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Version: 0.17.19

Create a Custom Query Expectation

QueryExpectations are a type of ExpectationA verifiable assertion about data., enabled for SQL and Spark, that enable a higher-complexity type of workflow when compared with core Expectation classes such as ColumnAggregate, ColumnMap, and Table.

QueryExpectations allow you to set Expectations against the results of your own custom queries, and make intermediate queries to your database. While this approach can result in extra roundtrips to your database, it can also unlock advanced functionality for your Custom ExpectationsAn extension of the `Expectation` class, developed outside of the Great Expectations library..

They are evaluated for the results of a query, and answer a semantic question about your data returned by that query. For example, expect_queried_table_row_count_to_equal answers how many rows are returned from your table by your query.

This guide will walk you through the process of creating your own custom QueryExpectation.

Prerequisites​

Choose a name for your Expectation​

First, decide on a name for your own Expectation. By convention, QueryExpectations always start with expect_queried_.

All QueryExpectations support the parameterization of your Active BatchA selection of records from a Data Asset.; some QueryExpectations also support the parameterization of a Column. This tutorial will detail both approaches.

Your Expectation will have two versions of the same name: a CamelCaseName and a snake_case_name. For example, this tutorial will use:

  • ExpectQueriedTableRowCountToBe
  • expect_queried_table_row_count_to_be
info

For more on Expectation naming conventions, see the Expectations section of the Code Style Guide.

Copy and rename the template file​

By convention, each Expectation is kept in its own python file, named with the snake_case version of the Expectation's name.

You can find the template file for a custom QueryExpectation here. Download the file, place it in the appropriate directory, and rename it to the appropriate name.

cp query_expectation_template.py /SOME_DIRECTORY/expect_queried_table_row_count_to_equal.py

Storing Expectation files​

During development, you don't need to store Expectation files in a specific location. Expectation files are self-contained and can be executed anywhere as long as GX is installed However, to use your new Expectation with other GX components, you'll need to make sure the file is stored one of the following locations:

  • If you're building a Custom ExpectationAn extension of the `Expectation` class, developed outside of the Great Expectations library. for personal use, you'll need to put it in the great_expectations/plugins/expectations folder of your GX deployment, and import your Custom Expectation from that directory whenever it will be used. When you instantiate the corresponding DataContext, it will automatically make all PluginsExtends Great Expectations' components and/or functionality. in the directory available for use.

  • If you're building a Custom Expectation to contribute to the open source project, you'll need to put it in the repo for the Great Expectations library itself. Most likely, this will be within a package within contrib/: great_expectations/contrib/SOME_PACKAGE/SOME_PACKAGE/expectations/. To use these Expectations, you'll need to install the package.

For more information about Custom Expectations, see Use a Custom Expectation.

Generate a diagnostic checklist for your Expectation​

Once you've copied and renamed the template file, you can execute it as follows.

python expect_queried_table_row_count_to_be.py

The template file is set up so that this will run the Expectation's print_diagnostic_checklist() method. This will run a diagnostic script on your new Expectation, and return a checklist of steps to get it to full production readiness.

Completeness checklist for ExpectQueryToMatchSomeCriteria:
  ✔ Has a valid library_metadata object
    Has a docstring, including a one-line short description
    Has at least one positive and negative example case, and all test cases pass
    Has core logic and passes tests on at least one Execution Engine
    Passes all linting checks
    Has basic input validation and type checking
    Has both Statement Renderers: prescriptive and diagnostic
    Has core logic that passes tests for all applicable Execution Engines and SQL dialects
    Has a robust suite of tests, as determined by a code owner
    Has passed a manual review by a code owner for code standards and style guides

When in doubt, the next step to implement is the first one that doesn't have a ✔ next to it. This guide will walk you through the first five steps, the minimum for a functioning Custom Expectation and all that is required for contribution back to open source at an Experimental level.

Change the Expectation class name and add a docstring​

Now we're going to begin laying the groundwork for the functionality of your Custom Expectation.

Let's start by updating your Expectation's name and docstring.

Replace the Expectation class name

class ExpectQueryToMatchSomeCriteria(QueryExpectation):

with your real Expectation class name, in upper camel case:

class ExpectQueriedTableRowCountToBe(QueryExpectation):

You can also go ahead and write a new one-line docstring, replacing

"""TODO: Add a docstring here"""

with something like:

"""Expect the expect the number of rows returned from a queried table to equal a specified value."""

Make sure your one-line docstring begins with "Expect " and ends with a period. You'll also need to change the class name at the bottom of the file, by replacing this line:

ExpectQueryToMatchSomeCriteria().print_diagnostic_checklist()

with this one:

ExpectQueriedTableRowCountToBe().print_diagnostic_checklist()

Later, you can go back and write a more thorough docstring. See Expectation Docstring Formatting.

At this point you can re-run your diagnostic checklist. You should see something like this:

$ python expect_queried_table_row_count_to_be.py

Completeness checklist for ExpectQueriedTableRowCountToBe:
  ✔ Has a valid library_metadata object
  ✔ Has a docstring, including a one-line short description
    Has at least one positive and negative example case, and all test cases pass
    Has core logic and passes tests on at least one Execution Engine
    Passes all linting checks
...

Metric classes​

If you've built a Custom Expectation before, you may have noticed that the template doesn't contain a MetricA computed attribute of data such as the mean of a column. class.

While you are still able to create a Custom Metric for your Custom Expectation if needed, the nature of QueryExpectations allows us to provide a small number of generic query.* Metrics are capable of supporting many use-cases.

Add example cases​

You're going to search for examples = [] in your file, and replace it with at least two test examples. These examples serve the following purposes:

  • They provide test fixtures that Great Expectations can execute automatically with pytest.

  • They help users understand the logic of your Expectation by providing tidy examples of paired input and output. If you contribute your Expectation to open source, these examples will appear in the Gallery.

Your examples will look similar to this example:

examples = [
    {
        "data": [
            {
                "data": {
                    "col1": [1, 2, 2, 3, 4],
                    "col2": ["a", "a", "b", "b", "a"],
                },
            },
        ],
        "tests": [
            {
                "title": "basic_positive_test",
                "exact_match_out": False,
                "include_in_gallery": True,
                "in": {
                    "value": 5,
                },
                "out": {"success": True},
                "only_for": ["sqlite", "spark"],
            },
            {
                "title": "basic_negative_test",
                "exact_match_out": False,
                "include_in_gallery": True,
                "in": {
                    "value": 2,
                },
                "out": {"success": False},
                "only_for": ["sqlite", "spark"],
            },
            {
                "title": "positive_test_static_data_asset",
                "exact_match_out": False,
                "include_in_gallery": True,
                "in": {
                    "value": 5,
                    "query": """
                             SELECT COUNT(*)
                             FROM test
                             """,
                },
                "out": {"success": True},
                "only_for": ["sqlite"],
            },
            {
                "title": "positive_test_row_condition",
                "exact_match_out": False,
                "include_in_gallery": True,
                "in": {
                    "value": 2,
                    "row_condition": 'col("col1")==2',
                    "condition_parser": "great_expectations__experimental__",
                },
                "out": {"success": True},
                "only_for": ["sqlite", "spark"],
            },
        ],
    },
]

Here's a quick overview of how to create test cases to populate examples. The overall structure is a list of dictionaries. Each dictionary has two keys:

  • data: defines the input data of the example as a table/data frame. In this example the table test has one column named col1 and a second column named col2. Both columns have 5 rows. (Note: if you define multiple columns, make sure that they have the same number of rows.)
  • tests: a list of test cases to ValidateThe act of applying an Expectation Suite to a Batch. against the data frame defined in the corresponding data.
    • title should be a descriptive name for the test case. Make sure to have no spaces.
    • include_in_gallery: This must be set to True if you want this test case to be visible in the Gallery as an example.
    • in contains exactly the parameters that you want to pass in to the Expectation. "in": {"value": 5} in the example above is equivalent to expect_queried_table_row_count_to_be(value=5)
    • out is based on the Validation ResultGenerated when data is Validated against an Expectation or Expectation Suite. returned when executing the Expectation.
    • exact_match_out: if you set exact_match_out=False, then you don’t need to include all the elements of the Validation Result object - only the ones that are important to test.

The only_for key​

only_for is an optional key you can pass to offer more granular control over which backends and SQL dialects your tests are run against.

If you run your Expectation file again, you won't see any new checkmarks, as the logic for your Custom Expectation hasn't been implemented yet. However, you should see that the tests you've written are now being caught and reported in your checklist:

$ python expect_queried_table_row_count_to_be.py

Completeness checklist for ExpectQueriedTableRowCountToBe:
  ✔ Has a valid library_metadata object
  ✔ Has a docstring, including a one-line short description
...
    Has core logic that passes tests for all applicable Execution Engines and SQL dialects
          Only 0 / 2 tests for sqlite are passing
            Failing: basic_positive_test, basic_negative_test
...
note

For more information on tests and example cases, see how to create example cases for a Custom Expectation.

Implement a Query & Connect a Metric to your Expectation​

The query is the core of a QueryExpectation; this query is what defines the scope of your expectations for your data.

To implement your query, replace the query attribute of your Custom Expectation.

This:

query = """
        SQL QUERY GOES HERE
        """

Becomes something like this:

query = """
        SELECT COUNT(*)
        FROM {active_batch}
        """
danger

As noted above, QueryExpectations support parameterization of your Active BatchA selection of records from a Data Asset..

We strongly recommend making use of that parameterization as above, by querying against {active_batch}. Not doing so could result in your Custom Expectation unintentionally being run against the wrong data!

Metrics for QueryExpectations are a thin wrapper, allowing you to execute that parameterized SQL query with Great Expectations. The results of that query are then validated to judge whether your data meets your expectations.

Great Expectations provides a small number of simple, ready-to-use query.* Metrics that can plug into your Custom Expectation, or serve as a basis for your own custom Metrics.

note

Query Metric functions have the @metric_value decorator, with the appropriate engine.

The @metric_value decorator allows us to explicitly structure queries and directly access our compute domain. While this can result in extra roundtrips to your database in some situations, it allows for advanced functionality and customization of your Custom Expectations.

See an example of a query.table metric here.

To connect this Metric to our Custom Expectation, we'll need to include the metric_name for this Metric in our metric_dependencies.

This tuple:

metric_dependencies = ("METRIC NAME GOES HERE",)

Becomes:

metric_dependencies = ("query.table",)

Other parameters​

Expectation Success Keys - A tuple consisting of values that must / could be provided by the user and defines how the Expectation evaluates success.

Expectation Default Kwarg Values (Optional) - Default values for success keys and the defined domain, among other values.

Metric Condition Value Keys (Optional) - Contains any additional arguments passed as parameters to compute the Metric.

Validate​

In this step, we simply need to validate that the results of our Metrics meet our Expectation.

The validate method is implemented as _validate(...):

def _validate(
    self,
    configuration: ExpectationConfiguration,
    metrics: dict,
    runtime_configuration: dict = None,
    execution_engine: ExecutionEngine = None,
) -> Union[ExpectationValidationResult, dict]:

This method takes a dictionary named metrics, which contains all Metrics requested by your Metric dependencies, and performs a simple validation against your success keys (i.e. important thresholds) in order to return a dictionary indicating whether the Expectation has evaluated successfully or not.

To do so, we'll be accessing our success keys, as well as the result of our previously-calculated Metrics. For example, here is the definition of a _validate(...) method to validate the results of our query.table Metric against our success keys:

def _validate(
    self,
    configuration: ExpectationConfiguration,
    metrics: dict,
    runtime_configuration: dict = None,
    execution_engine: ExecutionEngine = None,
) -> Union[ExpectationValidationResult, dict]:
    metrics = convert_to_json_serializable(data=metrics)
    query_result = list(metrics.get("query.table")[0].values())[0]
    value = configuration["kwargs"].get("value")

    success = query_result == value

    return {
        "success": success,
        "result": {"observed_value": query_result},
    }

Running your diagnostic checklist at this point should return something like this:

$ python expect_queried_table_row_count_to_be.py

Completeness checklist for ExpectQueriedTableRowCountToBe:
  ✔ Has a valid library_metadata object
  ✔ Has a docstring, including a one-line short description
  ✔ Has at least one positive and negative example case, and all test cases pass
  ✔ Has core logic and passes tests on at least one Execution Engine
    Passes all linting checks
...

Linting​

Finally, we need to lint our now-functioning Custom Expectation. Our CI system will test your code using black, and ruff.

If you've set up your dev environment, these libraries will already be available to you, and can be invoked from your command line to automatically lint your code:

black <PATH/TO/YOUR/EXPECTATION.py>
ruff <PATH/TO/YOUR/EXPECTATION.py> --fix
info

If desired, you can automate this to happen at commit time. See our guidance on linting for more on this process.

Once this is done, running your diagnostic checklist should now reflect your Custom Expectation as meeting our linting requirements:

$ python expect_queried_table_row_count_to_be.py

Completeness checklist for ExpectQueriedTableRowCountToBe:
  ✔ Has a valid library_metadata object
  ✔ Has a docstring, including a one-line short description
  ✔ Has at least one positive and negative example case, and all test cases pass
  ✔ Has core logic and passes tests on at least one Execution Engine
  ✔ Passes all linting checks
...

Other parameters​

Expectation Success Keys - A tuple consisting of values that must / could be provided by the user and defines how the Expectation evaluates success.

Expectation Default Kwarg Values (Optional) - Default values for success keys and the defined domain, among other values.

Metric Condition Value Keys (Optional) - Contains any additional arguments passed as parameters to compute the Metric.

Contribute (Optional)​

This guide will leave you with a Custom Expectation sufficient for contribution to Great Expectations at an Experimental level.

If you plan to contribute your Expectation to the public open source project, you should update the library_metadata object before submitting your Pull Request. For example:

library_metadata = {
    "tags": [],  # Tags for this Expectation in the Gallery
    "contributors": [  # Github handles for all contributors to this Expectation.
        "@your_name_here",  # Don't forget to add your github handle here!
    ],
}

would become

# This dictionary contains metadata for display in the public gallery
library_metadata = {
    "tags": ["query-based"],
    "contributors": ["@joegargery"],
}

This is particularly important because we want to make sure that you get credit for all your hard work!

note

For more information on our code standards and contribution, see our guide on Levels of Maturity for Expectations.

To view the full scripts used in this page, see them on GitHub: