altinity-datasets 0.1.1

Altinity Datasets for ClickHouse

Altinity Datasets for ClickHouse

Welcome! altinity-datasets loads test datasets for ClickHouse. It is inspired by Python libraries that automatically load standard datasets for quick testing.

Getting Started

Altinity-datasets requires Python 3.5 or greater. The clickhouse-client executable must be in the path to load data.

Before starting you must install the altinity-datasets package using pip3. First, set up a virtualy environment.

python3 -m venv my-env
. my-env/bin/activate

Now there are two quick options. Install current version directly from Github:

pip3 install git+https://github.com/altinity/altinity-datasets.git

Or, install local source. This is good for development.

git clone https://github.com/altinity/altinity-datasets.git
cd altinity-datasets
python3 setup.py develop 

You can also build an installable distribution:

# Get code and build source distribution. 
git clone https://github.com/altinity/altinity-datasets.git
cd altinity-datasets
python3 setup.py sdist
# Optionally copy somewhere else and install. 
pip3 install dist/altinity_datasets-0.1.0.tar.gz

To remove altinity-datasets run the following command:

pip3 uninstall altinity-datasets

Using datasets

The ad-cli command manages datasets. Here is a short tutorial. You can see available commands by typing ad-cli --help.

Listing repos

Let's start by listing repos, which are locations that contain datasets.

ad-cli repo list

This will return a list of repos that have datasets. For the time being there is just a built-in repo that is part of the altinity-datasets package.

Finding datasets

Next let's see the available datasets.

ad-cli dataset search

This gives you a list of datasets with detailed descriptions. You can restrict the search to a single dataset by typing the name, for example ad-cli search wine. You can also search other repos using the repo file system location, e.g., ad-cli search wine --repo-path=$HOME/myrepo.

Loading datasets

Now, let's load a dataset. This currently only works with ClickHouse servers that use the default user and unencrypted communications. (See limitations below.) Here's a command to load the iris dataset to a ClickHouse server running on localhost.

ad-cli dataset load iris

Here is a more complex example. It loads the iris dataset to the iris_new database on a remote server. Also, we parallize the upload with 10 threads.

ad-cli load iris --database=iris_new --host=my.remote.host.com --parallel=10

The command shown above is typical of the invocation when loading on a server that has a large number of cores and fast storage.

Note that it's common to reload datasets expecially during development. You can do this using ad-cli load --clean. IMPORTANT: This drops the database to get rid of dataset tables. If you have other tables in the same database they will be dropped as well.

Dumping datasets

You can make a dataset from any existing table or tables in ClickHouse that reside in a single database. Here's a simple example that shows how to dump the weather dataset to create a new dataset. (The weather dataset is a built-in that loads by default to the weather database.)

ad-cli dataset dump weather

There are additional options to control dataset dumps. For example, we can rename the dateset, restrict the dump to tables that start with 'central', compress data, and overwrite any existing data in the output directory.

ad-cli dataset dump new_weather -d weather --tables='^central' --compress \
  --overwrite

Repo and Dataset Format

Repos are directories on the file system. The exact location of the repo is known as the repo path. Data sets under the repo are child directories that in turn have subdirectories for DDL commands and data. The following listing shows part of the organization of the built-ins repo.

built-ins/
  iris/
    data/
      iris/
        iris.csv
    ddl/
      iris.sql
    manifest.yaml
  wine/
    data/
      wine/
        wine.csv
    ddl/
      wine.sql
    manifest.yaml

To create your own dataset you can dump existing tables using ad-cli dataset dump or copy the examples in built-ins. The format is is simple.

• The manifest.yaml file describes the dataset. If you put in extra fields they will be ignored.
• The DDL directory contains SQL scripts to run. By convention these should be named for the objects (i.e., tables) that they create.
• The data directory contains CSV data. There is a separate subdirectory for each table to be loaded. Its name must match the table name exactly.
• CSV files can be uncompressed .csv or gzipped .csv.gz. No other formats are supported and the file types must be correctly specified.

You can place new repos in any location you please. To load from your own repo run a load command and use the --repo-path option to point to the repo location. Here's an example:

ad-cli dataset load mydataset --repo-path=$HOME/my-repo

Development

Code conventions are kind of lax for now. Please keep the Python files need and properly documented.

Run tests as follows with virtual environment set. You will need a ClickHouse server with a null password on the default user.

cd tests
python3 -m unittest -v

Limitations

Really too many to mention but the most important are:

• Database connection parameters are not supported yet.
• There is no automatic way to populate large dataset like airline/ontime. You can add the extra data files yourself.