jello 1.2.1

Filter JSON and JSON Lines data with Python syntax.

jello

Filter JSON and JSON Lines data with Python syntax

jello is similar to jq in that it processes JSON and JSON Lines data except jello uses standard python dict and list syntax.

JSON or JSON Lines can be piped into jello (JSON Lines are automatically slurped into a list of dictionaries) and are available as the variable _. Processed data can be output as JSON, JSON Lines, bash array lines, or a grep-able schema.

For more information on the motivations for this project, see my blog post.

Install

pip3 install --upgrade jello

Usage

<JSON Data> | jello [OPTIONS] [QUERY]

QUERY is optional and can be most any valid python code. _ is the sanitized JSON from STDIN presented as a python dict or list of dicts. If QUERY is omitted then the original JSON input will simply be pretty printed.

A simple query:

$ cat data.json | jello '_["foo"]'

Options

• -c compact print JSON output instead of pretty printing
• -i initialize environment with a custom config file
• -l lines output (suitable for bash array assignment)
• -m monochrome output
• -n print selected null values
• -r raw output of selected strings (no quotes)
• -s print the JSON schema in grep-able format
• -h help
• -v version info

Assigning Results to a Bash Array

Use the -l option to print JSON array output in a manner suitable to be assigned to a bash array. The -r option can be used to remove quotation marks around strings. If you want null values to be printed as null, use the -n option, otherwise they are skipped.

variable=($(cat data.json | jello -rl '_["foo"]'))

Custom Configuration File

You can use the -i option to initialize the jello environment with your own configuration file. The configuration file accepts valid python code and can be as simple as setting the jello options you would like enabled or disabled, adding import statements for your favorite modules, or defining your own functions.

The file must be named .jelloconf.py and must be located in the proper directory based on the OS platform:

• Linux, unix, macOS: ~/
• Windows: %appdata%/

Setting Options

To set jello options in the .jelloconf.py file, add any of the following and set to True or False:

mono = True          # -m option
compact = True       # -c option
lines = True         # -l option
raw = True           # -r option
nulls = True         # -n option
schema = True        # -s option

Importing Modules

To import a module (e.g. glom) during initialization, just add the import statement to your .jelloconf.py file:

from glom import *

Then you can use glom in your jello filters without importing:

$ jc -a | jello -i 'glom(_, "parsers.25.name")'

"lsblk"

Adding Functions

You can also add functions to your initialization file. For example, you could simplify glom use by adding the following function to .jelloconf.py:

def g(q, data=_):
    import glom
    return glom.glom(data, q)

Then you can use the following syntax to filter the JSON data:

$ jc -a | jello -i 'g("parsers.6.compatible")'

[
  "linux",
  "darwin",
  "cygwin",
  "win32",
  "aix",
  "freebsd"
]

Examples:

Printing the Grep-able Schema

$ jc -a | jello -s

.name = "jc"
.version = "1.10.1"
.description = "jc cli output JSON conversion tool"
.author = "Kelly Brazil"
.author_email = "[e-mail]"
.parser_count = 50
.parsers.0.name = "airport"
.parsers.0.argument = "--airport"
.parsers.0.version = "1.0"
.parsers.0.description = "airport -I command parser"
.parsers.0.author = "Kelly Brazil"
.parsers.0.author_email = "[e-mail]"
.parsers.0.compatible.0 = "darwin"
.parsers.0.magic_commands.0 = "airport -I"
.parsers.1.name = "airport_s"
.parsers.1.argument = "--airport-s"
...

Lambda Functions and Math

$ echo '{"t1":-30, "t2":-20, "t3":-10, "t4":0}' | jello '\
keys = _.keys()
vals = _.values()
cel = list(map(lambda x: (float(5)/9)*(x-32), vals))
dict(zip(keys, cel))'

{
  "t1": -34.44444444444444,
  "t2": -28.88888888888889,
  "t3": -23.333333333333336,
  "t4": -17.77777777777778
}

$ jc -a | jello 'len([entry for entry in _["parsers"] if "darwin" in entry["compatible"]])'

32

For Loops

Output as JSON array

$ jc -a | jello '\
result = []
for entry in _["parsers"]:
  if "darwin" in entry["compatible"]:
    result.append(entry["name"])
result'

[
  "airport",
  "airport_s",
  "arp",
  "crontab",
  "crontab_u",
  ...
]

Output as bash array

$ jc -a | jello -rl '\
result = []
for entry in _["parsers"]:
  if "darwin" in entry["compatible"]:
    result.append(entry["name"])
result'

airport
airport_s
arp
crontab
crontab_u
...

List and Dictionary Comprehension

Output as JSON array

$ jc -a | jello '[entry["name"] for entry in _["parsers"] if "darwin" in entry["compatible"]]'

[
  "airport",
  "airport_s",
  "arp",
  "crontab",
  "crontab_u",
  ...
]

Output as bash array

$ jc -a | jello -rl '[entry["name"] for entry in _["parsers"] if "darwin" in entry["compatible"]]'

airport
airport_s
arp
crontab
crontab_u
...

Environment Variables

$ echo '{"login_name": "joeuser"}' | jello '\
True if os.getenv("LOGNAME") == _["login_name"] else False'

true

Using 3rd Party Modules

You can import and use your favorite modules to manipulate the data. For example, using glom:

$ jc -a | jello '\
from glom import *
glom(_, ("parsers", ["name"]))'

[
  "airport",
  "airport_s",
  "arp",
  "blkid",
  "crontab",
  "crontab_u",
  "csv",
  ...
]

Advanced JSON Manipulation

The data from this example comes from https://programminghistorian.org/assets/jq_twitter.json

Under Grouping and Counting, Matthew describes an advanced jq filter against a sample Twitter dataset that includes JSON Lines data. There he describes the following query:

“We can now create a table of users. Let’s create a table with columns for the user id, user name, followers count, and a column of their tweet ids separated by a semicolon.”

https://programminghistorian.org/en/lessons/json-and-jq

Here is a simple solution using jello:

$ cat jq_twitter.json | jello -l '\
user_ids = set()
for tweet in _:
    user_ids.add(tweet["user"]["id"])
result = []
for user in user_ids:
    user_profile = {}
    tweet_ids = []
    for tweet in _:
        if tweet["user"]["id"] == user:
            user_profile.update({
                "user_id": user,
                "user_name": tweet["user"]["screen_name"],
                "user_followers": tweet["user"]["followers_count"]})
            tweet_ids.append(str(tweet["id"]))
    user_profile["tweet_ids"] = ";".join(tweet_ids)
    result.append(user_profile)
result'

...
{"user_id": 2696111005, "user_name": "EGEVER142", "user_followers": 1433, "tweet_ids": "619172303654518784"}
{"user_id": 42226593, "user_name": "shirleycolleen", "user_followers": 2114, "tweet_ids": "619172281294655488;619172179960328192"}
{"user_id": 106948003, "user_name": "MrKneeGrow", "user_followers": 172, "tweet_ids": "501064228627705857"}
{"user_id": 18270633, "user_name": "ahhthatswhy", "user_followers": 559, "tweet_ids": "501064204661850113"}
{"user_id": 14331818, "user_name": "edsu", "user_followers": 4220, "tweet_ids": "615973042443956225;618602288781860864"}
{"user_id": 2569107372, "user_name": "SlavinOleg", "user_followers": 35, "tweet_ids": "501064198973960192;501064202794971136;501064214467731457;501064215759568897;501064220121632768"}
{"user_id": 22668719, "user_name": "nodehyena", "user_followers": 294, "tweet_ids": "501064222772445187"}
...