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https://github.com/altinity/altinity-datasets

Altinity Datasets for ClickHouse
https://github.com/altinity/altinity-datasets

Last synced: 12 months ago
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Altinity Datasets for ClickHouse

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README 

          
# 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:



```



    

         

            ...

            

                AcceptCertificateHandler

            

        

    

    ...



```



## 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.