The unexpected convenience of JSON on the commandline

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I was working with a migration from drupal to processwire CMS:es, where I wanted to be able to pipe data, including the body field with HTML formatting and all, through multiple processing steps in a flexible manner. I'd start with an extraction SQL query, through a few components to replace and massage the data, and finally over to an import command using processwire's wireshell tool. So, basically I needed a flexible format for structured data that could be sent as one "data object" per line, to work nicely with linux commandline tools like grep, sed and awk.

CSV didn't seem that suitable, because of the unpredictable HTML formatting of the body field (though I never really tried) ... and I don't even know how to wrap up YAML into one-liners.

But then it struck me how well JSON is suited for this.

I've heard folks using it before, and it is by no means any news to experienced data wranglers, I'm sure, but I just hadn't thought about it.

JSON has excellent escaping of any kind of characters, and can be formatted as one object per line, making it perfect for piping on the commandline. What's more, working with JSON in python, is extremely convenient, since all the typical python data structures (lists, tuples and dicts) map directly to counterparts in JSON. This means that you can just use json.dumps() or json.loads() to convert a nested python data structure to and from a JSON string.

Although this is for sure no news for experienced commandline data wranglers, I just wanted to illustrate just how easy this is:

So, for example, reading some basic info and the body field (of Drupal 7) from MySQL is as easy as:

import MySQLdb as mdb
import json
from sys import stdout

con = mdb.connect([HOST], [USER], [PASS], [DB])
cur = con.cursor()
query = '''
        SELECT n.nid, n.type, n.created, n.title, fdb.body_value
        FROM node AS n 
        LEFT JOIN field_data_body AS fdb ON n.vid = fdb.revision_id 
rows = cur.fetchall()
for row in rows:
    rowinfo = [i.decode('latin-1') if type(i) is str else i for i in row]
    pageinfo = {
            'Nid': rowinfo[0],
            'NodeType': rowinfo[1],
            'Created': rowinfo[2],
            'Title': rowinfo[3],
            'Body': rowinfo[4],
    stdout.write(json.dumps(pageinfo) + '\n') # The newline is important here!

But now, this is mostly MySQL stuff, so a smaller component might better demonstrate how little is needed to read from json. So, say that we want to change all occurances of "node" in NodeType to "page" (which is the more commonly used term for what Drupal calls a node):

from sys import stdin, stdout
import json

for row in stdin:
    r = json.loads(row)
    r['NodeType'] = r['NodeType'].replace('node', 'page')
    stdout.write(json.dumps(r) + '\n')

So, this is what little is required to create a pipeable component working with JSON data, using python!

So, now, we can use these two components together by just piping them, and using the normal linux commandline tools for convenient browsing on the commandline (such as my favorite less -Si for scrolling without wrapping lines:

$ python | python | less -Si

But, even better than custom python components, is that you have the jq tool available, which fits perfectly into this workflow. In short, jq can replace all of sed, awk and grep, in one combined package for working with json objects.

A more detailed foray into using jq is material for another post, but just a simple example that would be usable here is to filter out just the NodeType field, to see that it became correctly replaced. This is easily achieved with jq:

$ python | python | jq '.NodeType' | less -Si

As simple as that. Check the jq manual for more usage info on jq.

I'm sure I will use JSON for piping structured data on the commandline a lot more from now on.

About Bionics IT

Bionics IT currently serving as a research and development blog for Samuel Lampa, a PhD student (Pharmaceutical Bioinformatics at Uppsala University).

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