Revisiting Datagrepper Performance

Feb 27, 2015 | categories: fedmsg, datanommer, fedora, datagrepper, postgres View Comments

In Fedora Infrastructure, we run a service somewhat-hilariously called datagrepper which lets you make queries over HTTP about the history of our message bus. (The service that feeds the database is called datanommer.) We recently crossed the mark of 20 million messages in the store, and the thing still works but it has become noticeably slower over time. This affects other dependent services:

  • The releng dashboard and others make HTTP queries to datagrepper.
  • The fedora-packages app waits on datagrepper results to present brief histories of packages.
  • The Fedora Badges backend queries the db directly to figure out if it should award badges or not.
  • The notifications frontend queries the db to try an display what messages in the past would have matched a hypothetical set of rules.

I've written about this chokepoint before, but haven't had time to really do anything about it... until this week!

Measuring how bad it is

First, some stats -- I wrote this benchmarking script to try a handful of different queries on the service and report some average response times:

#!/usr/bin/env python
import requests
import itertools
import time
import sys

url = 'https://apps.fedoraproject.org/datagrepper/raw/'

attempts = 8

possible_arguments = [
    ('delta', 86400),
    ('user', 'ralph'),
    ('category', 'buildsys'),
    ('topic', 'org.fedoraproject.prod.buildsys.build.state.change'),
    ('not_package', 'bugwarrior'),

result_map = {}
for left, right in itertools.product(possible_arguments, possible_arguments):
    if left is right:
    key = hash(str(list(sorted(set(left + right)))))
    if key in result_map:

    results = []
    params = dict([left, right])
    for attempt in range(attempts):
        start = time.time()
        r = requests.get(url, params=params)
        assert(r.status_code == 200)
        results.append(time.time() - start)

    # Throw away the max and the min (outliers)

    average = sum(results) / len(results)
    result_map[key] = average

    print "%0.4f    %r" % (average, str(params))

The results get printed out in two columns.

  • The leftmost column is the average number of seconds it takes to make a query (we try 8 times, throw away the shortest and the longest and take the average of the remaining).
  • The rightmost column is a description of the query arguments passed to datagrepper. Different kinds of queries take different times.

This first set of results are from our production instance as-is:

7.7467    "{'user': 'ralph', 'delta': 86400}"
0.6984    "{'category': 'buildsys', 'delta': 86400}"
0.7801    "{'topic': 'org.fedoraproject.prod.buildsys.build.state.change', 'delta': 86400}"
6.0842    "{'not_package': 'bugwarrior', 'delta': 86400}"
7.9572    "{'category': 'buildsys', 'user': 'ralph'}"
7.2941    "{'topic': 'org.fedoraproject.prod.buildsys.build.state.change', 'user': 'ralph'}"
11.751    "{'user': 'ralph', 'not_package': 'bugwarrior'}"
34.402    "{'category': 'buildsys', 'topic': 'org.fedoraproject.prod.buildsys.build.state.change'}"
36.377    "{'category': 'buildsys', 'not_package': 'bugwarrior'}"
44.536    "{'topic': 'org.fedoraproject.prod.buildsys.build.state.change', 'not_package': 'bugwarrior'}"

Notice that a handful of queries are under one second but some are unbearably long. A seven second response time is too long, and a 44-second response time is way too long.

Setting up a dev instance

I grabbed the dump of our production database and imported it into a fresh postgres instance in our private cloud to mess around. Before making any further modifications, I ran the benchmarking script again on this new guy and got some different results:

5.4305    "{'user': 'ralph', 'delta': 86400}"
0.5391    "{'category': 'buildsys', 'delta': 86400}"
0.4992    "{'topic': 'org.fedoraproject.prod.buildsys.build.state.change', 'delta': 86400}"
4.5578    "{'not_package': 'bugwarrior', 'delta': 86400}"
6.4852    "{'category': 'buildsys', 'user': 'ralph'}"
6.3851    "{'topic': 'org.fedoraproject.prod.buildsys.build.state.change', 'user': 'ralph'}"
10.932    "{'user': 'ralph', 'not_package': 'bugwarrior'}"
9.1895    "{'category': 'buildsys', 'topic': 'org.fedoraproject.prod.buildsys.build.state.change'}"
14.950    "{'category': 'buildsys', 'not_package': 'bugwarrior'}"
12.044    "{'topic': 'org.fedoraproject.prod.buildsys.build.state.change', 'not_package': 'bugwarrior'}"

A couple things are faster here:

  • No ssl on the HTTP requests (almost irrelevant)
  • No other load on the db from other live requests (likely irrelevant)
  • The db was freshly imported (the last time we moved the db server things got magically faster. I think there's something about the way that postgres stores stuff internally that when you freshly import the data, it is organized more effectively. I have no data or real know-how to support this claim though).

Experimenting with indexes

I first tried adding indexes on the category and topic columns of the messages table (which are common columns used for filter operations). We already have an index on the timestamp column, without which the whole service is just unusable.

Some results after adding those:

0.1957    "{'user': 'ralph', 'delta': 86400}"
0.1966    "{'category': 'buildsys', 'delta': 86400}"
0.1936    "{'topic': 'org.fedoraproject.prod.buildsys.build.state.change', 'delta': 86400}"
0.1986    "{'not_package': 'bugwarrior', 'delta': 86400}"
6.6809    "{'category': 'buildsys', 'user': 'ralph'}"
6.4602    "{'topic': 'org.fedoraproject.prod.buildsys.build.state.change', 'user': 'ralph'}"
10.982    "{'user': 'ralph', 'not_package': 'bugwarrior'}"
3.7270    "{'category': 'buildsys', 'topic': 'org.fedoraproject.prod.buildsys.build.state.change'}"
14.906    "{'category': 'buildsys', 'not_package': 'bugwarrior'}"
7.6618    "{'topic': 'org.fedoraproject.prod.buildsys.build.state.change', 'not_package': 'bugwarrior'}"

Response times are faster in the cases you would expect.

Those columns are relatively simple one-to-many relationships. A message has one topic, and one category. Topics and categories are each associated with many messages. There is no JOIN required.

Handling the many-to-many cases

Speeding up the queries that require filtering on users and packages is more tricky. They are many-to-many relations -- each user is associated with multiple messages and a message may be associated with many users (or many packages).

I did some research, and through trial-and-error found that adding a composite primary key on the bridge tables gave a nice performance boost. See the results here:

0.2074    "{'user': 'ralph', 'delta': 86400}"
0.2091    "{'category': 'buildsys', 'delta': 86400}"
0.2099    "{'topic': 'org.fedoraproject.prod.buildsys.build.state.change', 'delta': 86400}"
0.2056    "{'not_package': 'bugwarrior', 'delta': 86400}"
1.4863    "{'category': 'buildsys', 'user': 'ralph'}"
1.4553    "{'topic': 'org.fedoraproject.prod.buildsys.build.state.change', 'user': 'ralph'}"
1.8186    "{'user': 'ralph', 'not_package': 'bugwarrior'}"
3.5525    "{'category': 'buildsys', 'topic': 'org.fedoraproject.prod.buildsys.build.state.change'}"
10.9242    "{'category': 'buildsys', 'not_package': 'bugwarrior'}"
3.5214    "{'topic': 'org.fedoraproject.prod.buildsys.build.state.change', 'not_package': 'bugwarrior'}"

The best so far! That one 10.9 second query is undesirable, but it also makes sense: we're asking it to first filter for all buildsys messages (the spammiest category) and then to prune those down to only the builds (a proper subset of that category). If you query just for the builds by topic and omit the category part (which is what you want anyways) the query takes 3.5s.

All around, I see a 3.5x speed increase.

Rolling it out

The code is set to be merged into datanommer and I wrote an ansible playbook to orchestrate pushing the change out. I'd push it out now, but we just entered the infrastructure freeze for the Fedora 22 Alpha release. Once we're through that and all thawed, we should be good to go.

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