Metadata-Version: 2.1
Name: altinity_datasets
Version: 0.1.2
Summary: Altinity Datasets for ClickHouse
Home-page: https://github.com/Altinity/altinity-datasets
Author: R Hodges
Author-email: info@altinity.com
License: Apache 2.0
Description: # Altinity Datasets for ClickHouse
        
        Welcome!  `altinity-datasets` loads test datasets for ClickHouse.  It is 
        inspired by Python libraries that [automatically load standard datasets](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html#sklearn.datasets.load_iris) 
        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. Following example shows install into a Python virtual environment. 
        First command is only required if you don't have clickhouse-client already
        installed on the host. 
        
        ```
        sudo apt install clickhouse-client
        sudo pip3 install altinity-datasets
        ```
        
        Many users will prefer to install within a Python3 virtual environment, 
        for example:
        
        ```
        python3 -m venv my-env
        . my-env/bin/activate
        pip3 install altinity-datasets
        ```
        
        You can also install a current version directly from Github:
        ```
        pip3 install git+https://github.com/altinity/altinity-datasets.git
        ```
        To remove altinity-datasets run the following command:
        ```
        pip3 uninstall altinity-datasets
        ```
        
        ## Using datasets
        
        The `ad-cli` command manages datasets.  You can see available commands by
        typing `ad-cli -h/--help`. All subcommands also accept -h/--help options.
        
        ### 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.  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
        ```
        
        ### Extra Connection Options
        
        The dataset load and dump commands by default connect to ClickHouse
        running on localhost with default user and empty password. The following
        example options connect using encrypted communications to a specific
        server with explicit user name and password. The last option suppresses
        certificate verification.  
        
        ```
        ad-cli dataset load iris -H 127.0.0.1 -P 9440 \
        -u special -p secret --secure --no-verify 
        ```
        
        Note: To use --no-verify you must also ensure that clickhouse-client is
        configured to accept invalid certificates. Validate by logging in using
        clickhouse-client with the --secure option.  Check and correct settings
        in /etc/clickhouse-client/config.xml if you have problems.
        
        ## 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
        
        To work on altinity-datasets clone from Github and install.  
        ```
        git clone https://github.com/altinity/altinity-datasets.git
        cd altinity-datasets
        python3 setup.py develop 
        ```
        
        After making changes you should run tests.
        ```
        cd tests
        python3 -m unittest --verbose
        ```
        
        The following commands build an installable and push to pypi.org.
        PyPI account credentials must be set in TWINE_USERNAME and TWINE_PASSWORD.
        
        ```
        python3 setup.py sdist
        twine upload --repository-url https://upload.pypi.org/legacy/ dist/*
        ```
        
        Code conventions are enforced using yapf and flake8. Run the
        dev-format-code.sh script to check formatting.
        
        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
        ```
        
        ## Errors
        
        ### Out-of-date pip3 causes installation failure
        
        If pip3 installs with the message `error: invalid command 'bdist_wheel'` you 
        may need to upgrade pip.  Run `pip3 install --upgrade pip` to correct the
        problem. 
        
        ### Materialized views cannot be dumped
        
        ad-cli will fail with an error if you try to dump a database that has
        materialized views. The workaround is to omit them from the dump operation 
        using a table regex as shown in the following example: 
        
        ```
        ad-cli dataset dump nyc_taxi_rides --repo-path=.  --compress --parallel=6 \
        --tables='^(tripdata|taxi_zones|central_park_weather_observations)$'
        ```
        
        ### --no-verify option fails on self-signed certs
        
        When using ad-cli --secure together with --no-verify options you need
        to also configure clickhouse-client to skip certificate verification.
        This only applies when the certificate is self-signed.  You must
        change /etc/clickhouse-client/config.xml as follows to skip certificate
        validation:
        
        ```
        <config>
            <openSSL>
                <client> <!-- Used for connection to server's secure tcp port -->
                    ...
                    <invalidCertificateHandler>
                        <name>AcceptCertificateHandler</name>
                    </invalidCertificateHandler>
                </client>
            </openSSL>
            ...
        </config>
        
        ```
        
        ## Limitations
        
        The most important are:
        
        * Error handling is spotty. If clickhouse-client is not in the path 
          things may fail mysteriously. 
        * Datasets have to be on the local file system.  In the future we will 
          use cloud object storage such as S3.
        
        Please file issues at https://github.com/Altinity/altinity-datasets/issues.
        Pull requests to fix problems are welcome. 
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: System Administrators
Description-Content-Type: text/markdown
