Release Notes | Python Wheel | Developer Docs | Examples | Tutorial
The dbldatgen Databricks Labs project is a Python library for generating synthetic data within the Databricks
environment using Spark. The generated data may be used for testing, benchmarking, demos and many
other uses.
It operates by defining a data generation specification in code that controls how the synthetic data is to be generated. The specification may incorporate use of existing schemas, or create data in an adhoc fashion.
It has no dependencies on any libraries that are not already incuded in the Databricks runtime, and you can use it from Scala, R or other languages by defining a view over the generated data.
It supports:
- Generating synthetic data at scale up to billions of rows within minutes using appropriately sized clusters
- Generating repeatable, predictable data supporting the needs for producing multiple tables, Change Data Capture, merge and join scenarios with consistency between primary and foreign keys
- Generating synthetic data for all of the Spark SQL supported primitive types as a Spark data frame which may be persisted, saved to external storage or used in other computations
- Generating ranges of dates, timestamps and numeric values
- Generation of discrete values - both numeric and text
- Generation of values at random and based on the values of other fields
(either based on the
hashof the underlying values or the values themselves) - Ability to specify a distribution for random data generation
- Generating arrays of values for ML style feature arrays
- Applying weights to the occurrence of values
- Generating values to conform to a schema or independent of an existing schema
- use of SQL expressions in test data generation
- plugin mechanism to allow use of 3rd party libraries such as Faker
Details of these features can be found in the Developer Docs and the online help (which contains the full documentation).
Please note that all projects in the databrickslabs github space are provided for your exploration only, and are not formally supported by Databricks with Service Level Agreements (SLAs). They are provided AS-IS and we do not make any guarantees of any kind. Please do not submit a support ticket relating to any issues arising from the use of these projects.
Any issues discovered through the use of this project should be filed as GitHub Issues on the Repo. They will be reviewed as time permits, but there are no formal SLAs for support.
The Databricks Labs data generator framework can be used with Pyspark 3.x and Python 3.6 or later
However prebuilt releases are tested against Pyspark 3.0.1 (compatible with the Databricks runtime 7.3 LTS or later) and built with Python 3.7.5
For full library compatibility for a specific Databricks Spark release, see the Databricks release notes for library compatibility
The release binaries can be accessed at:
- Databricks Labs Github Data Generator releases - https://github.com/databrickslabs/dbldatagen/releases
To use download a wheel file and install using the Databricks install mechanism to install a wheel based library into your workspace.
Latest pre-release is code complete and fully functional.
To use the project, the generated wheel should be installed in your Python notebook as a wheel based library
Once the library has been installed, you can use it to generate a test data frame.
For example
import dbldatagen as dg
from pyspark.sql.types import IntegerType, FloatType, StringType
column_count = 10
data_rows = 1000 * 1000
df_spec = (dg.DataGenerator(spark, name="test_data_set1", rows=data_rows,
partitions=4)
.withIdOutput()
.withColumn("r", FloatType(), expr="floor(rand() * 350) * (86400 + 3600)",
numColumns=column_count)
.withColumn("code1", IntegerType(), minValue=100, maxValue=200)
.withColumn("code2", IntegerType(), minValue=0, maxValue=10)
.withColumn("code3", StringType(), values=['a', 'b', 'c'])
.withColumn("code4", StringType(), values=['a', 'b', 'c'], random=True)
.withColumn("code5", StringType(), values=['a', 'b', 'c'], random=True, weights=[9, 1, 1])
)
df = df_spec.build()
num_rows=df.count()
See CONTRIBUTING.md for detailed build and testing instructions, including use of alternative build environments such as conda.
Dependencies are maintained by Pipenv. In order to start with depelopment,
you should install pipenv and pyenv.
Use make test-with-html-report to build and run the tests with a coverage report.
Use make dist to make the distributable. The resulting wheel file will be placed in the dist subdirectory.
Run make docs from the main project directory.
The main html document will be in the file (relative to the root of the build directory) ./python/docs/docs/build/html/index.html
If using an environment with multiple Python versions, make sure to use virtual env or similar to pick up correct python versions.
If necessary, set PYSPARK_PYTHON and PYSPARK_DRIVER_PYTHON to point to correct versions of Python.
Run make test from the main project directory to run the unit tests.
Issues with the application? Found a bug? Have a great idea for an addition? Feel free to file an issue.
Please note that all projects in the /databrickslabs github account are provided for your exploration only, and are not formally supported by Databricks with Service Level Agreements (SLAs). They are provided AS-IS and we do not make any guarantees of any kind. Please do not submit a support ticket relating to any issues arising from the use of these projects.
Any issues discovered through the use of this project should be filed as GitHub Issues on the Repo. They will be reviewed as time permits, but there are no formal SLAs for support.