Tuesday, January 29, 2013

Decoding JSON from a relational DB in Python

Lately I've been creating a fair number of APIs that store some JSON in a field in a database. Often this is a good reason to use a document database like Mongo or Couch, but sometimes a relational DB will do you better (e.g., if it's just a small amount of data for each row that is free-form, but the relational aspects are many and important). If you are using a relational DB with a JSON field, you will at some point have to deserialize that JSON for use as data.

I'm concerned with the speed of my APIs, so I began wondering what the best way to do this was. When returning a single record, there aren't many choices. I load the record, pull out the JSON field value as a string, deserialize it, and put it back on as a dictionary. However, when returning a potentially large set of records, my first inclination was to process each record individually, and compose them all into a list. Something like the following:

    def get_record(...):
        record = execute_query(...)
        data = process_record(record)
        return data

    def get_many_records(...):
        records = execute_query(...)
        data = [process_record(record) for record in records]
        return data

    def process_record(record):
        json_str = record['blob']
        record['blob'] = json.loads(json_str)
        return record

The issue here is that, if I have a number of records, json.loads will get called once for each one. The other option that I had was to compose all of the JSON data into a single list, deserialize it all at once, and then partition it back out to its original objects -- something like:

    def get_many_records(...):
        records = execute_query(...)
        json_str = '[%s]' % ', '.join(record['blob'] for record in records)
        blobs = json.loads(json_str)
        for record, blob in zip(records, blobs):  # izip for Python 2.x
            record['blob'] = blob
        return records

My first thoughts: I prefer the first code block, because it allows me to share code between my single- and multi-record getters, and it seems clearer. Also, I don't really know yet whether the latter would gain me anything significant. To test, I wrote up the following:

    from timeit import Timer

    list_str_code = """
        import json
        list_str = '[' + ', '.join(['{"a": 1}']*1000) + ']'
        data = json.loads(list_str)

    str_list_code = """
        import json
        str_list = ['{"a": 1}']*1000
        data = [json.loads(dict_str) for dict_str in str_list]

    list_str_timer = Timer(list_str_code)
    str_list_timer = Timer(str_list_code)

    print list_str_timer.repeat(number=1000)
    print str_list_timer.repeat(number=1000)

On my machine with Python 2.7, the list_str_code ran consistently more than 3 times faster than str_list_code (1.2 vs 3.9 seconds). With Python 3.2 it was nearly 5 times faster (0.7 vs 3.3 seconds). That's pretty significant.

It is worth noting that I tried this with lists of different sizes as well. Even if I construct list_str and str_list each with only 10 elements and run the code 100,000 times, the list_str_code is still several times faster.


At the suggestion of @mricordeau, I tried rerunning the timed code using Ultra JSON (ujson) instead of the core json module. I did this by just installing (pip install ujson) and replacing the lines that say import json with import ujson as json. It was, indeed, blazing fast. Notably, it brought the execution times much closer together (for the run above where I got results of 1.2 and 3.9 seconds, ujson gave me times of 0.21 and 0.27 seconds respectively)! This implies to me that much of the time in the core json module is spent in start-up (or tear-down) code each time you call loads.

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