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, Python, and JVM (Java/Spark) 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'...'
🛡️ 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
☕ JVM Bindings: Java/Spark UDFs (row and batched mapPartitions tiers) for Databricks Jobs/notebooks on classic compute and other Spark workloads -- see jvm/README.md

Why JSON Tools RS?
Features
Quick Start
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)

JVM / Spark

JNI-based Java bindings, mirroring the same JSONTools builder, for use as Apache Spark UDFs -- a simple row UDF and a higher-throughput batched mapPartitions transform. Built for Databricks Jobs/notebooks on classic compute and other Spark workloads (not usable inside a Databricks Lakeflow Declarative Pipeline -- Databricks doesn't permit JVM libraries on pipeline compute at all; use the Python bindings above, wrapped in a pandas_udf, for that case instead).

import io.github.amaye15.jsontoolsrs.JsonTools;
import io.github.amaye15.jsontoolsrs.JsonToolsHandle;

try (JsonToolsHandle tools = JsonTools.builder()
        .flatten()
        .separator("::")
        .keyReplacement("r'^admin_'", "")
        .removeNulls(true)
        .build()) {
    String result = tools.execute("{\"admin_name\": \"Jane\", \"age\": null}");
    // {"name":"Jane"}
}

See jvm/README.md for the Spark UDF API and Setting Up on Databricks for the full deployment walkthrough (both this and the pandas_udf path).

Runnable Examples

Every builder feature has a standalone, runnable example in all three 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). All three language versions use matching inputs and produce matching output.

	Individual features	Curated combinations
Rust	`examples/feature_by_feature.rs`	`examples/feature_combinations.rs`
Python	`python/examples/feature_by_feature.py`	`python/examples/feature_combinations.py`
Java	`jvm/examples/.../FeatureByFeature.java`	`jvm/examples/.../FeatureCombinations.java`

# 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

# Java (compiles examples/ as an extra source root, kept out of the packaged jar)
cd jvm
mvn -P examples compile exec:java -Dexec.mainClass=io.github.amaye15.jsontoolsrs.examples.FeatureByFeature
mvn -P examples compile exec:java -Dexec.mainClass=io.github.amaye15.jsontoolsrs.examples.FeatureCombinations

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

Method	Description	Example
`.flatten()`	Set operation mode to flatten	`JSONTools::new().flatten()`
`.unflatten()`	Set operation mode to unflatten	`JSONTools::new().unflatten()`
`.normal()`	Set mode to pass-through (transform only)	`JSONTools::new().normal()`
`.separator(sep)`	Set key separator (default: `"."`)	`.separator("::")`
`.lowercase_keys(bool)`	Convert keys to lowercase	`.lowercase_keys(true)`
`.remove_empty_strings(bool)`	Remove empty string values	`.remove_empty_strings(true)`
`.remove_nulls(bool)`	Remove null values	`.remove_nulls(true)`
`.remove_empty_objects(bool)`	Remove empty objects `{}`	`.remove_empty_objects(true)`
`.remove_empty_arrays(bool)`	Remove empty arrays `[]`	`.remove_empty_arrays(true)`
`.key_replacement(find, repl)`	Replace key patterns (literal, or regex via `r'...'`)	`.key_replacement("r'user_'", "")`
`.value_replacement(find, repl)`	Replace value patterns (literal, or regex via `r'...'`)	`.value_replacement("@old.com", "@new.com")`
`.handle_key_collision(bool)`	Collect colliding keys into arrays	`.handle_key_collision(true)`
`.auto_convert_types(bool)`	Convert types (nums, bools, dates)	`.auto_convert_types(true)`
`.parallel_threshold(n)`	Min batch size for parallelism	`.parallel_threshold(500)`
`.num_threads(n)`	Number of threads (default: CPU count)	`.num_threads(Some(4))`
`.nested_parallel_threshold(n)`	Nested object parallelism size	`.nested_parallel_threshold(50)`
`.max_array_index(n)`	Max array index for unflatten (DoS protection)	`.max_array_index(100_000)`

Automatic Type Conversion

When .auto_convert_types(true) is enabled, the library performs smart parsing on string values:

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.

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)

Booleans:

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

Nulls:

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

Installation

Rust

cargo add json-tools-rs

Python

pip install json-tools-rs

JVM / Spark

Published to Maven Central as io.github.amaye15:json-tools-rs-spark (live since v0.9.2, ships automatically on tagged releases):

<dependency>
  <groupId>io.github.amaye15</groupId>
  <artifactId>json-tools-rs-spark</artifactId>
  <version>0.9.5</version>
</dependency>

Or build from source (cargo build --release --features jvm && cd jvm && mvn package), or download the jar from a jvm-ci.yml CI run. See jvm/README.md for details.

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
├── jvm.rs            JVM bindings via JNI (Java/Spark UDFs, see jvm/)
├── tests.rs          Unit tests
└── main.rs           CLI examples

The processing pipeline:

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

Performance

Benchmark Results

Benchmark	Time	Description
Deep nesting (100 levels)	~2.17 µs	Deeply nested JSON objects
Wide objects (1,000 keys)	~24.8 µs	Flat objects with many keys
Large arrays (5,000 items)	~406 µs	Arrays with many elements
Parallel batch (10,000 items)	~635 µs	Batch processing with Rayon (`nested_parallel_threshold`)

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

v0.9.5 (Current)

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 cafÃ© \"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.

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json-tools-rs 0.9.5

JSON Tools RS

Why JSON Tools RS?

Features

Table of Contents

Quick Start

Rust - Unified JSONTools API

Basic Flattening

Advanced Flattening with Filtering

Automatic Type Conversion

Python - Unified JSONTools API

Basic Usage

Advanced Configuration & Parallelism

DataFrame & Series Support

JVM / Spark

Runnable Examples

Quick Reference

Method Cheat Sheet

Automatic Type Conversion

Installation

Rust

Python

JVM / Spark

Architecture

Performance

Benchmark Results

Optimization Techniques

CLI Demo

Contributing

License

Changelog

v0.9.5 (Current)

v0.9.4

v0.9.3

v0.9.2

v0.9.0

v0.8.0

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1 maintainer

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Algorithm	Hash digest
SHA256	`ee8845b77a2260f1e15b4e15f6813cdd043a071b492ce58400ba26f67184ee89`
MD5	`330d5c7fc78147d667b878567c9cf58a`
BLAKE2b-256	`3c0e15827c1ffb545b75b235be3fb6bc9292bafbe9299e86e22481589ee44237`

Algorithm	Hash digest
SHA256	`03de2ce8004d65f0b0e8fea384a3069bce42fda870ac269073e6d9917748109e`
MD5	`224742ba61bc9bd43bb9e74e2092b7d3`
BLAKE2b-256	`2b5287ace15fe2788e30c42b0c88c13646db30ddcf97a9f8ad0227fbbbac053c`

Algorithm	Hash digest
SHA256	`ada8a27f908734290a52618169bb1cce6dae7d7f72c8289024cf2aae87bf0bde`
MD5	`b340b37bd3939154db422c8a493b1246`
BLAKE2b-256	`5ce397180d2b952882feda2436ad80b012ed723d44431e1564bc485fed3e0253`

Algorithm	Hash digest
SHA256	`735c17aff492956016ecafaf8c19980fd20bbb95ff32e8e67743968ef8294f57`
MD5	`0db81e4a40a1d9bdf1e623a14c1e89ac`
BLAKE2b-256	`4388cef047a24e48caf77fcea87f9a25c3a1a34a5bf446d56e626a6a38ba354e`

Algorithm	Hash digest
SHA256	`86a6cedbba3fd943a299529f22baddfb67aa8831204d8f9139d6faa08d95cfed`
MD5	`a8e08ae7d33f0cb229b5aaf6cb882df3`
BLAKE2b-256	`be3371b005f6bc3c2045ffaf5837779d9d4f665bcf4df4451670b2709f677fcc`

Algorithm	Hash digest
SHA256	`d0d52bc5addb93924761c723329d6b9226072c23b7c8d2f1af6e6e162ed9b977`
MD5	`7c75efa4e5c9912c73301b56484288ef`
BLAKE2b-256	`3915adc33a9f21640fb0368b71f3fe972d9d6e40653026b78ec7d20cf33a1719`

Algorithm	Hash digest
SHA256	`1ba8fb4b752d3b6995f5611d3036619af99cdc76ffe8384537baebc0f1c77f76`
MD5	`93d1a57c7a407732a49e4a1ffa3576c0`
BLAKE2b-256	`855bc589c7a2510c808c6658cc9d4bbf9106d13ac88e42e61ee90dad64e97a0d`

Algorithm	Hash digest
SHA256	`6947c3f04833a03a347890ad15efc847fdc6f3447d800c36b8ff3506bc4573d2`
MD5	`2e7968e85a3133ffeeff8c416039981d`
BLAKE2b-256	`182324a2534eb0428486fe1f6c481f978acd7fdd087126698a99a814f9f7f501`

Algorithm	Hash digest
SHA256	`7d0a588afe409d933a1c99520bbf85eff59b72a74364c57f4ef62ce3c5e25970`
MD5	`ff3e4016b5ec3e3acb21bcca67b82733`
BLAKE2b-256	`de1a7f259414aaf70ef295a41f32caf552d6a6e77a52c7e114f940f2740891f3`

Algorithm	Hash digest
SHA256	`02b44748fc420b0c6527d3c77120c22d55a4e0e43c50c03ea0a3bb1f0ab3f67b`
MD5	`4c518112e797b49ba43683dcc405058e`
BLAKE2b-256	`e7898dbd29a604d25af1b1a40cee90af75def952ee3271ad1715be2caf6cc941`