This project starts the database container, and optionally (only available via API) also a test container. Both containers exist in the same Docker network. This allows you to connect from the test container to the Docker-DB container. Furthermore, this package provides two utility tasks which allow the population of some test data and upload of files to the BucketFS. Besides the test container, you can also access the Exasol database and the Bucket-FS from the host via forwarded ports.
- This project uses the Docker-DB, but it does the configuration, setup and startup for you. For example, it waits until the Database and the Bucket-FS accept connections. It also provides tasks which simplify the population the database with test data and upload of files to the BucketFS.
- The Exasol Testcontainers provide similar features for Java integration tests, so if you have a Java project use the Exasol Testcontainer, because they are more integrated in the Java ecosystem than this project. Testcontainers are designed to integrate with popular unit testing frameworks like JUnit4, JUnit5 and Spock. Most notably, lifecycle management of containers controlled by the tests lifecycle.
However, if you have a project in any other language you can use this project to start a test Exasol database via a command line interface. And if you have a Python project, you also have the possibility to start the additional test-container, which is started in the same docker network as the database.
In order to start a Docker-DB Test Environment, you need:
- Tested Operating System:
- Linux
- Mac OS X with Docker Desktop on Mac and Intel processor
- Windows is currently not supported
- Docker with privileged mode
- At least 2 GiB RAM
- We recommend at least 15 GB free disk space on the partition where
Docker stores its images and containers. On Linux Docker typically
stores the images under
/var/lib/docker.
Clone the repository
git clone https://github.com/exasol/integration-test-docker-environment
Starting the test environment:
./start-test-env spawn-test-environment --environment-name <NAME>
or if you work on the code of the Test Environment (requires Python >=3.8 with poetry):
./start-test-env-with-poetry spawn-test-environment --environment-name <NAME>
Shutdown of the test environment is currently done manually.
The following options are available to customize the test environment.
Usage: main.py spawn-test-environment [OPTIONS]
This command spawn a test environment with a docker-db container.
Options:
--environment-name TEXT Name of the docker environment. This name
gets used as suffix for the container
db_container_<name> and
test_container_<name> [required]
--database-port-forward INTEGER
Host port to which the database port gets
forwarded
--bucketfs-port-forward INTEGER
Host port to which the BucketFS port gets
forwarded
--ssh-port-forward INTEGER
Host port to which the SSH port gets
forwarded
--db-mem-size TEXT The main memory used by the database. Format
<number> <unit>, e.g. 1 GiB. The minimum
size is 1 GB, below that the database will
not start. [default: 2 GiB]
--db-disk-size TEXT The disk size available for the database.
Format <number> <unit>, e.g. 1 GiB. The
minimum size is 100 MiB. However, the setup
creates volume files with at least 2 GB
larger size, because the database needs at
least so much more disk. [default: 2 GiB]
--nameserver TEXT Add a name server to the list of DNS
nameservers which the docker-db should use
for resolving domain names. You can repeat
this option to add further nameservers.
--docker-runtime TEXT The docker runtime used to start all
containers
--docker-db-image-version TEXT Docker DB Image Version against which the
tests should run. [default: 8.18.1]
--docker-db-image-name TEXT Docker DB Image Name against which the tests
should run. [default: exasol/docker-db]
--db-os-access METHOD How to access file system and command
line of the database operating
system. Experimental option, will show no
effect until implementation of feature
SSH access is completed. [default:
DOCKER_EXEC]
--output-directory DIRECTORY Output directory where the system stores all
output and log files. [default:
.build_output]
--temporary-base-directory DIRECTORY
Directory where the system creates temporary
directories. [default: /tmp]
You can look at them on the command line with:
./start-test-env spawn-test-environment --help
Besides, the already mentioned command spawn-test-environment, the
integration-test-docker-environment command line tool provides a couple
of other helpful tools.
Run main.py --help, to get a list of the available commands:
Usage: main.py [OPTIONS] COMMAND [ARGS]...
Options:
--help Show this message and exit.
Commands:
health Check the health of the execution environment.
spawn-test-environment This command spawn a test environment with a...To get more details on a specific command run
main.py <command> --help, e.g. main.py health --help:
Usage: main.py health [OPTIONS]
Check the health of the execution environment.
If no issues have been found, using the library or executing the test should
work just fine. For all found issues there will be a proposed fix/solution.
If the environment was found to be healthy the exit code will be 0.
Options:
--help Show this message and exit.t.b.d. Link to sphinx documentation
The default credentials for the database are
- User:
sys - Password:
exasol
and for the Bucket-FS:
- User:
w - Password:
write
or
- User:
r - Password:
read
The python setup script creates configuration files on the host and in the test container.
On the host the container information get stored in the build output
directory usually under .build_output/cache/<ITDE_NAME>/*. In
the test container the config file is stored at the root directory
/.
The following config files are available:
environment_info.sh: This file is meant to be source by the bash and then provides the information as environment variables. Here an example for the content:export ITDE_NAME=test export ITDE_TYPE=EnvironmentType.docker_db # Database IP in environment docker network export ITDE_DATABASE_HOST=172.21.0.2 export ITDE_DATABASE_DB_PORT=8563 export ITDE_DATABASE_BUCKETFS_PORT=2580 export ITDE_DATABASE_SSH_PORT=22 export ITDE_DATABASE_CONTAINER_NAME=db_container_test export ITDE_DATABASE_CONTAINER_NETWORK_ALIASES="exasol_test_database db_container_test" # Database IP in the environment docker network export ITDE_DATABASE_CONTAINER_IP_ADDRESS=172.21.0.2 export ITDE_DATABASE_CONTAINER_VOLUME_NAME=db_container_test_volume # Database IP on the docker default bridge which under Linux available from the host export ITDE_DATABASE_CONTAINER_DEFAULT_BRIDGE_IP_ADDRESS=172.17.0.3 export ITDE_TEST_CONTAINER_NAME=test_container_test export ITDE_TEST_CONTAINER_NETWORK_ALIASES="test_container test_container_test" # Test Container IP in the environment docker network export ITDE_TEST_CONTAINER_IP_ADDRESS=172.21.0.3
environment_info.json: Contains the EnvironmentInfo objects pickled with JsonPickle.
- 7.0: up to 7.0.20 except 7.0.5
- 7.1: up to 7.1.17
- 8: 8.17.0 and 8.18.1
If you need further versions, please open an issue.
Up to version 1.6.0 ITDE used docker_exec to access the operating system
of the Exasol database inside the Docker Container, e.g. to analyze the
content of log files or execute some shell commands. With version 8 of Exasol
database the format of the Docker Containers might change so that
docker_exec is no longer possible. Instead ITDE will then need to use SSH
access.
You can select the access method with command line option
--db-os-access. The default value is DOCKER_EXEC.
ITDE will create a random SSH key pair and store it to the file
~/.cache/exasol/itde/id_rsa with access permissions limited to the current
user only. By this ITDE enables to reuse the same SSH keys for future sessions
which leaves the container unchanged and hence reusable.
The public key will be added as file /root/.ssh/authorized_keys inside the
Docker Container to enable SSH access with public key authentication.
You can use command line option --ssh-port-forward to specify a port on
your host machine to which ITDE forwards the SSH port of the Docker Container
running the Exasol database. If you do not specify a port then ITDE will
select a random free port.
- Docker Default Runtime
- NVIDIA Container Runtime for GPU accelerated UDFs
The Exasol Docker-DB needs per default a bit more than 2 GB of RAM,
however the Docker VM on Mac OS X provides often not enough RAM to
accommodate this. You should increase the RAM of the Docker VM to at
least 4.25 GB or reduce the DB Mem Size for the Exasol Docker-DB to less
than 2 GB with --db-mem-size 1 GiB.
Mac OS X with Docker Desktop for Mac uses a lightweight virtual machine with linux in which the docker daemon runs and the containers get started. This makes networking and shared directories more complicated then on Linux.
We start the Python setup script for the test environment in its own
Docker container, lets call it docker runner, because the library
Luigi can have problems
with Mac OS X and to avoid the installation of further dependencies. To
support Mac OS X, the start-test-env script starts the
docker runner container and mounts the docker socket at
/var/run/docker.sock and the directory of the test environment from
the Mac OS X host to the container. Then, it starts
start-test-env-without-docker which then starts the python script.
It is important, that the repository gets cloned to the Mac OS X host
and not to a docker container, because the python scripts tries to start
further docker container which use host mounts to share the tests
directory of the test environment with the docker container.
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