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1.1.1  Oct 31, 2023 
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1.0.0beta  Aug 4, 2023 
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jaq
jaq (pronounced like Jacques[^jacques]) is a clone of the JSON data processing tool jq. jaq aims to support a large subset of jq's syntax and operations.
jaq focuses on three goals:

Correctness: jaq aims to provide a more correct and predictable implementation of jq, while preserving compatibility with jq in most cases.
Examples of surprising jq behaviour
nan > nan
is false, whilenan < nan
is true.[[]]  implode
crashes jq, and this was not fixed at the time of writing despite being known since five years. The jq manual claims that
limit(n; exp)
"extracts up ton
outputs fromexp
". This holds for values ofn > 1
, e.g.jq n '[limit(2; 1, 2, 3)]'
yields[1, 2]
, but whenn == 0
,jq n '[limit(0; 1, 2, 3)]'
yields[1]
instead of[]
. And perhaps even worse, whenn < 0
, thenlimit
yields all outputs fromexp
, which is not documented.

Performance: I created jaq originally because I was bothered by jq's long startup time, which amounts to about 50ms on my machine. This can particularly show when processing a large number of small files. jaq starts up about 30 times faster than jq 1.6 and outperforms jq also on many other benchmarks.

Simplicity: jaq aims to have a simple and small implementation, in order to reduce the potential for bugs and to facilitate contributions.
I drew inspiration from another Rust program, namely jql. However, unlike jql, jaq aims to closely imitate jq's syntax and semantics. This should allow users proficient in jq to easily use jaq.
[^jacques]: I wanted to create a tool that should be discreet and obliging, like a good waiter. And when I think of a typical name for a (French) waiter, to my mind comes "Jacques". Later, I found out about the old French word jacquet, meaning "squirrel", which makes for a nice ex post inspiration for the name.
Installation
From Source
To compile jaq, you need a Rust toolchain. See https://rustup.rs/ for instructions. (Note that Rust compilers shipped with Linux distributions may be too outdated to compile jaq.)
Any of the following commands install jaq:
$ cargo install locked jaq
$ cargo install locked git https://github.com/01mf02/jaq # latest development version
On my system, both commands place the executable at ~/.cargo/bin/jaq
.
If you have cloned this repository, you can also build jaq by executing one of the commands in the cloned repository:
$ cargo build release # places binary into target/release/jaq
$ cargo install locked path jaq # installs binary
jaq should work on any system supported by Rust. If it does not, please file an issue.
Binaries
You may also install jaq using homebrew on macOS or Linux:
$ brew install jaq
$ brew install HEAD jaq # latest development version
Examples
The following examples should give an impression of what jaq can currently do. You should obtain the same outputs by replacing jaq with jq. If not, your filing an issue would be appreciated. :) The syntax is documented in the jq manual.
Access a field:
$ echo '{"a": 1, "b": 2}'  jaq '.a'
1
Add values:
$ echo '{"a": 1, "b": 2}'  jaq 'add'
3
Construct an array from an object in two ways and show that they are equal:
$ echo '{"a": 1, "b": 2}'  jaq '[.a, .b] == [.[]]'
true
Apply a filter to all elements of an array and filter the results:
$ echo '[0, 1, 2, 3]'  jaq 'map(.*2)  [.[]  select(. < 5)]'
[0, 2, 4]
Read (slurp) input values into an array and get the average of its elements:
$ echo '1 2 3 4'  jaq s 'add / length'
2.5
Repeatedly apply a filter to itself and output the intermediate results:
$ echo '0'  jaq '[recurse(.+1; . < 3)]'
[0, 1, 2]
Lazily fold over inputs and output intermediate results:
$ seq 1000  jaq n 'foreach inputs as $x (0; . + $x)'
1 3 6 10 15 [...]
Performance
The following evaluation consists of several benchmarks that
allow comparing the performance of jaq, jq, and gojq.
The empty
benchmark runs n
times the filter empty
with null input,
serving to measure the startup time.
The bffib
benchmark runs a Brainfuck interpreter written in jq,
interpreting a Brainfuck script that produces n
Fibonacci numbers.
The other benchmarks evaluate various filters with n
as input;
see bench.sh
for details.
I generated the benchmark data with
bench.sh target/release/jaq jq1.7 gojq0.12.13 jq1.6  tee bench.json
on a Linux system with an AMD Ryzen 5 5500U.[^binaries]
I then processed the results with a "oneliner" (stretching the term and the line a bit):
jq rs '.[]  "`\(.name)`\(.n)" + ([.time[]  min  (.*1000round)? // "N/A"]  min as $total_min  map(if . == $total_min then "**\(.)**" else "\(.)" end)  join(""))' bench.json
(Of course, you can also use jaq here instead of jq.)
Finally, I concatenated the table header with the output and piped it through pandoc t gfm
.
[^binaries]: The binaries for jq1.7 and gojq0.12.13 were retrieved from their GitHub release pages, the binary for jq1.6 was installed from the standard Ubuntu repository.
Table: Evaluation results in milliseconds ("N/A" if more than 10 seconds).
Benchmark  n  jaq1.2  jq1.7  gojq0.12.13  jq1.6 

empty 
512  650  790  740  8340 
bffib 
13  410  1280  820  1420 
reverse 
1048576  60  680  310  630 
sort 
1048576  140  530  600  670 
groupby 
1048576  420  1850  1680  2830 
minmax 
1048576  220  320  290  310 
add 
1048576  480  650  1540  750 
kv 
131072  160  150  250  200 
kvupdate 
131072  190  530  570  N/A 
kventries 
131072  580  1170  820  1110 
eximplode 
1048576  460  1110  740  1080 
reduce 
1048576  740  880  N/A  850 
trycatch 
1048576  180  330  480  650 
treeflatten 
17  650  360  0  480 
treeupdate 
17  450  980  1850  1180 
treepaths 
17  450  380  920  470 
tofromjson 
65536  40  370  100  380 
ack 
7  570  680  1090  610 
rangeprop 
128  260  310  320  580 
The results show that
jaq1.2 is fastest on 16 benchmarks, whereas
jq1.7 is fastest on 2 benchmarks and
gojq0.12.13 is fastest on 1 benchmark.
gojq is much faster on treeflatten
because it implements the filter flatten
natively instead of by definition.
Features
Here is an overview that summarises:
 features already implemented, and
 features not yet implemented.
Contributions to extend jaq are highly welcome.
Basics
 Identity (
.
)  Recursion (
..
)  Basic data types (null, boolean, number, string, array, object)
 ifthenelse (
if .a < .b then .a else .b end
)  Folding (
reduce .[] as $x (0; . + $x)
,foreach .[] as $x (0; . + $x; . + .)
)  Error handling (
try ... catch ...
) (see the differences from jq)  String interpolation (
"The successor of \(.) is \(.+1)."
)  Format strings (
@json
,@text
,@csv
,@tsv
,@html
,@sh
,@base64
,@base64d
)
Paths
 Indexing of arrays/objects (
.[0]
,.a
,.["a"]
)  Iterating over arrays/objects (
.[]
)  Optional indexing/iteration (
.a?
,.[]?
)  Array slices (
.[3:7]
,.[0:1]
)  String slices
Operators
 Composition (

)  Binding (
. as $x  $x
)  Concatenation (
,
)  Plain assignment (
=
)  Update assignment (
=
,+=
,=
)  Alternation (
//
)  Logic (
or
,and
)  Equality and comparison (
.a == .b
,.a < .b
)  Arithmetic (
+
,
,*
,/
,%
)  Negation (

)  Error suppression (
?
)
Definitions
 Basic definitions (
def map(f): [.[]  f];
)  Recursive definitions (
def r: r; r
)
Core filters
 Empty (
empty
)  Errors (
error
)  Input (
inputs
)  Length (
length
,utf8bytelength
)  Rounding (
floor
,round
,ceil
)  String <> JSON (
fromjson
,tojson
)  String <> integers (
explode
,implode
)  String normalisation (
ascii_downcase
,ascii_upcase
)  String prefix/postfix (
startswith
,endswith
,ltrimstr
,rtrimstr
)  String splitting (
split("foo")
)  Array filters (
reverse
,sort
,sort_by(.)
,group_by
,min_by
,max_by
)  Stream consumers (
first
,last
,range
,fold
)  Stream generators (
range
,recurse
)  Time (
now
,fromdateiso8601
,todateiso8601
)  More numeric filters (
sqrt
,sin
,log
,pow
, ...) (list of numeric filters)  More time filters (
strptime
,strftime
,strflocaltime
,mktime
,gmtime
, andlocaltime
)
Standard filters
These filters are defined via more basic filters.
Their definitions are at std.jq
.
 Undefined (
null
)  Booleans (
true
,false
,not
)  Special numbers (
nan
,infinite
,isnan
,isinfinite
,isfinite
,isnormal
)  Type (
type
)  Filtering (
select(. >= 0)
)  Selection (
values
,nulls
,booleans
,numbers
,strings
,arrays
,objects
,iterables
,scalars
)  Conversion (
tostring
,tonumber
)  Iterable filters (
map(.+1)
,map_values(.+1)
,add
,join("a")
)  Array filters (
transpose
,first
,last
,nth(10)
,flatten
,min
,max
)  Objectarray conversion (
to_entries
,from_entries
,with_entries
)  Universal/existential (
all
,any
)  Recursion (
walk
)  I/O (
input
)  Regular expressions (
test
,scan
,match
,capture
,splits
,sub
,gsub
)  Time (
fromdate
,todate
)
Numeric filters
jaq imports many filters from libm and follows their type signature.
Full list of numeric filters defined in jaq
Zeroargument filters:

acos

acosh

asin

asinh

atan

atanh

cbrt

cos

cosh

erf

erfc

exp

exp10

exp2

expm1

fabs

frexp
, which returns pairs of (float, integer). 
ilogb
, which returns integers. 
j0

j1

lgamma

log

log10

log1p

log2

logb

modf
, which returns pairs of (float, float). 
nearbyint

pow10

rint

significand

sin

sinh

sqrt

tan

tanh

tgamma

trunc

y0

y1
Twoargument filters that ignore .
:

atan2

copysign

drem

fdim

fmax

fmin

fmod

hypot

jn
, which takes an integer as first argument. 
ldexp
, which takes an integer as second argument. 
nextafter

nexttoward

pow

remainder

scalb

scalbln
, which takes as integer as second argument. 
yn
, which takes an integer as first argument.
Threeargument filters that ignore .
:

fma
Advanced features
jaq currently does not aim to support several features of jq, such as:
 Modules
 SQLstyle operators
 Streaming
Differences between jq and jaq
Numbers
jq uses 64bit floatingpoint numbers (floats) for any number. By contrast, jaq interprets numbers such as 0 or 42 as machinesized integers and numbers such as 0.0 or 3e8 as 64bit floats. Many operations in jaq, such as array indexing, check whether the passed numbers are indeed integer. The motivation behind this is to avoid rounding errors that may silently lead to wrong results. For example:
$ jq n '[0, 1, 2]  .[1.0000000000000001]'
1
$ jaq n '[0, 1, 2]  .[1.0000000000000001]'
Error: cannot use 1.0 as integer
$ jaq n '[0, 1, 2]  .[1]'
1
The rules of jaq are:
 The sum, difference, product, and remainder of two integers is integer.
 Any other operation between two numbers yields a float.
Examples:
$ jaq n '1 + 2'
3
$ jaq n '10 / 2'
5.0
$ jaq n '1.0 + 2'
3.0
You can convert an integer to a floatingpoint number e.g.
by adding 0.0, by multiplying with 1.0, or by dividing with 1.
You can convert a floatingpoint number to an integer by
round
, floor
, or ceil
:
$ jaq n '1.2  [floor, round, ceil]'
[1, 1, 2]
NaN and infinity
In jq, division by 0 has some surprising properties; for example,
0 / 0
yields nan
, whereas
0 as $n  $n / 0
yields an error.
In jaq, n / 0
yields nan
if n == 0
, infinite
if n > 0
, and infinite
if n < 0
.
jaq's behaviour is closer to the IEEE standard for floatingpoint arithmetic (IEEE 754).
jaq implements a total ordering on floatingpoint numbers to allow sorting values.
Therefore, it unfortunately has to enforce that nan == nan
.
(jq gets around this by enforcing nan < nan
, which breaks basic laws about total orders.)
Like jq, jaq prints nan
and infinite
as null
in JSON,
because JSON does not support encoding these values as numbers.
Preservation of fractional numbers
jaq preserves fractional numbers coming from JSON data perfectly (as long as they are not used in some arithmetic operation), whereas jq 1.6 may silently convert to 64bit floatingpoint numbers:
$ echo '1e500'  jq '.'
1.7976931348623157e+308
$ echo '1e500'  jaq '.'
1e500
Therefore, unlike jq 1.6, jaq satisfies the following paragraph in the jq manual:
An important point about the identity filter is that it guarantees to preserve the literal decimal representation of values. This is particularly important when dealing with numbers which can't be losslessly converted to an IEEE754 double precision representation.
Please note that newer versions of jq, e.g. 1.7, seem to preserve the literal decimal representation as well.
Assignments
Like jq, jaq allows for assignments of the form p = f
.
However, jaq interprets these assignments differently.
Fortunately, in most cases, the result is the same.
In jq, an assignment p = f
first constructs paths to all values that match p
.
Only then, it applies the filter f
to these values.
In jaq, an assignment p = f
applies f
immediately to any value matching p
.
Unlike in jq, assignment does not explicitly construct paths.
jaq's implementation of assignment likely yields higher performance,
because it does not construct paths.
Furthermore, this also prevents several bugs in jq "by design".
For example, given the filter [0, 1, 2, 3]  .[] = empty
,
jq yields [1, 3]
, whereas
jaq yields []
.
What happens here?
jq first constructs the paths corresponding to .[]
, which are .0, .1, .2, .3
.
Then, it removes the element at each of these paths.
However, each of these removals changes the value that the remaining paths refer to.
That is, after removing .0
(value 0), .1
does not refer to value 1, but value 2!
That is also why value 1 (and in consequence also value 3) is not removed.
There is more weirdness ahead in jq;
for example, 0  0 = .+1
yields 1
in jq,
although 0
is not a valid path expression.
However, 1  0 = .+1
yields an error.
In jaq, any such assignment yields an error.
jaq attempts to use multiple outputs of the righthand side, whereas
jq uses only the first.
For example, 0  (., .) = (., .+1)
yields 0 1 1 2
in jaq,
whereas it yields only 0
in jq.
However, {a: 1}  .a = (2, 3)
yields {"a": 2}
in both jaq and jq,
because an object can only associate a single value with any given key,
so we cannot use multiple outputs in a meaningful way here.
Because jaq does not construct paths,
it does not allow some filters on the lefthand side of assignments,
for example first
, last
, limit
:
For example, [1, 2, 3]  first(.[]) = .1
yields [0, 2, 3]
in jq, but is invalid in jaq.
Similarly, [1, 2, 3]  limit(2; .[]) = .1
yields [0, 1, 3]
in jq, but is invalid in jaq.
(Inconsequentially, jq also does not allow for last
.)
Definitions
Like jq, jaq allows for the definition of filters, such as:
def map(f): [.[]  f];
Arguments can also be passed by value, such as:
def cartesian($f; $g): [$f, $g];
Filter definitions can be nested and recursive, i.e. refer to themselves.
That is, a filter such as recurse
can be defined in jaq:
def recurse(f): def r: ., (f  r); r;
Since jaq 1.2, jaq optimises tail calls, like jq. Since jaq 1.1, recursive filters can also have nonvariable arguments, like in jq. For example:
def f(a): a, f(1+a);
Recursive filters with nonvariable arguments can yield surprising effects;
for example, a call f(0)
builds up calls of the shape f(1+(..(1+0)...))
,
which leads to exponential execution times.
Recursive filters with nonvariable arguments can very frequently be alternatively implemented by either:
 A nested filter: for example, instead of
def walk(f): (.[]? = walk(f))  f;
, you can usedef walk(f): def rec: (.[]? = rec)  f; rec;
.  A filter with variable arguments: for example, instead of
def f(a): a, f(1+a);
, you can equally well writedef f($a): $a, f(1+$a);
.  A filter with
recurse
: for example, you may writedef f(a): a  recurse(1+.);
. If you expect your filter to recurse deeply, it is advised to implement it usingrecurse
, because jaq has an optimised implementation ofrecurse
.
All of these options are supported by jaq.
Arguments
Like jq, jaq allows to define arguments via the command line,
in particular by the options arg
, rawfile
, slurpfile
.
This binds variables to values, and
for every variable $x
bound to v
this way,
$ARGS.named
contains an entry with key x
and value v
.
For example:
$ jaq n arg x 1 arg y 2 '$x, $y, $ARGS.named'
"1"
"2"
{
"x": "1",
"y": "2"
}
Folding
jq and jaq provide filters
reduce xs as $x (init; f)
and
foreach xs as $x (init; f)
.
In jaq, the output of these filters is defined very simply:
Assuming that xs
evaluates to x0
, x1
, ..., xn
,
reduce xs as $x (init; f)
evaluates to
init
 x0 as $x  f
 ...
 xn as $x  f
and foreach xs as $x (init; f)
evaluates to
init
 x0 as $x  f  (.,
 ...
 xn as $x  f  (.,
empty)...)
Additionally, jaq provides the filter for xs as $x (init; f)
that evaluates to
init
 ., (x0 as $x  f
 ...
 ., (xn as $x  f
)...)
The difference between foreach
and for
is that
for
yields the output of init
, whereas foreach
omits it.
For example,
foreach (1, 2, 3) as $x (0; .+$x)
yields 1, 3, 6
, whereas
for (1, 2, 3) as $x (0; .+$x)
yields 0, 1, 3, 6
.
The interpretation of reduce
/foreach
in jaq has the following advantages over jq:
 It deals very naturally with filters that yield multiple outputs.
In contrast, jq discriminates outputs of
f
, because it recurses only on the last of them, although it outputs all of them.Example
`foreach (5, 10) as $x (1; .+$x, .)` yields `6, 1, 9, 1` in jq, whereas it yields `6, 16, 6, 1, 9, 1` in jaq. We can see that both jq and jaq yield the values `6` and `1` resulting from the first iteration (where `$x` is 5), namely `1  5 as $x  (.+$x, .)`. However, jq performs the second iteration (where `$x` is 10) *only on the last value* returned from the first iteration, namely `1`, yielding the values `9` and `1` resulting from `1  10 as $x  (.+$x, .)`. jaq yields these values too, but it also performs the second iteration on all other values returned from the first iteration, namely `6`, yielding the values `16` and `6` that result from ` 6  10 as $x  (.+$x, .)`.  It makes the implementation of
reduce
andforeach
special cases of the same code, reducing the potential for bugs.
Compared to foreach ...
, the filter for ...
(where ...
refers to xs as $x (init; f)
)
has a stronger relationship with reduce
.
In particular,
the values yielded by reduce ...
are a subset of
the values yielded by for ...
.
This does not hold if you replace for
by foreach
.
Furthermore, jq provides the filter
foreach xs as $x (init; f; proj)
(foreach/3
) and interprets
foreach xs as $x (init; f)
(foreach/2
) as
foreach xs as $x (init; f; .)
, whereas
jaq does not provide foreach/3
because
it requires completely separate logic from foreach/2
and reduce
in both the parser and the interpreter.
Error handling
In jq, the try f catch g
expression breaks out of the f
stream as
soon as an error occurs, ceding control to g
after that. This is
mentioned in its manual as a possible mechanism for breaking out of
loops
(here). jaq
however doesn't interrupt the f
stream, but instead sends each
error value emitted to the g
filter; the result is a stream of
values emitted from f
with values emitted from g
interspersed
where errors occurred.
Consider the following example: this expression is true
in jq,
because the first error(2)
interrupts the stream:
[try (1, error(2), 3, error(4)) catch .] == [1, 2]
In jaq however, this holds:
[try (1, error(2), 3, error(4)) catch .] == [1, 2, 3, 4]
Miscellaneous
 Slurping: When files are slurped in (via the
s
/slurp
option), jq combines the inputs of all files into one single array, whereas jaq yields an array for every file. The behaviour of jq can be approximated in jaq; for example, to achieve the output ofjq s . a b
, you may usejaq s . <(cat a b)
.  Cartesian products:
In jq,
[(1,2) * (3,4)]
yields[3, 6, 4, 8]
, whereas[{a: (1,2), b: (3,4)}  .a * .b]
yields[3, 4, 6, 8]
. jaq yields[3, 4, 6, 8]
in both cases.  List updating:
In jq,
[0, 1]  .[3] = 3
yields[0, 1, null, 3]
; that is, jq fills up the list withnull
s if we update beyond its size. In contrast, jaq fails with an outofbounds error in such a case.  Input reading:
When there is no more input value left,
in jq,
input
yields an error, whereas in jaq, it yields no output value.  Joining:
When given an array
[x0, x1, ..., xn]
, in jq,join(x)
converts all elements of the input array to strings and intersperses them withx
, whereas in jaq,join(x)
simply calculatesx0 + x + x1 + x + ... + xn
. When all elements of the input array andx
are strings, jq and jaq yield the same output.
Contributing
Contributions to jaq are welcome.
Please make sure that after your change, cargo test
runs successfully.
Acknowledgements
jaq has profited tremendously from:
 serde_json to read and colored_json to output JSON,
 chumsky to parse and ariadne to prettyprint parse errors,
 mimalloc to boost the performance of memory allocation, and
 the Rust standard library, in particular its awesome Iterator, which builds the rocksolid base of jaq's filter execution
Dependencies
~2–3MB
~56K SLoC