DataStation The Open-Source Data IDE
Blog

Experimenting with column- and row-oriented datastructures

Published on by

DataStation is an open-source data IDE for developers. It allows you to easily build graphs and tables with data pulled from SQL databases, logging databases, metrics databases, HTTP servers, and all kinds of text and binary files. Need to join or munge data? Write embedded scripts as needed in Python, JavaScript, Ruby, R, or Julia. All in one application.

DataStation stores intermediate results as a JSON-encoded array of objects (e.g. [{ "a": 1, "b": "y" }, { "a": 2, "b": "z" }]). It uses JSON since DataStation supports scripting with intermediate results in many languages and finding support for other encodings is more challenging (even though I may eventually switch). But the choice of storing data as an array of objects was a shortcut I took despite knowing more efficient alternatives exist even within JSON. The two most obvious alternatives that come to mind are array of arrays (e.g. [["a", "b"], [1, "y"], [2, "z"]]) and columnar (e.g. [["a", 1, 2], ["b", "y", "z"]]).

All code for this post is available on Github.

Generating N rows in a schema

Over the weekend I was thinking through what exactly I'd stand to gain (or lose) if I switched representation, other than a potentially more challenging programmer interface. So I pulled out Faker and wrote a script to generate a schema and N rows of data, writing to disk as JSON in each of the above mentioned forms.

import json
import sys

from faker import Faker

fake = Faker()

N = int(sys.argv[1])

keys = fake.words(200)
schema = {}
for key in keys:
    schema[key] = fake.random_choices((fake.iso8601, fake.paragraph, fake.random_int, fake.word), length=1)[0]

array_of_objects = []
for i in range(N):
    obj = {}
    if i % 10_000 == 0:
        print(f'Done generating {i} of {N} ({i / N * 100:.0f}%)')
    for key in keys:
        obj[key] = schema[key]()
    array_of_objects.append(obj)

print('Generated data')

with open('array_of_objects.json', 'w') as f:
    json.dump(array_of_objects, f)

print('Dumped array of objects')

array_of_arrays = [keys]
for row in array_of_objects:
    array_row = []
    for key in keys:
        array_row.append(row[key])
    array_of_arrays.append(array_row)

with open('array_of_arrays.json', 'w') as f:
    json.dump(array_of_arrays, f)

print('Dumped array of arrays')

columnar = [[] for key in keys]
for row in array_of_arrays:
    for i in range(len(keys)):
        columnar[i].append(row[i])

with open('columnar.json', 'w') as f:
    json.dump(columnar, f)

print('Dumped columnar')

Benchmarking in-memory "queries"

Then I sketched out a few common "queries" that might exercise different aspects of the representations:

Then I wrote a simple benchmark function and the code needed to run each of these queries against each representation.

import json
from time import time

from beautifultable import BeautifulTable

table = BeautifulTable()
table.max_table_width=150

tests = {}

def bench_avg(storage, f, runs, name, sv=None):
    start = time();
    for i in range(runs):
        res = f()
    end = time()
    if name not in tests:
        tests[name] = []
    tests[name].append({ 'time': f'{((end - start) / runs):.2f}s', 'signal': sv(res) if sv else 'N/A', 'storage': storage })
    return res

print('Testing arrays')

with open('array_of_arrays.json') as f:
    [header, *data] = bench_avg('array of arrays', lambda: json.load(f), 1, 'Read JSON')
    first_int_column = header[0]
    first_int_column_index = 0
    for i, key in enumerate(header):
        try:
            int(data[0][i])
            first_int_column = key
            first_int_column_index = i
            break
        except:
            pass
    bench_avg('array of arrays', lambda: sum([row[first_int_column_index] for row in data]), 5, 'Sum int field', lambda a: a)
    bench_avg('array of arrays', lambda: sorted(data, key=lambda r: r[0])[:100], 5, 'Sort by first field and take first 100', lambda a: a[0][0])
    bench_avg('array of arrays', lambda: sorted(data, key=lambda r: r[0]), 5, 'Sort by first field')

    def group():
        matches = {}
        for row in data:
            if row[0] not in matches:
                matches[row[0]] = 0
            matches[row[0]] += 1

        return list(matches.items())

    bench_avg('array of arrays', group, 5, 'Group by first field, count', lambda g: len(g))

print('Testing objects')

with open('array_of_objects.json') as f:
    data = bench_avg('array of objects', lambda: json.load(f), 1, 'Read JSON')
    bench_avg('array of objects', lambda: sum([row[key] for row in data]), 5, 'Sum int field', lambda a: a)
    bench_avg('array of objects', lambda: sorted(data, key=lambda r: r[header[0]])[:100], 5, 'Sort by first field and take first 100', lambda n: n[0][header[0]])
    bench_avg('array of objects', lambda: sorted(data, key=lambda r: r[header[0]]), 5, 'Sort by first field')

    def group():
        matches = {}
        key = header[0]
        for row in data:
            if row[key] not in matches:
                matches[row[key]] = 0
            matches[row[key]] += 1

        return list(matches.items())

    bench_avg('array of objects', group, 5, 'Group by first field, count', lambda g: len(g))

print('Testing columnar')

with open('columnar.json') as f:
    data = bench_avg('columnar', lambda: json.load(f), 1, 'Read JSON')
    columns = [r[0] for r in data]
    data = [r[1:] for r in data]
    bench_avg('columnar', lambda: sum(data[first_int_column_index]), 5, 'Sum int field', lambda a: a)
    def data_sort(n=None):
        guide = sorted(range(len(data[0])), key=lambda i: data[0][i])
        return [[row[i] for i in (guide[:n] if n else guide)] for row in data]
    bench_avg('columnar', lambda: data_sort(100), 5, 'Sort by first field and take first 100', lambda n: n[0][0])
    bench_avg('columnar', data_sort, 5, 'Sort by first field')

    def group():
        matches = {}
        for val in data[0]:
            if val not in matches:
                matches[val] = 0
            matches[val] += 1

        return list(matches.items())

    bench_avg('columnar', group, 5, 'Group by first field, count', lambda g: len(g))

table.columns.header = tests.keys()
storages = [t['storage'] for t in tests[list(tests.keys())[0]]]

for storage in storages:
    row = []
    for testname in tests.keys():
        for t in tests[testname]:
            if (t['storage'] == storage):
                row.append(t['time'] + f' ({t["signal"]})')

    table.rows.append(row)

table.rows.header = storages
print(table)

After a pip3 install faker beautifultable, I could run the generate script and then the benchmarks.

$ python3 generate_schema_data.py 1_000_000 && python3 benchmark.py
... other stuff ...
+------------------+--------------+--------------------+----------------------------------------+---------------------+-----------------------------+
|                  |  Read JSON   |   Sum int field    | Sort by first field and take first 100 | Sort by first field | Group by first field, count |
+------------------+--------------+--------------------+----------------------------------------+---------------------+-----------------------------+
| array of arrays  | 40.54s (N/A) | 0.27s (4999013444) |      1.02s (1970-01-01T00:18:47)       |     1.01s (N/A)     |       0.69s (999668)        |
+------------------+--------------+--------------------+----------------------------------------+---------------------+-----------------------------+
| array of objects | 47.35s (N/A) | 0.35s (4999013444) |      1.09s (1970-01-01T00:18:47)       |     1.09s (N/A)     |       0.72s (999668)        |
+------------------+--------------+--------------------+----------------------------------------+---------------------+-----------------------------+
|     columnar     | 29.19s (N/A) | 0.01s (4999013444) |      0.51s (1970-01-01T00:18:47)       |    49.30s (N/A)     |       0.94s (999668)        |
+------------------+--------------+--------------------+----------------------------------------+---------------------+-----------------------------+

And one more note before analysis, the file sizes on disk:

$ ls -lah *.json
-rw-r--r-- 1 phil phil 6.4G Oct 18 17:49 array_of_arrays.json
-rw-r--r-- 1 phil phil 7.6G Oct 18 17:47 array_of_objects.json
-rw-r--r-- 1 phil phil 6.4G Oct 18 17:50 columnar.json

Analysis

In this experiment it became clear that array of objects is the least efficient representation for storage and has the poorest performance for every query. Array of arrays is still a row-oriented representation but it is more compact which is presumably why it does better than the array of objects representation. And it's still fairly easy to program.

The really interesting results though are about the columnar data. It takes a meaningfully smaller amount of time to parse. But it's about the same size on disk as the array of arrays. Summing a single field is extremely efficient in this representation. And sorting on a field and taking a few results back is twice as efficient than the other representations.

Most surprising to me was how slow sorting all rows while in columnar representation is compared to the row-oriented representations. Maybe I'm missing an obviously better way to sort there.

But the way this terrible performance makes sense to me after seeing the numbers is that sorting column-oriented data is dependent on both the size of the input and the number of columns since you need to move data in each column around independently. In contrast, sorting in row-oriented data is not dependent on the number of columns, only the size of the input.

I know that some databases (like DuckDB reports here) switch to a row-oriented representation for some operations like sorting to get around this inefficiency.

On a sidenote, one interesting kind of query I didn't have time to look at was JOINs where I expect the row-oriented database would have won out.

On another sidennote, I tried to replicate these results in JavaScript/Node.js but Node fails to load files larger than 2GB and there's no builtin streaming JSON library for JavaScript. You also can't have a string with a value larger than 1GB. So I gave up on Node.js for easy large file analysis. Nice work, Python.

Takeaways

The intent of this little script was to look at vanilla storage choices keeping programming language choice, the amount of parallelism, the non-existence of indexes, and various other tricks constant. In reality when you are examining columnar or row-oriented database products (PostgreSQL, ClickHouse, DuckDB, etc.) they will have many tricks that speed up these results in different ways.

For the purposes of DataStation it would probably be best to move to the array of arrays representation to save some space and time. It's not immediately clear using a column-oriented store makes sense given common workloads on DataStation.

Share

Questions? Feedback? Feel free to reach the author at phil@multiprocess.io.

Subscribe for more posts like this!