LazyJSON is an interface for reading JSON data in Julia programs.
If you find it useful, or not, please report your experiance in the discourse thread.
LazyJSON provides direct access to values stored in a JSON text though standard Julia
interfaces: Number, AbstractString, AbstractVector and AbstractDict.
The function LazyJSON.value constructs an object representing the value(s) of a JSON text.
LazyJSON.value(jsontext::AbstractString) -> Union{Bool,
LazyJSON.Number,
LazyJSON.String,
LazyJSON.Array,
LazyJSON.Object,
Nothing}
LazyJSON.Number <: Base.Number
LazyJSON.String <: AbstractString
LazyJSON.Array <: AbstractVector{Any}
LazyJSON.Object <: AbstractDict{AbstractString,Any}e.g.
julia> j = LazyJSON.value("""{
"foo": [1, 2, 3, "four"]
"bar": null
}""")
LazyJSON.Object with 2 entries:
"foo" => Any[1, 2, 3, "four"]
"bar" => nothing
julia> j["foo"]
4-element LazyJSON.Array:
1
2
3
"four"
julia> j["foo"][4]
"four"
julia> typeof(j["bar"])
NothingThe fields of JSON objects can also be accessed using '.' (getproperty)
syntax.
e.g.
julia> j = LazyJSON.value("""{
"foo": [1, 2, 3, "four"]
"bar": null
}"""; getproperty=true)
julia> j.foo
4-element LazyJSON.Array:
1
2
3
"four"JSON Objects can be converted to struct types.
e.g.
julia> struct Point
x::Int
y::Int
end
julia> struct Line
a::Point
b::Point
end
julia> struct Arrow
label::String
segments::Vector{Line}
dashed::Bool
end
julia> convert(Arrow, LazyJSON.value("""{
"label": "Hello",
"segments": [
{"a": {"x": 1, "y": 1}, "b": {"x": 2, "y": 2}},
{"a": {"x": 2, "y": 2}, "b": {"x": 3, "y": 3}}
],
"dashed": false
}"""))
Arrow("Hello", Line[Line(Point(1, 1), Point(2, 2)), Line(Point(2, 2), Point(3, 3))], false)For compatibility with other JSON interfaces that have a parse function,
LazyJSON.parse is provided as an alias for LazyJSON.value. e.g.
e.g.
julia> j = LazyJSON.parse("""{
"foo": [1, 2, 3, "four"]
"bar": null
}""")
julia> j["foo"][4]
"four"LazyJSON is lazy in the sense that it assumes that its input is well formed JSON text. It does not try to detect every type of JSON syntax error. If security is a concern, JSON data of unknown providence should probably be validated before use.
LazyJSON is also lazy in the sense that it does not process any part of the JSON
text until values are requested through the AbstractVector and AbstractDict
interfaces.
i.e. j = LazyJSON.value(jsontext) does no parsing and immediately
returns a thin wrapper object.
j["foo"] calls get(::AbstractDict, "foo"), which parses just enough to find
the "foo" field.
j["foo"][4] calls getindex(::AbstractArray, 4), which continues paring up to
the fourth item in the array.
This results in much less memory allocation compared to non-lazy parsers:
JSON.jl:
j = String(read("ec2-2016-11-15.normal.json"))
julia> function f(json)
v = JSON.parse(json)
v["shapes"]["scope"]["enum"][1]
end
julia> @time f(j)
0.066773 seconds (66.43 k allocations: 7.087 MiB)
"Availability Zone"LazyJSON.jl:
julia> function f(json)
v = LazyJSON.parse(json)
v["shapes"]["scope"]["enum"][1]
end
julia> @time f(j)
0.001392 seconds (12 allocations: 384 bytes)
"Availability Zone"LazyJSON's AbstractString and Number implementations are lazy too.
The text of a LazyJSON.Number is not parsed to Int64 or Float64 form
until it is needed for a numeric operation. If the number is only used in a
textual context, it need never be parsed at all. e.g.
j = LazyJSON.value(jsontext)
html = """<img width=$(j["width"]), height=$(j["height"])>"""Likewise, the content of a LazyJSON.String is not interpreted until it is
accessed. If a LazyJSON.String containing complex UTF16 escape sequences is
compared to a UTF8 Base.String, and the two strings differ in the first
few characters, then the comparison will terminate before the any unescaping
work needs to be done.
The LazyJSON.Array does not keep track of the indices of its items.
Every array[i] access scans all the values in the array until it reaches
the ith value. This is fast if you only need to access a single item,
even near the end of the array, because the alternative of transforming the
LazyJSON.Array into a Base.Array must scan the entire array and allocate
new memory for each item. It is also fast to access multiple items near the
start of the array. However, if you need random access to many items in a large
array it is better to convert it to a Base.Array.
e.g.
v = LazyJSON.value(jsontext)["foo"]["bar"]["an_array"]
v = convert(Vector{Any}, v)
If you need to access the items in the array sequentially, the iteration
interface is very efficient, but incrementing an index is very inefficient.
length(::LazyJSON.Array) is also inefficient, in that it must scan the whole
array.
e.g.
v = LazyJSON.value(jsontext)["foo"]["bar"]["an_array"]
for i in v ✅
println(i)
end
r = map(i -> f(i), v) ✅
i = 1
while i < length(v) ❌
println(v[i]) ❌
endThe performance considerations for LazyJSON.Object are similar to those
described above for LazyJSON.Array. The LazyJSON.Object does not keep a
hash table of keys. Every object["key"] access scans all the keys in the
object until it finds a match. Accessing a keys in an object with a small
number of keys is efficient. Accessing a few keys in an object with many keys
is effiecient. However, if you need random acess to many keys in a large object
it is better to convert it to a Base.Dict.
e.g.
v = LazyJSON.value(jsontext)["foo"]["bar"]["an_object_with_many_keys"]
v = convert(Dict, v)length(::LazyJSON.Object) is inefficient, in that it must scan the whole
object.
If you need to access the key value pairs sequentially, the iteration
interface is very efficient.
e.g.
o = LazyJSON.value(jsontext)["foo"]["bar"]["an_object_with_many_keys"]
for (k, v) in o ✅
println(k, v)
end
r = filter((k, v) -> contains(i, r".jpg$", o)) ✅
for k in long_list_of_keys
println(o[k]) ❌
end
d = convert(Dict, o)
for k in long_list_of_keys
println(d[k]) ✅
endWhenever a LazyJSON.Number is used in a numeric operation it must be parsed
from its string form into an Int or a Float. If you are only using each
each numetic value once, there is no performance penalty, as the string is only
parsed once. However if you need to use the numeric value many times, it is
better to convert it to a normal Base number type.
e.g.
i = LazyJSON.value(jsontext)["foo"]
x = origin.x + i["width"], ✅ used once in an addition operation
y = origin.y + i["height"] ✅
draw(i["data"], x, y)
limit = LazyJSON.value(jsontext)["foo"]["limit"]
i = 0
while i < limit ❌ re-parsed every time the less than operation is evaluated
i += 1
...
end
limit = convert(Int, LazyJSON.value(jsontext)["foo"]["limit"]) ✅
v = LazyJSON.value(jsontext)["foo"]["ammounts"]
total = sum(v) ✅ iteration is efficient, each number is parsed once.
struct Foo
x::Int
y::Int
end
i = LazyJSON.value(jsontext)["foo"]
Foo(i["x"], i["y"]) ✅ converted to `Int` on assignment to struct fields.
v = LazyJSON.value(jsontext)["foo"]["values"]
ints = convert(Vector{Int}, v) ✅ manual conversion when neededValues are represented by a reference to the JSON text String
and the byte index of the value text. The LazyJSON.value(jsontext) function
simply returns a LazyJSON.Value object with s = jsontext and i = 1.
String: {"foo": 1, "bar": [1, 2, 3, "four"]}
▲ ▲ ▲ ▲
│ │ │ │
├─────────────────┐ │ │ │
│ LazyJSON.Array( s, i=9) │ │ == Any[1, 2, 3, "four"]
│ │ │
├─────────────────┐ ┌──────┘ │
│ LazyJSON.Number(s, i=16) │ == 3
│ │
├─────────────────┐ ┌─────────┘
│ LazyJSON.String(s, i=19) == "four"
│
└─────────────────┬──┐
LazyJSON.Object(s, i=1)
LazyJSON does not parse and translate values into concrete Julia Number,
String, Array or Dict objects. Instead it provides interface methods that
conform to the protocols of Base.Number, AbstractString, AbstractVector
and AbstractDict. These methods interpret the JSON text on the fly and parse
only as much as is needed return the requested values.
LazyJSON can process JSON files that are too big to fit in available RAM
by using the mmap interface.
e.g.
using Mmap
f = open("huge_file_that_wont_fit_in_ram.json", "r")
s = String(Mmap.mmap(f))
j = LazyJSON.value(s)
v = j["foo"]["bar"]The operating stytem will lazily load enough chunks of the file into RAM to
reach field "bar" of opject "foo".
For some workloads lazyness makes LazyJSON faster and less memory intensive than JSON parsers that parse the entire JSON text and allocate a tree of collection and value objects.
The test/benchmark.jl test uses a 1MB AWS API definition JSON file
to compare performance vs JSON.jl. When accessing a value close to the
start of the file the lazy parser is orders of magnitude faster than JSON.jl,
for values near then end of the file, the lazy parser is about 6 times faster.
(Each test case is run once for JIT warmup, then 190 times for measurement.)
Julia Version 0.7.0-DEV.3761
JSON.jl master Tue Feb 6, 98727675b635c8428effa30a2287a9fe6370e664
Access value close to start:
LazyJSON.jl: 0.000568 seconds (3.42 k allocations: 139.531 KiB)
JSON.jl: 6.410700 seconds (13.28 M allocations: 1.337 GiB, 3.17% gc time)
Access 2 values close to end:
LazyJSON.jl: 0.177059 seconds (7.79 k allocations: 347.344 KiB)
JSON.jl: 6.417241 seconds (13.28 M allocations: 1.337 GiB, 3.18% gc time)
Note, until recently JSON.jl was taking ~1 second for the tests above.
It seems that it may be hampered deprecation of IOBuffer(maxsize::Integer).
The test/benchmark_geo.jl test uses a 1.2MB GeoJSON file
to compare performance vs JSON.jl. The first test extracts a country name
near the middle of the file. The second test checks that the country outline
polygon is at the expected coordinates.
Country name
LazyJSON.jl: 0.004762 seconds (190 allocations: 5.938 KiB)
JSON.jl: 1.063652 seconds (8.62 M allocations: 373.471 MiB, 11.19% gc time)
Map data
LazyJSON.jl: 0.011075 seconds (27.30 k allocations: 679.547 KiB)
JSON.jl: 1.064750 seconds (8.62 M allocations: 373.541 MiB, 10.75% gc time)
- New Lazyer parser looses some format validation, consider recovering old
code validation code from
src/OldLazyJSON.jl
- Another lazy JSON parser: https://github.com/doubledutch/LazyJSON
- RFC 7159: https://tools.ietf.org/html/rfc7159