Torque
High-performance JSON library for Elixir via Rustler NIFs, powered by sonic-rs (SIMD-accelerated).
Torque provides the fastest JSON encoding and decoding available in the BEAM ecosystem, with a selective field extraction API for workloads that only need a subset of fields from each document.
Features
- SIMD-accelerated decoding (AVX2/SSE4.2 on x86, NEON on ARM)
- Ultra-low memory encoder (64 B per encode vs ~4 KB for OTP
json/jason) - Parse-then-get API for selective field extraction via JSON Pointer (RFC 6901)
- Batch field extraction (
get_many/2) with single NIF call - Automatic dirty CPU scheduler dispatch for inputs larger than 20 KB
- jiffy-compatible
{proplist}encoding
Installation
Add to your mix.exs:
def deps do
[
{:torque, "~> 0.2.2"}
]
end
Precompiled binaries are available for common targets. To compile from source, install a stable Rust toolchain and set TORQUE_BUILD=true.
CPU-optimized variants
On x86_64, precompiled binaries are available for three CPU feature levels:
| Variant | CPU features | target-cpu |
|---|---|---|
| baseline | SSE2 | x86-64 |
| v2 | SSE4.2, SSSE3, POPCNT | x86-64-v2 |
| v3 | AVX2, AVX, BMI1, BMI2, FMA | x86-64-v3 |
At compile time, Torque auto-detects the host CPU and downloads the best matching variant. To override detection (e.g., when cross-compiling for a different target):
TORQUE_CPU_VARIANT=v2 mix compile # force SSE4.2 variant
TORQUE_CPU_VARIANT=v3 mix compile # force AVX2 variant
TORQUE_CPU_VARIANT=base mix compile # force baseline
Usage
Decoding
{:ok, data} = Torque.decode(~s({"name":"Alice","age":30}))
# %{"name" => "Alice", "age" => 30}
data = Torque.decode!(json)
Selective Field Extraction
Parse once, extract many fields without building the full Elixir term tree:
{:ok, doc} = Torque.parse(json)
{:ok, "example.com"} = Torque.get(doc, "/site/domain")
nil = Torque.get(doc, "/missing/field", nil)
# Batch extraction (single NIF call, fastest path)
results = Torque.get_many(doc, ["/id", "/site/domain", "/device/ip"])
# [{:ok, "req-1"}, {:ok, "example.com"}, {:ok, "1.2.3.4"}]
When your JSON is known to have no duplicate object keys, pass unique_keys: true
for faster field lookups (uses sonic-rs internal indexing instead of linear scan):
{:ok, doc} = Torque.parse(json, unique_keys: true)
Encoding
# Maps with atom or binary keys
{:ok, json} = Torque.encode(%{id: "abc", price: 1.5})
# "{\"id\":\"abc\",\"price\":1.5}"
# Bang variant
json = Torque.encode!(%{id: "abc"})
# iodata variant (fastest, no {:ok, ...} tuple wrapping)
json = Torque.encode_to_iodata(%{id: "abc"})
# jiffy-compatible proplist format
{:ok, json} = Torque.encode({[{:id, "abc"}, {:price, 1.5}]})
API
| Function | Description |
|---|---|
Torque.decode(binary) | Decode JSON to Elixir terms |
Torque.decode!(binary) | Decode JSON, raising on error |
Torque.parse(binary, opts) | Parse JSON into opaque document reference |
Torque.get(doc, path) | Extract field by JSON Pointer path |
Torque.get(doc, path, default) | Extract field with default for missing paths |
Torque.get_many(doc, paths) | Extract multiple fields in one NIF call |
Torque.get_many_nil(doc, paths) | Extract multiple fields, nil for missing |
Torque.length(doc, path) | Return length of array at path |
Torque.encode(term) | Encode term to JSON binary |
Torque.encode!(term) | Encode term, raising on error |
Torque.encode_to_iodata(term) | Encode term, returns binary directly (fastest) |
Type Conversion
JSON to Elixir
| JSON | Elixir |
|---|---|
| object | map (binary keys) |
| array | list |
| string | binary |
| integer | integer |
| float | float |
true, false | true, false |
null | nil |
For objects with duplicate keys, the last value wins (unless unique_keys: true is passed to parse/2).
Integers outside the signed/unsigned 64-bit range decode as exact arbitrary-precision integers (Erlang bignums) via decode/1, rather than degrading to lossy floats. The parse/2 + get/2 path returns them as floats, since the parsed document cannot hold a bignum.
Elixir to JSON
| Elixir | JSON |
|---|---|
| map (atom/binary keys) | object |
| list | array |
| binary | string |
| integer | number |
| float | number |
true, false | true, false |
nil | null |
| atom | string |
{keyword_list} | object |
Errors
Functions return {:error, reason} tuples (or raise ArgumentError for bang/iodata variants). Possible reason atoms:
Decode / Parse
| Atom | Returned by | Meaning |
|---|---|---|
:nesting_too_deep | decode/1, parse/1, get/2, get_many/2 | Document exceeds 128 nesting levels |
parse/1 and decode/1 also return {:error, binary} with a message from sonic-rs for malformed JSON.
Encode
| Atom | Returned by | Meaning |
|---|---|---|
:unsupported_type | encode/1 | Term has no JSON representation (PID, reference, port, …) |
:invalid_utf8 | encode/1 | Binary string or map key is not valid UTF-8 |
:invalid_key | encode/1 | Map key is not an atom or binary (e.g. integer key) |
:malformed_proplist | encode/1 | {proplist} contains a non-{key, value} element |
:non_finite_float | encode/1 | Float is infinity or NaN (unreachable from normal BEAM code) |
:nesting_too_deep | encode/1 | Term exceeds 128 nesting levels |
Benchmarks
Apple M2 Pro, OTP 29, Elixir 1.20:
Decode (1.2 KB OpenRTB)
| Library | ips | mean | median | p99 | memory |
|---|---|---|---|---|---|
| torque | 392.8K | 2.55 μs | 2.46 μs | 4.25 μs | 1.56 KB |
| glazer | 298.9K | 3.35 μs | 3.00 μs | 8.92 μs | 1.56 KB |
| jiffy | 208.8K | 4.79 μs | 4.33 μs | 10.13 μs | 1.55 KB |
| simdjsone | 181.6K | 5.51 μs | 5.29 μs | 9.88 μs | 1.59 KB |
| otp json | 125.2K | 7.99 μs | 7.71 μs | 13.08 μs | 7.73 KB |
| jason | 92.6K | 10.80 μs | 10.13 μs | 22.33 μs | 9.54 KB |
Decode (750 KB Twitter)
| Library | ips | mean | median | p99 | memory |
|---|---|---|---|---|---|
| torque | 655.1 | 1.53 ms | 1.32 ms | 2.16 ms | 1.57 KB |
| glazer | 590.8 | 1.69 ms | 1.53 ms | 2.56 ms | 1.58 KB |
| simdjsone | 392.5 | 2.55 ms | 2.10 ms | 4.53 ms | 1.59 KB |
| jiffy | 286.3 | 3.49 ms | 3.59 ms | 3.92 ms | 2.30 MB |
| otp json | 165.2 | 6.05 ms | 5.42 ms | 16.57 ms | 2.48 MB |
| jason | 142.1 | 7.04 ms | 7.01 ms | 8.15 ms | 3.54 MB |
Encode (1.2 KB OpenRTB)
| Library | ips | mean | median | p99 | memory |
|---|---|---|---|---|---|
| torque [proplist() :: iodata()] | 1280K | 0.78 μs | 0.71 μs | 0.88 μs | 64 B |
| torque [proplist() :: binary()] | 1190K | 0.84 μs | 0.75 μs | 1.63 μs | 88 B |
| glazer [map() :: binary()] | 1090K | 0.92 μs | 0.83 μs | 1.13 μs | 64 B |
| otp json [map() :: iodata()] | 1080K | 0.92 μs | 0.79 μs | 2.04 μs | 3928 B |
| torque [map() :: iodata()] | 1050K | 0.95 μs | 0.88 μs | 1.13 μs | 64 B |
| torque [map() :: binary()] | 1040K | 0.96 μs | 0.88 μs | 1.13 μs | 88 B |
| jiffy [proplist() :: iodata()] | 730K | 1.37 μs | 1.13 μs | 2.33 μs | 120 B |
| jiffy [map() :: iodata()] | 620K | 1.62 μs | 1.42 μs | 2.04 μs | 824 B |
| jason [map() :: iodata()] | 610K | 1.63 μs | 1.50 μs | 2.88 μs | 3848 B |
| simdjsone [proplist() :: iodata()] | 440K | 2.25 μs | 2.04 μs | 2.88 μs | 184 B |
| jason [map() :: binary()] | 380K | 2.66 μs | 2.38 μs | 6.29 μs | 3912 B |
| simdjsone [map() :: iodata()] | 370K | 2.69 μs | 2.42 μs | 4.79 μs | 888 B |
Encode (750 KB Twitter)
| Library | ips | mean | median | p99 | memory |
|---|---|---|---|---|---|
| torque [proplist() :: iodata()] | 1228.7 | 0.81 ms | 0.80 ms | 1.00 ms | 64 B |
| torque [proplist() :: binary()] | 1226.6 | 0.82 ms | 0.80 ms | 0.99 ms | 88 B |
| torque [map() :: binary()] | 1098.9 | 0.91 ms | 0.89 ms | 1.17 ms | 88 B |
| torque [map() :: iodata()] | 1077.4 | 0.93 ms | 0.91 ms | 1.12 ms | 64 B |
| glazer [map() :: binary()] | 981.9 | 1.02 ms | 1.00 ms | 1.28 ms | 64 B |
| jiffy [proplist() :: iodata()] | 536.5 | 1.86 ms | 1.82 ms | 2.98 ms | 37.7 KB |
| jiffy [map() :: iodata()] | 439.8 | 2.27 ms | 2.26 ms | 2.48 ms | 1.06 MB |
| otp json [map() :: iodata()] | 267.3 | 3.74 ms | 4.08 ms | 6.51 ms | 5.40 MB |
| jason [map() :: iodata()] | 260.6 | 3.84 ms | 3.58 ms | 6.07 ms | 4.96 MB |
| simdjsone [proplist() :: iodata()] | 255.6 | 3.91 ms | 3.78 ms | 6.60 ms | 37.7 KB |
| simdjsone [map() :: iodata()] | 214.7 | 4.66 ms | 4.64 ms | 6.61 ms | 1.06 MB |
| jason [map() :: binary()] | 136.6 | 7.32 ms | 7.10 ms | 9.02 ms | 4.96 MB |
Parse (1.2 KB OpenRTB)
| Library | ips | mean | median | p99 |
|---|---|---|---|---|
| torque parse(unique_keys) | 572.1K | 1.75 μs | 1.33 μs | 5.75 μs |
| torque parse | 549.5K | 1.82 μs | 1.33 μs | 6.08 μs |
| simdjsone parse | 320.4K | 3.12 μs | 1.21 μs | 5.96 μs |
Extract 5 fields from raw JSON (1.2 KB OpenRTB)
End-to-end cost of pulling 5 fields out of a JSON blob: parse + get
(torque, simdjsone) vs decode + find (glazer has no lazy handle, so it must
fully decode first). This is the apples-to-apples version of "get" — torque's
selective extraction skips materializing the whole document.
| Library | ips | mean | median | p99 |
|---|---|---|---|---|
| torque parse(unique_keys) + get_many | 443.3K | 2.26 μs | 1.71 μs | 6.67 μs |
| torque parse + get_many | 425.3K | 2.35 μs | 1.79 μs | 6.04 μs |
| torque parse + get x5 | 419.9K | 2.38 μs | 2.00 μs | 6.92 μs |
| simdjsone parse + get x5 | 371.7K | 2.69 μs | 1.67 μs | 7.00 μs |
| glazer decode + find x5 | 317.0K | 3.15 μs | 2.83 μs | 7.71 μs |
Run benchmarks locally:
MIX_ENV=bench mix run bench/torque_bench.exs
Limitations
- Nesting depth: JSON documents nested deeper than 128 levels return
{:error, :nesting_too_deep}fromdecode/1,parse/1,get/2,get_many/2, andencode/1rather than crashing the VM. Real-world documents are never this deep; the limit exists to prevent stack overflow in the NIF (the dirty CPU scheduler, used for inputs over 20 KB, has a small stack).
License
MIT