Skeem
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About
Skeem infers SQL DDL statements from tabular data.
Skeem is, amongst others, based on the excellent ddlgenerator,
frictionless, fsspec, pandas, ScipPy, SQLAlchemy
and xarray_ packages, and can be used both as a standalone program,
and as a library.
Supported input data:
Apache Parquet_CSV_Google Sheets_GRIB_InfluxDB line protocol_JSON_NetCDF_NDJSON_ (formerly LDJSON) aka. JSON Lines, see also JSON streamingOffice Open XML Workbook_ (Microsoft Excel_)OpenDocument Spreadsheet_ (LibreOffice_)Supported input sources:
Amazon S3_File system_GitHub_Google Cloud Storage_HTTP_Please note that Skeem is beta-quality software, and a work in progress. Contributions of all kinds are very welcome, in order to make it more solid. Breaking changes should be expected until a 1.0 release, so version pinning is recommended, especially when you use it as a library.
Synopsis
.. code-block:: sh
skeem infer-ddl --dialect=postgresql data.ndjson
.. code-block:: sql
CREATE TABLE "data" (
"id" SERIAL NOT NULL,
"name" TEXT NOT NULL,
"date" TIMESTAMP WITHOUT TIME ZONE,
"fruits" TEXT NOT NULL,
"price" DECIMAL(2, 2) NOT NULL,
PRIMARY KEY ("id")
);
Quickstart
If you are in a hurry, and want to run Skeem without any installation, just use the OCI image on Podman or Docker.
.. code-block:: sh
docker run --rm ghcr.io/daq-tools/skeem-standard \
skeem infer-ddl --dialect=postgresql \
https://github.com/daq-tools/skeem/raw/main/tests/testdata/basic.ndjson
Setup
Install Skeem from PyPI.
.. code-block:: sh
pip install skeem
Install Skeem with support for additional data formats like NetCDF.
.. code-block:: sh
pip install 'skeem[scientific]'
Usage
This section outlines some example invocations of Skeem, both on the command
line, and per library use. Other than the resources available from the web,
testing data can be acquired from the repository's testdata_ folder.
.. code-block:: sh
skeem info
skeem --help
skeem infer-ddl --help
.. code-block:: sh
# NDJSON, Parquet, and InfluxDB line protocol (ILP) formats.
skeem infer-ddl --dialect=postgresql data.ndjson
skeem infer-ddl --dialect=postgresql data.parquet
skeem infer-ddl --dialect=postgresql data.lp
# CSV, JSON, ODS, and XLSX formats.
skeem infer-ddl --dialect=postgresql data.csv
skeem infer-ddl --dialect=postgresql data.json
skeem infer-ddl --dialect=postgresql data.ods
skeem infer-ddl --dialect=postgresql data.xlsx
skeem infer-ddl --dialect=postgresql data.xlsx --address="Sheet2"
.. code-block:: sh
# CSV, NDJSON, XLSX
skeem infer-ddl --dialect=postgresql https://github.com/daq-tools/skeem/raw/main/tests/testdata/basic.csv
skeem infer-ddl --dialect=postgresql https://github.com/daq-tools/skeem/raw/main/tests/testdata/basic.ndjson
skeem infer-ddl --dialect=postgresql https://github.com/daq-tools/skeem/raw/main/tests/testdata/basic.xlsx --address="Sheet2"
# Google Sheets: Address first sheet, and specific sheet of workbook.
skeem infer-ddl --dialect=postgresql --table-name=foo https://docs.google.com/spreadsheets/d/1ExyrawjlyksbC6DOM6nLolJDbU8qiRrrhxSuxf5ScB0/view
skeem infer-ddl --dialect=postgresql --table-name=foo https://docs.google.com/spreadsheets/d/1ExyrawjlyksbC6DOM6nLolJDbU8qiRrrhxSuxf5ScB0/view#gid=883324548
# InfluxDB line protocol (ILP)
skeem infer-ddl --dialect=postgresql https://github.com/influxdata/influxdb2-sample-data/raw/master/air-sensor-data/air-sensor-data.lp
# Compressed files in gzip format
skeem --verbose infer-ddl --dialect=crate --content-type=ndjson https://s3.amazonaws.com/crate.sampledata/nyc.yellowcab/yc.2019.07.gz
# CSV on S3
skeem --verbose infer-ddl --dialect=postgresql s3://noaa-ghcn-pds/csv/by_year/2022.csv
# CSV on Google Cloud Storage
skeem --verbose infer-ddl --dialect=postgresql gs://tinybird-assets/datasets/nations.csv
skeem --verbose infer-ddl --dialect=postgresql gs://tinybird-assets/datasets/medals1.csv
# CSV on GitHub
skeem --verbose infer-ddl --dialect=postgresql github://daq-tools:skeem@/tests/testdata/basic.csv
# GRIB2, NetCDF
skeem infer-ddl --dialect=postgresql https://github.com/earthobservations/testdata/raw/main/opendata.dwd.de/weather/nwp/icon/grib/18/t/icon-global_regular-lat-lon_air-temperature_level-90.grib2
skeem infer-ddl --dialect=postgresql https://www.unidata.ucar.edu/software/netcdf/examples/sresa1b_ncar_ccsm3-example.nc
skeem infer-ddl --dialect=postgresql https://www.unidata.ucar.edu/software/netcdf/examples/WMI_Lear.nc
OCI images are available on the GitHub Container Registry (GHCR). In order to run them on Podman or Docker, invoke:
.. code-block:: sh
docker run --rm ghcr.io/daq-tools/skeem-standard \
skeem infer-ddl --dialect=postgresql \
https://github.com/daq-tools/skeem/raw/main/tests/testdata/basic.csv
If you want to work with files on your filesystem, you will need to either
mount the working directory into the container using the --volume option,
or use the --interactive option to consume STDIN, like:
.. code-block:: sh
docker run --rm --volume=$(pwd):/data ghcr.io/daq-tools/skeem-standard \
skeem infer-ddl --dialect=postgresql /data/basic.ndjson
docker run --rm --interactive ghcr.io/daq-tools/skeem-standard \
skeem infer-ddl --dialect=postgresql --content-type=ndjson - < basic.ndjson
In order to always run the latest nightly development version, and to use a
shortcut for that, this section outlines how to use an alias for skeem, and
a variable for storing the input URL. It may be useful to save a few keystrokes
on subsequent invocations.
.. code-block:: sh
docker pull ghcr.io/daq-tools/skeem-standard:nightly
alias skeem="docker run --rm --interactive ghcr.io/daq-tools/skeem-standard:nightly skeem"
URL=https://github.com/daq-tools/skeem/raw/main/tests/testdata/basic.ndjson
skeem infer-ddl --dialect=postgresql $URL
Use a different backend (default: ddlgen)::
skeem infer-ddl --dialect=postgresql --backend=frictionless data.ndjson
Reading data from STDIN needs to obtain both the table name and content type separately::
skeem infer-ddl --dialect=crate --table-name=foo --content-type=ndjson - < data.ndjson
Reading data from STDIN also works like this, if you prefer to use pipes::
cat data.ndjson | skeem infer-ddl --dialect=crate --table-name=foo --content-type=ndjson -
.. code-block:: python
import io
from skeem.core import SchemaGenerator
from skeem.model import Resource, SqlTarget
INDATA = io.StringIO(
"""
{"id":1,"name":"foo","date":"2014-10-31 09:22:56","fruits":"apple,banana","price":0.42}
{"id":2,"name":"bar","date":null,"fruits":"pear","price":0.84}
"""
)
sg = SchemaGenerator(
resource=Resource(data=INDATA, content_type="ndjson"),
target=SqlTarget(dialect="crate", table_name="testdrive"),
)
print(sg.to_sql_ddl().pretty)
.. code-block:: sql
CREATE TABLE "testdrive" (
"id" INT NOT NULL,
"name" STRING NOT NULL,
"date" TIMESTAMP,
"fruits" STRING NOT NULL,
"price" DOUBLE NOT NULL,
PRIMARY KEY ("id")
);
Development
For installing the project from source, please follow the development_
documentation.
Project information
Catherine Devlin_ for ddlgenerator_ and data_dispenser_.Mike Bayer_ for SQLAlchemy_.Paul Walsh_ and Evgeny Karev_ for frictionless_.Wes McKinney_ for pandas_.We are maintaining a list of other projects_ with the same or similar goals
like Skeem.
The program was about to be called Eskema, but it turned out that there is
already another Eskema_ out there. So, it has been renamed to Skeem, which
is Estonian, and means "schema", "outline", or "(to) plan".
.. _Amazon S3: https://en.wikipedia.org/wiki/Amazon_S3 .. _Apache Parquet: https://en.wikipedia.org/wiki/Apache_Parquet .. _Catherine Devlin: https://github.com/catherinedevlin .. _CSV: https://en.wikipedia.org/wiki/Comma-separated_values .. _data_dispenser: https://pypi.org/project/data_dispenser/ .. _ddlgenerator: https://pypi.org/project/ddlgenerator/ .. _development: doc/development.rst .. _Eskema: https://github.com/nombrekeff/eskema .. _Evgeny Karev: https://github.com/roll .. _file system: https://en.wikipedia.org/wiki/File_system .. _frictionless: https://github.com/frictionlessdata/framework .. _fsspec: https://pypi.org/project/fsspec/ .. _GitHub: https://github.com/ .. _Google Cloud Storage: https://en.wikipedia.org/wiki/Google_Cloud_Storage .. _Google Sheets: https://en.wikipedia.org/wiki/Google_Sheets .. _GRIB: https://en.wikipedia.org/wiki/GRIB .. _HTTP: https://en.wikipedia.org/wiki/HTTP .. _InfluxDB line protocol: https://docs.influxdata.com/influxdb/latest/reference/syntax/line-protocol/ .. _JSON: https://www.json.org/ .. _JSON Lines: https://jsonlines.org/ .. _JSON streaming: https://en.wikipedia.org/wiki/JSON_streaming .. _LibreOffice: https://en.wikipedia.org/wiki/LibreOffice .. _list of other projects: doc/prior-art.rst .. _Microsoft Excel: https://en.wikipedia.org/wiki/Microsoft_Excel .. _Mike Bayer: https://github.com/zzzeek .. _NDJSON: http://ndjson.org/ .. _NetCDF: https://en.wikipedia.org/wiki/NetCDF .. _Office Open XML Workbook: https://en.wikipedia.org/wiki/Office_Open_XML .. _OpenDocument Spreadsheet: https://en.wikipedia.org/wiki/OpenDocument .. _pandas: https://pandas.pydata.org/ .. _Paul Walsh: https://github.com/pwalsh .. _ScipPy: https://scipy.org/ .. _SQLAlchemy: https://pypi.org/project/SQLAlchemy/ .. _testdata: https://github.com/daq-tools/skeem/tree/main/tests/testdata .. _Wes McKinney: https://github.com/wesm .. _xarray: https://xarray.dev/
FAQs
Infer SQL DDL statements from tabular data
We found that skeem demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 3 open source maintainers collaborating on the project.
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