replacement/value_replacement, see below): new Spark UDF bindings (jvm/) with full feature parity, via a JNI shim over the same Rust core -- see jvm/README.md.
• key_replacement/value_replacement pattern syntax (BREAKING): patterns are now literal (exact substring match) by default; wrap in r'...' (e.g. r'^admin_') for regex. Previously every pattern was always compiled as regex.
• Rayon parallelism: batch processing switched back from std::thread::scope (per-call OS thread spawn) to Rayon's persistent work-stealing pool -- measurably faster for small-to-medium batches.
• has_escape scanner bug fix: escape sequences not adjacent to a quote (\n, \t, \r, \uXXXX) were previously invisible to the tape scanner, silently skipping auto_convert_types/replacements/lowercase_keys for affected strings.
• crates.io and Maven Central publishing enabled on tagged releases.

See CHANGELOG.md for full details on all of the above.

v0.9.0

• Crossbeam Parallelism: Migrated from Rayon to Crossbeam for finer-grained parallel control.
• DataFrame/Series Support: Native Python support for Pandas, Polars, PyArrow, and PySpark DataFrames and Series.
• Modular Architecture: Refactored into 10 focused modules for maintainability (zero API changes).
• Performance Optimizations: Eliminated per-entry HashMap in parallel flatten, early-exit discriminators, SIMD literal fallback, thread-local regex cache half-eviction, vectorized clean_number_string().
• Python Binding Optimizations: mem::take for zero-cost builder mutations, O(1) DataFrame/Series reconstruction.

v0.8.0

• Python Feature Parity: Added auto_convert_types, parallel_threshold, num_threads, and nested_parallel_threshold to Python bindings.
• Enhanced Type Conversion: Added support for ISO-8601 dates, currency codes (USD, EUR), basis points (bps), and suffixed numbers (K/M/B).
• Date Normalization: Automatic detection and UTC normalization of date strings.

See CHANGELOG.md for full history.