The Ceilometer project supports various database backend that can be used as storage. Among them are MongoDB, SQLite MySQL, PostgreSQL, HBase, DB2… All Ceilometer's code is unit tested, but when dealing with external storage services, one cannot be sure that the code is really working. You could be inserting data with an incorrect SQL statement, or in the wrong table. Only having the real database storage running and being used can tell you that.

Over the months, we developed integration testing on top of our unit testing to validate that our storage drivers are able to deal with real world databases. That is not really different from generic integration testing. Integration testing is about plugging all the pieces of your software all together and running. In what I call "database integration testing", the pieces will be both your software and the database system that you are going to rely on.

The only difference here is that one of the module is not coming from the application itself but is an external project. The type of database that you use (RDBMS, NoSQL…) does not matter. Taking a step back, what I will describe here could also apply to a lot of other different software modules, even something that would not be a database sytem at all.

Writing tests for integration

Presumably, your Python application has unit tests. In order to test against a database back-end, you need to write a few specific classes of tests that will use the database subsystem for real. For example:

import unittest
import os
import sqlalchemy
 
class TestDB(unittest.TestCase):
def setUp(self):
url = os.getenv("DB_TEST_URL")
if not url:
self.skipTest("No database URL set")
self.engine = sqlalchemy.create_engine(url)


This code will try to fetch the database URL to use from an environment variable, and then will rely on SQLAlchemy to create a database connection.

import unittest
import os
import sqlalchemy
 
import myapp
 
class TestDB(unittest.TestCase):
def setUp(self):
url = os.getenv("DB_TEST_URL")
if not url:
self.skipTest("No database URL set")
self.engine = sqlalchemy.create_engine(url)
 
def test_foobar(self):
self.assertTrue(myapp.store_integer(self.engine, 42))


You can then add as many tests as you want using the connection stored in self.engine. If no test database URL is, the tests will be skipped; however that decision is up to you. You may want to have these tests always run and fail if they can't be run.

In the setUp() method, you may also need to do more work, like create a database and delete a database.

import unittest
import os
import sqlalchemy
 
class TestDB(unittest.TestCase):
def setUp(self):
url = os.getenv("DB_TEST_URL")
if not url:
self.skipTest("No database URL set")
self.engine = sqlalchemy.create_engine(url)
self.connection = self.engine.connect()
self.connection.execute("CREATE DATABASE testdb")
 
def tearDown(self):
self.connection.execute("DROP DATABASE testdb")


This will make sure that the database you need is clean and ready to be used to testing.

Launching modules, a.k.a. databases

The main problem we encountered when building integration testing with databases, is to find a way to start them. Most users are used to start them system-wide with some sort of init script, but when running sandboxed tests, that is not really a good option. Browsing the documentation of each storage allowed us to find a way to start them in foreground and control them "interactively" via a shell script.

The following is a script that you can use to run Python tests using nose and is heavily inspired by the one we wrote for Ceilometer.

#!/bin/bash
set -e
 
clean_exit() {
local error_code="$?"
kill -9 $(jobs -p) >/dev/null 2>&1 || true
rm -rf "PGSQL_DATA"
return $error_code
}
 
check_for_cmd () {
if ! which "$1" >/dev/null 2>&1
then
echo "Could not find $1 command" 1>&2
exit 1
fi
}
 
wait_for_line () {
while read line
do
echo "$line" | grep -q "$1" && break
done < "$2"
# Read the fifo for ever otherwise process would block
cat "$2" >/dev/null &
}
 
check_for_cmd mongod
 
trap "clean_exit" EXIT
 
# Start PostgreSQL process for tests
PGSQL_DATA=`mktemp -d /tmp/PGSQL-XXXXX`
PGSQL_PATH=`pg_config --bindir`
{PGSQL_PATH}/initdb ${PGSQL_DATA}
mkfifo ${PGSQL_DATA}/out
${PGSQL_PATH}/postgres -F -k ${PGSQL_DATA} -D ${PGSQL_DATA} &> ${PGSQL_DATA}/out &
# Wait for PostgreSQL to start listening to connections
wait_for_line "database system is ready to accept connections" ${PGSQL_DATA}/out
export DB_TEST_URL="postgresql:///?host=${PGSQL_DATA}&dbname=template1"
 
# Run the tests
nosetests


If you use tox to automatize your test run, you can use this scripts (I call it run-test.sh) in your tox.ini file.

[testenv]
commands = {toxinidir}/run-tests.sh {posargs}


Most databases are able to be run in some sort of standalone mode where you can connect to them using a either a Unix domain socket, or a fixed port. Here are the snippet used in Ceilometer to run with MongoDB and MySQL:

# Start MongoDB process for tests
MONGO_DATA=$(mktemp -d /tmp/MONGODB-XXXXX)
MONGO_PORT=29000
mkfifo ${MONGO_DATA}/out
mongod --maxConns 32 --nojournal --noprealloc --smallfiles --quiet --noauth --port ${MONGO_PORT} --dbpath "${MONGO_DATA}" --bind_ip localhost &>${MONGO_DATA}/out &
# Wait for Mongo to start listening to connections
wait_for_line "waiting for connections on port ${MONGO_PORT}" ${MONGO_DATA}/out
export DB_TEST_URL="mongodb://localhost:${MONGO_PORT}/testdb"


# Start MySQL process for tests
MYSQL_DATA=$(mktemp -d /tmp/MYSQL-XXXXX)
mkfifo ${MYSQL_DATA}/out
mysqld --datadir=${MYSQL_DATA} --pid-file=${MYSQL_DATA}/mysql.pid --socket=${MYSQL_DATA}/mysql.socket --skip-networking --skip-grant-tables &> ${MYSQL_DATA}/out &
# Wait for MySQL to start listening to connections
wait_for_line "mysqld: ready for connections." ${MYSQL_DATA}/out
export DB_TEST_URL="mysql://root@localhost/testdb?unix_socket=${MYSQL_DATA}/mysql.socket&charset=utf8"


The mechanism is always the same. We create a fifo with mkfifo, and then run the database daemon with the output redirected to that fifo. We then read from it until we find a line stating the the database is ready to be used. At that point, we can continue and start running the tests. You have to read continuously from the fifo, otherwise the process writing to it will block. We redirect the output to /dev/null, but you could also redirect it to a different log file, or not at all.

Note: Evgeni Golov pointed it exists a pg_virtualenv for PostgreSQL and my_virtualenv for MySQL that does the same kind of thing, but with more bells and whistles.

One step further: using parallelism and scenarios

The described approach is quite simple, as it only support one database type. When using an abstraction layer, such as SQLAlchemy, it would be a good idea to run all these tests against different RDBMS, such as MySQL and PostgreSQL for example.

The snippet above allows to run both RDBMS in parallel, but the classic approach of unit tests does not allow that. Using one scenario for each database backend would be a great idea. To that end, you can use the testscenarios library.

import unittest
import os
import sqlalchemy
import testscenarios
 
load_tests = testscenarios.load_tests_apply_scenarios
 
class TestDB(unittest.TestCase):
scenarios = [
('mysql', dict(database_connection=os.getenv("MYSQL_TEST_URL")),
('postgresql', dict(database_connection=os.getenv("PGSQL_TEST_URL")),
]
 
def setUp(self):
if not self.database_connection:
self.skipTest("No database URL set")
self.engine = sqlalchemy.create_engine(self.database_connection)
self.connection = self.engine.connect()
self.connection.execute("CREATE DATABASE testdb")
 
def tearDown(self):
self.connection.execute("DROP DATABASE testdb")


$ python -m subunit.run test_scenario | subunit2pyunit
test_scenario.TestDB.test_foobar(mysql)
test_scenario.TestDB.test_foobar(mysql) ... ok
test_scenario.TestDB.test_foobar(postgresql)
test_scenario.TestDB.test_foobar(postgresql) ... ok
 
---------------------------------------------------------
Ran 2 tests in 0.061s
 
OK


To speed up tests run, you could also run the test in parallel. It can be intesting as you'll be able to spread the workload among a lot of different CPUs. However, note that it can require a different database for each test or a locking mechanism to be in place. It's likely that your tests won't be able to work altogether at the same time on only one database.

(Both usage of scenarios and parallelism in testing will be covered in The Hacker's Guide to Python, in case you wonder.)

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