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Python jsonl query engine

Project description

Build Status Coverage PyPI


JF, aka “jndex fingers” or more commonly “json filter pipeline”, is a jq-clone written in python. It supports evaluation of python oneliners, making it especially appealing for data scientists who are used to working with python.


pip install jf

How does it work

JF works by converting json or yaml data structure through a map/filter-pipeline. The pipeline is compiled from a string representing a comma-separated list filters and mappers. The query parser assumes that each function of the pipeline reads items from a generator. The generator is given as the last non-keyword parameter to the function, so “map(conversion)” is interpreted as “map(conversion, inputgenerator)”. The result from a previous function is given as the input generator for the next function in the pipeline. The pipeline conversion is shown below as pseudocode:

def build_pipeline(input, conversions):
    pipeline = input
    for convert in conversions:
        pipeline = convert(pipeline)
    return pipeline

The pipeline generated by the previous function is then iterated and printed to the user. The basic building blocks of a pipeline are

  • map(val) = map item to new object
  • filter(cond) = filter to show only items matching condition
  • update(val) = update item values
  • hide(dict_keys) = hide dict_keys from output

Some built-in functions headers have been remodeled to be more intuitive with the framework. Most noticeable is the sorted-function, which normally has the key defined as a keyword argument. This was done since it seems more logical to sort items by id by writing “sorted(” than “sorted(key=lambda x:”. Similar changes are done for some other useful functions:

  • islice(stop) => islice(arr, start=0, stop, step=1)
  • islice(start, stop, step=1) => islice(arr, start, stop, step)
  • first(N=1) => islice(arr, N)
  • last(N=1) => iter(deque(arr, maxlen=N))
  • I = arr (== identity operation)
  • yield_from(x) => yield items from x
  • group_by(key) => group items by data key value
  • chain() => chain(*arr) - combine items into a list

For datetime processing, two useful helper functions are imported by default:

  • date(string) for parsing string into a python datetime-object
  • age(string) for calculating timedelta between now() and date(string)

These are useful for sorting or filtering items in based on timestamps. Some of these functions have aliases predefined, such as head(), tail(), yield_all(), group() and reduce_list().

For shortened syntax, ‘{…}’ is interpreted as ‘map({…})’ and (…) is interpreted as filter(…).

Basic usage

Filter selected fields

$ cat samples.jsonl | jf 'map({id:, subject: x.fields.subject})'
{"id": "87086895", "subject": "Swedish children stories"}
{"id": "87114792", "subject": "New Finnish storybooks"}

Filter selected items

$ cat samples.jsonl | jf 'map({id:, subject: x.fields.subject}),
        filter( == "87114792")'
{"id": "87114792", "subject": "New Finnish storybooks"}

Filter selected items with shortened syntax

$ cat samples.jsonl | jf '{id:, subject: x.fields.subject},
        ( == "87114792")'
{"id": "87114792", "subject": "New Finnish storybooks"}

Filter selected values

$ cat samples.jsonl | jf 'map('

Filter items by age (and output yaml)

$ cat samples.jsonl | jf 'map({id:, datetime: x["content-datetime"]}),
        filter(age(x.datetime) > age("456 days")),
        update({age: age(x.datetime)})' --indent=5 --yaml
age: 457 days, 4:07:54.932587
datetime: '2016-10-29 10:55:42+03:00'
id: '87086895'

Sort items by age and print their id, length and age

$ cat samples.jsonl|jf 'update({age: age(x["content-datetime"])}),
        map(.id, "length: %d" % len(.content), .age)' --indent=3 --yaml
- '14941692'
- 'length: 63'
- 184 days, 0:02:20.421829
- '90332110'
- 'length: 191'
- 215 days, 22:15:46.403613
- '88773908'
- 'length: 80'
- 350 days, 3:11:06.412088
- '14558799'
- 'length: 1228'
- 450 days, 6:30:54.419461

Filter items after a given datetime (test.json is a git commit history):

$ jf 'update({age: age(}),
        filter(date( > date("2018-01-30T17:00:00Z")),
        sorted(x.age, reverse=True), map(.sha, .age,' test.json
  "2 days, 9:40:12.137919",
  "2 days, 9:18:07.134418",
  "2 days, 8:50:09.129790",

Import your own modules and hide fields:

$ cat test.json|jf --import_from modules/ --import demomodule --yaml 'update({id: x.sha}),
        hide("sha", "committer", "parents", "html_url", "author", "commit",
             "comments_url"), islice(3,5)'
- Pipemod: was here at 2018-01-31 09:26:12.366465
  id: f5f879dd7303c35fa3712586af1e7df884a5b98b
- Pipemod: was here at 2018-01-31 09:26:12.368438
  id: b393d09215efc4fc0382dd82ec3f38ae59a287e5

Read yaml:

$ cat test.yaml | jf --yamli 'update({id: x.sha, age: age(}),
        filter(x.age < age("1 days"))' --indent=2 --yaml
- age: 0 days, 22:45:56.388477

Group duplicates (age is within the same hour):

$ cat test.json|jf --import_from modules/ --import demomodule 'update({id: x.sha}),
        sorted(, reverse=True),
        group=1).process(lambda x: {"duplicate":}),
        map(list(map(lambda y: {age: age(, id:,
                     date:, duplicate_of: y["duplicate"],
                     comment: y.commit.message}, x))),
    "comment": "Add support for hiding fields",
    "duplicate_of": null,
    "id": "f8ba0ba559e39611bc0b63f236a3e67085fe8b40",
    "age": "16:19:00.102299",
    "date": "2018-01-30 19:25:30+00:00"
    "comment": "Enhance error handling",
    "duplicate_of": "f8ba0ba559e39611bc0b63f236a3e67085fe8b40",
    "id": "d3211e1141d8b2bf480cbbebd376b57bae9d8bdf",
    "age": "16:46:58.104188",
    "date": "2018-01-30 18:57:32+00:00"
    "comment": "Reduce verbosity when debugging",
    "duplicate_of": null,
    "id": "f5f879dd7303c35fa3712586af1e7df884a5b98b",
    "age": "19:26:00.106777",
    "date": "2018-01-30 16:18:30+00:00"
    "comment": "Print help if no input is given",
    "duplicate_of": "f5f879dd7303c35fa3712586af1e7df884a5b98b",
    "id": "b393d09215efc4fc0382dd82ec3f38ae59a287e5",
    "age": "19:35:16.108654",
    "date": "2018-01-30 16:09:14+00:00"

Use pythonic conditional operation, string.split() and complex string and date formatting with built-in python syntax. Also you can combine the power of regular expressions by including the re-library.

$ jf --import_from modules/ --import re --import demomodule --input skype.json 'yield_from(x.messages),
        update({from: x.from.split(":")[-1], mid: x.skypeeditedid if x.skypeeditedid else x.clientmessageid}),
        sorted(age(x.composetime), reverse=True),
        demomodule.DuplicateRemover(x.mid, group=1).process(),
        sorted(age(.composetime), reverse=True),
        map("%s %s: %s" % (date(x.composetime).strftime("%d.%m.%Y %H:%M"), x.from, re.sub(r"(<[^>]+>)+", " ", x.content)))' --raw
27.01.2018 11:02  1518001760666 8:live:matti_3426 8:live:matti_6656 8:live:suvi_56 8:jukka.mattinen
27.01.2018 11:12 matti_7626: Required competence: PHP programmer (Mika D, Markus H, Heidi), some JavaScript (e.g. for GUI)
27.01.2018 11:12 matti_7626: Matti: parameters part
27.01.2018 11:15 matti_7626: 1.) Clarify customer requirements - AP: Suvi/Joseph
27.01.2018 11:22 matti_7626: This week - initial installation and setup
27.01.2018 11:22 matti_7626: Next week (pending customer requirements) - system configuration
27.01.2018 11:25 matti_7626: configuration = parameters, configuration files (audio files, from customer, ask Suvi to request today?), add audio files to system (via GUI)
27.01.2018 11:26 matti_7626: Testing = specify how we do testing, for example written test cases by the customer.
27.01.2018 11:28 matti_7626: Need test group (testgroup 1 prob easiest to recognise says Lasse)


  • json, jsonl and yaml files for input and output
  • bz2 and gzip compressed input for json, jsonl and yaml
  • csv and xlsx support if pandas and xlrd is installed
  • markdown table output support
  • construct generator pipeline with map, hide, filter
  • access json dict as classes with dot-notation for attributes
  • datetime and timedelta comparison
  • age() for timedelta between datetime and current time
  • first(N), last(N), islice(start, stop, step)
  • head and tail alias for last and first
  • firstnlast(N) (or headntail(N))
  • import your own modules for more complex filtering
  • Support stateful classes for complex interactions between items
  • drop your filtered data to IPython for manual data exploration
  • pandas profiling support for quick data exploration
  • user –ordered_dict to keep items in order

Known bugs

  • IPython doesn’t launch perfectly with piped data

Project details

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