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PyDeequ

PyDeequ is a Python API for Deequ, a library built on top of Apache Spark for defining "unit tests for data", which measure data quality in large datasets. PyDeequ is written to support usage of Deequ in Python.

License Coverage

There are 4 main components of Deequ, and they are:

  • Metrics Computation:
    • Profiles leverages Analyzers to analyze each column of a dataset.
    • Analyzers serve here as a foundational module that computes metrics for data profiling and validation at scale.
  • Constraint Suggestion:
    • Specify rules for various groups of Analyzers to be run over a dataset to return back a collection of constraints suggested to run in a Verification Suite.
  • Constraint Verification:
    • Perform data validation on a dataset with respect to various constraints set by you.
  • Metrics Repository
    • Allows for persistence and tracking of Deequ runs over time.

🎉 Announcements 🎉

Quickstart

The following will quickstart you with some basic usage. For more in-depth examples, take a look in the tutorials/ directory for executable Jupyter notebooks of each module. For documentation on supported interfaces, view the documentation.

Installation

You can install PyDeequ via pip.

pip install pydeequ

Set up a PySpark session

from pyspark.sql import SparkSession, Row
import pydeequ

spark = (SparkSession
    .builder
    .config("spark.jars.packages", pydeequ.deequ_maven_coord)
    .config("spark.jars.excludes", pydeequ.f2j_maven_coord)
    .getOrCreate())

df = spark.sparkContext.parallelize([
            Row(a="foo", b=1, c=5),
            Row(a="bar", b=2, c=6),
            Row(a="baz", b=3, c=None)]).toDF()

Analyzers

from pydeequ.analyzers import *

analysisResult = AnalysisRunner(spark) \
                    .onData(df) \
                    .addAnalyzer(Size()) \
                    .addAnalyzer(Completeness("b")) \
                    .run()

analysisResult_df = AnalyzerContext.successMetricsAsDataFrame(spark, analysisResult)
analysisResult_df.show()

Profile

from pydeequ.profiles import *

result = ColumnProfilerRunner(spark) \
    .onData(df) \
    .run()

for col, profile in result.profiles.items():
    print(profile)

Constraint Suggestions

from pydeequ.suggestions import *

suggestionResult = ConstraintSuggestionRunner(spark) \
             .onData(df) \
             .addConstraintRule(DEFAULT()) \
             .run()

# Constraint Suggestions in JSON format
print(suggestionResult)

Constraint Verification

from pydeequ.checks import *
from pydeequ.verification import *

check = Check(spark, CheckLevel.Warning, "Review Check")

checkResult = VerificationSuite(spark) \
    .onData(df) \
    .addCheck(
        check.hasSize(lambda x: x >= 3) \
        .hasMin("b", lambda x: x == 0) \
        .isComplete("c")  \
        .isUnique("a")  \
        .isContainedIn("a", ["foo", "bar", "baz"]) \
        .isNonNegative("b")) \
    .run()

checkResult_df = VerificationResult.checkResultsAsDataFrame(spark, checkResult)
checkResult_df.show()

Repository

Save to a Metrics Repository by adding the useRepository() and saveOrAppendResult() calls to your Analysis Runner.

from pydeequ.repository import *
from pydeequ.analyzers import *

metrics_file = FileSystemMetricsRepository.helper_metrics_file(spark, 'metrics.json')
repository = FileSystemMetricsRepository(spark, metrics_file)
key_tags = {'tag': 'pydeequ hello world'}
resultKey = ResultKey(spark, ResultKey.current_milli_time(), key_tags)

analysisResult = AnalysisRunner(spark) \
    .onData(df) \
    .addAnalyzer(ApproxCountDistinct('b')) \
    .useRepository(repository) \
    .saveOrAppendResult(resultKey) \
    .run()

To load previous runs, use the repository object to load previous results back in.

result_metrep_df = repository.load() \
    .before(ResultKey.current_milli_time()) \
    .forAnalyzers([ApproxCountDistinct('b')]) \
    .getSuccessMetricsAsDataFrame()

Contributing

Please refer to the contributing doc for how to contribute to PyDeequ.

License

This library is licensed under the Apache 2.0 License.

Contributing Developer Setup

  1. Setup SDKMAN
  2. Setup Java
  3. Setup Apache Spark
  4. Install Poetry
  5. Run tests locally

Setup SDKMAN

SDKMAN is a tool for managing parallel Versions of multiple Software Development Kits on any Unix based system. It provides a convenient command line interface for installing, switching, removing and listing Candidates. SDKMAN! installs smoothly on Mac OSX, Linux, WSL, Cygwin, etc... Support Bash and ZSH shells. See documentation on the SDKMAN! website.

Open your favourite terminal and enter the following:

$ curl -s https://get.sdkman.io | bash
If the environment needs tweaking for SDKMAN to be installed,
the installer will prompt you accordingly and ask you to restart.

Next, open a new terminal or enter:

$ source "$HOME/.sdkman/bin/sdkman-init.sh"

Lastly, run the following code snippet to ensure that installation succeeded:

$ sdk version

Setup Java

Install Java Now open favourite terminal and enter the following:

List the AdoptOpenJDK OpenJDK versions
$ sdk list java

To install For Java 11
$ sdk install java 11.0.10.hs-adpt

To install For Java 11
$ sdk install java 8.0.292.hs-adpt

Setup Apache Spark

Install Java Now open favourite terminal and enter the following:

List the Apache Spark versions:
$ sdk list spark

To install For Spark 3
$ sdk install spark 3.0.2

Poetry

Poetry Commands

poetry install

poetry update

# --tree: List the dependencies as a tree.
# --latest (-l): Show the latest version.
# --outdated (-o): Show the latest version but only for packages that are outdated.
poetry show -o

Running Tests Locally

Take a look at tests in tests/dataquality and tests/jobs

$ poetry run pytest