Functional tests are the most simple and convenient to use. Most of ClickHouse features can be tested with functional tests and they are mandatory to use for every change in ClickHouse code that can be tested that way.
Each functional test sends one or multiple queries to the running ClickHouse server and compares the result with reference.
Tests are located in `dbms/tests/queries` directory. There are two subdirectories: `stateless` and `stateful`. Stateless tests run queries without any preloaded test data - they often create small synthetic datasets on the fly, within the test itself. Stateful tests require preloaded test data from Yandex.Metrica and not available to general public. We tend to use only `stateless` tests and avoid adding new `stateful` tests.
Each test can be one of two types: `.sql` and `.sh`. `.sql` test is the simple SQL script that is piped to `clickhouse-client --multiquery --testmode`. `.sh` test is a script that is run by itself.
To run all tests, use `dbms/tests/clickhouse-test` tool. Look `--help` for the list of possible options. You can simply run all tests or run subset of tests filtered by substring in test name: `./clickhouse-test substring`.
The most simple way to invoke functional tests is to copy `clickhouse-client` to `/usr/bin/`, run `clickhouse-server` and then run `./clickhouse-test` from its own directory.
To add new test, create a `.sql` or `.sh` file in `dbms/tests/queries/0_stateless` directory, check it manually and then generate `.reference` file in the following way: `clickhouse-client -n --testmode < 00000_test.sql > 00000_test.reference` or `./00000_test.sh > ./00000_test.reference`.
Tests should use (create, drop, etc) only tables in `test` database that is assumed to be created beforehand; also tests can use temporary tables.
If you want to use distributed queries in functional tests, you can leverage `remote` table function with `127.0.0.{1..2}` addresses for the server to query itself; or you can use predefined test clusters in server configuration file like `test_shard_localhost`.
If we know some bugs that can be easily reproduced by functional tests, we place prepared functional tests in `dbms/tests/queries/bugs` directory. These tests will be moved to `dbms/tests/queries/0_stateless` when bugs are fixed.
Integration tests allow to test ClickHouse in clustered configuration and ClickHouse interaction with other servers like MySQL, Postgres, MongoDB. They are useful to emulate network splits, packet drops, etc. These tests are run under Docker and create multiple containers with various software.
See `dbms/tests/integration/README.md` on how to run these tests.
Note that integration of ClickHouse with third-party drivers is not tested. Also we currently don't have integration tests with our JDBC and ODBC drivers.
## Unit Tests
Unit tests are useful when you want to test not the ClickHouse as a whole, but a single isolated library or class. You can enable or disable build of tests with `ENABLE_TESTS` CMake option. Unit tests (and other test programs) are located in `tests` subdirectories across the code. To run unit tests, type `ninja test`. Some tests use `gtest`, but some are just programs that return non-zero exit code on test failure.
It's not necessarily to have unit tests if the code is already covered by functional tests (and functional tests are usually much more simple to use).
## Performance Tests
Performance tests allow to measure and compare performance of some isolated part of ClickHouse on synthetic queries. Tests are located at `dbms/tests/performance`. Each test is represented by `.xml` file with description of test case. Tests are run with `clickhouse performance-test` tool (that is embedded in `clickhouse` binary). See `--help` for invocation.
Each test run one or miltiple queries (possibly with combinations of parameters) in a loop with some conditions for stop (like "maximum execution speed is not changing in three seconds") and measure some metrics about query performance (like "maximum execution speed"). Some tests can contain preconditions on preloaded test dataset.
If you want to improve performance of ClickHouse in some scenario, and if improvements can be observed on simple queries, it is highly recommended to write a performance test. It always makes sense to use `perf top` or other perf tools during your tests.
Some programs in `tests` directory are not prepared tests, but are test tools. For example, for `Lexer` there is a tool `dbms/src/Parsers/tests/lexer` that just do tokenization of stdin and writes colorized result to stdout. You can use these kind of tools as a code examples and for exploration and manual testing.
You can also place pair of files `.sh` and `.reference` along with the tool to run it on some predefined input - then script result can be compared to `.reference` file. These kind of tests are not automated.
There are tests for external dictionaries located at `dbms/tests/external_dictionaries` and for machine learned models in `dbms/tests/external_models`. These tests are not updated and must be transferred to integration tests.
There is separate test for quorum inserts. This test run ClickHouse cluster on separate servers and emulate various failure cases: network split, packet drop (between ClickHouse nodes, between ClickHouse and ZooKeeper, between ClickHouse server and client, etc.), `kill -9`, `kill -STOP` and `kill -CONT` , like [Jepsen](https://aphyr.com/tags/Jepsen). Then the test checks that all acknowledged inserts was written and all rejected inserts was not.
Quorum test was written by separate team before ClickHouse was open-sourced. This team no longer work with ClickHouse. Test was accidentially written in Java. For these reasons, quorum test must be rewritten and moved to integration tests.
## Manual Testing
When you develop a new feature, it is reasonable to also test it manually. You can do it with the following steps:
Build ClickHouse. Run ClickHouse from the terminal: change directory to `dbms/src/programs/clickhouse-server` and run it with `./clickhouse-server`. It will use configuration (`config.xml`, `users.xml` and files within `config.d` and `users.d` directories) from the current directory by default. To connect to ClickHouse server, run `dbms/src/programs/clickhouse-client/clickhouse-client`.
Note that all clickhouse tools (server, client, etc) are just symlinks to a single binary named `clickhouse`. You can find this binary at `dbms/src/programs/clickhouse`. All tools can also be invoked as `clickhouse tool` instead of `clickhouse-tool`.
Alternatively you can install ClickHouse package: either stable release from Yandex repository or you can build package for yourself with `./release` in ClickHouse sources root. Then start the server with `sudo service clickhouse-server start` (or stop to stop the server). Look for logs at `/etc/clickhouse-server/clickhouse-server.log`.
When ClickHouse is already installed on your system, you can build a new `clickhouse` binary and replace the existing binary:
If the system clickhouse-server is already running and you don't want to stop it, you can change port numbers in your `config.xml` (or override them in a file in `config.d` directory), provide appropriate data path, and run it.
`clickhouse` binary has almost no dependencies and works across wide range of Linux distributions. To quick and dirty test your changes on a server, you can simply `scp` your fresh built `clickhouse` binary to your server and then run it as in examples above.
Before publishing release as stable we deploy it on testing environment. Testing environment is a cluster that process 1/39 part of [Yandex.Metrica](https://metrica.yandex.com/) data. We share our testing environment with Yandex.Metrica team. ClickHouse is upgraded without downtime on top of existing data. We look at first that data is processed successfully without lagging from realtime, the replication continue to work and there is no issues visible to Yandex.Metrica team. First check can be done in the following way:
SELECT hostName() AS h, any(version()), any(uptime()), max(UTCEventTime), count() FROM remote('example01-01-{1..3}t', merge, hits) WHERE EventDate >= today() - 2 GROUP BY h ORDER BY h;
```
In some cases we also deploy to testing environment of our friend teams in Yandex: Market, Cloud, etc. Also we have some hardware servers that are used for development purposes.
$ clickhouse-client --query="SELECT DISTINCT query FROM system.query_log WHERE event_date = today() AND query LIKE '%ym:%' AND query NOT LIKE '%system.query_log%' AND type = 2 AND is_initial_query" > queries.tsv
This is a way complicated example. `type = 2` will filter queries that are executed successfully. `query LIKE '%ym:%'` is to select relevant queries from Yandex.Metrica. `is_initial_query` is to select only queries that are initiated by client, not by ClickHouse itself (as parts of distributed query processing).
`scp` this log to your testing cluster and run it as following:
Then leave it for a night or weekend and go take a rest.
You should check that `clickhouse-server` doesn't crash, memory footprint is bounded and performance not degrading over time.
Precise query execution timings are not recorded and not compared due to high variability of queries and environment.
## Build Tests
Build tests allow to check that build is not broken on various alternative configurations and on some foreign systems. Tests are located at `ci` directory. They run build from source inside Docker, Vagrant, and sometimes with `qemu-user-static` inside Docker. These tests are under development and test runs are not automated.
Motivation:
Normally we release and run all tests on a single variant of ClickHouse build. But there are alternative build variants that are not thoroughly tested. Examples:
- build on FreeBSD;
- build on Debian with libraries from system packages;
For example, build with system packages is bad practice, because we cannot guarantee what exact version of packages a system will have. But this is really needed by Debian maintainers. For this reason we at least have to support this variant of build. Another example: shared linking is a common source of trouble, but it is needed for some enthusiasts.
Though we cannot run all tests on all variant of builds, we want to check at least that various build variants are not broken. For this purpose we use build tests.
When we extend ClickHouse network protocol, we test manually that old clickhouse-client works with new clickhouse-server and new clickhouse-client works with old clickhouse-server (simply by running binaries from corresponding packages).
## Help From The Compiler
Main ClickHouse code (that is located in `dbms` directory) is built with `-Wall -Wextra -Werror` and with some additional enabled warnings. Although these options are not enabled for third-party libraries.
Clang has even more useful warnings - you can look for them with `-Weverything` and pick something to default build.
For production builds, gcc is used (it still generates slightly more efficient code than clang). For development, clang is usually more convenient to use. You can build on your own machine with debug mode (to save battery of your laptop), but please note that compiler is able to generate more warnings with `-O3` due to better control flow and inter-procedure analysis. When building with clang, `libc++` is used instead of `libstdc++` and when building with debug mode, debug version of `libc++` is used that allows to catch more errors at runtime.
We run functional tests under Valgrind overnight. It takes multiple hours. Currently there is one known false positive in `re2` library, see [this article](https://research.swtch.com/sparse).
We use simple fuzz test to generate random SQL queries and to check that the server doesn't die. Fuzz testing is performed with Address sanitizer. You can find it in `00746_sql_fuzzy.pl`. This test should be run continuously (overnight and longer).
As of December 2018, we still don't use isolated fuzz testing of library code.
We run `PVS-Studio` on per-commit basis. We have evaluated `clang-tidy`, `Coverity`, `cppcheck`, `PVS-Studio`, `tscancode`. You will find instructions for usage in `dbms/tests/instructions/` directory. Also you can read [the article in russian](https://habr.com/company/yandex/blog/342018/).
If you use `CLion` as an IDE, you can leverage some `clang-tidy` checks out of the box.
## Hardening
`FORTIFY_SOURCE` is used by default. It is almost useless, but still makes sense in rare cases and we don't disable it.
## Code Style
Code style rules are described [here](https://clickhouse.yandex/docs/en/development/style/).
To check for some common style violations, you can use `utils/check-style` script.
To force proper style of your code, you can use `clang-format`. File `.clang-format` is located at the sources root. It mostly corresponding with our actual code style. But it's not recommended to apply `clang-format` to existing files because it makes formatting worse. You can use `clang-format-diff` tool that you can find in clang source repository.
Alternatively you can try `uncrustify` tool to reformat your code. Configuration is in `uncrustify.cfg` in the sources root. It is less tested than `clang-format`.
Each ClickHouse release is tested with Yandex Metrica and AppMetrica engines. Testing and stable versions of ClickHouse are deployed on VMs and run with a small copy of Metrica engine that is processing fixed sample of input data. Then results of two instances of Metrica engine are compared together.
These tests are automated by separate team. Due to high number of moving parts, tests are fail most of the time by completely unrelated reasons, that are very difficult to figure out. Most likely these tests have negative value for us. Nevertheless these tests was proved to be useful in about one or two times out of hundreds.
Build jobs and tests are run in Sandbox on per commit basis. Resulting packages and test results are published in GitHub and can be downloaded by direct links. Artifacts are stored eternally. When you send a pull request on GitHub, we tag it as "can be tested" and our CI system will build ClickHouse packages (release, debug, with address sanitizer, etc) for you.
