Added profiling job

.github/ISSUE_TEMPLATE/bug.yml

@@ -57,7 +57,7 @@ body:
     id: os
     attributes:
       label: Operating System
-      description: Which operating system do you have UCX installed on?
+      description: Which operating system do you have DQX installed on?
       options:
         - macOS
         - Linux

README.md

@@ -20,7 +20,7 @@ Simplified Data Quality checking at Scale for PySpark Workloads on streaming and
     * [Uninstall DQX from the Databricks workspace](#uninstall-dqx-from-the-databricks-workspace)
 * [How to use it](#how-to-use-it)
   * [Demos](#demos)
-  * [Data Profiling](#data-profiling)
+  * [Data Profiling and Quality Rules Generation](#data-profiling-and-quality-rules-generation)
     * [In Python](#in-python)
     * [Using CLI](#using-cli)
   * [Validating quality rules (checks)](#validating-quality-rules--checks-)
@@ -125,6 +125,7 @@ and other configuration options.
 The cli command will install the following components in the workspace:
 - A Python [wheel file](https://peps.python.org/pep-0427/) with the library packaged.
 - DQX configuration file ('config.yml').
+- Profiling workflow for generating quality rule candidates.
 - Quality dashboard for monitoring to display information about the data quality issues.
 
 DQX configuration file can contain multiple run configurations defining specific set of input, output and quarantine locations etc.
@@ -136,7 +137,7 @@ run_config:
 - name: default
   checks_file: checks.yml
   curated_location: main.dqx.curated
-  input_locations: main.dqx.input
+  input_location: main.dqx.input
   output_location: main.dqx.output
   profile_summary_stats_file: profile_summary_stats.yml
   quarantine_location: main.dqx.quarantine
@@ -152,6 +153,11 @@ by setting 'DQX_FORCE_INSTALL' environment variable. The following options are a
 * `DQX_FORCE_INSTALL=global databricks labs install dqx`: will force the installation to be for root only (`/Applications/dqx`)
   * `DQX_FORCE_INSTALL=user databricks labs install dqx`: will force the installation to be for user only (`/Users/<user>/.dqx`)
 
+To list all installed dqx workflows in the workspace and their latest run state, execute the following command:
+```commandline
+databricks labs dqx workflows
+```
+
 ### Install the tool on the Databricks cluster
 
 After you install the tool on the workspace, you need to install the DQX package on a Databricks cluster.
@@ -212,7 +218,7 @@ you can upload the following notebooks in the Databricks workspace to try it out
 * [DQX Demo Notebook](demos/dqx_demo.py) - demonstrates how to use DQX for data quality checks.
 * [DQX DLT Demo Notebook](demos/dqx_dlt_demo.py) - demonstrates how to use DQX with Delta Live Tables (DLT).
 
-## Data Profiling
+## Data Profiling and Quality Rules Generation
 
 Data profiling is run to profile the input data and generate quality rule candidates with summary statistics.
 The generated rules/checks are input for the quality checking (see [Adding quality checks to the application](#adding-quality-checks-to-the-application)).
@@ -246,19 +252,27 @@ dlt_expectations = dlt_generator.generate_dlt_rules(profiles)
 ### Using CLI 
 
 You must install DQX in the workspace before (see [installation](#installation-in-a-databricks-workspace)).
+As part of the installation, profiler workflow is installed. It can be run manually in the workspace UI or using the CLI as below.
 
-Run profiling job:
+Run profiler workflow:
 ```commandline
 databricks labs dqx profile --run-config "default"
 ```
 
-If run config is not provided, the "default" run config will be used. The run config is used to select specific run configuration from 'config.yml'.
+You will find the generated quality rule candidates and summary statistics in the installation folder as defined in the run config.
+If run config is not provided, the "default" run config will be used. The run config is used to select specific run configuration from the 'config.yml'.
 
-The following DQX configuration from 'config.yml' will be used by default:
+The following DQX configuration from 'config.yml' are used:
 - 'input_location': input data as a path or a table.
-- 'input_format': input data format.
-- 'checks_file': relative location of the generated quality rule candidates (default: `checks.yml`). Can be json or yaml file.
-- 'profile_summary_stats_file': relative location of the summary statistics (default: `profile_summary.yml`). Can be json or yaml file.
+- 'input_format': input data format. Required if input data is a path.
+- 'checks_file': relative location of the generated quality rule candidates (default: `checks.yml`).
+- 'profile_summary_stats_file': relative location of the summary statistics (default: `profile_summary.yml`).
+
+Logs are be printed in the console and saved in the installation folder.
+To show the saved logs from the latest profiler workflow run, visit the Databricks workspace UI or execute the following command:
+```commandline
+databricks labs dqx logs --workflow profiler
+```
 
 ## Validating quality rules (checks)
 

demos/dqx_demo.py

@@ -7,7 +7,7 @@
 # MAGIC %md
 # MAGIC ### Installation DQX in the workspace
 # MAGIC
-# MAGIC Install DQX in the workspace as per the instructions [here](https://github.com/databrickslabs/dqx?tab=readme-ov-file#installation).
+# MAGIC Install DQX in the workspace (default user installation) as per the instructions [here](https://github.com/databrickslabs/dqx?tab=readme-ov-file#installation).
 # MAGIC Use default filename for data quality rules.
 
 # COMMAND ----------
@@ -17,11 +17,13 @@
 
 # COMMAND ----------
 
-import subprocess
+import glob
+import os
 
 user_name = spark.sql('select current_user() as user').collect()[0]['user']
-pip_install_path = f"/Workspace/Users/{user_name}/.dqx/wheels/databricks_labs_dqx-*.whl"
-%pip install {pip_install_path}
+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}
 
 # COMMAND ----------
 

demos/dqx_dlt_demo.py

@@ -1,11 +1,15 @@
 # Databricks notebook source
-# 1. Install DQX in the workspace as per the instructions here: https://github.com/databrickslabs/dqx?tab=readme-ov-file#installation
+# 1. Install DQX in the workspace (default user installation) as per the instructions here: https://github.com/databrickslabs/dqx?tab=readme-ov-file#installation
 # Use default filename for data quality rules.
 
 # 2. Install DQX in the cluster
+import glob
+import os
+
 user_name = "[email protected]" # cannot dynamically retrieve user name as "System-User" is always returned: spark.sql('select current_user() as user').collect()[0]['user']
-pip_install_path = f"/Workspace/Users/{user_name}/.dqx/wheels/databricks_labs_dqx-*.whl"
-%pip install {pip_install_path}
+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}
 
 # COMMAND ----------
 

labs.yml

@@ -2,9 +2,9 @@
 name: dqx
 description: Common libraries for Databricks Labs
 install:
-  script: src/databricks/labs/dqx/install.py
+  script: src/databricks/labs/dqx/installer/install.py
 uninstall:
-  script: src/databricks/labs/dqx/uninstall.py
+  script: src/databricks/labs/dqx/installer/uninstall.py
 entrypoint: src/databricks/labs/dqx/cli.py
 min_python: 3.10
 commands:
@@ -23,4 +23,20 @@ commands:
         description: Run config to use
     table_template: |-
       {{range .}}{{.error}}
-      {{end}}
+      {{end}}
+  - name: profile
+    description: Profile input data and generate quality rule (checks) candidates
+    flags:
+      - name: run-config
+        description: (Optional) Selects run configuration from installation config. If not provided, use the "default" run configuration.
+  - name: workflows
+    description: Show deployed workflows and their latest run state
+    table_template: |-
+      Workflow\tWorkflow ID\tState\tStarted
+      {{range .}}{{.workflow}}\t{{.workflow_id}}\t{{.state}}\t{{.started}}
+      {{end}}
+  - name: logs
+    description: Show logs from the latest job run
+    flags:
+      - name: workflow
+        description: Name of the workflow to show logs for, e.g. profiler

pyproject.toml

@@ -32,6 +32,9 @@ dependencies = ["databricks-labs-blueprint>=0.9.1,<0.10",
                 "databricks-sdk~=0.30",
                 "databricks-labs-lsql>=0.5,<0.13"]
 
+[project.entry-points.databricks]
+runtime = "databricks.labs.dqx.runtime:main"
+
 [project.urls]
 Issues = "https://github.com/databrickslabs/dqx/issues"
 Source = "https://github.com/databrickslabs/dqx"
@@ -76,8 +79,8 @@ path = ".venv"
 
 [tool.hatch.envs.default.scripts]
 test        = "pytest -n 10 --cov src --cov-report=xml --timeout 30 tests/unit --durations 20"
-coverage    = "pytest -n 10 --cov src tests/ --timeout 240 --cov-report=html --durations 20"
-integration = "pytest -n 10 --timeout 240 --cov src tests/integration --durations 20"
+coverage    = "pytest -n 10 --cov src tests/ --timeout 480 --cov-report=html --durations 20"
+integration = "pytest -n 10 --timeout 480 --cov src tests/integration --durations 20"
 fmt         = ["black . --extend-exclude 'demos/'",
                "ruff check . --fix",
                "mypy . --exclude 'demos/*'",

src/databricks/labs/dqx/__init__.py

@@ -14,8 +14,8 @@
     r"(?:\+(?P<build>[0-9a-zA-Z-]+(?:\.[0-9a-zA-Z-]+)*))?$"
 )
 
-# Add ucx/<version> for projects depending on ucx as a library
+# Add dqx/<version> for projects depending on dqx as a library
 ua.with_extra("dqx", __version__)
 
-# Add ucx/<version> for re-packaging of ucx, where product name is omitted
+# Add dqx/<version> for re-packaging of dqx, where product name is omitted
 ua.with_product("dqx", __version__)

src/databricks/labs/dqx/cli.py

@@ -82,5 +82,46 @@ def validate_checks(
     return errors_list
 
 
+@dqx.command
+def profile(w: WorkspaceClient, *, run_config: str = "default", ctx: WorkspaceContext | None = None) -> None:
+    """
+    Profile input data and generate quality rule (checks) candidates.
+
+    :param w: The WorkspaceClient instance to use for accessing the workspace.
+    :param run_config: The name of the run configuration to use.
+    :param ctx: The WorkspaceContext instance to use for accessing the workspace.
+    """
+    ctx = ctx or WorkspaceContext(w)
+    ctx.deployed_workflows.run_workflow("profiler", run_config)
+
+
+@dqx.command
+def workflows(w: WorkspaceClient, *, ctx: WorkspaceContext | None = None):
+    """
+    Show deployed workflows and their state
+
+    :param w: The WorkspaceClient instance to use for accessing the workspace.
+    :param ctx: The WorkspaceContext instance to use for accessing the workspace.
+    """
+    ctx = ctx or WorkspaceContext(w)
+    logger.info("Fetching deployed jobs...")
+    latest_job_status = ctx.deployed_workflows.latest_job_status()
+    print(json.dumps(latest_job_status))
+    return latest_job_status
+
+
+@dqx.command
+def logs(w: WorkspaceClient, *, workflow: str | None = None, ctx: WorkspaceContext | None = None):
+    """
+    Show logs of the latest job run.
+
+    :param w: The WorkspaceClient instance to use for accessing the workspace.
+    :param workflow: The name of the workflow to show logs for.
+    :param ctx: The WorkspaceContext instance to use for accessing the workspace
+    """
+    ctx = ctx or WorkspaceContext(w)
+    ctx.deployed_workflows.relay_logs(workflow)
+
+
 if __name__ == "__main__":
     dqx()

src/databricks/labs/dqx/config.py

@@ -10,12 +10,14 @@ class RunConfig:
     """Configuration class for the data quality checks"""
 
     name: str = "default"  # name of the run configuration
-    input_locations: str | None = None  # input data path or a table
+    input_location: str | None = None  # input data path or a table
     input_format: str | None = "delta"  # input data format
     output_table: str | None = None  # output data table
     quarantine_table: str | None = None  # quarantined data table
     checks_file: str | None = "checks.yml"  # file containing quality rules / checks
     profile_summary_stats_file: str | None = "profile_summary_stats.yml"  # file containing profile summary statistics
+    override_clusters: dict[str, str] | None = None
+    spark_conf: dict[str, str] | None = None
 
 
 @dataclass

src/databricks/labs/dqx/contexts/application.py

@@ -1,12 +1,12 @@
 import abc
 import logging
-from datetime import timedelta
 from functools import cached_property
 
 from databricks.labs.blueprint.installation import Installation
 from databricks.labs.blueprint.installer import InstallState
 from databricks.labs.blueprint.tui import Prompts
 from databricks.labs.blueprint.wheels import ProductInfo, WheelsV2
+from databricks.labs.dqx.installer.workflows_installer import DeployedWorkflows
 from databricks.sdk import WorkspaceClient
 
 from databricks.labs.dqx.config import WorkspaceConfig
@@ -57,10 +57,6 @@ def installation(self):
     def config(self) -> WorkspaceConfig:
         return self.installation.load(WorkspaceConfig)
 
-    @cached_property
-    def verify_timeout(self):
-        return timedelta(minutes=2)
-
     @cached_property
     def wheels(self):
         return WheelsV2(self.installation, self.product_info)
@@ -69,12 +65,14 @@ def wheels(self):
     def install_state(self):
         return InstallState.from_installation(self.installation)
 
+    @cached_property
+    def deployed_workflows(self) -> DeployedWorkflows:
+        return DeployedWorkflows(self.workspace_client, self.install_state)
+
 
 class CliContext(GlobalContext, abc.ABC):
     """
     Abstract base class for global context, providing common properties and methods for workspace management.
-
-    :param named_parameters: Optional dictionary of named parameters.
     """
 
     @cached_property