Introducing to Spark Remote for Unit Tests

.github/scripts/setup_spark_remote.sh

@@ -0,0 +1,61 @@
+#!/usr/bin/env bash
+
+set -xve
+echo "Setting up spark-connect"
+
+mkdir -p "$HOME"/spark
+cd "$HOME"/spark || exit 1
+
+version=$(wget -O - https://dlcdn.apache.org/spark/ | grep 'href="spark' | grep -v 'preview' | sed 's:</a>:\n:g' | sed -n 's/.*>//p' | tr -d spark- | tr -d / | sort -r --version-sort | head -1)
+if [ -z "$version" ]; then
+  echo "Failed to extract Spark version"
+   exit 1
+fi
+
+spark=spark-${version}-bin-hadoop3
+spark_connect="spark-connect_2.12"
+
+mkdir -p "${spark}"
+
+
+SERVER_SCRIPT=$HOME/spark/${spark}/sbin/start-connect-server.sh
+
+## check the spark version already exist, if not download the respective version
+if [ -f "${SERVER_SCRIPT}" ];then
+  echo "Spark Version already exists"
+else
+  if [ -f "${spark}.tgz" ];then
+    echo "${spark}.tgz already exists"
+  else
+    wget "https://dlcdn.apache.org/spark/spark-${version}/${spark}.tgz"
+  fi
+  tar -xvf "${spark}.tgz"
+fi
+
+cd "${spark}" || exit 1
+## check spark remote is running,if not start the spark remote
+result=$(${SERVER_SCRIPT} --packages org.apache.spark:${spark_connect}:"${version}" > "$HOME"/spark/log.out; echo $?)
+
+if [ "$result" -ne 0 ]; then
+    count=$(tail "${HOME}"/spark/log.out | grep -c "SparkConnectServer running as process")
+    if [ "${count}" == "0" ]; then
+            echo "Failed to start the server"
+        exit 1
+    fi
+    # Wait for the server to start by pinging localhost:4040
+    echo "Waiting for the server to start..."
+    for i in {1..30}; do
+        if nc -z localhost 4040; then
+            echo "Server is up and running"
+            break
+        fi
+        echo "Server not yet available, retrying in 5 seconds..."
+        sleep 5
+    done
+
+    if ! nc -z localhost 4040; then
+        echo "Failed to start the server within the expected time"
+        exit 1
+    fi
+fi
+echo "Started the Server"

.github/workflows/push.yml

@@ -35,10 +35,15 @@ jobs:
           cache-dependency-path: '**/pyproject.toml'
           python-version: ${{ matrix.pyVersion }}
 
+      - name: Setup Spark Remote
+        run: |
+          pip install hatch==1.9.4
+          make setup_spark_remote
+
       - name: Run unit tests
         run: |
           pip install hatch==1.9.4
-          make test
+          make ci-test
 
       - name: Publish test coverage
         uses: codecov/codecov-action@v5

Makefile

@@ -17,12 +17,17 @@ lint:
 fmt:
 	hatch run fmt
 
-test:
+ci-test:
 	hatch run test
 
 integration:
 	hatch run integration
 
+setup_spark_remote:
+	.github/scripts/setup_spark_remote.sh
+
+test: setup_spark_remote ci-test
+
 coverage:
 	hatch run coverage && open htmlcov/index.html
 

demos/dqx_demo_library.py

@@ -59,7 +59,7 @@
 print(dlt_expectations)
 
 # save generated checks in a workspace file
-user_name = spark.sql('select current_user() as user').collect()[0]['user']
+user_name = spark.sql("select current_user() as user").collect()[0]["user"]
 checks_file = f"/Workspace/Users/{user_name}/dqx_demo_checks.yml"
 dq_engine = DQEngine(ws)
 dq_engine.save_checks_in_workspace_file(checks, workspace_path=checks_file)
@@ -143,7 +143,7 @@
       col_name: col3
 
 - criticality: error
-  filter: col1<3
+  filter: col1 < 3
   check:
     function: is_not_null_and_not_empty
     arguments:
@@ -193,17 +193,17 @@
             criticality="error",
             check_func=is_not_null).get_rules() + [
          DQRule( # define rule for a single column
-            name='col3_is_null_or_empty',
-            criticality='error',
-            check=is_not_null_and_not_empty('col3')),
+            name="col3_is_null_or_empty",
+            criticality="error",
+            check=is_not_null_and_not_empty("col3")),
          DQRule( # define rule with a filter
-            name='col_4_is_null_or_empty',
-            criticality='error', 
-            filter='col1<3',
-            check=is_not_null_and_not_empty('col4')),
+            name="col_4_is_null_or_empty",
+            criticality="error",
+            filter="col1 < 3",
+            check=is_not_null_and_not_empty("col4")),
          DQRule( # name auto-generated if not provided
-            criticality='warn',
-            check=value_is_in_list('col4', ['1', '2']))
+            criticality="warn",
+            check=value_is_in_list("col4", ["1", "2"]))
         ]
 
 schema = "col1: int, col2: int, col3: int, col4 int"
@@ -384,9 +384,9 @@ def ends_with_foo(col_name: str) -> Column:
 input_df = spark.createDataFrame([["str1"], ["foo"], ["str3"]], schema)
 
 checks = [ DQRule(
-            name='col_1_is_null_or_empty',
-            criticality='error',
-            check=is_not_null_and_not_empty('col1')),
+            name="col_1_is_null_or_empty",
+            criticality="error",
+            check=is_not_null_and_not_empty("col1")),
         ]
 
 valid_and_quarantined_df = dq_engine.apply_checks(input_df, checks)

demos/dqx_demo_tool.py

@@ -45,7 +45,7 @@
 import glob
 import os
 
-user_name = spark.sql('select current_user() as user').collect()[0]['user']
+user_name = spark.sql("select current_user() as user").collect()[0]["user"]
 dqx_wheel_files = glob.glob(f"/Workspace/Users/{user_name}/.dqx/wheels/databricks_labs_dqx-*.whl")
 dqx_latest_wheel = max(dqx_wheel_files, key=os.path.getctime)
 %pip install {dqx_latest_wheel}
@@ -210,7 +210,7 @@
 # COMMAND ----------
 
 print(f"Saving quarantined data to {run_config.quarantine_table}")
-quarantine_catalog, quarantine_schema, _ = run_config.quarantine_table.split('.')
+quarantine_catalog, quarantine_schema, _ = run_config.quarantine_table.split(".")
 
 spark.sql(f"CREATE CATALOG IF NOT EXISTS {quarantine_catalog}")
 spark.sql(f"CREATE SCHEMA IF NOT EXISTS {quarantine_catalog}.{quarantine_schema}")

docs/dqx/docs/dev/contributing.mdx

@@ -86,13 +86,18 @@ Before every commit, apply the consistent formatting of the code, as we want our
 make fmt
 ```
 
-Before every commit, run automated bug detector (`make lint`) and unit tests (`make test`) to ensure that automated
-pull request checks do pass, before your code is reviewed by others: 
+Before every commit, run automated bug detector and unit tests to ensure that automated
+pull request checks do pass, before your code is reviewed by others:
 ```shell
 make lint
+make setup_spark_remote
 make test
 ```
 
+The command `make setup_spark_remote` sets up the environment for running unit tests and is required one time only.
+DQX uses Databricks Connect as a test dependency, which restricts the creation of a Spark session in local mode.
+To enable spark local execution for unit testing, the command install spark remote.
+
 ### Local setup for integration tests and code coverage
 
 Note that integration tests and code coverage are run automatically when you create a Pull Request in Github.
@@ -215,7 +220,7 @@ Here are the example steps to submit your first contribution:
 7. `make fmt`
 8. `make lint`
 9. .. fix if any
-10. `make test` and `make integration`, optionally `make coverage` to get test coverage report
+10. `make setup_spark_remote`, make test` and `make integration`, optionally `make coverage` to get test coverage report
 11. .. fix if any issues
 12. `git commit -S -a -m "message"`. 
 Make sure to enter a meaningful commit message title. 

docs/dqx/docs/guide.mdx

@@ -251,11 +251,11 @@ checks = DQRuleColSet( # define rule for multiple columns at once
          DQRule( # define rule with a filter
             name="col_4_is_null_or_empty",
             criticality="error", 
-            filter="col1<3",
+            filter="col1 < 3",
             check=is_not_null_and_not_empty("col4")),
          DQRule( # name auto-generated if not provided       
-            criticality='warn', 
-            check=value_is_in_list('col4', ['1', '2']))
+            criticality="warn",
+            check=value_is_in_list("col4", ["1", "2"]))
         ]
 
 input_df = spark.read.table("catalog1.schema1.table1")
@@ -294,7 +294,7 @@ checks = yaml.safe_load("""
       col_name: col3
 
 - criticality: error
-  filter: col1<3
+  filter: col1 < 3
   check:
     function: is_not_null_and_not_empty
     arguments:

docs/dqx/docs/reference.mdx

@@ -36,17 +36,17 @@ The following quality rules / functions are currently available:
 
 You can check implementation details of the rules [here](https://github.com/databrickslabs/dqx/blob/main/src/databricks/labs/dqx/col_functions.py).
 
-#### Apply Filter on quality rule
+### Apply filters on checks
 
-If you want to apply a filter to a part of the dataframe, you can add a `filter` to the rule.
-For example, if you want to check that a col `a` is not null when `b` is positive, you can do it like this:
+You can apply checks to a part of the DataFrame by using a `filter`.
+For example, to ensure that a column `a` is not null only when a column `b` is positive, you can define the check as follows:
 ```yaml
-- criticality: "error"
-  filter: b>0
+- criticality: error
+  filter: b > 0
   check:
-    function: "is_not_null"
+    function: is_not_null
     arguments:
-      col_name: "a"
+      col_name: a
 ```
 
 ### Creating your own checks
@@ -265,7 +265,7 @@ def test_dq():
 
     schema = "a: int, b: int, c: int"
     expected_schema = schema + ", _errors: map<string,string>, _warnings: map<string,string>"
-    test_df = spark.createDataFrame([[1, 3, 3]], schema)
+    test_df = spark.createDataFrame([[1, None, 3]], schema)
 
     checks = [
         DQRule(name="col_a_is_null_or_empty", criticality="warn", check=is_not_null_and_not_empty("a")),
@@ -275,6 +275,8 @@ def test_dq():
     dq_engine = DQEngine(ws)
     df = dq_engine.apply_checks(test_df, checks)
 
-    expected_df = spark.createDataFrame([[1, 3, 3, None, None]], expected_schema)
+    expected_df = spark.createDataFrame(
+        [[1, None, 3, {"col_b_is_null_or_empty": "Column b is null or empty"}, None]], expected_schema
+    )
     assert_df_equality(df, expected_df)
 ```

src/databricks/labs/dqx/utils.py

@@ -38,6 +38,8 @@ def read_input_data(spark: SparkSession, input_location: str | None, input_forma
     if STORAGE_PATH_PATTERN.match(input_location):
         if not input_format:
             raise ValueError("Input format not configured")
+        # TODO handle spark options while reading data from a file location
+        # https://github.com/databrickslabs/dqx/issues/161
         return spark.read.format(str(input_format)).load(input_location)
 
     raise ValueError(

tests/integration/test_utils.py

@@ -0,0 +1,31 @@
+from chispa.dataframe_comparer import assert_df_equality  # type: ignore
+from databricks.labs.dqx.utils import read_input_data
+
+
+def test_read_input_data_unity_catalog_table(spark, make_schema, make_random):
+    catalog_name = "main"
+    schema_name = make_schema(catalog_name=catalog_name).name
+    input_location = f"{catalog_name}.{schema_name}.{make_random(6).lower()}"
+    input_format = None
+
+    schema = "a: int, b: int"
+    input_df = spark.createDataFrame([[1, 2]], schema)
+    input_df.write.format("delta").saveAsTable(input_location)
+
+    result_df = read_input_data(spark, input_location, input_format)
+    assert_df_equality(input_df, result_df)
+
+
+def test_read_input_data_workspace_file(spark, make_schema, make_volume):
+    catalog_name = "main"
+    schema_name = make_schema(catalog_name=catalog_name).name
+    info = make_volume(catalog_name=catalog_name, schema_name=schema_name)
+    input_location = info.full_name
+    input_format = "delta"
+
+    schema = "a: int, b: int"
+    input_df = spark.createDataFrame([[1, 2]], schema)
+    input_df.write.format("delta").saveAsTable(input_location)
+
+    result_df = read_input_data(spark, input_location, input_format)
+    assert_df_equality(input_df, result_df)

tests/unit/conftest.py

@@ -1,13 +1,12 @@
 import os
 from pathlib import Path
-from unittest.mock import Mock
 from pyspark.sql import SparkSession
 import pytest
 
 
 @pytest.fixture
-def spark_session_mock():
-    return Mock(spec=SparkSession)
+def spark_local():
+    return SparkSession.builder.appName("DQX Test").remote("sc://localhost").getOrCreate()
 
 
 @pytest.fixture

tests/unit/test_apply_checks.py

@@ -0,0 +1,28 @@
+from unittest.mock import MagicMock
+
+from chispa.dataframe_comparer import assert_df_equality  # type: ignore
+from databricks.labs.dqx.col_functions import is_not_null_and_not_empty
+from databricks.labs.dqx.engine import DQEngine
+from databricks.labs.dqx.rule import DQRule
+from databricks.sdk import WorkspaceClient
+
+
+def test_apply_checks(spark_local):
+    ws = MagicMock(spec=WorkspaceClient, **{"catalogs.list.return_value": []})
+
+    schema = "a: int, b: int, c: int"
+    expected_schema = schema + ", _errors: map<string,string>, _warnings: map<string,string>"
+    test_df = spark_local.createDataFrame([[1, None, 3]], schema)
+
+    checks = [
+        DQRule(name="col_a_is_null_or_empty", criticality="warn", check=is_not_null_and_not_empty("a")),
+        DQRule(name="col_b_is_null_or_empty", criticality="error", check=is_not_null_and_not_empty("b")),
+    ]
+
+    dq_engine = DQEngine(ws)
+    df = dq_engine.apply_checks(test_df, checks)
+
+    expected_df = spark_local.createDataFrame(
+        [[1, None, 3, {"col_b_is_null_or_empty": "Column b is null or empty"}, None]], expected_schema
+    )
+    assert_df_equality(df, expected_df)