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 streaming
- Office Open XML Workbook (Microsoft Excel)
- OpenDocument Spreadsheet (LibreOffice)
Supported input sources:
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.
skeem infer-ddl --dialect=postgresql data.ndjsonCREATE 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")
);If you are in a hurry, and want to run Skeem without any installation, just use the OCI image on Podman or Docker.
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.ndjsonInstall Skeem from PyPI.
pip install skeemInstall Skeem with support for additional data formats like NetCDF.
pip install 'skeem[scientific]'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.
skeem info
skeem --help
skeem infer-ddl --help# 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"# 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.ncOCI images are available on the GitHub Container Registry (GHCR). In order to run them on Podman or Docker, invoke:
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.csvIf 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:
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.ndjsonIn 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.
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 $URLUse 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 -
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)CREATE TABLE "testdrive" (
"id" INT NOT NULL,
"name" STRING NOT NULL,
"date" TIMESTAMP,
"fruits" STRING NOT NULL,
"price" DOUBLE NOT NULL,
PRIMARY KEY ("id")
);For installing the project from source, please follow the development documentation.
- Catherine Devlin for ddlgenerator and data_dispenser.
- Mike Bayer for SQLAlchemy.
- Paul Walsh and Evgeny Karev for frictionless.
- Wes McKinney for pandas.
- All other countless contributors and authors of excellent Python packages, Python itself, and turtles all the way down.
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".