From 6351d472cb2a71863d0d61547863a5f829a0ecc9 Mon Sep 17 00:00:00 2001 From: fedeflowers Date: Mon, 30 Mar 2026 19:49:10 +0200 Subject: [PATCH 1/4] add demos for banking and fashion industry, changed path for manufacturing industry under proper folder --- demos/dqx_demo_industry/dqx_banking_demo.py | 424 +++++++++++++++ demos/dqx_demo_industry/dqx_fashion_demo.py | 489 ++++++++++++++++++ .../dqx_manufacturing_demo.py | 0 docs/dqx/docs/demos.mdx | 6 +- src/databricks/labs/dqx/io.py | 2 +- 5 files changed, 919 insertions(+), 2 deletions(-) create mode 100644 demos/dqx_demo_industry/dqx_banking_demo.py create mode 100644 demos/dqx_demo_industry/dqx_fashion_demo.py rename demos/{ => dqx_demo_industry}/dqx_manufacturing_demo.py (100%) diff --git a/demos/dqx_demo_industry/dqx_banking_demo.py b/demos/dqx_demo_industry/dqx_banking_demo.py new file mode 100644 index 000000000..bf30991d9 --- /dev/null +++ b/demos/dqx_demo_industry/dqx_banking_demo.py @@ -0,0 +1,424 @@ +# Databricks notebook source +# MAGIC %md +# MAGIC # DQX - Banking / FSI Industry Accelerator Demo +# MAGIC ## Anti-Money Laundering (AML) & Transaction Monitoring +# MAGIC +# MAGIC This demo showcases DQX data quality rules tailored for the **Banking and Financial Services industry**, with a focus on **fraud detection, transaction structuring prevention, and regulatory compliance**. +# MAGIC +# MAGIC ### Use Case: Real-Time Transaction Quality & Fraud Monitoring +# MAGIC +# MAGIC Process incoming transaction batches and automatically: +# MAGIC * **Quarantine invalid transactions** (errors) for immediate investigation +# MAGIC * **Flag suspicious patterns** (warnings) for AML review while allowing transactions to process +# MAGIC * **Enforce data integrity** to ensure downstream systems receive clean, compliant data +# MAGIC +# MAGIC ### Implemented Data Quality Checks +# MAGIC +# MAGIC **1. Custom Fraud Detection (AML Red Flags)** +# MAGIC * **Structuring Detection**: Perfectly round amounts (\$5K, \$10K, \$15K) often indicate structuring to avoid reporting +# MAGIC * **Threshold Avoidance**: Amounts just below \$10K reporting threshold (\$9,950-\$9,999) +# MAGIC * **Anomalous Patterns**: Repeated digit patterns (\$7,777.77, \$3,333.33) statistically unlikely in legitimate transactions +# MAGIC +# MAGIC **2. Critical Field Validation (Errors)** +# MAGIC * Required fields: `transaction_id`, `currency` must not be null +# MAGIC * Amount constraints: Not null, not zero, within system bounds (-999M to 999M) +# MAGIC +# MAGIC **3. Business Logic Validation (Errors)** +# MAGIC * **Transaction Sign Consistency**: CREDIT/DEPOSIT/INCOMING_TRANSFER must be positive; DEBIT/WITHDRAWAL/FEE/CHARGEBACK must be negative +# MAGIC * Prevents accounting errors and reconciliation failures +# MAGIC +# MAGIC **4. Data Quality Standards (Warnings)** +# MAGIC * **Currency Validation**: ISO-4217 compliance (USD, EUR, GBP, etc.) +# MAGIC * **Precision Standards**: Maximum 2 decimal places for monetary amounts +# MAGIC +# MAGIC ### Key DQX Features Demonstrated +# MAGIC +# MAGIC * **Dual-Routing Split Behavior**: Warnings go to BOTH valid and invalid DataFrames (monitoring without blocking) +# MAGIC * **Custom Check Functions**: Industry-specific fraud detection logic integrated with built-in checks +# MAGIC * **Centralized Rule Management**: Rules stored in Delta tables for versioning and governance +# MAGIC * **Single-Pass Execution**: All checks applied in one data scan for optimal performance + +# COMMAND ---------- + +# MAGIC %md +# MAGIC ### Install DQX + +# COMMAND ---------- + +# Install DQX +%pip install databricks-labs-dqx + +# COMMAND ---------- + +dbutils.library.restartPython() + +# COMMAND ---------- + +# MAGIC %md +# MAGIC ### Production Best Practice: Pin DQX Version +# MAGIC +# MAGIC To ensure consistent behavior and avoid unexpected issues from automatic upgrades, it is highly recommended to always pin DQX to a specific version in your production pipelines. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC ### Setup Catalog and Schema +# MAGIC +# MAGIC Specify the catalog and schema where the quality rules table will be stored. + +# COMMAND ---------- + +# DBTITLE 1,Set Catalog and Schema for Demo +default_catalog_name = "main" +default_schema_name = "default" + +dbutils.widgets.text("demo_catalog", default_catalog_name, "Catalog Name") +dbutils.widgets.text("demo_schema", default_schema_name, "Schema Name") + +catalog = dbutils.widgets.get("demo_catalog") +schema = dbutils.widgets.get("demo_schema") + +print(f"Selected Catalog: {catalog}") +print(f"Selected Schema: {schema}") + +# COMMAND ---------- + +# MAGIC %md +# MAGIC ### Setup Sample Financial Data +# MAGIC +# MAGIC We'll create a DataFrame simulating a typical transaction stream or daily batch (`transactions_df`). + +# COMMAND ---------- + +from pyspark.sql import SparkSession, Row +from datetime import datetime + +# Create a comprehensive sample DataFrame simulating international banking transactions +transactions = [ + # Valid transactions + Row(transaction_id="TRX-101", account_id="ACC-US-001", country="USA", transaction_type="CREDIT", currency="USD", amount=1247.89, timestamp="2024-03-15 10:30:00"), + Row(transaction_id="TRX-102", account_id="ACC-UK-002", country="GBR", transaction_type="DEPOSIT", currency="GBP", amount=876.45, timestamp="2024-03-15 14:20:00"), + Row(transaction_id="TRX-103", account_id="ACC-JP-003", country="JPN", transaction_type="WITHDRAWAL", currency="JPY", amount=-23456.00, timestamp="2024-03-15 09:15:00"), + Row(transaction_id="TRX-104", account_id="ACC-FR-004", country="FRA", transaction_type="CREDIT", currency="EUR", amount=543.21, timestamp="2024-03-15 11:45:00"), + + # Suspicious Pattern: Perfectly round amounts (structuring) + Row(transaction_id="TRX-201", account_id="ACC-US-005", country="USA", transaction_type="DEPOSIT", currency="USD", amount=10000.00, timestamp="2024-03-15 10:00:00"), # Warn: Perfectly round 10K + Row(transaction_id="TRX-202", account_id="ACC-CA-006", country="CAN", transaction_type="CREDIT", currency="CAD", amount=5000.00, timestamp="2024-03-15 15:30:00"), # Warn: Perfectly round 5K + Row(transaction_id="TRX-203", account_id="ACC-AU-007", country="AUS", transaction_type="DEPOSIT", currency="AUD", amount=15000.00, timestamp="2024-03-16 08:20:00"), # Warn: Perfectly round 15K + + # Suspicious Pattern: Just below reporting thresholds + Row(transaction_id="TRX-301", account_id="ACC-US-008", country="USA", transaction_type="DEPOSIT", currency="USD", amount=9995.00, timestamp="2024-03-16 11:00:00"), # Warn: Just below 10K threshold + Row(transaction_id="TRX-302", account_id="ACC-US-009", country="USA", transaction_type="CREDIT", currency="USD", amount=4975.50, timestamp="2024-03-16 13:45:00"), # Warn: Just below 5K threshold + Row(transaction_id="TRX-303", account_id="ACC-CH-010", country="CHE", transaction_type="DEPOSIT", currency="CHF", amount=2980.00, timestamp="2024-03-16 16:20:00"), # Warn: Just below 3K threshold + + # Suspicious Pattern: Repeated digit patterns + Row(transaction_id="TRX-401", account_id="ACC-BR-011", country="BRA", transaction_type="CREDIT", currency="BRL", amount=7777.77, timestamp="2024-03-17 09:30:00"), # Warn: Repeated 7s + Row(transaction_id="TRX-402", account_id="ACC-MX-012", country="MEX", transaction_type="DEPOSIT", currency="MXN", amount=3333.33, timestamp="2024-03-17 10:15:00"), # Warn: Repeated 3s + Row(transaction_id="TRX-403", account_id="ACC-IN-013", country="IND", transaction_type="CREDIT", currency="INR", amount=8888.88, timestamp="2024-03-17 14:00:00"), # Warn: Repeated 8s + + # Data quality errors: Sign inconsistency + Row(transaction_id="TRX-501", account_id="ACC-DE-014", country="DEU", transaction_type="CREDIT", currency="EUR", amount=-500.00, timestamp="2024-03-18 10:00:00"), # Error: Credit must be positive + Row(transaction_id="TRX-502", account_id="ACC-ES-015", country="ESP", transaction_type="WITHDRAWAL", currency="EUR", amount=300.00, timestamp="2024-03-18 11:30:00"), # Error: Withdrawal must be negative + Row(transaction_id="TRX-503", account_id="ACC-IT-016", country="ITA", transaction_type="CHARGEBACK", currency="EUR", amount=125.50, timestamp="2024-03-18 13:15:00"), # Error: Chargeback must be negative + + # Data quality errors: Precision issues + Row(transaction_id="TRX-601", account_id="ACC-SG-017", country="SGP", transaction_type="FEE", currency="USD", amount=-1.234, timestamp="2024-03-19 09:00:00"), # Warn: Too many decimals + Row(transaction_id="TRX-602", account_id="ACC-CN-018", country="CHN", transaction_type="CREDIT", currency="CNY", amount=567.8901, timestamp="2024-03-19 10:30:00"), # Warn: Too many decimals + + # Data quality errors: Null values + Row(transaction_id="TRX-701", account_id="ACC-NL-019", country="NLD", transaction_type="DEBIT", currency=None, amount=-150.00, timestamp="2024-03-20 08:45:00"), # Error: Null currency + Row(transaction_id=None, account_id="ACC-SE-020", country="SWE", transaction_type="CREDIT", currency="EUR", amount=200.00, timestamp="2024-03-20 09:30:00"), # Error: Null transaction_id + + # More valid international transactions + Row(transaction_id="TRX-801", account_id="ACC-KR-021", country="KOR", transaction_type="INCOMING_TRANSFER", currency="USD", amount=2345.67, timestamp="2024-03-20 11:00:00"), + Row(transaction_id="TRX-802", account_id="ACC-ZA-022", country="ZAF", transaction_type="FEE", currency="USD", amount=-5.50, timestamp="2024-03-20 12:15:00"), + Row(transaction_id="TRX-803", account_id="ACC-AR-023", country="ARG", transaction_type="WITHDRAWAL", currency="USD", amount=-789.25, timestamp="2024-03-20 14:30:00"), + Row(transaction_id="TRX-804", account_id="ACC-PL-024", country="POL", transaction_type="DEPOSIT", currency="EUR", amount=1456.88, timestamp="2024-03-20 15:45:00"), +] + +transactions_df = spark.createDataFrame(transactions) +display(transactions_df) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC ### Defining Financial Data Quality Checks +# MAGIC +# MAGIC We define our industry-specific checks using a YAML configuration. This is the recommended approach for DQX as it separates rule definitions from logic. +# MAGIC +# MAGIC We also define a **custom check function** for detecting suspicious amount patterns commonly associated with financial crime and structuring: +# MAGIC * **Perfectly round amounts** above threshold (e.g., exactly $5,000, $10,000) which may indicate attempts to structure transactions +# MAGIC * **Just below $10K reporting threshold** (e.g., $9,950-$9,999) to avoid regulatory reporting +# MAGIC * **Repeated digit patterns** (e.g., $7,777.77, $3,333.33) which are statistically unlikely in legitimate transactions +# MAGIC +# MAGIC These patterns are red flags for anti-money laundering (AML) and fraud detection systems. + +# COMMAND ---------- + +# DBTITLE 1,Custom Industry Check: IBAN MOD-97 Validation +import pyspark.sql.functions as F +from pyspark.sql import Column +from databricks.labs.dqx.check_funcs import make_condition, get_normalized_column_and_expr +from databricks.labs.dqx.rule import register_rule + +@register_rule("row") +def is_suspicious_amount_pattern(column: str | Column, threshold: float = 5000.0) -> Column: + """ + Detects suspicious amount patterns commonly associated with structuring or financial crime: + 1. Perfectly round amounts above threshold (e.g., 5000.00, 10000.00) + 2. Amounts just below the $5K reporting threshold (e.g., 4950-4999) + 3. Repeated digit patterns (e.g., 7777.77, 3333.33) + + These patterns may indicate attempts to avoid regulatory reporting requirements. + """ + col_str_norm, col_expr_str, col_expr = get_normalized_column_and_expr(column) + + # Take absolute value for the checks + abs_amount = F.abs(col_expr) + + # Check 1: Perfectly round amounts above threshold (e.g., 5000.00, 10000.00) + # Amount equals its floor and is divisible by 1000 or 5000 + is_round = ( + (abs_amount >= threshold) & + (abs_amount == F.floor(abs_amount)) & + ((abs_amount % 1000 == 0) | (abs_amount % 5000 == 0)) + ) + + # Check 2: Just below the $10K reporting threshold + # Within $50 below $10,000 (e.g., $9,950 - $9,999.99) + near_threshold = (abs_amount >= 9950) & (abs_amount < 10000) + + # Check 3: Repeated digit patterns (e.g., 7777.77, 3333.33) + amount_str = F.format_number(abs_amount, 2) + # Remove decimal point and check if all digits are the same + digits_only = F.regexp_replace(amount_str, "[^0-9]", "") + first_digit = F.substring(digits_only, 1, 1) + all_same_digit = ( + (F.length(digits_only) >= 4) & + (F.length(F.regexp_replace(digits_only, first_digit, "")) == 0) + ) + + # Flag as suspicious if any pattern is detected + suspicious = is_round | near_threshold | all_same_digit + + return make_condition( + suspicious, + F.concat( + F.lit(f"Column '{col_expr_str}' value '"), + col_expr.cast("string"), + F.lit("' matches suspicious amount pattern (possible structuring/fraud indicator)") + ), + f"{col_str_norm}_suspicious_amount_pattern" + ) + +# COMMAND ---------- + +import yaml + +# Define industry-specific checks in YAML format +banking_checks_yaml = f""" +# 1. Critical fields must not be null +- criticality: error + check: + function: is_not_null_and_not_empty + for_each_column: + - transaction_id + - currency + user_metadata: + version: v1 + location: {catalog}.{schema}.banking_quality_rules + +# 2. Suspicious Amount Pattern Detection (Custom Fraud Check) +- name: suspicious_amount_pattern + criticality: warn + check: + function: is_suspicious_amount_pattern + arguments: + column: amount + threshold: 5000.0 + user_metadata: + version: v1 + location: {catalog}.{schema}.banking_quality_rules + +# 3. Currency validation +- criticality: warn + check: + function: is_in_list + arguments: + column: currency + allowed: ["USD", "EUR", "GBP", "JPY", "CHF", "CAD", "AUD", "CNY", "INR", "BRL", "MXN"] + name: valid_iso_currency + user_metadata: + version: v1 + +# 4. Amount Basic Checks +- criticality: error + check: + function: is_not_null + arguments: + column: amount + name: amount_not_null + user_metadata: + version: v1 + +- criticality: error + check: + function: is_not_equal_to + arguments: + column: amount + value: 0 + name: amount_not_zero + user_metadata: + version: v1 + +# 5. Business Boundary Checks +- criticality: error + check: + function: is_in_range + arguments: + column: amount + min_limit: -999999999.99 + max_limit: 999999999.99 + name: amount_within_system_bounds + user_metadata: + version: v1 + +# 6. Transaction Sign Consistency +- criticality: error + check: + function: sql_expression + arguments: + expression: > + NOT ( + transaction_type IN ('CREDIT', 'DEPOSIT', 'INCOMING_TRANSFER') + AND amount < 0 + ) + name: credit_transaction_must_be_positive + user_metadata: + version: v1 + +- criticality: error + check: + function: sql_expression + arguments: + expression: > + NOT ( + transaction_type IN ('DEBIT', 'WITHDRAWAL', 'FEE', 'CHARGEBACK') + AND amount > 0 + ) + name: debit_transaction_must_be_negative + user_metadata: + version: v1 + +# 7. Banking Precision (max 2 decimals) +- criticality: warn + check: + function: sql_expression + arguments: + expression: "amount = ROUND(amount, 2)" + name: amount_max_two_decimal_places + user_metadata: + version: v1 +""" + +checks = yaml.safe_load(banking_checks_yaml) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC ### Setup `DQEngine` + +# COMMAND ---------- + +from databricks.labs.dqx.engine import DQEngine +from databricks.labs.dqx.config import TableChecksStorageConfig +from databricks.sdk import WorkspaceClient + +ws = WorkspaceClient() # auto-authenticated inside Databricks +dq_engine = DQEngine(ws) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC ### Production Best Practice: Centralized Rule Management +# MAGIC +# MAGIC Instead of hardcoding rules in every pipeline, DQX recommends maintaining them in a **centralized Delta table**. This enables versioning, governance, sharing across teams, and easier discoverability. +# MAGIC +# MAGIC **Recommended Practices:** +# MAGIC * **Access Control**: Grant read-only access to users; restrict write access to service principals. +# MAGIC * **Granularity**: Prioritize row-level checks for distributed performance. +# MAGIC * **One-Pass Execution**: Apply all checks (row, dataset, anomaly) in a single pass to minimize redundant data scans. + +# COMMAND ---------- + +# DBTITLE 1,Centralize Rules in Delta Table +banking_rules_table = f"{catalog}.{schema}.banking_quality_rules" + +# Note: Custom check functions (like is_suspicious_amount_pattern) cannot be saved to Delta tables. +# Only built-in checks are persisted. Custom checks must be applied directly from the YAML list. + +# Filter out custom checks for table storage (keep only built-in checks) +built_in_checks = [check for check in checks if check.get('check', {}).get('function') != 'is_suspicious_amount_pattern'] + +# 1. Save built-in checks to a Delta table +dq_engine.save_checks( + built_in_checks, + config=TableChecksStorageConfig(location=banking_rules_table, run_config_name="banking_prod_v1") +) + +# 2. Load checks from the Delta table +quality_checks = dq_engine.load_checks( + config=TableChecksStorageConfig(location=banking_rules_table, run_config_name="banking_prod_v1") +) + +# 3. Add the custom check back to the loaded checks for runtime application +custom_amount_check = next(check for check in checks if check.get('check', {}).get('function') == 'is_suspicious_amount_pattern') +quality_checks.append(custom_amount_check) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC ### Apply Checks & Quarantine Invalid Records +# MAGIC +# MAGIC We'll use `apply_checks_by_metadata_and_split` to process the **loaded** checks. +# MAGIC +# MAGIC **Custom Function Handling**: +# MAGIC * **Better practice**: Pass only specific functions: `{'is_suspicious_amount_pattern': is_suspicious_amount_pattern}` +# MAGIC * **Production best practice**: Package custom checks as an importable module instead of notebook-defined functions +# MAGIC +# MAGIC **Split Behavior**: +# MAGIC * Records with **`error`** criticality violations → go **only** to `invalid_df` (quarantined) +# MAGIC * Records with **`warn`** criticality violations → go to **BOTH** `valid_df` and `invalid_df` (allows processing while flagging for review) +# MAGIC * This dual-routing for warnings enables fraud monitoring without blocking legitimate transactions +# MAGIC * For different behavior, use `apply_checks_by_metadata()` and filter manually + +# COMMAND ---------- + +# DQX Split Behavior: +# - Records with 'error' criticality violations → ONLY in invalid_df (quarantined) +# - Records with 'warn' criticality violations → BOTH valid_df AND invalid_df (dual-routing) +# +# This design allows suspicious patterns (warnings) to be monitored in the quarantine dataset +# while still flowing through the pipeline for processing. + +valid_df, invalid_df = dq_engine.apply_checks_by_metadata_and_split( + transactions_df, + quality_checks, + custom_check_functions={'is_suspicious_amount_pattern': is_suspicious_amount_pattern} +) + +# COMMAND ---------- + +display(valid_df) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC #### Invalid / Quarantined Transactions +# MAGIC Data quality issues are captured in the invalid DataFrame via the `_errors` and `_warnings` array columns mapping directly to our named checks. + +# COMMAND ---------- + +display(invalid_df) \ No newline at end of file diff --git a/demos/dqx_demo_industry/dqx_fashion_demo.py b/demos/dqx_demo_industry/dqx_fashion_demo.py new file mode 100644 index 000000000..45877a188 --- /dev/null +++ b/demos/dqx_demo_industry/dqx_fashion_demo.py @@ -0,0 +1,489 @@ +# Databricks notebook source +# MAGIC %md +# MAGIC # DQX - Fashion & Retail Industry Accelerator Demo +# MAGIC ## Product Catalog Quality & Merchandising Compliance +# MAGIC +# MAGIC This demo showcases DQX data quality rules tailored for the **Fashion and Retail industry**, with a focus on **product catalog integrity, pricing compliance, and merchandising standards**. +# MAGIC +# MAGIC ### Use Case: Product Catalog Ingestion & Quality Gate +# MAGIC +# MAGIC Process incoming product catalog uploads from vendors and internal merchandising teams and automatically: +# MAGIC * **Quarantine invalid products** (errors) that must be fixed before going live on the storefront +# MAGIC * **Flag merchandising issues** (warnings) for buyer review while still allowing products into the pipeline +# MAGIC * **Enforce data integrity** to ensure downstream inventory, pricing, and recommendation systems receive clean data +# MAGIC +# MAGIC ### Implemented Data Quality Checks +# MAGIC +# MAGIC **1. Custom Pricing Intelligence (Suspicious Pricing Detection)** +# MAGIC * **Pennies-off psychological pricing**: Prices ending in .95, .97, .98, .99 are expected — but extreme penny-off patterns (e.g., $99.01) may indicate data entry errors +# MAGIC * **Suspiciously round wholesale prices**: Perfectly round prices ($100.00, $500.00) in retail contexts often indicate placeholder or draft pricing +# MAGIC * **Extreme margin outliers**: Unit cost exceeding retail price (negative margin) or margin above 95% (likely data error) +# MAGIC +# MAGIC **2. Critical Field Validation (Errors)** +# MAGIC * Required fields: `sku`, `product_name`, `category` must not be null or empty +# MAGIC * SKU format: Must follow standard pattern (e.g., `TSHIRT-BLK-M`, `JEANS-BLU-32`) +# MAGIC * Hex color code: Must be valid CSS hex color (#RRGGBB or #RGB) +# MAGIC * Positive price: Listing price must be greater than zero +# MAGIC +# MAGIC **3. Business Logic Validation (Errors)** +# MAGIC * **Margin consistency**: Unit cost must not exceed retail price (negative margin) +# MAGIC * **Weight plausibility**: Shipping weight must be within plausible bounds for apparel (0.01–25 kg) +# MAGIC +# MAGIC **4. Data Quality Standards (Warnings)** +# MAGIC * **Apparel sizing**: Must follow standard sizing codes (XS, S, M, L, XL, XXL, OS, or numeric like 32-L) +# MAGIC * **Category taxonomy**: Must belong to known product categories +# MAGIC * **Season code**: Must follow format (e.g., SS25, FW24, AW25, RS25) +# MAGIC * **Brand-category consistency**: Luxury brands should not have products priced below $50 +# MAGIC +# MAGIC ### Key DQX Features Demonstrated +# MAGIC +# MAGIC * **Dual-Routing Split Behavior**: Warnings go to BOTH valid and invalid DataFrames (monitoring without blocking) +# MAGIC * **Custom Check Functions**: Industry-specific pricing intelligence integrated with built-in checks +# MAGIC * **Centralized Rule Management**: Rules stored in Delta tables for versioning and governance +# MAGIC * **Single-Pass Execution**: All checks applied in one data scan for optimal performance + +# COMMAND ---------- + +# MAGIC %md +# MAGIC ### Install DQX + +# COMMAND ---------- + +# Install DQX +%pip install databricks-labs-dqx + +# COMMAND ---------- + +dbutils.library.restartPython() + +# COMMAND ---------- + +# MAGIC %md +# MAGIC ### Production Best Practice: Pin DQX Version +# MAGIC +# MAGIC To ensure consistent behavior and avoid unexpected issues from automatic upgrades, it is highly recommended to always pin DQX to a specific version in your production pipelines. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC ### Setup Catalog and Schema +# MAGIC +# MAGIC Specify the catalog and schema where the quality rules table will be stored. + +# COMMAND ---------- + +# DBTITLE 1,Set Catalog and Schema for Demo +default_catalog_name = "main" +default_schema_name = "default" + +dbutils.widgets.text("demo_catalog", default_catalog_name, "Catalog Name") +dbutils.widgets.text("demo_schema", default_schema_name, "Schema Name") + +catalog = dbutils.widgets.get("demo_catalog") +schema = dbutils.widgets.get("demo_schema") + +print(f"Selected Catalog: {catalog}") +print(f"Selected Schema: {schema}") + +# COMMAND ---------- + +# MAGIC %md +# MAGIC ### Setup Sample Fashion Product Catalog Data +# MAGIC +# MAGIC We'll create a DataFrame simulating a typical product catalog upload from vendors or a merchandising system sync. +# MAGIC +# MAGIC The dataset includes fields common in fashion/retail: +# MAGIC * **`sku`** — Stock Keeping Unit identifier following `CATEGORY-COLOR-SIZE` convention +# MAGIC * **`product_name`** — Human-readable product description +# MAGIC * **`category`** / **`brand`** — Product taxonomy and brand attribution +# MAGIC * **`size_code`** — Apparel sizing (XS–XXL, OS, numeric) +# MAGIC * **`color_hex`** — CSS hex color for storefront display +# MAGIC * **`retail_price`** / **`unit_cost`** — Pricing and cost for margin analysis +# MAGIC * **`weight_kg`** — Shipping weight for logistics +# MAGIC * **`season_code`** — Merchandising season (e.g., SS25 = Spring/Summer 2025) + +# COMMAND ---------- + +from pyspark.sql import SparkSession, Row + +# Create a comprehensive sample DataFrame simulating a fashion product catalog ingestion +products = [ + # ── Valid products ────────────────────────────────────────────────────────── + Row(sku="TSHIRT-BLK-M", product_name="Classic Crew Tee", category="Tops", brand="BasicsLab", size_code="M", color_hex="#000000", retail_price=29.99, unit_cost=8.50, weight_kg=0.18, season_code="SS25"), + Row(sku="JEANS-BLU-32", product_name="Slim Fit Denim", category="Bottoms", brand="DenimCo", size_code="32-L", color_hex="#1A3A5C", retail_price=89.99, unit_cost=28.00, weight_kg=0.65, season_code="FW24"), + Row(sku="HOODIE-RED-XL", product_name="Oversized Hoodie", category="Outerwear", brand="StreetPulse", size_code="XL", color_hex="#C0392B", retail_price=65.00, unit_cost=22.00, weight_kg=0.55, season_code="AW25"), + Row(sku="DRESS-PNK-S", product_name="Midi Wrap Dress", category="Dresses", brand="Atelier Lux", size_code="S", color_hex="#F5A6C8", retail_price=185.00, unit_cost=52.00, weight_kg=0.35, season_code="SS25"), + Row(sku="SNEAKER-WHT-42", product_name="Retro Runner", category="Footwear", brand="StepForward", size_code="42", color_hex="#F8F8F8", retail_price=120.00, unit_cost=38.00, weight_kg=0.75, season_code="RS25"), + Row(sku="SCARF-GLD-OS", product_name="Silk Evening Scarf", category="Accessories", brand="Atelier Lux", size_code="OS", color_hex="#D4AF37", retail_price=95.00, unit_cost=30.00, weight_kg=0.08, season_code="FW24"), + Row(sku="BLAZER-NVY-L", product_name="Tailored Wool Blazer", category="Outerwear", brand="Atelier Lux", size_code="L", color_hex="#1B2631", retail_price=320.00, unit_cost=110.00, weight_kg=0.90, season_code="AW25"), + Row(sku="SHORTS-KHK-M", product_name="Chino Shorts", category="Bottoms", brand="BasicsLab", size_code="M", color_hex="#C8B88A", retail_price=45.00, unit_cost=12.00, weight_kg=0.28, season_code="SS25"), + + # ── Suspicious pricing patterns (warnings) ───────────────────────────────── + Row(sku="COAT-BLK-L", product_name="Trench Coat", category="Outerwear", brand="BasicsLab", size_code="L", color_hex="#0D0D0D", retail_price=200.00, unit_cost=60.00, weight_kg=1.20, season_code="FW24"), # Warn: Perfectly round retail price + Row(sku="POLO-GRN-M", product_name="Piqué Polo Shirt", category="Tops", brand="BasicsLab", size_code="M", color_hex="#2E8B57", retail_price=500.00, unit_cost=15.00, weight_kg=0.22, season_code="SS25"), # Warn: Round price + extreme 97% margin + Row(sku="BAG-TAN-OS", product_name="Leather Tote Bag", category="Accessories", brand="Atelier Lux", size_code="OS", color_hex="#D2B48C", retail_price=350.00, unit_cost=12.00, weight_kg=0.60, season_code="AW25"), # Warn: Round price + extreme 96.6% margin + + # ── Invalid sizing (warning) ──────────────────────────────────────────────── + Row(sku="SOCKS-WHT-PK3", product_name="Cotton Ankle Socks 3-Pack",category="Accessories", brand="BasicsLab", size_code="HUGE", color_hex="#FFFFFF", retail_price=12.50, unit_cost=3.00, weight_kg=0.10, season_code="SS25"), # Warn: Non-standard size "HUGE" + + # ── Invalid hex color (error) ─────────────────────────────────────────────── + Row(sku="HAT-GRN-OS", product_name="Bucket Hat", category="Accessories", brand="StreetPulse", size_code="OS", color_hex="GREEN", retail_price=25.00, unit_cost=7.00, weight_kg=0.12, season_code="SS25"), # Error: "GREEN" is not a valid hex color + Row(sku="VEST-CRM-M", product_name="Knit Vest", category="Tops", brand="BasicsLab", size_code="M", color_hex="#GGHHII",retail_price=55.00, unit_cost=18.00, weight_kg=0.30, season_code="FW24"), # Error: Invalid hex chars + + # ── Negative / zero price (error) ─────────────────────────────────────────── + Row(sku="BELT-BRN-36", product_name="Leather Belt", category="Accessories", brand="DenimCo", size_code="36", color_hex="#8B4513", retail_price=-15.00, unit_cost=5.00, weight_kg=0.15, season_code="SS25"), # Error: Negative price + Row(sku="TIE-BLU-OS", product_name="Silk Tie", category="Accessories", brand="Atelier Lux", size_code="OS", color_hex="#2C3E50", retail_price=0.00, unit_cost=25.00, weight_kg=0.05, season_code="FW24"), # Error: Zero price + + # ── Null required fields (error) ──────────────────────────────────────────── + Row(sku=None, product_name="Mystery Item", category="Tops", brand="BasicsLab", size_code="S", color_hex="#FFC0CB", retail_price=40.00, unit_cost=12.00, weight_kg=0.20, season_code="SS25"), # Error: Null SKU + Row(sku="JACKET-GRY-L", product_name=None, category="Outerwear", brand="StreetPulse", size_code="L", color_hex="#808080", retail_price=110.00, unit_cost=35.00, weight_kg=0.70, season_code="AW25"), # Error: Null product name + + # ── Invalid category (warning) ────────────────────────────────────────────── + Row(sku="LAMP-WHT-OS", product_name="Table Lamp", category="HomeGoods", brand="BasicsLab", size_code="OS", color_hex="#EEEEEE", retail_price=75.00, unit_cost=20.00, weight_kg=2.50, season_code="SS25"), # Warn: Category not in taxonomy + + # ── Invalid season code (warning) ─────────────────────────────────────────── + Row(sku="CARDIGAN-BRG-M", product_name="Cashmere Cardigan", category="Tops", brand="Atelier Lux", size_code="M", color_hex="#800020", retail_price=245.00, unit_cost=85.00, weight_kg=0.40, season_code="XMAS"), # Warn: Non-standard season code + + # ── Negative margin: cost > price (error) ─────────────────────────────────── + Row(sku="BOOT-BLK-40", product_name="Chelsea Boot", category="Footwear", brand="StepForward", size_code="40", color_hex="#1C1C1C", retail_price=89.00, unit_cost=120.00, weight_kg=1.10, season_code="AW25"), # Error: Cost exceeds price + + # ── Implausible weight (error) ────────────────────────────────────────────── + Row(sku="RING-SLV-OS", product_name="Sterling Silver Ring", category="Accessories", brand="Atelier Lux", size_code="OS", color_hex="#C0C0C0", retail_price=150.00, unit_cost=45.00, weight_kg=0.005,season_code="RS25"), # Error: Weight below 0.01 kg minimum + + # ── Brand-price inconsistency (warning) ───────────────────────────────────── + Row(sku="TEE-WHT-XS", product_name="Basic White Tee", category="Tops", brand="Atelier Lux", size_code="XS", color_hex="#FAFAFA", retail_price=15.00, unit_cost=4.00, weight_kg=0.16, season_code="SS25"), # Warn: Luxury brand priced under $50 + + # ── More valid products for pipeline flow ─────────────────────────────────── + Row(sku="PARKA-OLV-XL", product_name="Expedition Parka", category="Outerwear", brand="StreetPulse", size_code="XL", color_hex="#556B2F", retail_price=275.00, unit_cost=95.00, weight_kg=1.40, season_code="FW24"), +] + +products_df = spark.createDataFrame(products) +display(products_df) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC ### Defining Fashion Product Quality Checks +# MAGIC +# MAGIC We define our industry-specific checks using a YAML configuration. This is the recommended approach for DQX as it separates rule definitions from logic. +# MAGIC +# MAGIC We also define a **custom check function** for detecting suspicious pricing patterns common in retail product catalogs: +# MAGIC * **Perfectly round retail prices** (e.g., exactly $100.00, $200.00) which often indicate placeholder or draft pricing that was never finalized +# MAGIC * **Extreme margin outliers** — margins above 95% typically indicate a data entry error (e.g., cost entered in the wrong currency) +# MAGIC * **Negative margins** — unit cost exceeding retail price signals a pricing or cost error +# MAGIC +# MAGIC These patterns are red flags for merchandising and pricing compliance teams. + +# COMMAND ---------- + +# DBTITLE 1,Custom Industry Check: Suspicious Pricing Pattern Detection +import pyspark.sql.functions as F +from pyspark.sql import Column +from databricks.labs.dqx.check_funcs import make_condition, get_normalized_column_and_expr +from databricks.labs.dqx.rule import register_rule + +@register_rule("row") +def is_suspicious_pricing_pattern( + price_column: str | Column, + cost_column: str | Column, + round_price_threshold: float = 100.0, + max_margin_pct: float = 95.0, +) -> Column: + """ + Detects suspicious pricing patterns common in fashion/retail product catalogs: + 1. Perfectly round retail prices above threshold (e.g., $100.00, $200.00, $500.00) + — often indicates placeholder/draft pricing + 2. Extreme margin percentages above max_margin_pct (e.g., 95%+) + — likely a data entry error (wrong currency, missing digit, etc.) + 3. Negative margins (cost > price) + — pricing or cost error + + These patterns may indicate catalog data quality issues requiring merchandising review. + """ + price_norm, price_expr_str, price_expr = get_normalized_column_and_expr(price_column) + _cost_norm, cost_expr_str, cost_expr = get_normalized_column_and_expr(cost_column) + + # Check 1: Perfectly round retail prices above threshold + is_round = ( + (price_expr >= round_price_threshold) + & (price_expr == F.floor(price_expr)) + & ((price_expr % 50 == 0) | (price_expr % 100 == 0)) + ) + + # Check 2: Extreme margin (margin% = (price - cost) / price * 100) + margin_pct = ((price_expr - cost_expr) / price_expr) * 100 + extreme_margin = (price_expr > 0) & (cost_expr > 0) & (margin_pct > max_margin_pct) + + # Check 3: Negative margin (cost exceeds price) + negative_margin = (price_expr > 0) & (cost_expr > price_expr) + + suspicious = is_round | extreme_margin | negative_margin + + return make_condition( + suspicious, + F.concat( + F.lit(f"Suspicious pricing: '{price_expr_str}'="), + price_expr.cast("string"), + F.lit(f", '{cost_expr_str}'="), + cost_expr.cast("string"), + F.lit(" (possible placeholder, margin outlier, or cost/price inversion)"), + ), + f"{price_norm}_suspicious_pricing_pattern", + ) + +# COMMAND ---------- + +import yaml + +# Define industry-specific checks in YAML format +fashion_checks_yaml = f""" +# ──── 1. Critical Fields: Must not be null or empty ──────────────────────────── +- criticality: error + check: + function: is_not_null_and_not_empty + for_each_column: + - sku + - product_name + - category + user_metadata: + version: v1 + location: {catalog}.{schema}.fashion_quality_rules + +# ──── 2. SKU Format Validation ──────────────────────────────────────────────── +# Standard pattern: CATEGORY-COLOR-SIZE (e.g., TSHIRT-BLK-M, JEANS-BLU-32) +- criticality: error + check: + function: regex_match + arguments: + column: sku + regex: "^[A-Z]{{2,10}}-[A-Z]{{2,4}}-[A-Z0-9]{{1,4}}$" + name: sku_format_valid + user_metadata: + version: v1 + location: {catalog}.{schema}.fashion_quality_rules + +# ──── 3. Hex Color Code Validation ──────────────────────────────────────────── +# Must be valid CSS hex color: #RRGGBB or #RGB +- criticality: error + check: + function: regex_match + arguments: + column: color_hex + regex: "^#([A-Fa-f0-9]{{6}}|[A-Fa-f0-9]{{3}})$" + name: valid_css_hex_color + user_metadata: + version: v1 + location: {catalog}.{schema}.fashion_quality_rules + +# ──── 4. Listing Price: Must be positive ────────────────────────────────────── +- criticality: error + check: + function: is_not_less_than + arguments: + column: retail_price + limit: 0.01 + name: positive_listing_price + user_metadata: + version: v1 + location: {catalog}.{schema}.fashion_quality_rules + +# ──── 5. Suspicious Pricing Pattern (Custom Check) ─────────────────────────── +- name: suspicious_pricing_pattern + criticality: warn + check: + function: is_suspicious_pricing_pattern + arguments: + price_column: retail_price + cost_column: unit_cost + round_price_threshold: 100.0 + max_margin_pct: 95.0 + user_metadata: + version: v1 + location: {catalog}.{schema}.fashion_quality_rules + +# ──── 6. Apparel Sizing Validation ──────────────────────────────────────────── +# Accepted formats: XS, S, M, L, XL, XXL, OS, or numeric (32, 42, 32-L) +- criticality: warn + check: + function: regex_match + arguments: + column: size_code + regex: "^(XS|S|M|L|XL|XXL|OS|[0-9]{{2}}(-[A-Z])?)$" + name: standard_apparel_sizing + user_metadata: + version: v1 + location: {catalog}.{schema}.fashion_quality_rules + +# ──── 7. Product Category Taxonomy ──────────────────────────────────────────── +- criticality: warn + check: + function: is_in_list + arguments: + column: category + allowed: ["Tops", "Bottoms", "Dresses", "Outerwear", "Footwear", "Accessories", "Activewear", "Swimwear", "Loungewear"] + name: valid_product_category + user_metadata: + version: v1 + location: {catalog}.{schema}.fashion_quality_rules + +# ──── 8. Season Code Format ────────────────────────────────────────────────── +# Format: SS25 (Spring/Summer), FW24 (Fall/Winter), AW25 (Autumn/Winter), RS25 (Resort) +- criticality: warn + check: + function: regex_match + arguments: + column: season_code + regex: "^(SS|FW|AW|RS|PF|CR)[0-9]{{2}}$" + name: valid_season_code + user_metadata: + version: v1 + location: {catalog}.{schema}.fashion_quality_rules + +# ──── 9. Shipping Weight Plausibility ──────────────────────────────────────── +# Apparel and accessories typically weigh between 0.01 kg and 25 kg +- criticality: error + check: + function: is_in_range + arguments: + column: weight_kg + min_limit: 0.01 + max_limit: 25.0 + name: weight_within_bounds + user_metadata: + version: v1 + location: {catalog}.{schema}.fashion_quality_rules + +# ──── 10. Margin Consistency: Cost must not exceed Price ───────────────────── +- criticality: error + check: + function: sql_expression + arguments: + expression: "unit_cost <= retail_price OR retail_price <= 0" + name: cost_does_not_exceed_price + user_metadata: + version: v1 + location: {catalog}.{schema}.fashion_quality_rules + +# ──── 11. Brand-Price Consistency: Luxury brands must be priced >= $50 ─────── +- criticality: warn + check: + function: sql_expression + arguments: + expression: > + NOT ( + brand IN ('Atelier Lux') + AND retail_price < 50 + ) + name: luxury_brand_minimum_price + user_metadata: + version: v1 + location: {catalog}.{schema}.fashion_quality_rules +""" + +checks = yaml.safe_load(fashion_checks_yaml) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC ### Setup `DQEngine` + +# COMMAND ---------- + +from databricks.labs.dqx.engine import DQEngine +from databricks.labs.dqx.config import TableChecksStorageConfig +from databricks.sdk import WorkspaceClient + +ws = WorkspaceClient() # auto-authenticated inside Databricks +dq_engine = DQEngine(ws) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC ### Production Best Practice: Centralized Rule Management +# MAGIC +# MAGIC Instead of hardcoding rules in every pipeline, DQX recommends maintaining them in a **centralized Delta table**. This enables versioning, governance, sharing across teams, and easier discoverability. +# MAGIC +# MAGIC **Recommended Practices:** +# MAGIC * **Access Control**: Grant read-only access to users; restrict write access to service principals. +# MAGIC * **Granularity**: Prioritize row-level checks for distributed performance. +# MAGIC * **One-Pass Execution**: Apply all checks (row, dataset, anomaly) in a single pass to minimize redundant data scans. + +# COMMAND ---------- + +# DBTITLE 1,Centralize Rules in Delta Table +fashion_rules_table = f"{catalog}.{schema}.fashion_quality_rules" + +# Note: Custom check functions (like is_suspicious_pricing_pattern) cannot be saved to Delta tables. +# Only built-in checks are persisted. Custom checks must be applied directly from the YAML list. + +# Filter out custom checks for table storage (keep only built-in checks) +built_in_checks = [check for check in checks if check.get('check', {}).get('function') != 'is_suspicious_pricing_pattern'] + +# 1. Save built-in checks to a Delta table +dq_engine.save_checks( + built_in_checks, + config=TableChecksStorageConfig(location=fashion_rules_table, run_config_name="fashion_prod_v1") +) + +# 2. Load checks from the Delta table +quality_checks = dq_engine.load_checks( + config=TableChecksStorageConfig(location=fashion_rules_table, run_config_name="fashion_prod_v1") +) + +# 3. Add the custom check back to the loaded checks for runtime application +custom_pricing_check = next(check for check in checks if check.get('check', {}).get('function') == 'is_suspicious_pricing_pattern') +quality_checks.append(custom_pricing_check) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC ### Apply Checks & Quarantine Invalid Products +# MAGIC +# MAGIC We'll use `apply_checks_by_metadata_and_split` to process the **loaded** checks. +# MAGIC +# MAGIC **Custom Function Handling**: +# MAGIC * **Better practice**: Pass only specific functions: `{'is_suspicious_pricing_pattern': is_suspicious_pricing_pattern}` +# MAGIC * **Production best practice**: Package custom checks as an importable module instead of notebook-defined functions +# MAGIC +# MAGIC **Split Behavior**: +# MAGIC * Records with **`error`** criticality violations → go **only** to `invalid_df` (quarantined) +# MAGIC * Records with **`warn`** criticality violations → go to **BOTH** `valid_df` and `invalid_df` (allows processing while flagging for review) +# MAGIC * This dual-routing for warnings enables merchandising review without blocking catalog publication +# MAGIC * For different behavior, use `apply_checks_by_metadata()` and filter manually + +# COMMAND ---------- + +# DQX Split Behavior: +# - Records with 'error' criticality violations → ONLY in invalid_df (quarantined) +# - Records with 'warn' criticality violations → BOTH valid_df AND invalid_df (dual-routing) +# +# This design allows merchandising issues (warnings) to be reviewed in the quarantine dataset +# while still flowing through the pipeline to the storefront. + +valid_df, invalid_df = dq_engine.apply_checks_by_metadata_and_split( + products_df, + quality_checks, + custom_check_functions={'is_suspicious_pricing_pattern': is_suspicious_pricing_pattern} +) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC #### ✅ Valid Products — Ready for Storefront +# MAGIC Products that pass all error-level checks. Warning-flagged products still appear here for downstream processing. + +# COMMAND ---------- + +display(valid_df) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC #### ❌ Invalid / Quarantined Products — Merchandising Review Queue +# MAGIC Data quality issues are captured in the invalid DataFrame via the `_errors` and `_warnings` array columns mapping directly to our named checks. + +# COMMAND ---------- + +display(invalid_df) diff --git a/demos/dqx_manufacturing_demo.py b/demos/dqx_demo_industry/dqx_manufacturing_demo.py similarity index 100% rename from demos/dqx_manufacturing_demo.py rename to demos/dqx_demo_industry/dqx_manufacturing_demo.py diff --git a/docs/dqx/docs/demos.mdx b/docs/dqx/docs/demos.mdx index 9c136903d..bb6921aec 100644 --- a/docs/dqx/docs/demos.mdx +++ b/docs/dqx/docs/demos.mdx @@ -26,9 +26,13 @@ Import the following notebooks in the Databricks workspace to try DQX out: ## Deploy as Workspace Tool * [DQX Demo Notebook](https://github.com/databrickslabs/dqx/blob/v0.13.0/demos/dqx_demo_tool.py) - demonstrates how to use DQX as a tool when installed in the workspace. +## Industry Accelerators +* [DQX for Banking / FSI Industry Accelerator Demo](https://github.com/databrickslabs/dqx/blob/v0.13.0/demos/dqx_demo_industry/dqx_banking_demo.py) - showcases DQX data quality rules tailored for the Banking and Financial Services industry, focusing on fraud detection, transaction structuring, and regulatory compliance. +* [DQX for Fashion & Retail Accelerator Demo](https://github.com/databrickslabs/dqx/blob/v0.13.0/demos/dqx_demo_industry/dqx_fashion_demo.py) - demonstrates DQX rules tailored for the Fashion and Retail industry, including SKU validation, hex color formats, and pricing compliance. +* [DQX for Manufacturing Industry Accelerator Demo](https://github.com/databrickslabs/dqx/blob/v0.13.0/demos/dqx_demo_industry/dqx_manufacturing_demo.py) - demonstrates how to use DQX to check data quality for Manufacturing Industry datasets, specifically for machine maintenance and sensor data. + ## Use Cases * [DQX for PII Detection Notebook](https://github.com/databrickslabs/dqx/blob/v0.13.0/demos/dqx_demo_pii_detection.py) - demonstrates how to use DQX to check data for Personally Identifiable Information (PII). -* [DQX for Manufacturing Notebook](https://github.com/databrickslabs/dqx/blob/v0.13.0/demos/dqx_manufacturing_demo.py) - demonstrates how to use DQX to check data quality for Manufacturing Industry datasets.
diff --git a/src/databricks/labs/dqx/io.py b/src/databricks/labs/dqx/io.py index 2da6950a3..96e45a369 100644 --- a/src/databricks/labs/dqx/io.py +++ b/src/databricks/labs/dqx/io.py @@ -125,7 +125,7 @@ def save_dataframe_as_table(df: DataFrame, output_config: OutputConfig) -> Strea if output_config.cluster_by: if _supports_streaming_cluster_on_write(df.sparkSession): - stream_writer = stream_writer.clusterBy(*output_config.cluster_by) + stream_writer = stream_writer.clusterBy(*output_config.cluster_by) # type: ignore[attr-defined] if output_config.partition_by: stream_writer = stream_writer.partitionBy(*output_config.partition_by) From 520ef10fb181c9b517c6470567041056eeb2b330 Mon Sep 17 00:00:00 2001 From: fedeflowers Date: Mon, 30 Mar 2026 19:56:09 +0200 Subject: [PATCH 2/4] remove pylint ignore in another unralated file to the PR --- src/databricks/labs/dqx/io.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/databricks/labs/dqx/io.py b/src/databricks/labs/dqx/io.py index 96e45a369..2da6950a3 100644 --- a/src/databricks/labs/dqx/io.py +++ b/src/databricks/labs/dqx/io.py @@ -125,7 +125,7 @@ def save_dataframe_as_table(df: DataFrame, output_config: OutputConfig) -> Strea if output_config.cluster_by: if _supports_streaming_cluster_on_write(df.sparkSession): - stream_writer = stream_writer.clusterBy(*output_config.cluster_by) # type: ignore[attr-defined] + stream_writer = stream_writer.clusterBy(*output_config.cluster_by) if output_config.partition_by: stream_writer = stream_writer.partitionBy(*output_config.partition_by) From 292965606f7dceabbf388bc48d325b5b327f9119 Mon Sep 17 00:00:00 2001 From: fedeflowers Date: Thu, 2 Apr 2026 14:42:23 +0200 Subject: [PATCH 3/4] add conformity to previous e2e, add tests, modified conftest for e2e testing on forked repo --- demos/dqx_demo_industry/dqx_banking_demo.py | 35 ++++++++++----- demos/dqx_demo_industry/dqx_fashion_demo.py | 12 +++-- tests/e2e/conftest.py | 23 ++++++++++ tests/e2e/test_run_demos.py | 50 ++++++++++++++++++++- 4 files changed, 104 insertions(+), 16 deletions(-) diff --git a/demos/dqx_demo_industry/dqx_banking_demo.py b/demos/dqx_demo_industry/dqx_banking_demo.py index bf30991d9..c08d4a527 100644 --- a/demos/dqx_demo_industry/dqx_banking_demo.py +++ b/demos/dqx_demo_industry/dqx_banking_demo.py @@ -45,12 +45,16 @@ # COMMAND ---------- -# Install DQX -%pip install databricks-labs-dqx +# DBTITLE 1,Install DQX Library -# COMMAND ---------- +dbutils.widgets.text("test_library_ref", "", "Test Library Ref") + +if dbutils.widgets.get("test_library_ref") != "": + %pip install '{dbutils.widgets.get("test_library_ref")}' +else: + %pip install databricks-labs-dqx -dbutils.library.restartPython() +%restart_python # COMMAND ---------- @@ -163,11 +167,16 @@ from databricks.labs.dqx.rule import register_rule @register_rule("row") -def is_suspicious_amount_pattern(column: str | Column, threshold: float = 5000.0) -> Column: +def is_suspicious_amount_pattern( + column: str | Column, + threshold: float = 5000.0, + reporting_thresholds: float | list[float] = 10000.0, + margin: float = 50.0 +) -> Column: """ Detects suspicious amount patterns commonly associated with structuring or financial crime: - 1. Perfectly round amounts above threshold (e.g., 5000.00, 10000.00) - 2. Amounts just below the $5K reporting threshold (e.g., 4950-4999) + 1. Perfectly round amounts above threshold (e.g., exactly 5000.00, 10000.00) + 2. Amounts just below configurable reporting thresholds (e.g., 9950-9999 for a 10K threshold) 3. Repeated digit patterns (e.g., 7777.77, 3333.33) These patterns may indicate attempts to avoid regulatory reporting requirements. @@ -185,9 +194,13 @@ def is_suspicious_amount_pattern(column: str | Column, threshold: float = 5000.0 ((abs_amount % 1000 == 0) | (abs_amount % 5000 == 0)) ) - # Check 2: Just below the $10K reporting threshold - # Within $50 below $10,000 (e.g., $9,950 - $9,999.99) - near_threshold = (abs_amount >= 9950) & (abs_amount < 10000) + # Check 2: Just below the reporting thresholds + if isinstance(reporting_thresholds, (int, float)): + reporting_thresholds = [float(reporting_thresholds)] + + near_threshold = F.lit(False) + for t in reporting_thresholds: + near_threshold = near_threshold | ((abs_amount >= t - margin) & (abs_amount < t)) # Check 3: Repeated digit patterns (e.g., 7777.77, 3333.33) amount_str = F.format_number(abs_amount, 2) @@ -237,6 +250,8 @@ def is_suspicious_amount_pattern(column: str | Column, threshold: float = 5000.0 arguments: column: amount threshold: 5000.0 + reporting_thresholds: [10000.0, 5000.0, 3000.0] + margin: 50.0 user_metadata: version: v1 location: {catalog}.{schema}.banking_quality_rules diff --git a/demos/dqx_demo_industry/dqx_fashion_demo.py b/demos/dqx_demo_industry/dqx_fashion_demo.py index 45877a188..45b8b6135 100644 --- a/demos/dqx_demo_industry/dqx_fashion_demo.py +++ b/demos/dqx_demo_industry/dqx_fashion_demo.py @@ -49,12 +49,16 @@ # COMMAND ---------- -# Install DQX -%pip install databricks-labs-dqx +# DBTITLE 1,Install DQX Library -# COMMAND ---------- +dbutils.widgets.text("test_library_ref", "", "Test Library Ref") + +if dbutils.widgets.get("test_library_ref") != "": + %pip install '{dbutils.widgets.get("test_library_ref")}' +else: + %pip install databricks-labs-dqx -dbutils.library.restartPython() +%restart_python # COMMAND ---------- diff --git a/tests/e2e/conftest.py b/tests/e2e/conftest.py index c759f56da..c47df75aa 100644 --- a/tests/e2e/conftest.py +++ b/tests/e2e/conftest.py @@ -26,6 +26,26 @@ def _get_local_git_branch() -> str | None: return None +def _get_remote_url(branch: str) -> str | None: + """Get the git remote URL for the current branch, or None if not available.""" + try: + remote = subprocess.check_output( + ["git", "config", f"branch.{branch}.remote"], + stderr=subprocess.DEVNULL, + text=True, + ).strip() + url = subprocess.check_output( + ["git", "config", f"remote.{remote}.url"], + stderr=subprocess.DEVNULL, + text=True, + ).strip() + if url.endswith(".git"): + url = url[:-4] + return url + except Exception: + return None + + @pytest.fixture def library_ref() -> str: """ @@ -55,6 +75,9 @@ def library_ref() -> str: # Local behavior: use current git branch ref_name = _get_local_git_branch() if ref_name and ref_name != "HEAD": + remote_url = _get_remote_url(ref_name) + if remote_url: + return f"git+{remote_url}.git@{ref_name}" return f"{base_ref}.git@{ref_name}" # Default to main branch diff --git a/tests/e2e/test_run_demos.py b/tests/e2e/test_run_demos.py index 468a2f1cd..94e9adc84 100644 --- a/tests/e2e/test_run_demos.py +++ b/tests/e2e/test_run_demos.py @@ -73,10 +73,10 @@ def test_run_intermediate_dqx_demo_library(ws, make_notebook, make_schema, make_ def test_run_dqx_manufacturing_demo(ws, make_notebook, make_directory, make_schema, make_job, library_ref): - path = Path(__file__).parent.parent.parent / "demos" / "dqx_manufacturing_demo.py" + path = Path(__file__).parent.parent.parent / "demos" / "dqx_demo_industry" / "dqx_manufacturing_demo.py" with open(path, "rb") as f: notebook = make_notebook(content=f, format=ImportFormat.SOURCE) - folder = notebook.as_fuse().parent / "quality_rules" + folder = (notebook.as_fuse().parent / "quality_checks").as_posix() make_directory(path=folder) catalog = TEST_CATALOG @@ -97,6 +97,52 @@ def test_run_dqx_manufacturing_demo(ws, make_notebook, make_directory, make_sche logging.info(f"Job run {run.run_id} completed successfully for dqx_manufacturing_demo") +def test_run_dqx_banking_demo(ws, make_notebook, make_schema, make_job, library_ref): + path = Path(__file__).parent.parent.parent / "demos" / "dqx_demo_industry" / "dqx_banking_demo.py" + with open(path, "rb") as f: + notebook = make_notebook(content=f, format=ImportFormat.SOURCE) + + catalog = TEST_CATALOG + schema = make_schema(catalog_name=catalog).name + notebook_path = notebook.as_fuse().as_posix() + notebook_task = NotebookTask( + notebook_path=notebook_path, + base_parameters={"demo_catalog": catalog, "demo_schema": schema, "test_library_ref": library_ref}, + ) + job = make_job(tasks=[Task(task_key="dqx_banking_demo", notebook_task=notebook_task)]) + + waiter = ws.jobs.run_now_and_wait(job.job_id) + run = ws.jobs.wait_get_run_job_terminated_or_skipped( + run_id=waiter.run_id, + timeout=timedelta(minutes=30), + callback=lambda r: validate_run_status(r, ws), + ) + logging.info(f"Job run {run.run_id} completed successfully for dqx_banking_demo") + + +def test_run_dqx_fashion_demo(ws, make_notebook, make_schema, make_job, library_ref): + path = Path(__file__).parent.parent.parent / "demos" / "dqx_demo_industry" / "dqx_fashion_demo.py" + with open(path, "rb") as f: + notebook = make_notebook(content=f, format=ImportFormat.SOURCE) + + catalog = TEST_CATALOG + schema = make_schema(catalog_name=catalog).name + notebook_path = notebook.as_fuse().as_posix() + notebook_task = NotebookTask( + notebook_path=notebook_path, + base_parameters={"demo_catalog": catalog, "demo_schema": schema, "test_library_ref": library_ref}, + ) + job = make_job(tasks=[Task(task_key="dqx_fashion_demo", notebook_task=notebook_task)]) + + waiter = ws.jobs.run_now_and_wait(job.job_id) + run = ws.jobs.wait_get_run_job_terminated_or_skipped( + run_id=waiter.run_id, + timeout=timedelta(minutes=30), + callback=lambda r: validate_run_status(r, ws), + ) + logging.info(f"Job run {run.run_id} completed successfully for dqx_fashion_demo") + + def test_run_dqx_quick_start_demo_library(ws, make_notebook, make_job, library_ref): path = Path(__file__).parent.parent.parent / "demos" / "dqx_quick_start_demo_library.py" with open(path, "rb") as f: From 718dc9c797c8bc384c8f886c54179bb767858f1f Mon Sep 17 00:00:00 2001 From: Marcin Wojtyczka Date: Wed, 15 Apr 2026 13:57:51 +0200 Subject: [PATCH 4/4] fix getting remote url --- .build-constraints.txt | 23 + .codegen.json | 9 +- .github/ISSUE_TEMPLATE/bug.yml | 6 +- .github/actions/jfrog-auth/action.yml | 50 + .github/actions/jfrog-auth/jfrog-auth | 37 + .github/dependabot.yml | 10 +- .github/workflows/acceptance.yml | 118 +- .github/workflows/anomaly.yml | 154 +- .github/workflows/check-if-commit-signed.yml | 3 + .github/workflows/docs-release.yml | 34 +- .github/workflows/nightly.yml | 471 +- .github/workflows/no-cheat.yml | 7 +- .github/workflows/performance.yml | 212 - .github/workflows/push.yml | 86 +- .github/workflows/release.yml | 32 +- .gitignore | 1 - .python-version | 1 + Makefile | 77 +- app/.gitignore | 3 - demos/dqx_demo_industry/dqx_banking_demo.py | 213 +- demos/dqx_demo_industry/dqx_fashion_demo.py | 214 +- .../dqx_manufacturing_demo.py | 7 + docs/dqx/docs/dev/contributing.mdx | 74 +- docs/dqx/package-lock.json | 19852 ---------------- docs/dqx/package.json | 2 +- docs/dqx/yarn.lock | 5539 ++--- pyproject.toml | 96 +- tests/conftest.py | 14 +- tests/e2e/.codegen.json | 6 +- tests/e2e/conftest.py | 3 + tests/e2e/test_run_demos.py | 78 +- tests/integration/.codegen.json | 6 +- tests/integration_anomaly/.codegen.json | 11 + tests/integration_anomaly/conftest.py | 19 + uv.lock | 5928 +++-- 35 files changed, 7128 insertions(+), 26268 deletions(-) create mode 100644 .build-constraints.txt create mode 100644 .github/actions/jfrog-auth/action.yml create mode 100755 .github/actions/jfrog-auth/jfrog-auth delete mode 100644 .github/workflows/performance.yml create mode 100644 .python-version delete mode 100644 docs/dqx/package-lock.json create mode 100644 tests/integration_anomaly/.codegen.json diff --git a/.build-constraints.txt b/.build-constraints.txt new file mode 100644 index 000000000..59b14636e --- /dev/null +++ b/.build-constraints.txt @@ -0,0 +1,23 @@ +hatch-fancy-pypi-readme==25.1.0 \ + --hash=sha256:9c58ed3dff90d51f43414ce37009ad1d5b0f08ffc9fc216998a06380f01c0045 \ + --hash=sha256:ce0134c40d63d874ac48f48ccc678b8f3b62b8e50e9318520d2bffc752eedaf3 +hatchling==1.29.0 \ + --hash=sha256:50af9343281f34785fab12da82e445ed987a6efb34fd8c2fc0f6e6630dbcc1b0 \ + --hash=sha256:793c31816d952cee405b83488ce001c719f325d9cda69f1fc4cd750527640ea6 + # via hatch-fancy-pypi-readme +packaging==26.0 \ + --hash=sha256:00243ae351a257117b6a241061796684b084ed1c516a08c48a3f7e147a9d80b4 \ + --hash=sha256:b36f1fef9334a5588b4166f8bcd26a14e521f2b55e6b9de3aaa80d3ff7a37529 + # via hatchling +pathspec==1.0.4 \ + --hash=sha256:0210e2ae8a21a9137c0d470578cb0e595af87edaa6ebf12ff176f14a02e0e645 \ + --hash=sha256:fb6ae2fd4e7c921a165808a552060e722767cfa526f99ca5156ed2ce45a5c723 + # via hatchling +pluggy==1.6.0 \ + --hash=sha256:7dcc130b76258d33b90f61b658791dede3486c3e6bfb003ee5c9bfb396dd22f3 \ + --hash=sha256:e920276dd6813095e9377c0bc5566d94c932c33b27a3e3945d8389c374dd4746 + # via hatchling +trove-classifiers==2026.1.14.14 \ + --hash=sha256:00492545a1402b09d4858605ba190ea33243d361e2b01c9c296ce06b5c3325f3 \ + --hash=sha256:1f9553927f18d0513d8e5ff80ab8980b8202ce37ecae0e3274ed2ef11880e74d + # via hatchling diff --git a/.codegen.json b/.codegen.json index 74b9ad344..9abe21982 100644 --- a/.codegen.json +++ b/.codegen.json @@ -3,8 +3,9 @@ "src/databricks/labs/dqx/__about__.py": "__version__ = \"$VERSION\"" }, "toolchain": { - "required": ["python3", "hatch"], - "pre_setup": ["hatch env create"], - "prepend_path": ".venv/bin" + "required": ["make", "uv"], + "pre_setup": ["make dev"], + "prepend_path": ".venv/bin", + "test": ["make test"] } -} \ No newline at end of file +} diff --git a/.github/ISSUE_TEMPLATE/bug.yml b/.github/ISSUE_TEMPLATE/bug.yml index b06405b6f..0b83e7652 100644 --- a/.github/ISSUE_TEMPLATE/bug.yml +++ b/.github/ISSUE_TEMPLATE/bug.yml @@ -20,7 +20,7 @@ body: label: Current Behavior description: | A concise description of what you're experiencing. - **Do not paste links to attachments with logs and/or images, as all issues will attachments will get deleted.** + **Do not paste links to attachments with logs and/or images, as all issues with attachments will get deleted.** Use the `Relevant log output` field to paste redacted log output without personal identifying information (PII). You can Ctrl/Cmd+V the screenshot, which would appear as a rendered image if it doesn't contain any PII. validations: @@ -67,6 +67,6 @@ body: - type: textarea id: logs attributes: - label: Relevant log output - description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks. + label: Relevant log output, error message and full stacktrace + description: Please copy and paste any relevant log output, error message and full stacktrace. This will be automatically formatted into code, so no need for backticks. render: shell diff --git a/.github/actions/jfrog-auth/action.yml b/.github/actions/jfrog-auth/action.yml new file mode 100644 index 000000000..92b617ebd --- /dev/null +++ b/.github/actions/jfrog-auth/action.yml @@ -0,0 +1,50 @@ +name: 'Authenticate for JFrog' +description: 'Authenticate with JFrog using OIDC based on the GitHub repository.' +# TODO: Factor out into an external action, once releases are allowed. +outputs: + jfrog-access-token: + description: "Access token for JFrog" + value: "${{ steps.jfrog-auth.outputs.jfrog-access-token }}" +runs: + using: "composite" + steps: + - id: jfrog-auth + name: Authenticate against JFrog + shell: bash + run: | + "${GITHUB_ACTION_PATH}/jfrog-auth" "${ACTIONS_ID_TOKEN_REQUEST_URL}" "${ACTIONS_ID_TOKEN_REQUEST_TOKEN}" + + - id: detect-cmds + name: Detecting python package/project managers. + shell: bash + run: | + for cmd in pip3 uv + do + command -v "${cmd}" > /dev/null && found=true || found=false + printf '::debug::%s\n' "Found ${cmd}: ${found}" + printf '%s=%s\n' "command_${cmd}" "${found}" >> "${GITHUB_OUTPUT}" + done + + - name: Configure pip for JFrog + if: "${{ steps.detect-cmds.outputs.command_pip3 == 'true' }}" + shell: bash + env: + JFROG_ACCESS_TOKEN: "${{ steps.jfrog-auth.outputs.jfrog-access-token }}" + run: | + umask 077 + cat > "$RUNNER_TEMP/.pip.conf" <> "${GITHUB_ENV}" + + - name: Configure uv for JFrog + if: "${{ steps.detect-cmds.outputs.command_uv == 'true' }}" + shell: bash + env: + JFROG_ACCESS_TOKEN: "${{ steps.jfrog-auth.outputs.jfrog-access-token }}" + UV_INDEX_URL: 'https://databricks.jfrog.io/artifactory/api/pypi/db-pypi/simple' + run: | + uv auth login "${UV_INDEX_URL}" --username gha-service-account --password "${JFROG_ACCESS_TOKEN}" + printf "%s=%s\n" 'UV_INDEX_URL' "${UV_INDEX_URL}" >> "${GITHUB_ENV}" + printf "%s=%s\n" 'UV_FROZEN' '1' >> "${GITHUB_ENV}" diff --git a/.github/actions/jfrog-auth/jfrog-auth b/.github/actions/jfrog-auth/jfrog-auth new file mode 100755 index 000000000..6f02005ec --- /dev/null +++ b/.github/actions/jfrog-auth/jfrog-auth @@ -0,0 +1,37 @@ +#!/usr/bin/env bash +# +# Obtain a JFrog access token, assuming GitHub OIDC. +# +set -eu + +_request_url="$1" +_request_token="$2" + +# +# Step 1: Obtain the OIDC identifier token from GitHub. +# +printf '::debug::%s\n' "Fetching OIDC identifier token from GitHub..." +_id_token="$(curl -sLS \ + -H 'User-Agent: actions/oidc-client' \ + -H "Authorization: Bearer ${_request_token}" \ + "${_request_url}&audience=jfrog-github" | + jq -r .value)" +printf '::add-mask::%s\n' "${_id_token}" + +# +# Step 2: Exchange it for the JFrog access token. +# +printf '::debug::%s\n' "Exchanging OIDC identifier token for JFrog access token..." +_access_token=$(curl -sLS \ + --json "{\"grant_type\": \"urn:ietf:params:oauth:grant-type:token-exchange\", \"subject_token_type\":\"urn:ietf:params:oauth:token-type:id_token\", \"subject_token\": \"${_id_token}\", \"provider_name\": \"github-actions\"}" \ + "https://databricks.jfrog.io/access/api/v1/oidc/token" | + jq -r .access_token) +printf '::add-mask::%s\n' "${_access_token}" + +if [ -z "${_access_token}" ] || [ "${_access_token}" = 'null' ] +then + printf '::error::%s\n' "Could not fetch JFrog access token." + exit 1 +fi + +printf '%s=%s\n' 'jfrog-access-token' "${_access_token}" >> "${GITHUB_OUTPUT}" diff --git a/.github/dependabot.yml b/.github/dependabot.yml index 8c763bf21..fd193b117 100644 --- a/.github/dependabot.yml +++ b/.github/dependabot.yml @@ -1,10 +1,10 @@ version: 2 updates: - - package-ecosystem: "pip" + - package-ecosystem: "uv" directory: "/" + cooldown: + default-days: 7 + exclude: + - "databricks*" schedule: interval: "daily" - - package-ecosystem: "github-actions" - directory: "/" - schedule: - interval: "daily" \ No newline at end of file diff --git a/.github/workflows/acceptance.yml b/.github/workflows/acceptance.yml index f8b1fe972..0da7338ce 100644 --- a/.github/workflows/acceptance.yml +++ b/.github/workflows/acceptance.yml @@ -10,9 +10,7 @@ on: - main permissions: - id-token: write contents: read - pull-requests: write concurrency: group: ${{ github.workflow }}-${{ github.ref }} @@ -27,21 +25,25 @@ jobs: if: github.event_name == 'pull_request' && !github.event.pull_request.draft && !github.event.pull_request.head.repo.fork environment: tool runs-on: larger + permissions: + # Access to the integration testing infrastructure. + id-token: write + # Write test results to the PR. + pull-requests: write steps: - name: Checkout Code uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2 with: fetch-depth: 0 - - name: Install Python - uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6.2.0 + - name: Setup uv + uses: astral-sh/setup-uv@5a095e7a2014a4212f075830d4f7277575a9d098 # v7.3.1 with: - cache: 'pip' - cache-dependency-path: '**/pyproject.toml' - python-version: '3.12' + version: "0.11.2" + checksum: "7ac2ca0449c8d68dae9b99e635cd3bc9b22a4cb1de64b7c43716398447d42981" - - name: Install hatch - run: pip install hatch==1.16.5 + - name: Setup for JFrog + uses: ./.github/actions/jfrog-auth - name: Run unit tests and generate test coverage report run: make test @@ -72,8 +74,7 @@ jobs: COVERAGE_FILE: ${{ github.workspace }}/.coverage # make sure the coverage report is preserved - name: Merge coverage reports and convert them to XML - run: | - hatch run combine_coverage + run: make combine-coverage # Recursively search the entire workspace directory for all coverage reports. # All uploaded test coverage reports will be used even if publish is done multiple time. @@ -89,6 +90,9 @@ jobs: if: github.event_name == 'pull_request' && !github.event.pull_request.draft && !github.event.pull_request.head.repo.fork environment: tool runs-on: larger + permissions: + id-token: write + pull-requests: write env: DATABRICKS_SERVERLESS_COMPUTE_ID: auto steps: @@ -97,15 +101,14 @@ jobs: with: fetch-depth: 0 - - name: Install Python - uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6.2.0 + - name: Setup uv + uses: astral-sh/setup-uv@5a095e7a2014a4212f075830d4f7277575a9d098 # v7.3.1 with: - cache: 'pip' - cache-dependency-path: '**/pyproject.toml' - python-version: '3.12' + version: "0.11.2" + checksum: "7ac2ca0449c8d68dae9b99e635cd3bc9b22a4cb1de64b7c43716398447d42981" - - name: Install hatch - run: pip install hatch==1.16.5 + - name: Setup for JFrog + uses: ./.github/actions/jfrog-auth # Integration tests are run from within tests/integration folder. # Create .coveragerc with correct relative path to source code. @@ -131,8 +134,7 @@ jobs: COVERAGE_FILE: ${{ github.workspace }}/.coverage # make sure the coverage report is preserved - name: Merge coverage reports and convert them to XML - run: | - hatch run combine_coverage + run: make combine-coverage # collects all coverage reports - name: Publish test coverage @@ -144,43 +146,27 @@ jobs: if: github.event_name == 'pull_request' && !github.event.pull_request.draft && !github.event.pull_request.head.repo.fork environment: tool runs-on: larger + permissions: + id-token: write + pull-requests: write steps: - name: Checkout Code uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2 with: fetch-depth: 0 - - name: Install Python - uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6.2.0 + - name: Setup uv + uses: astral-sh/setup-uv@5a095e7a2014a4212f075830d4f7277575a9d098 # v7.3.1 with: - cache: 'pip' - cache-dependency-path: '**/pyproject.toml' - python-version: '3.12' + version: "0.11.2" + checksum: "7ac2ca0449c8d68dae9b99e635cd3bc9b22a4cb1de64b7c43716398447d42981" - - name: Install hatch - run: pip install hatch==1.16.5 - - - name: Install dbt - run: | - pip install dbt-core==1.10.9 dbt-databricks==1.10.9 + - name: Setup for JFrog + uses: ./.github/actions/jfrog-auth + # Required for DAB (Databricks Asset Bundle) e2e tests - name: Install Databricks CLI - run: | - curl -fsSL https://raw.githubusercontent.com/databricks/setup-cli/main/install.sh | sh - databricks --version - - - name: Azure login (OIDC) - uses: azure/login@a457da9ea143d694b1b9c7c869ebb04ebe844ef5 # v2.3.0 - with: - client-id: ${{ secrets.ARM_CLIENT_ID }} - tenant-id: ${{ secrets.ARM_TENANT_ID }} - allow-no-subscriptions: true - - - name: Set env vars for Azure CLI auth - run: | - val=$(az keyvault secret show --id "${{ secrets.VAULT_URI }}/secrets/DATABRICKS-HOST" --query value -o tsv) - echo "DATABRICKS_HOST=$val" >> $GITHUB_ENV - echo "DATABRICKS_AUTH_TYPE=azure-cli" >> $GITHUB_ENV + uses: databricks/setup-cli@acd0e77a1ed7f15f528faca1e1f7f5590bcfdff8 # v0.296.0 - name: Run e2e tests uses: databrickslabs/sandbox/acceptance@3313d06ce86227537b3f37f5974f7eecb2a8e59a # acceptance/v0.4.4 @@ -198,6 +184,9 @@ jobs: if: github.event_name == 'pull_request' && !github.event.pull_request.draft && !github.event.pull_request.head.repo.fork environment: tool runs-on: larger + permissions: + id-token: write + pull-requests: write env: DATABRICKS_SERVERLESS_COMPUTE_ID: auto steps: @@ -206,37 +195,18 @@ jobs: with: fetch-depth: 0 - - name: Install Python - uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6.2.0 + - name: Setup uv + uses: astral-sh/setup-uv@5a095e7a2014a4212f075830d4f7277575a9d098 # v7.3.1 with: - cache: 'pip' - cache-dependency-path: '**/pyproject.toml' - python-version: '3.12' - - - name: Install hatch - run: pip install hatch==1.16.5 + version: "0.11.2" + checksum: "7ac2ca0449c8d68dae9b99e635cd3bc9b22a4cb1de64b7c43716398447d42981" - - name: Install dbt - run: | - pip install dbt-core==1.10.9 dbt-databricks==1.10.9 + - name: Setup for JFrog + uses: ./.github/actions/jfrog-auth + # Required for DAB (Databricks Asset Bundle) e2e tests - name: Install Databricks CLI - run: | - curl -fsSL https://raw.githubusercontent.com/databricks/setup-cli/main/install.sh | sh - databricks --version - - - name: Azure login (OIDC) - uses: azure/login@a457da9ea143d694b1b9c7c869ebb04ebe844ef5 # v2.3.0 - with: - client-id: ${{ secrets.ARM_CLIENT_ID }} - tenant-id: ${{ secrets.ARM_TENANT_ID }} - allow-no-subscriptions: true - - - name: Set env vars for Azure CLI auth - run: | - val=$(az keyvault secret show --id "${{ secrets.VAULT_URI }}/secrets/DATABRICKS-HOST" --query value -o tsv) - echo "DATABRICKS_HOST=$val" >> $GITHUB_ENV - echo "DATABRICKS_AUTH_TYPE=azure-cli" >> $GITHUB_ENV + uses: databricks/setup-cli@acd0e77a1ed7f15f528faca1e1f7f5590bcfdff8 # v0.296.0 - name: Run e2e tests on serverless cluster uses: databrickslabs/sandbox/acceptance@3313d06ce86227537b3f37f5974f7eecb2a8e59a # acceptance/v0.4.4 diff --git a/.github/workflows/anomaly.yml b/.github/workflows/anomaly.yml index 6534b07f4..ed65953db 100644 --- a/.github/workflows/anomaly.yml +++ b/.github/workflows/anomaly.yml @@ -10,9 +10,7 @@ on: - main permissions: - id-token: write contents: read - pull-requests: write concurrency: group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} @@ -23,78 +21,52 @@ jobs: if: github.event_name == 'pull_request' && !github.event.pull_request.draft && !github.event.pull_request.head.repo.fork environment: tool runs-on: larger + permissions: + id-token: write + pull-requests: write steps: - name: Checkout Code uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2 with: fetch-depth: 0 - - name: Install Python - uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6.2.0 + - name: Setup uv + uses: astral-sh/setup-uv@5a095e7a2014a4212f075830d4f7277575a9d098 # v7.3.1 with: - cache: 'pip' - cache-dependency-path: '**/pyproject.toml' - python-version: '3.12' + version: "0.11.2" + checksum: "7ac2ca0449c8d68dae9b99e635cd3bc9b22a4cb1de64b7c43716398447d42981" - - name: Install hatch - run: pip install hatch==1.16.5 + - name: Setup for JFrog + uses: ./.github/actions/jfrog-auth - # Anomaly tests are run from within tests/integration_anomaly folder. # Create .coveragerc with correct relative path to source code. - name: Prepare code coverage configuration for anomaly tests run: | cat > tests/integration_anomaly/.coveragerc << EOF [run] - source = ../../../src - parallel = true + source = ../../src relative_files = true EOF - - name: Azure login (OIDC) - uses: azure/login@a457da9ea143d694b1b9c7c869ebb04ebe844ef5 # v2.3.0 + - name: Run anomaly integration tests + uses: databrickslabs/sandbox/acceptance@3313d06ce86227537b3f37f5974f7eecb2a8e59a # acceptance/v0.4.4 with: - client-id: ${{ secrets.ARM_CLIENT_ID }} - tenant-id: ${{ secrets.ARM_TENANT_ID }} - allow-no-subscriptions: true - - - name: Set env vars for Azure CLI auth + MLflow - shell: bash - run: | - val=$(az keyvault secret show --id "${{ secrets.VAULT_URI }}/secrets/DATABRICKS-HOST" --query value -o tsv) - # Ensure host has https:// prefix (SDK and MLflow expect full URL) - if [[ ! "$val" =~ ^https?:// ]]; then - val="https://$val" - fi - # Workaround for MLflow OIDC auth: MLflow requires a profile to exist even when it uses SDK auth. - dummy_profile="${RUNNER_TEMP}/databricks_profile" - cat > "$dummy_profile" << EOF - [DEFAULT] - host = $val - token = dummy - EOF - echo "DATABRICKS_HOST=$val" >> $GITHUB_ENV - # Set cluster ID without printing to logs - echo "DATABRICKS_CLUSTER_ID=$(az keyvault secret show --id "${{ secrets.VAULT_URI }}/secrets/DATABRICKS-CLUSTER-ID" --query value -o tsv)" >> $GITHUB_ENV - echo "DATABRICKS_DQX_CLUSTER_ID=$(az keyvault secret show --id "${{ secrets.VAULT_URI }}/secrets/DATABRICKS-DQX-CLUSTER-ID" --query value -o tsv)" >> $GITHUB_ENV - - # Set warehouse ID without printing to logs - echo "DATABRICKS_WAREHOUSE_ID=$(az keyvault secret show --id "${{ secrets.VAULT_URI }}/secrets/DATABRICKS-WAREHOUSE-ID" --query value -o tsv)" >> $GITHUB_ENV - echo "DATABRICKS_AUTH_TYPE=azure-cli" >> $GITHUB_ENV - echo "DATABRICKS_CONFIG_FILE=$dummy_profile" >> $GITHUB_ENV - - # MLflow: Use databricks scheme so MLflow uses SDK auth (with dummy profile present). - echo "MLFLOW_ENABLE_DB_SDK=true" >> $GITHUB_ENV - echo "MLFLOW_TRACKING_URI=databricks" >> $GITHUB_ENV - echo "MLFLOW_REGISTRY_URI=databricks-uc" >> $GITHUB_ENV - echo "MLFLOW_HTTP_REQUEST_TIMEOUT=600" >> $GITHUB_ENV - echo "MLFLOW_HTTP_REQUEST_MAX_RETRIES=10" >> $GITHUB_ENV - - - name: Run anomaly integration tests and generate test coverage report - timeout-minutes: 200 + vault_uri: ${{ secrets.VAULT_URI }} + timeout: 3h + codegen_path: tests/integration_anomaly/.codegen.json env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + ARM_CLIENT_ID: ${{ secrets.ARM_CLIENT_ID }} + ARM_TENANT_ID: ${{ secrets.ARM_TENANT_ID }} COVERAGE_FILE: ${{ github.workspace }}/.coverage - run: | - hatch run pytest tests/integration_anomaly/ -v -rs -n 10 --cov --cov-report=xml --timeout=2000 --reruns 1 --reruns-delay 5 --durations=10 + MLFLOW_ENABLE_DB_SDK: "true" + MLFLOW_TRACKING_URI: databricks + MLFLOW_REGISTRY_URI: databricks-uc + MLFLOW_HTTP_REQUEST_TIMEOUT: "600" + MLFLOW_HTTP_REQUEST_MAX_RETRIES: "10" + + - name: Merge coverage reports and convert them to XML + run: make combine-coverage # Recursively search the entire workspace directory for all coverage reports. # All uploaded test coverage reports will be used even if publish is done multiple time. @@ -107,75 +79,53 @@ jobs: if: github.event_name == 'pull_request' && !github.event.pull_request.draft && !github.event.pull_request.head.repo.fork environment: tool runs-on: larger + permissions: + id-token: write + pull-requests: write steps: - name: Checkout Code uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2 with: fetch-depth: 0 - - name: Install Python - uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6.2.0 + - name: Setup uv + uses: astral-sh/setup-uv@5a095e7a2014a4212f075830d4f7277575a9d098 # v7.3.1 with: - cache: 'pip' - cache-dependency-path: '**/pyproject.toml' - python-version: '3.12' + version: "0.11.2" + checksum: "7ac2ca0449c8d68dae9b99e635cd3bc9b22a4cb1de64b7c43716398447d42981" - - name: Install hatch - run: pip install hatch==1.16.5 + - name: Setup for JFrog + uses: ./.github/actions/jfrog-auth - # Anomaly tests are run from within tests/integration_anomaly folder. # Create .coveragerc with correct relative path to source code. - name: Prepare code coverage configuration for anomaly tests run: | cat > tests/integration_anomaly/.coveragerc << EOF [run] - source = ../../../src - parallel = true + source = ../../src relative_files = true EOF - - name: Azure login (OIDC) - uses: azure/login@a457da9ea143d694b1b9c7c869ebb04ebe844ef5 # v2.3.0 - with: - client-id: ${{ secrets.ARM_CLIENT_ID }} - tenant-id: ${{ secrets.ARM_TENANT_ID }} - allow-no-subscriptions: true - - - name: Set env vars for Azure CLI auth + MLflow - shell: bash - run: | - val=$(az keyvault secret show --id "${{ secrets.VAULT_URI }}/secrets/DATABRICKS-HOST" --query value -o tsv) - # Ensure host has https:// prefix (SDK and MLflow expect full URL) - if [[ ! "$val" =~ ^https?:// ]]; then - val="https://$val" - fi - # Workaround for MLflow OIDC auth: MLflow requires a profile to exist even when it uses SDK auth. - dummy_profile="${RUNNER_TEMP}/databricks_profile" - cat > "$dummy_profile" << EOF - [DEFAULT] - host = $val - token = dummy - EOF - echo "DATABRICKS_HOST=$val" >> $GITHUB_ENV - echo "DATABRICKS_AUTH_TYPE=azure-cli" >> $GITHUB_ENV - echo "DATABRICKS_CONFIG_FILE=$dummy_profile" >> $GITHUB_ENV - # Set warehouse ID without printing to logs - echo "DATABRICKS_WAREHOUSE_ID=$(az keyvault secret show --id "${{ secrets.VAULT_URI }}/secrets/DATABRICKS-WAREHOUSE-ID" --query value -o tsv)" >> $GITHUB_ENV - - # MLflow: Use databricks scheme so MLflow uses SDK auth (with dummy profile present). - echo "MLFLOW_ENABLE_DB_SDK=true" >> $GITHUB_ENV - echo "MLFLOW_TRACKING_URI=databricks" >> $GITHUB_ENV - echo "MLFLOW_REGISTRY_URI=databricks-uc" >> $GITHUB_ENV - echo "MLFLOW_HTTP_REQUEST_TIMEOUT=600" >> $GITHUB_ENV - echo "MLFLOW_HTTP_REQUEST_MAX_RETRIES=10" >> $GITHUB_ENV - - name: Run anomaly integration tests on serverless cluster - timeout-minutes: 200 + uses: databrickslabs/sandbox/acceptance@3313d06ce86227537b3f37f5974f7eecb2a8e59a # acceptance/v0.4.4 + with: + vault_uri: ${{ secrets.VAULT_URI }} + timeout: 3h + codegen_path: tests/integration_anomaly/.codegen.json env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + ARM_CLIENT_ID: ${{ secrets.ARM_CLIENT_ID }} + ARM_TENANT_ID: ${{ secrets.ARM_TENANT_ID }} COVERAGE_FILE: ${{ github.workspace }}/.coverage DATABRICKS_SERVERLESS_COMPUTE_ID: auto - run: | - hatch run pytest tests/integration_anomaly/ -v -rs -n 10 --cov --cov-report=xml --timeout=2000 --reruns 1 --reruns-delay 5 --durations=10 + MLFLOW_ENABLE_DB_SDK: "true" + MLFLOW_TRACKING_URI: databricks + MLFLOW_REGISTRY_URI: databricks-uc + MLFLOW_HTTP_REQUEST_TIMEOUT: "600" + MLFLOW_HTTP_REQUEST_MAX_RETRIES: "10" + + - name: Merge coverage reports and convert them to XML + run: make combine-coverage # collects all coverage reports - name: Publish test coverage diff --git a/.github/workflows/check-if-commit-signed.yml b/.github/workflows/check-if-commit-signed.yml index ff35510a4..5d606350f 100644 --- a/.github/workflows/check-if-commit-signed.yml +++ b/.github/workflows/check-if-commit-signed.yml @@ -1,6 +1,9 @@ name: Check signed commits in PR on: pull_request_target +permissions: + contents: read + jobs: check-signed-commits: name: Check signed commits in PR diff --git a/.github/workflows/docs-release.yml b/.github/workflows/docs-release.yml index b1d08b48a..2c9586bf8 100644 --- a/.github/workflows/docs-release.yml +++ b/.github/workflows/docs-release.yml @@ -1,10 +1,13 @@ name: Docs Release -on: - push: - tags: - - 'v[0-9]+.[0-9]+.[0-9]+' # Must match semantic version tags like 'v1.2.3' - workflow_dispatch: # Allows manual triggering of the workflow +on: {} +# push: +# tags: +# - 'v[0-9]+.[0-9]+.[0-9]+' # Must match semantic version tags like 'v1.2.3' +# workflow_dispatch: # Allows manual triggering of the workflow + +permissions: + contents: read jobs: build: @@ -13,22 +16,23 @@ jobs: group: databrickslabs-protected-runner-group labels: linux-ubuntu-latest environment: release + permissions: + # Access to JFrog via OIDC on IT-managed runners. + id-token: write steps: - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2 with: fetch-depth: 0 - - name: Install Python - uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6.2.0 + - name: Setup uv + uses: astral-sh/setup-uv@5a095e7a2014a4212f075830d4f7277575a9d098 # v7.3.1 with: - cache: 'pip' - cache-dependency-path: '**/pyproject.toml' - python-version: '3.12' + version: "0.11.2" + checksum: "7ac2ca0449c8d68dae9b99e635cd3bc9b22a4cb1de64b7c43716398447d42981" - - name: Install Hatch - run: | - pip install hatch==1.16.5 + - name: Setup for JFrog + uses: ./.github/actions/jfrog-auth - uses: actions/setup-node@53b83947a5a98c8d113130e565377fae1a50d02f # v6.3.0 with: @@ -54,7 +58,7 @@ jobs: run: working-directory: docs/dqx - # Grant GITHUB_TOKEN the permissions required to make a Pages deployment + # Grant GITHUB_TOKEN the permissions required to make a Pages deployment permissions: pages: write # to deploy to Pages id-token: write # to verify the deployment originates from an appropriate source @@ -70,4 +74,4 @@ jobs: steps: - name: Deploy to GitHub Pages id: deployment - uses: actions/deploy-pages@d6db90164ac5ed86f2b6aed7e0febac5b3c0c03e # v4.0.5 \ No newline at end of file + uses: actions/deploy-pages@d6db90164ac5ed86f2b6aed7e0febac5b3c0c03e # v4.0.5 diff --git a/.github/workflows/nightly.yml b/.github/workflows/nightly.yml index adbb5e869..bc22af6e6 100644 --- a/.github/workflows/nightly.yml +++ b/.github/workflows/nightly.yml @@ -6,10 +6,7 @@ on: - cron: '0 4 * * *' # Runs automatically at 4:00 AM UTC every day permissions: - id-token: write - issues: write contents: read - pull-requests: read concurrency: group: single-acceptance-job-per-repo @@ -19,21 +16,23 @@ jobs: integration: environment: tool runs-on: larger + permissions: + id-token: write + pull-requests: read steps: - name: Checkout Code uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2 with: fetch-depth: 0 - - name: Install Python - uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6.2.0 + - name: Setup uv + uses: astral-sh/setup-uv@5a095e7a2014a4212f075830d4f7277575a9d098 # v7.3.1 with: - cache: 'pip' - cache-dependency-path: '**/pyproject.toml' - python-version: '3.12' + version: "0.11.2" + checksum: "7ac2ca0449c8d68dae9b99e635cd3bc9b22a4cb1de64b7c43716398447d42981" - - name: Install hatch - run: pip install hatch==1.16.5 + - name: Setup for JFrog + uses: ./.github/actions/jfrog-auth - name: Run unit tests and generate test coverage report run: make test @@ -64,8 +63,7 @@ jobs: COVERAGE_FILE: ${{ github.workspace }}/.coverage # make sure the coverage report is preserved - name: Merge coverage reports and convert them to XML - run: | - hatch run combine_coverage + run: make combine-coverage # collects all coverage reports - name: Publish test coverage @@ -76,6 +74,9 @@ jobs: integration_serverless: environment: tool runs-on: larger + permissions: + id-token: write + pull-requests: read env: DATABRICKS_SERVERLESS_COMPUTE_ID: auto steps: @@ -84,15 +85,14 @@ jobs: with: fetch-depth: 0 - - name: Install Python - uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6.2.0 + - name: Setup uv + uses: astral-sh/setup-uv@5a095e7a2014a4212f075830d4f7277575a9d098 # v7.3.1 with: - cache: 'pip' - cache-dependency-path: '**/pyproject.toml' - python-version: '3.12' + version: "0.11.2" + checksum: "7ac2ca0449c8d68dae9b99e635cd3bc9b22a4cb1de64b7c43716398447d42981" - - name: Install hatch - run: pip install hatch==1.16.5 + - name: Setup for JFrog + uses: ./.github/actions/jfrog-auth # Integration tests are run from within tests/integration folder. # Create .coveragerc with correct relative path to source code. @@ -119,8 +119,7 @@ jobs: COVERAGE_FILE: ${{ github.workspace }}/.coverage # make sure the coverage report is preserved - name: Merge coverage reports and convert them to XML - run: | - hatch run combine_coverage + run: make combine-coverage # collects all coverage reports - name: Publish test coverage @@ -131,80 +130,54 @@ jobs: anomaly-tests: environment: tool runs-on: larger + permissions: + id-token: write + pull-requests: read steps: - name: Checkout Code uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2 with: fetch-depth: 0 - - name: Install Python - uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6.2.0 + - name: Setup uv + uses: astral-sh/setup-uv@5a095e7a2014a4212f075830d4f7277575a9d098 # v7.3.1 with: - cache: 'pip' - cache-dependency-path: '**/pyproject.toml' - python-version: '3.12' + version: "0.11.2" + checksum: "7ac2ca0449c8d68dae9b99e635cd3bc9b22a4cb1de64b7c43716398447d42981" - - name: Install hatch - run: pip install hatch==1.16.5 + - name: Setup for JFrog + uses: ./.github/actions/jfrog-auth - # Anomaly tests are run from within tests/integration_anomaly folder. # Create .coveragerc with correct relative path to source code. - name: Prepare code coverage configuration for anomaly tests run: | cat > tests/integration_anomaly/.coveragerc << EOF [run] - source = ../../../src - parallel = true + source = ../../src relative_files = true EOF - - name: Azure login (OIDC) - uses: azure/login@a457da9ea143d694b1b9c7c869ebb04ebe844ef5 # v2.3.0 + - name: Run anomaly integration tests + uses: databrickslabs/sandbox/acceptance@3313d06ce86227537b3f37f5974f7eecb2a8e59a # acceptance/v0.4.4 with: - client-id: ${{ secrets.ARM_CLIENT_ID }} - tenant-id: ${{ secrets.ARM_TENANT_ID }} - allow-no-subscriptions: true - - - name: Set env vars for Azure CLI auth + MLflow - shell: bash - run: | - val=$(az keyvault secret show --id "${{ secrets.VAULT_URI }}/secrets/DATABRICKS-HOST" --query value -o tsv) - # Ensure host has https:// prefix (SDK and MLflow expect full URL) - if [[ ! "$val" =~ ^https?:// ]]; then - val="https://$val" - fi - # Workaround for MLflow OIDC auth: MLflow requires a profile to exist even when it uses SDK auth. - dummy_profile="${RUNNER_TEMP}/databricks_profile" - cat > "$dummy_profile" << EOF - [DEFAULT] - host = $val - token = dummy - EOF - echo "DATABRICKS_HOST=$val" >> $GITHUB_ENV - # Set cluster ID without printing to logs - echo "DATABRICKS_CLUSTER_ID=$(az keyvault secret show --id "${{ secrets.VAULT_URI }}/secrets/DATABRICKS-CLUSTER-ID" --query value -o tsv)" >> $GITHUB_ENV - echo "DATABRICKS_DQX_CLUSTER_ID=$(az keyvault secret show --id "${{ secrets.VAULT_URI }}/secrets/DATABRICKS-DQX-CLUSTER-ID" --query value -o tsv)" >> $GITHUB_ENV - # Set warehouse ID without printing to logs - echo "DATABRICKS_WAREHOUSE_ID=$(az keyvault secret show --id "${{ secrets.VAULT_URI }}/secrets/DATABRICKS-WAREHOUSE-ID" --query value -o tsv)" >> $GITHUB_ENV - echo "DATABRICKS_AUTH_TYPE=azure-cli" >> $GITHUB_ENV - echo "DATABRICKS_CONFIG_FILE=$dummy_profile" >> $GITHUB_ENV - - # MLflow: Use databricks scheme so MLflow uses SDK auth (with dummy profile present). - echo "MLFLOW_ENABLE_DB_SDK=true" >> $GITHUB_ENV - echo "MLFLOW_TRACKING_URI=databricks" >> $GITHUB_ENV - echo "MLFLOW_REGISTRY_URI=databricks-uc" >> $GITHUB_ENV - echo "MLFLOW_HTTP_REQUEST_TIMEOUT=600" >> $GITHUB_ENV - echo "MLFLOW_HTTP_REQUEST_MAX_RETRIES=10" >> $GITHUB_ENV - - - name: Run anomaly integration tests and generate test coverage report - timeout-minutes: 200 + vault_uri: ${{ secrets.VAULT_URI }} + timeout: 3h + create_issues: false + codegen_path: tests/integration_anomaly/.codegen.json env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + ARM_CLIENT_ID: ${{ secrets.ARM_CLIENT_ID }} + ARM_TENANT_ID: ${{ secrets.ARM_TENANT_ID }} COVERAGE_FILE: ${{ github.workspace }}/.coverage - run: | - hatch run pytest tests/integration_anomaly/ -v -rs -n 10 --cov --cov-report=xml --timeout=2000 --reruns 1 --reruns-delay 5 --durations=10 + MLFLOW_ENABLE_DB_SDK: "true" + MLFLOW_TRACKING_URI: databricks + MLFLOW_REGISTRY_URI: databricks-uc + MLFLOW_HTTP_REQUEST_TIMEOUT: "600" + MLFLOW_HTTP_REQUEST_MAX_RETRIES: "10" + + - name: Merge coverage reports and convert them to XML + run: make combine-coverage - # Recursively search the entire workspace directory for all coverage reports. - # All uploaded test coverage reports will be used even if publish is done multiple time. - name: Publish test coverage uses: codecov/codecov-action@b9fd7d16f6d7d1b5d2bec1a2887e65ceed900238 # v4.6.0 with: @@ -213,77 +186,55 @@ jobs: anomaly-tests-serverless: environment: tool runs-on: larger + permissions: + id-token: write + pull-requests: read steps: - name: Checkout Code uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2 with: fetch-depth: 0 - - name: Install Python - uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6.2.0 + - name: Setup uv + uses: astral-sh/setup-uv@5a095e7a2014a4212f075830d4f7277575a9d098 # v7.3.1 with: - cache: 'pip' - cache-dependency-path: '**/pyproject.toml' - python-version: '3.12' + version: "0.11.2" + checksum: "7ac2ca0449c8d68dae9b99e635cd3bc9b22a4cb1de64b7c43716398447d42981" - - name: Install hatch - run: pip install hatch==1.16.5 + - name: Setup for JFrog + uses: ./.github/actions/jfrog-auth - # Anomaly tests are run from within tests/integration_anomaly folder. # Create .coveragerc with correct relative path to source code. - name: Prepare code coverage configuration for anomaly tests run: | cat > tests/integration_anomaly/.coveragerc << EOF [run] - source = ../../../src - parallel = true + source = ../../src relative_files = true EOF - - name: Azure login (OIDC) - uses: azure/login@a457da9ea143d694b1b9c7c869ebb04ebe844ef5 # v2.3.0 - with: - client-id: ${{ secrets.ARM_CLIENT_ID }} - tenant-id: ${{ secrets.ARM_TENANT_ID }} - allow-no-subscriptions: true - - - name: Set env vars for Azure CLI auth + MLflow - shell: bash - run: | - val=$(az keyvault secret show --id "${{ secrets.VAULT_URI }}/secrets/DATABRICKS-HOST" --query value -o tsv) - # Ensure host has https:// prefix (SDK and MLflow expect full URL) - if [[ ! "$val" =~ ^https?:// ]]; then - val="https://$val" - fi - # Workaround for MLflow OIDC auth: MLflow requires a profile to exist even when it uses SDK auth. - dummy_profile="${RUNNER_TEMP}/databricks_profile" - cat > "$dummy_profile" << EOF - [DEFAULT] - host = $val - token = dummy - EOF - echo "DATABRICKS_HOST=$val" >> $GITHUB_ENV - echo "DATABRICKS_AUTH_TYPE=azure-cli" >> $GITHUB_ENV - echo "DATABRICKS_CONFIG_FILE=$dummy_profile" >> $GITHUB_ENV - # Set warehouse ID without printing to logs - echo "DATABRICKS_WAREHOUSE_ID=$(az keyvault secret show --id "${{ secrets.VAULT_URI }}/secrets/DATABRICKS-WAREHOUSE-ID" --query value -o tsv)" >> $GITHUB_ENV - - # MLflow: Use databricks scheme so MLflow uses SDK auth (with dummy profile present). - echo "MLFLOW_ENABLE_DB_SDK=true" >> $GITHUB_ENV - echo "MLFLOW_TRACKING_URI=databricks" >> $GITHUB_ENV - echo "MLFLOW_REGISTRY_URI=databricks-uc" >> $GITHUB_ENV - echo "MLFLOW_HTTP_REQUEST_TIMEOUT=600" >> $GITHUB_ENV - echo "MLFLOW_HTTP_REQUEST_MAX_RETRIES=10" >> $GITHUB_ENV - - name: Run anomaly integration tests on serverless cluster - timeout-minutes: 200 + uses: databrickslabs/sandbox/acceptance@3313d06ce86227537b3f37f5974f7eecb2a8e59a # acceptance/v0.4.4 + with: + vault_uri: ${{ secrets.VAULT_URI }} + timeout: 3h + create_issues: false + codegen_path: tests/integration_anomaly/.codegen.json env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + ARM_CLIENT_ID: ${{ secrets.ARM_CLIENT_ID }} + ARM_TENANT_ID: ${{ secrets.ARM_TENANT_ID }} COVERAGE_FILE: ${{ github.workspace }}/.coverage DATABRICKS_SERVERLESS_COMPUTE_ID: auto - run: | - hatch run pytest tests/integration_anomaly/ -v -rs -n 10 --cov --cov-report=xml --timeout=2000 --reruns 1 --reruns-delay 5 --durations=10 + MLFLOW_ENABLE_DB_SDK: "true" + MLFLOW_TRACKING_URI: databricks + MLFLOW_REGISTRY_URI: databricks-uc + MLFLOW_HTTP_REQUEST_TIMEOUT: "600" + MLFLOW_HTTP_REQUEST_MAX_RETRIES: "10" + + - name: Merge coverage reports and convert them to XML + run: make combine-coverage - # collects all coverage reports - name: Publish test coverage uses: codecov/codecov-action@b9fd7d16f6d7d1b5d2bec1a2887e65ceed900238 # v4.6.0 with: @@ -292,43 +243,27 @@ jobs: e2e: environment: tool runs-on: larger + permissions: + id-token: write + pull-requests: read steps: - name: Checkout Code uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2 with: fetch-depth: 0 - - name: Install Python - uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6.2.0 + - name: Setup uv + uses: astral-sh/setup-uv@5a095e7a2014a4212f075830d4f7277575a9d098 # v7.3.1 with: - cache: 'pip' - cache-dependency-path: '**/pyproject.toml' - python-version: '3.12' + version: "0.11.2" + checksum: "7ac2ca0449c8d68dae9b99e635cd3bc9b22a4cb1de64b7c43716398447d42981" - - name: Install hatch - run: pip install hatch==1.16.5 - - - name: Install dbt - run: | - pip install dbt-core==1.10.9 dbt-databricks==1.10.9 + - name: Setup for JFrog + uses: ./.github/actions/jfrog-auth + # Required for DAB (Databricks Asset Bundle) e2e tests - name: Install Databricks CLI - run: | - curl -fsSL https://raw.githubusercontent.com/databricks/setup-cli/main/install.sh | sh - databricks --version - - - name: Azure login (OIDC) - uses: azure/login@a457da9ea143d694b1b9c7c869ebb04ebe844ef5 # v2.3.0 - with: - client-id: ${{ secrets.ARM_CLIENT_ID }} - tenant-id: ${{ secrets.ARM_TENANT_ID }} - allow-no-subscriptions: true - - - name: Set env vars for Azure CLI auth - run: | - val=$(az keyvault secret show --id "${{ secrets.VAULT_URI }}/secrets/DATABRICKS-HOST" --query value -o tsv) - echo "DATABRICKS_HOST=$val" >> $GITHUB_ENV - echo "DATABRICKS_AUTH_TYPE=azure-cli" >> $GITHUB_ENV + uses: databricks/setup-cli@acd0e77a1ed7f15f528faca1e1f7f5590bcfdff8 # v0.296.0 - name: Run e2e tests uses: databrickslabs/sandbox/acceptance@3313d06ce86227537b3f37f5974f7eecb2a8e59a # acceptance/v0.4.4 @@ -345,6 +280,9 @@ jobs: e2e_serverless: environment: tool runs-on: larger + permissions: + id-token: write + pull-requests: read env: DATABRICKS_SERVERLESS_COMPUTE_ID: auto steps: @@ -353,37 +291,18 @@ jobs: with: fetch-depth: 0 - - name: Install Python - uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6.2.0 + - name: Setup uv + uses: astral-sh/setup-uv@5a095e7a2014a4212f075830d4f7277575a9d098 # v7.3.1 with: - cache: 'pip' - cache-dependency-path: '**/pyproject.toml' - python-version: '3.12' + version: "0.11.2" + checksum: "7ac2ca0449c8d68dae9b99e635cd3bc9b22a4cb1de64b7c43716398447d42981" - - name: Install hatch - run: pip install hatch==1.16.5 + - name: Setup for JFrog + uses: ./.github/actions/jfrog-auth - - name: Install dbt - run: | - pip install dbt-core==1.10.9 dbt-databricks==1.10.9 - + # Required for DAB (Databricks Asset Bundle) e2e tests - name: Install Databricks CLI - run: | - curl -fsSL https://raw.githubusercontent.com/databricks/setup-cli/main/install.sh | sh - databricks --version - - - name: Azure login (OIDC) - uses: azure/login@a457da9ea143d694b1b9c7c869ebb04ebe844ef5 # v2.3.0 - with: - client-id: ${{ secrets.ARM_CLIENT_ID }} - tenant-id: ${{ secrets.ARM_TENANT_ID }} - allow-no-subscriptions: true - - - name: Set env vars for Azure CLI auth - run: | - val=$(az keyvault secret show --id "${{ secrets.VAULT_URI }}/secrets/DATABRICKS-HOST" --query value -o tsv) - echo "DATABRICKS_HOST=$val" >> $GITHUB_ENV - echo "DATABRICKS_AUTH_TYPE=azure-cli" >> $GITHUB_ENV + uses: databricks/setup-cli@acd0e77a1ed7f15f528faca1e1f7f5590bcfdff8 # v0.296.0 - name: Run e2e tests on serverless cluster uses: databrickslabs/sandbox/acceptance@3313d06ce86227537b3f37f5974f7eecb2a8e59a # acceptance/v0.4.4 @@ -398,27 +317,39 @@ jobs: ARM_TENANT_ID: ${{ secrets.ARM_TENANT_ID }} DATABRICKS_SERVERLESS_COMPUTE_ID: ${{ env.DATABRICKS_SERVERLESS_COMPUTE_ID }} + # Performance benchmarks: + # 1. run tests and generate a new perf baseline + # 2. merge it with the existing one + # 3. compare against it to detect regressions (>25% mean degradation) + # 4. if the baseline changed and new benchmarks are added or removed, open a PR in the repo + # 5. PR commit is unsigned — a maintainer must pull, sign, and push before merging benchmark: + permissions: + id-token: write + contents: write # required for creating benchmark baseline branches + pull-requests: write # required for creating benchmark baseline PRs environment: tool runs-on: larger env: DATABRICKS_SERVERLESS_COMPUTE_ID: auto BENCHMARKS_DIR: tests/perf/.benchmarks + FINAL_BASELINE: tests/perf/.benchmarks/baseline.json + UPDATED_BASELINE: tests/perf/.benchmarks/baseline_updated.json + NEW_BASELINE: tests/perf/.benchmarks/baseline_new.json steps: - name: Checkout Code uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2 with: fetch-depth: 0 - - name: Install Python - uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6.2.0 + - name: Setup uv + uses: astral-sh/setup-uv@5a095e7a2014a4212f075830d4f7277575a9d098 # v7.3.1 with: - cache: 'pip' - cache-dependency-path: '**/pyproject.toml' - python-version: '3.12' + version: "0.11.2" + checksum: "7ac2ca0449c8d68dae9b99e635cd3bc9b22a4cb1de64b7c43716398447d42981" - - name: Install hatch - run: pip install hatch==1.16.5 + - name: Setup for JFrog + uses: ./.github/actions/jfrog-auth - name: Login to Azure for azure-cli authentication uses: azure/login@a457da9ea143d694b1b9c7c869ebb04ebe844ef5 # v2.3.0 @@ -427,6 +358,172 @@ jobs: tenant-id: ${{ secrets.ARM_TENANT_ID }} allow-no-subscriptions: true + - name: Generate new benchmark baseline + timeout-minutes: 120 + env: + DATABRICKS_SERVERLESS_COMPUTE_ID: ${{ env.DATABRICKS_SERVERLESS_COMPUTE_ID }} + run: | + export DATABRICKS_HOST=$(az keyvault secret show --id "${{ secrets.VAULT_URI }}/secrets/DATABRICKS-HOST" --query value -o tsv) + export DATABRICKS_AUTH_TYPE=azure-cli + # We don't use the acceptance action here as it may finish before all benchmark files are written. + # Tests are run sequentially to reduce variability. + # Create baseline benchmarks under: $BENCHMARKS_DIR//*_baseline.json + # Do at least 5 rounds to get more stable results. + UV_FROZEN=1 uv run --all-extras pytest tests/perf -v -n 1 \ + --benchmark-storage=$BENCHMARKS_DIR \ + --benchmark-save=baseline \ + --benchmark-min-rounds=5 + + - name: Update benchmark baseline + run: | + echo "----- Benchmark Files -----" + find $BENCHMARKS_DIR + + # find the benchmark subfolder dynamically + BENCHMARK_SUBDIR=$(find $BENCHMARKS_DIR -mindepth 1 -maxdepth 1 -type d | head -n1) + echo "New benchmarks located in sub-folder '$BENCHMARK_SUBDIR'" + + # combine individual benchmark files into a single file for easier comparison later + mkdir -p "$(dirname "$NEW_BASELINE")" + jq -s ' + .[0] * { + benchmarks: ( + map(.benchmarks) | add | sort_by(.fullname) + ) + } + ' $BENCHMARK_SUBDIR/*_baseline.json > $NEW_BASELINE + + # remove the subfolder to avoid confusion + rm -rf $BENCHMARK_SUBDIR + + # If baseline.json does not exist -> just use the new baseline_new.json as baseline. + # If baseline.json exists -> merge the two with this logic: + # * If a test exists only in old baseline -> remove it (since it's missing in new run). + # * If a test exists only in new -> add it. + # * If a test exists in both -> keep the old one (no overwrite). + if [ ! -f "$FINAL_BASELINE" ]; then + echo "No baseline exists, using new baseline" + cp $NEW_BASELINE $FINAL_BASELINE + else + echo "Merging new baseline into existing baseline" + + # Determine added/removed benchmarks + ADDED=$(jq -s ' + (.[0].benchmarks // [] | map(.fullname)) as $old + | (.[1].benchmarks // [] | map(.fullname)) as $new + | ($new - $old)[]? + ' $FINAL_BASELINE $NEW_BASELINE) + + REMOVED=$(jq -s ' + (.[0].benchmarks // [] | map(.fullname)) as $old + | (.[1].benchmarks // [] | map(.fullname)) as $new + | ($old - $new)[]? + ' $FINAL_BASELINE $NEW_BASELINE) + + # Print info about added/removed + if [ -n "$ADDED" ]; then + echo "Added benchmarks:" + echo "$ADDED" + fi + + if [ -n "$REMOVED" ]; then + echo "Removed benchmarks:" + echo "$REMOVED" + fi + + # Merge: prefer existing benchmarks if they exist, add new, remove missing + jq -s ' + (.[0].benchmarks // [] | map({(.fullname): .}) | add) as $old + | (.[1].benchmarks // [] | map({(.fullname): .}) | add) as $new + | (.[0] + { + benchmarks: + ( + ($new | keys_unsorted) as $keys + | [ $keys[] | + if ($old[.] != null) then $old[.] else $new[.] end + ] + ) + }) + ' $FINAL_BASELINE $NEW_BASELINE > $UPDATED_BASELINE + + # Replace the old baseline with the updated one + rm -rf $FINAL_BASELINE + cp $UPDATED_BASELINE $FINAL_BASELINE + fi + + echo "----- Benchmark Files -----" + find $BENCHMARKS_DIR + + echo "Updating performance benchmark report" + OUT=$(UV_FROZEN=1 uv run --all-extras python tests/perf/generate_md_report.py | grep REPORT_PATH | cut -d '=' -f2) + echo "Benchmark report updated at $OUT" + echo "BENCHMARK_REPORT=$OUT" >> $GITHUB_ENV + + # For troubleshooting only + - name: Upload baseline artifacts + uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4.6.2 + with: + name: baseline-artifacts + path: | + ${{ env.FINAL_BASELINE }} + ${{ env.NEW_BASELINE }} + ${{ env.UPDATED_BASELINE }} + + - name: Create PR with updated baseline if changed + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + run: | + git config user.name "github-actions[bot]" + git config user.email "github-actions[bot]@users.noreply.github.com" + + # Stage baseline and report + git add $FINAL_BASELINE || true + git add $BENCHMARK_REPORT || true + + # Check if there are actual changes + if git diff --cached --quiet; then + echo "No changes to benchmark baseline or report, skipping PR creation" + else + BRANCH_NAME="update-benchmark-baseline-$(date +%Y%m%d)" + + git checkout -b "$BRANCH_NAME" + + # Note: this commit is NOT GPG-signed. + # The reviewer should pull, sign the commit, and push to trigger CI and satisfy branch protection. + git commit -m "Update pytest-benchmark performance baseline" + + git remote set-url origin "https://x-access-token:${GITHUB_TOKEN}@github.com/${GITHUB_REPOSITORY}.git" + git push origin "$BRANCH_NAME" --force-with-lease + + # Create a PR only if one doesn't already exist for this branch + EXISTING_PR=$(gh pr list --head "$BRANCH_NAME" --state open --json number --jq '.[0].number') + if [ -z "$EXISTING_PR" ]; then + gh pr create \ + --title "Update performance benchmark baseline" \ + --body "$(cat <<'EOF' + ## Summary + - Updated `tests/perf/.benchmarks/baseline.json` with latest nightly benchmark results + - Regenerated `docs/dqx/docs/reference/benchmarks.mdx` report + + ## Action required + This commit is **not GPG-signed** (created by GitHub Actions). + To merge, pull this branch, sign the commit, and push: + ```bash + git fetch origin + git checkout + git commit --amend --no-edit # re-signs with your GPG key + git push --force-with-lease + ``` + + This pull request was created automatically by the nightly performance workflow. + EOF + )" \ + --base main + else + echo "PR #$EXISTING_PR already exists for $BRANCH_NAME, force-pushed updated baseline" + fi + fi + - name: Run performance tests and compare with baseline timeout-minutes: 120 env: @@ -435,11 +532,15 @@ jobs: export DATABRICKS_HOST=$(az keyvault secret show --id "${{ secrets.VAULT_URI }}/secrets/DATABRICKS-HOST" --query value -o tsv) export DATABRICKS_AUTH_TYPE=azure-cli + # remove the new and updated baseline files to avoid confusion, only FINAL_BASELINE is needed for comparison + rm -rf $NEW_BASELINE + rm -rf $UPDATED_BASELINE + # We are not using acceptance action as it does not show the comparison results. # The run fails if performance degrades by more than 25%. # Tests are run sequentially to reduce variability. # Do at least 5 rounds to get more stable results. - hatch run pytest tests/perf -v -n 1 \ + UV_FROZEN=1 uv run --all-extras pytest tests/perf -v -n 1 \ --benchmark-storage=$BENCHMARKS_DIR \ --benchmark-compare=baseline \ --benchmark-compare-fail=mean:25% \ diff --git a/.github/workflows/no-cheat.yml b/.github/workflows/no-cheat.yml index 349d4acce..5ea2b1d14 100644 --- a/.github/workflows/no-cheat.yml +++ b/.github/workflows/no-cheat.yml @@ -10,9 +10,14 @@ on: branches: - main +permissions: + contents: read + jobs: no-pylint-disable: - runs-on: ubuntu-latest + runs-on: + group: databrickslabs-protected-runner-group + labels: linux-ubuntu-latest if: github.event_name == 'pull_request' && (github.event.action == 'opened' || github.event.action == 'synchronize') steps: - name: Checkout diff --git a/.github/workflows/performance.yml b/.github/workflows/performance.yml deleted file mode 100644 index a3d21e924..000000000 --- a/.github/workflows/performance.yml +++ /dev/null @@ -1,212 +0,0 @@ -name: performance - -on: - pull_request: - types: [ opened, synchronize, ready_for_review ] - merge_group: - types: [ checks_requested ] - push: - branches: - - main - -permissions: - id-token: write - contents: write # <-- required for pushing benchmark baseline changes - pull-requests: write - -concurrency: - group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} - cancel-in-progress: true - -jobs: - benchmark: - if: github.event_name == 'pull_request' && !github.event.pull_request.draft && !github.event.pull_request.head.repo.fork - environment: tool - runs-on: larger - env: - DATABRICKS_SERVERLESS_COMPUTE_ID: auto - BENCHMARKS_DIR: tests/perf/.benchmarks - FINAL_BASELINE: tests/perf/.benchmarks/baseline.json - UPDATED_BASELINE: tests/perf/.benchmarks/baseline_updated.json - NEW_BASELINE: tests/perf/.benchmarks/baseline_new.json - steps: - - name: Checkout - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2 - with: - fetch-depth: 0 - - - name: Set up Python - uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6.2.0 - with: - python-version: '3.12' - cache: pip - cache-dependency-path: '**/pyproject.toml' - - - name: Install hatch - run: pip install hatch==1.16.5 - - - name: Login to Azure for azure-cli authentication - uses: azure/login@a457da9ea143d694b1b9c7c869ebb04ebe844ef5 # v2.3.0 - with: - client-id: ${{ secrets.ARM_CLIENT_ID }} - tenant-id: ${{ secrets.ARM_TENANT_ID }} - allow-no-subscriptions: true - - - name: Generate new benchmark baseline - timeout-minutes: 120 - env: - DATABRICKS_SERVERLESS_COMPUTE_ID: ${{ env.DATABRICKS_SERVERLESS_COMPUTE_ID }} - run: | - export DATABRICKS_HOST=$(az keyvault secret show --id "${{ secrets.VAULT_URI }}/secrets/DATABRICKS-HOST" --query value -o tsv) - export DATABRICKS_AUTH_TYPE=azure-cli - - # We don't use the acceptance action here as it may finish before all benchmark files are written. - # The run fails if performance degrades by more than 25%. - # Tests are run sequentially to reduce variability. - # Create baseline benchmarks under: $BENCHMARKS_DIR//*_baseline.json - # Do at least 5 rounds to get more stable results. - hatch run pytest tests/perf -v -n 1 \ - --benchmark-storage=$BENCHMARKS_DIR \ - --benchmark-save=baseline \ - --benchmark-min-rounds=5 - - - name: Update benchmark baseline - run: | - echo "----- Benchmark Files -----" - find $BENCHMARKS_DIR - - # find the benchmark subfolder dynamically - BENCHMARK_SUBDIR=$(find $BENCHMARKS_DIR -mindepth 1 -maxdepth 1 -type d | head -n1) - echo "New benchmarks located in sub-folder '$BENCHMARK_SUBDIR'" - - # combine individual benchmark files into a single file for easier comparison later - mkdir -p "$(dirname "$NEW_BASELINE")" - jq -s ' - .[0] * { - benchmarks: ( - map(.benchmarks) | add | sort_by(.fullname) - ) - } - ' $BENCHMARK_SUBDIR/*_baseline.json > $NEW_BASELINE - - # remove the subfolder to avoid confusion - rm -rf $BENCHMARK_SUBDIR - - # If baseline.json does not exist → just use the new baseline_new.json as baseline. - # If baseline.json exists → merge the two with this logic: - # * If a test exists only in old baseline → remove it (since it’s missing in new run). - # * If a test exists only in new → add it. - # * If a test exists in both → keep the old one (no overwrite). - if [ ! -f "$FINAL_BASELINE" ]; then - echo "No baseline exists, using new baseline" - cp $NEW_BASELINE $FINAL_BASELINE - else - echo "Merging new baseline into existing baseline" - - # Determine added/removed benchmarks - ADDED=$(jq -s ' - (.[0].benchmarks // [] | map(.fullname)) as $old - | (.[1].benchmarks // [] | map(.fullname)) as $new - | ($new - $old)[]? - ' $FINAL_BASELINE $NEW_BASELINE) - - REMOVED=$(jq -s ' - (.[0].benchmarks // [] | map(.fullname)) as $old - | (.[1].benchmarks // [] | map(.fullname)) as $new - | ($old - $new)[]? - ' $FINAL_BASELINE $NEW_BASELINE) - - # Print info about added/removed - if [ -n "$ADDED" ]; then - echo "🔹 Added benchmarks:" - echo "$ADDED" - fi - - if [ -n "$REMOVED" ]; then - echo "🔸 Removed benchmarks:" - echo "$REMOVED" - fi - - # Merge: prefer existing benchmarks if they exist, add new, remove missing - jq -s ' - (.[0].benchmarks // [] | map({(.fullname): .}) | add) as $old - | (.[1].benchmarks // [] | map({(.fullname): .}) | add) as $new - | (.[0] + { - benchmarks: - ( - ($new | keys_unsorted) as $keys - | [ $keys[] | - if ($old[.] != null) then $old[.] else $new[.] end - ] - ) - }) - ' $FINAL_BASELINE $NEW_BASELINE > $UPDATED_BASELINE - - # Replace the old baseline with the updated one - rm -rf $FINAL_BASELINE - cp $UPDATED_BASELINE $FINAL_BASELINE - fi - - echo "----- Benchmark Files -----" - find $BENCHMARKS_DIR - - echo "Updating performance benchmark report" - OUT=$(hatch run python tests/perf/generate_md_report.py | grep REPORT_PATH | cut -d '=' -f2) - echo "Benchmark report updated at $OUT" - echo "BENCHMARK_REPORT=$OUT" >> $GITHUB_ENV - - # For troubleshooting only - - name: Upload baseline artifacts - uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4.6.2 - with: - name: baseline-artifacts - path: | - ${{ env.FINAL_BASELINE }} - ${{ env.NEW_BASELINE }} - ${{ env.UPDATED_BASELINE }} - - - name: Commit benchmark baseline to the current branch - env: - GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} - run: | - git config user.name "${{ github.actor }}" - git config user.email "${{ github.actor }}@users.noreply.github.com" - - # Make sure we are on the PR branch - git fetch origin ${{ github.head_ref }} - git checkout ${{ github.head_ref }} - - # Stage changes if there are any - git add $FINAL_BASELINE || echo "No changes to add" - git add $BENCHMARK_REPORT || echo "No changes to add" - - # Commit changes if there are any - # Note: this does not sign the commit! - # User should sing and re-apply the commit before PR is merged - git commit -m "Add pytest-benchmark performance baseline" || echo "No changes to commit" - - # Push back to the same branch if there are changes - git remote set-url origin "https://x-access-token:${GITHUB_TOKEN}@github.com/${GITHUB_REPOSITORY}.git" - git push origin HEAD:${{ github.head_ref }} || echo "No changes to push" - - - name: Run performance tests and compare with baseline - timeout-minutes: 120 - env: - DATABRICKS_SERVERLESS_COMPUTE_ID: ${{ env.DATABRICKS_SERVERLESS_COMPUTE_ID }} - run: | - export DATABRICKS_HOST=$(az keyvault secret show --id "${{ secrets.VAULT_URI }}/secrets/DATABRICKS-HOST" --query value -o tsv) - export DATABRICKS_AUTH_TYPE=azure-cli - - # remove the new and updated baseline files to avoid confusion, only FINAL_BASELINE is needed for comparison - rm -rf $NEW_BASELINE - rm -rf $UPDATED_BASELINE - - # We are not using acceptance action as it does not show the comparison results. - # The run fails if performance degrades by more than 25%. - # Tests are run sequentially to reduce variability. - # Do at least 5 rounds to get more stable results. - hatch run pytest tests/perf -v -n 1 \ - --benchmark-storage=$BENCHMARKS_DIR \ - --benchmark-compare=baseline \ - --benchmark-compare-fail=mean:25% \ - --benchmark-min-rounds=5 diff --git a/.github/workflows/push.yml b/.github/workflows/push.yml index 7c8e07642..19d14446d 100644 --- a/.github/workflows/push.yml +++ b/.github/workflows/push.yml @@ -15,37 +15,56 @@ on: - main permissions: - id-token: write + contents: read + jobs: ci: strategy: fail-fast: false matrix: pyVersion: [ '3.10', '3.11', '3.12' ] - runs-on: ubuntu-latest - + runs-on: + group: databrickslabs-protected-runner-group + labels: linux-ubuntu-latest + permissions: + id-token: write + env: + UV_PYTHON: ${{ matrix.pyVersion }} steps: - name: Checkout uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2 - - name: Install Python - uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6.2.0 + - name: Setup uv + uses: astral-sh/setup-uv@5a095e7a2014a4212f075830d4f7277575a9d098 # v7.3.1 with: - cache: 'pip' - cache-dependency-path: '**/pyproject.toml' - python-version: ${{ matrix.pyVersion }} + version: "0.11.2" + checksum: "7ac2ca0449c8d68dae9b99e635cd3bc9b22a4cb1de64b7c43716398447d42981" + + - name: Setup for JFrog + uses: ./.github/actions/jfrog-auth - name: Run unit tests - run: | - pip install hatch==1.16.5 - make test + run: make test fmt: - runs-on: ubuntu-latest + runs-on: + group: databrickslabs-protected-runner-group + labels: linux-ubuntu-latest + permissions: + id-token: write steps: - name: Checkout uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2 + - name: Setup uv + uses: astral-sh/setup-uv@5a095e7a2014a4212f075830d4f7277575a9d098 # v7.3.1 + with: + version: "0.11.2" + checksum: "7ac2ca0449c8d68dae9b99e635cd3bc9b22a4cb1de64b7c43716398447d42981" + + - name: Setup for JFrog + uses: ./.github/actions/jfrog-auth + - name: Format all files run: make dev fmt @@ -54,22 +73,24 @@ jobs: docs: name: Build Docusaurus - runs-on: ubuntu-latest + runs-on: + group: databrickslabs-protected-runner-group + labels: linux-ubuntu-latest + permissions: + id-token: write steps: - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2 with: fetch-depth: 0 - - name: Install Python - uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6.2.0 + - name: Setup uv + uses: astral-sh/setup-uv@5a095e7a2014a4212f075830d4f7277575a9d098 # v7.3.1 with: - cache: 'pip' - cache-dependency-path: '**/pyproject.toml' - python-version: '3.12' + version: "0.11.2" + checksum: "7ac2ca0449c8d68dae9b99e635cd3bc9b22a4cb1de64b7c43716398447d42981" - - name: Install Hatch - run: | - pip install hatch==1.16.5 + - name: Setup for JFrog + uses: ./.github/actions/jfrog-auth - uses: actions/setup-node@53b83947a5a98c8d113130e565377fae1a50d02f # v6.3.0 with: @@ -85,29 +106,30 @@ jobs: app: name: Build and Check App - runs-on: ubuntu-latest + runs-on: + group: databrickslabs-protected-runner-group + labels: linux-ubuntu-latest + permissions: + id-token: write defaults: run: working-directory: app steps: - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2 - - name: Install Python - uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6.2.0 + - name: Setup uv + uses: astral-sh/setup-uv@5a095e7a2014a4212f075830d4f7277575a9d098 # v7.3.1 with: - cache: 'pip' - cache-dependency-path: '**/pyproject.toml' - python-version: '3.12' + version: "0.11.2" + checksum: "7ac2ca0449c8d68dae9b99e635cd3bc9b22a4cb1de64b7c43716398447d42981" - - name: Install uv - uses: astral-sh/setup-uv@d4b2f3b6ecc6e67c4457f6d3e41ec42d3d0fcb86 # v5.4.2 - with: - version: "latest" + - name: Setup for JFrog + uses: ./.github/actions/jfrog-auth - name: Install Bun uses: oven-sh/setup-bun@ecf28ddc73e819eb6fa29df6b34ef8921c743461 # v2.1.3 with: - bun-version: latest + bun-version: "1.3.11" - name: Install JavaScript dependencies run: bun install diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 0c44b21e3..c5727438d 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -1,9 +1,10 @@ name: Release -on: - push: - tags: - - 'v[0-9]+.[0-9]+.[0-9]+' # Must match semantic version tags like 'v1.2.3' +on: {} +# Disabled for now, pending further work. +# push: +# tags: +# - 'v[0-9]+.[0-9]+.[0-9]+' # Must match semantic version tags like 'v1.2.3' jobs: publish: @@ -19,17 +20,18 @@ jobs: steps: - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2 - - uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6.2.0 + - name: Setup uv + uses: astral-sh/setup-uv@5a095e7a2014a4212f075830d4f7277575a9d098 # v7.3.1 with: - cache: 'pip' - cache-dependency-path: '**/pyproject.toml' - python-version: '3.12' - + version: "0.11.2" + checksum: "7ac2ca0449c8d68dae9b99e635cd3bc9b22a4cb1de64b7c43716398447d42981" + + - name: Setup for JFrog + uses: ./.github/actions/jfrog-auth + - name: Build wheels - run: | - pip install hatch==1.16.5 - hatch build - + run: make build + - name: Github release uses: softprops/action-gh-release@a06a81a03ee405af7f2048a818ed3f03bbf83c7b # v2.5.0 with: @@ -39,11 +41,11 @@ jobs: - uses: pypa/gh-action-pypi-publish@ed0c53931b1dc9bd32cbe73a98c7f6766f8a527e # v1.13.0 name: Publish package distributions to PyPI - + - name: Sign artifacts with Sigstore uses: sigstore/gh-action-sigstore-python@f514d46b907ebcd5bedc05145c03b69c1edd8b46 # v3.0.0 with: inputs: | dist/databricks_*.whl dist/databricks_*.tar.gz - release-signing-artifacts: true \ No newline at end of file + release-signing-artifacts: true diff --git a/.gitignore b/.gitignore index 48e303fd6..4238951fd 100644 --- a/.gitignore +++ b/.gitignore @@ -168,7 +168,6 @@ dev/cleanup.py .databricks .vscode -.python-version .databricks-login.json .local-dev diff --git a/.python-version b/.python-version new file mode 100644 index 000000000..e4fba2183 --- /dev/null +++ b/.python-version @@ -0,0 +1 @@ +3.12 diff --git a/Makefile b/Makefile index 129820709..0bdeffff4 100644 --- a/Makefile +++ b/Makefile @@ -1,57 +1,76 @@ -all: clean dev lint fmt test integration perf coverage e2e +all: clean lint fmt test coverage + +# Prevent uv from modifying the lock file. UV_FROZEN skips resolution entirely, +# which is required because the lock file uses public PyPI URLs while the actual +# index may be an internal proxy. Use `make lock-dependencies` to update the lock file. +export UV_FROZEN := 1 +# Ensure that hatchling is pinned when builds are needed. +export UV_BUILD_CONSTRAINT := .build-constraints.txt + +UV_RUN := uv run --exact --all-extras +UV_TEST := $(UV_RUN) pytest -n 10 --timeout 60 --durations 20 clean: docs-clean rm -fr .venv clean htmlcov .mypy_cache .pytest_cache .ruff_cache .coverage coverage.xml ./mlruns - rm -fr **/*.pyc + find . -name '__pycache__' -print0 | xargs -0 rm -fr -.venv/bin/python: - pip install hatch 'virtualenv<21' - hatch env create - hatch run pip install ".[llm,pii,datacontract,anomaly]" - -dev: .venv/bin/python - @hatch run which python +dev: + uv sync --all-extras lint: - hatch run verify + $(UV_RUN) black --check . + $(UV_RUN) ruff check . + $(UV_RUN) mypy . + $(UV_RUN) pylint --output-format=colorized -j 0 src tests fmt: - hatch run fmt - hatch run update_github_urls + $(UV_RUN) black . + $(UV_RUN) ruff check . --fix + $(UV_RUN) mypy . + $(UV_RUN) pylint --output-format=colorized -j 0 src tests + $(UV_RUN) python docs/dqx/update_github_urls.py test: - hatch run test + $(UV_TEST) --cov --cov-report=xml:coverage-unit.xml tests/unit/ integration: - hatch run integration + $(UV_TEST) --timeout 1200 --cov --cov-report=xml tests/integration/ e2e: - hatch run e2e + $(UV_TEST) --timeout 1200 --cov --cov-report=xml tests/e2e/ perf: - hatch run perf + $(UV_TEST) --timeout 600 --cov --cov-report=xml tests/perf/ + +anomaly: + $(UV_RUN) pytest tests/integration_anomaly/ -v -n 10 --timeout 1200 --durations 20 --reruns 2 --reruns-delay 5 coverage: - hatch run test_coverage; open htmlcov/index.html + $(UV_TEST) --ignore=tests/e2e --ignore=tests/perf --cov --cov-report=html tests/ + open htmlcov/index.html + +combine-coverage: + $(UV_RUN) coverage combine + $(UV_RUN) coverage xml -o coverage-integration.xml + $(UV_RUN) coverage erase docs-build: - hatch run docs:pydoc-markdown + $(UV_RUN) --group docs pydoc-markdown yarn --cwd docs/dqx build docs-serve-dev: - hatch run docs:pydoc-markdown + $(UV_RUN) --group docs pydoc-markdown yarn --cwd docs/dqx start docs-install: yarn --cwd docs/dqx install --frozen-lockfile docs-serve: docs-build - hatch run docs:pydoc-markdown + $(UV_RUN) --group docs pydoc-markdown yarn --cwd docs/dqx serve docs-clean: - rm -rf docs/dqx/build - rm -rf docs/dqx/.docusaurus docs/dqx/.cache + rm -rf docs/dqx/build docs/dqx/.docusaurus docs/dqx/.cache find docs/dqx/docs/reference/api -mindepth 1 -not -name 'index.mdx' -exec rm -rf {} + # Sync fork PR to branch in main repo for CI testing (acceptance/anomaly/perf run on non-fork PRs). @@ -59,3 +78,17 @@ docs-clean: fork-sync: @test -n "$(PR)" || (echo "Usage: make fork-sync PR="; exit 1) ./.github/scripts/fork-sync-pr.sh $(PR) + +build: + uv build --require-hashes --build-constraints=.build-constraints.txt + +lock-dependencies: export UV_FROZEN := 0 +lock-dependencies: + uv lock + $(UV_RUN) --group yq tomlq -r '.["build-system"].requires[]' pyproject.toml | \ + uv pip compile --generate-hashes --universal --no-header - > build-constraints-new.txt + mv build-constraints-new.txt .build-constraints.txt + perl -pi -e 's|registry = "https://[^"]*"|registry = "https://pypi.org/simple"|g' uv.lock + +.DEFAULT: all +.PHONY: all clean dev lint fmt test integration e2e perf anomaly coverage combine-coverage docs-build docs-serve-dev docs-install docs-serve docs-clean fork-sync build lock-dependencies diff --git a/app/.gitignore b/app/.gitignore index 715ae30e3..a8d60a75b 100644 --- a/app/.gitignore +++ b/app/.gitignore @@ -35,6 +35,3 @@ node_modules/ # apx .apx/ - -# lock files -uv.lock \ No newline at end of file diff --git a/demos/dqx_demo_industry/dqx_banking_demo.py b/demos/dqx_demo_industry/dqx_banking_demo.py index c08d4a527..dfd4b4e58 100644 --- a/demos/dqx_demo_industry/dqx_banking_demo.py +++ b/demos/dqx_demo_industry/dqx_banking_demo.py @@ -58,6 +58,22 @@ # COMMAND ---------- +import os + +workspace_root_path = os.getcwd() +quality_rules_path = f"{workspace_root_path}/quality_checks" + +# Cleanup existing DQ Rules files, if already exists +if os.path.exists(quality_rules_path): + for filename in os.listdir(quality_rules_path): + file_path = os.path.join(quality_rules_path, filename) + # Only delete files, not subdirectories + if os.path.isfile(file_path): + os.remove(file_path) + print(f"Deleted: {file_path}") + +# COMMAND ---------- + # MAGIC %md # MAGIC ### Production Best Practice: Pin DQX Version # MAGIC @@ -140,27 +156,147 @@ Row(transaction_id="TRX-804", account_id="ACC-PL-024", country="POL", transaction_type="DEPOSIT", currency="EUR", amount=1456.88, timestamp="2024-03-20 15:45:00"), ] -transactions_df = spark.createDataFrame(transactions) -display(transactions_df) +transactions_table = f"{catalog}.{schema}.banking_transactions" + +if spark.catalog.tableExists(transactions_table) and spark.table(transactions_table).count() > 0: + print(f"Table {transactions_table} already exists with demo data. Skipping data generation") +else: + transactions_df = spark.createDataFrame(transactions) + transactions_df.write.mode("overwrite").saveAsTable(transactions_table) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC ### Understanding the Dataset +# MAGIC +# MAGIC ### Banking Transactions Dataset +# MAGIC +# MAGIC | Column Name | Data Type | Description | Example Value | +# MAGIC |--------------------|-----------|------------------------------------------------------|------------------------| +# MAGIC | `transaction_id` | string | Unique transaction identifier | TRX-101 | +# MAGIC | `account_id` | string | Account identifier | ACC-US-001 | +# MAGIC | `country` | string | Country code (ISO 3166-1 alpha-3) | USA | +# MAGIC | `transaction_type` | string | Type of transaction (CREDIT, DEBIT, DEPOSIT, etc.) | CREDIT | +# MAGIC | `currency` | string | Currency code (ISO 4217) | USD | +# MAGIC | `amount` | double | Transaction amount (positive for credits, negative for debits) | 1247.89 | +# MAGIC | `timestamp` | string | Transaction timestamp | 2024-03-15 10:30:00 | + +# COMMAND ---------- + +# MAGIC %md +# MAGIC ### Some Sample Transaction Data + +# COMMAND ---------- + +# DBTITLE 1,Transactions Bronze Table +transactions_df = spark.read.table(transactions_table) +print("=== Transactions Data Sample ===") +display(transactions_df.limit(10)) + +# COMMAND ---------- + +# DBTITLE 1,Common Imports +import yaml +from pprint import pprint + +from databricks.sdk import WorkspaceClient +from databricks.labs.dqx.profiler.profiler import DQProfiler +from databricks.labs.dqx.profiler.generator import DQGenerator +from databricks.labs.dqx.engine import DQEngine +from databricks.labs.dqx.config import WorkspaceFileChecksStorageConfig, TableChecksStorageConfig # COMMAND ---------- # MAGIC %md -# MAGIC -# MAGIC ### Defining Financial Data Quality Checks +# MAGIC ### Auto-Infer Quality Rules with DQProfiler # MAGIC -# MAGIC We define our industry-specific checks using a YAML configuration. This is the recommended approach for DQX as it separates rule definitions from logic. +# MAGIC Before defining rules manually, DQX can **automatically infer data quality rules** from the data profile. This is useful for bootstrapping a quality rule set for a new dataset. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC **Step 1**: Read raw data and instantiate DQX + +# COMMAND ---------- + +# Read Input Data +transactions_df = spark.read.table(transactions_table) + +# Instantiate DQX engine +ws = WorkspaceClient() +dq_engine = DQEngine(ws) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC **Step 2**: Run DQProfiler to infer quality rules + +# COMMAND ---------- + +# DBTITLE 1,Profile Data and Infer Quality Rules +profiler = DQProfiler(ws) +summary_stats, profiles = profiler.profile(transactions_df) + +generator = DQGenerator(ws) +inferred_checks = generator.generate_dq_rules(profiles) + +print("=== Inferred DQ Checks ===\n") +for idx, check in enumerate(inferred_checks): + print(f"========Check {idx} ==========\n") + pprint(check) + +# COMMAND ---------- + +# DBTITLE 1,Save Inferred Rules to Workspace File +banking_inferred_rules_yaml = f"{quality_rules_path}/banking_inferred_dq_rules.yml" +dq_engine.save_checks(inferred_checks, config=WorkspaceFileChecksStorageConfig(location=banking_inferred_rules_yaml)) +displayHTML(f'Banking Inferred Quality Rules YAML') + +# COMMAND ---------- + +# DBTITLE 1,Save Inferred Rules to Delta Table +banking_inferred_rules_table = f"{catalog}.{schema}.banking_inferred_quality_rules" +dq_engine.save_checks(inferred_checks, config=TableChecksStorageConfig(location=banking_inferred_rules_table, run_config_name="banking_inferred")) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC **Step 3**: Apply Inferred Quality Rules to Input Data + +# COMMAND ---------- + +# Load checks from workspace file +inferred_quality_checks = dq_engine.load_checks(config=WorkspaceFileChecksStorageConfig(location=banking_inferred_rules_yaml)) + +# Apply checks on input data +valid_inferred_df, quarantined_inferred_df = dq_engine.apply_checks_by_metadata_and_split(transactions_df, inferred_quality_checks) + +print("=== Transactions Quarantined by Inferred Rules ===") +display(quarantined_inferred_df) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC ### Bring Your Own Rule: Suspicious Amount Pattern Detection # MAGIC -# MAGIC We also define a **custom check function** for detecting suspicious amount patterns commonly associated with financial crime and structuring: -# MAGIC * **Perfectly round amounts** above threshold (e.g., exactly $5,000, $10,000) which may indicate attempts to structure transactions -# MAGIC * **Just below $10K reporting threshold** (e.g., $9,950-$9,999) to avoid regulatory reporting -# MAGIC * **Repeated digit patterns** (e.g., $7,777.77, $3,333.33) which are statistically unlikely in legitimate transactions +# MAGIC This section demonstrates how to extend DQX with a **custom quality rule**. The workflow follows 3 steps: +# MAGIC 1. **Define** the custom rule function +# MAGIC 2. **Add** the rule to the YAML definition +# MAGIC 3. **Apply** the DQ rules on input data # MAGIC -# MAGIC These patterns are red flags for anti-money laundering (AML) and fraud detection systems. +# MAGIC For this demo, we define a custom fraud detection rule that flags suspicious amount patterns commonly associated with financial crime and structuring: +# MAGIC * **Perfectly round amounts** above threshold (e.g., exactly \$5,000, \$10,000) which may indicate attempts to structure transactions +# MAGIC * **Just below reporting thresholds** (e.g., \$9,950-\$9,999) to avoid regulatory reporting +# MAGIC * **Repeated digit patterns** (e.g., \$7,777.77, \$3,333.33) which are statistically unlikely in legitimate transactions + +# COMMAND ---------- + +# MAGIC %md +# MAGIC #### Step 1: Define the Custom Rule Function # COMMAND ---------- -# DBTITLE 1,Custom Industry Check: IBAN MOD-97 Validation +# DBTITLE 1,Custom Industry Check: Suspicious Amount Pattern Detection import pyspark.sql.functions as F from pyspark.sql import Column from databricks.labs.dqx.check_funcs import make_condition, get_normalized_column_and_expr @@ -227,7 +363,12 @@ def is_suspicious_amount_pattern( # COMMAND ---------- -import yaml +# MAGIC %md +# MAGIC #### Step 2: Add the Custom Rule to YAML Definition +# MAGIC +# MAGIC We define all quality checks — both built-in and custom — in a single YAML configuration. + +# COMMAND ---------- # Define industry-specific checks in YAML format banking_checks_yaml = f""" @@ -341,17 +482,13 @@ def is_suspicious_amount_pattern( # COMMAND ---------- -# MAGIC %md -# MAGIC ### Setup `DQEngine` - -# COMMAND ---------- - -from databricks.labs.dqx.engine import DQEngine -from databricks.labs.dqx.config import TableChecksStorageConfig -from databricks.sdk import WorkspaceClient - -ws = WorkspaceClient() # auto-authenticated inside Databricks -dq_engine = DQEngine(ws) +# DBTITLE 1,Validate Checks +status = DQEngine.validate_checks( + checks, + custom_check_functions={'is_suspicious_amount_pattern': is_suspicious_amount_pattern} +) +print(status) +assert not status.has_errors # COMMAND ---------- @@ -393,9 +530,19 @@ def is_suspicious_amount_pattern( # COMMAND ---------- +# DBTITLE 1,Save Rules to Workspace File (Alternative Storage) +# DQX also supports storing rules as workspace YAML files +banking_rules_yaml = f"{quality_rules_path}/banking_quality_rules.yml" +dq_engine.save_checks(built_in_checks, config=WorkspaceFileChecksStorageConfig(location=banking_rules_yaml)) + +# Display a link to the saved checks file +displayHTML(f'Banking Quality Rules YAML') + +# COMMAND ---------- + # MAGIC %md -# MAGIC -# MAGIC ### Apply Checks & Quarantine Invalid Records +# MAGIC +# MAGIC ### Step 3: Apply Checks & Quarantine Invalid Records # MAGIC # MAGIC We'll use `apply_checks_by_metadata_and_split` to process the **loaded** checks. # MAGIC @@ -436,4 +583,18 @@ def is_suspicious_amount_pattern( # COMMAND ---------- -display(invalid_df) \ No newline at end of file +display(invalid_df) + +# COMMAND ---------- + +# DBTITLE 1,Persist Quarantine Table +quarantine_table = f"{catalog}.{schema}.banking_transactions_quarantine" +invalid_df.write.mode("overwrite").saveAsTable(quarantine_table) +print(f"Quarantined transactions saved to {quarantine_table}") + +# COMMAND ---------- + +# MAGIC %md +# MAGIC ### Visualize Quality on Pre-Configured Dashboard +# MAGIC +# MAGIC When you deploy DQX as [`workspace tool`](https://databrickslabs.github.io/dqx/docs/installation/#dqx-installation-as-a-tool-in-a-databricks-workspace), it automatically generates a Quality Dashboard.
You can open the dashboard using Databricks CLI: `databricks labs dqx open-dashboards` diff --git a/demos/dqx_demo_industry/dqx_fashion_demo.py b/demos/dqx_demo_industry/dqx_fashion_demo.py index 45b8b6135..66fcc7b36 100644 --- a/demos/dqx_demo_industry/dqx_fashion_demo.py +++ b/demos/dqx_demo_industry/dqx_fashion_demo.py @@ -62,6 +62,22 @@ # COMMAND ---------- +import os + +workspace_root_path = os.getcwd() +quality_rules_path = f"{workspace_root_path}/quality_checks" + +# Cleanup existing DQ Rules files, if already exists +if os.path.exists(quality_rules_path): + for filename in os.listdir(quality_rules_path): + file_path = os.path.join(quality_rules_path, filename) + # Only delete files, not subdirectories + if os.path.isfile(file_path): + os.remove(file_path) + print(f"Deleted: {file_path}") + +# COMMAND ---------- + # MAGIC %md # MAGIC ### Production Best Practice: Pin DQX Version # MAGIC @@ -95,16 +111,6 @@ # MAGIC ### Setup Sample Fashion Product Catalog Data # MAGIC # MAGIC We'll create a DataFrame simulating a typical product catalog upload from vendors or a merchandising system sync. -# MAGIC -# MAGIC The dataset includes fields common in fashion/retail: -# MAGIC * **`sku`** — Stock Keeping Unit identifier following `CATEGORY-COLOR-SIZE` convention -# MAGIC * **`product_name`** — Human-readable product description -# MAGIC * **`category`** / **`brand`** — Product taxonomy and brand attribution -# MAGIC * **`size_code`** — Apparel sizing (XS–XXL, OS, numeric) -# MAGIC * **`color_hex`** — CSS hex color for storefront display -# MAGIC * **`retail_price`** / **`unit_cost`** — Pricing and cost for margin analysis -# MAGIC * **`weight_kg`** — Shipping weight for logistics -# MAGIC * **`season_code`** — Merchandising season (e.g., SS25 = Spring/Summer 2025) # COMMAND ---------- @@ -161,23 +167,139 @@ Row(sku="PARKA-OLV-XL", product_name="Expedition Parka", category="Outerwear", brand="StreetPulse", size_code="XL", color_hex="#556B2F", retail_price=275.00, unit_cost=95.00, weight_kg=1.40, season_code="FW24"), ] -products_df = spark.createDataFrame(products) -display(products_df) +products_table = f"{catalog}.{schema}.fashion_products" + +if spark.catalog.tableExists(products_table) and spark.table(products_table).count() > 0: + print(f"Table {products_table} already exists with demo data. Skipping data generation") +else: + products_df = spark.createDataFrame(products) + products_df.write.mode("overwrite").saveAsTable(products_table) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC ### Understanding the Dataset +# MAGIC +# MAGIC ### Fashion Product Catalog Dataset +# MAGIC +# MAGIC | Column Name | Data Type | Description | Example Value | +# MAGIC |----------------|-----------|----------------------------------------------------------------|-------------------| +# MAGIC | `sku` | string | Stock Keeping Unit — `CATEGORY-COLOR-SIZE` format | TSHIRT-BLK-M | +# MAGIC | `product_name` | string | Human-readable product description | Classic Crew Tee | +# MAGIC | `category` | string | Product taxonomy category | Tops | +# MAGIC | `brand` | string | Brand attribution | BasicsLab | +# MAGIC | `size_code` | string | Apparel sizing (XS–XXL, OS, or numeric like 32-L) | M | +# MAGIC | `color_hex` | string | CSS hex color for storefront display | #000000 | +# MAGIC | `retail_price` | double | Listed retail price (must be positive) | 29.99 | +# MAGIC | `unit_cost` | double | Vendor unit cost (must not exceed retail price) | 8.50 | +# MAGIC | `weight_kg` | double | Shipping weight in kilograms (0.01–25 kg) | 0.18 | +# MAGIC | `season_code` | string | Merchandising season code (e.g., SS25, FW24, AW25, RS25) | SS25 | + +# COMMAND ---------- + +# MAGIC %md +# MAGIC ### Some Sample Product Catalog Data + +# COMMAND ---------- + +# DBTITLE 1,Products Bronze Table +products_df = spark.read.table(products_table) +print("=== Product Catalog Data Sample ===") +display(products_df.limit(10)) # COMMAND ---------- # MAGIC %md +# MAGIC ### Auto-Infer Quality Rules with DQProfiler # MAGIC -# MAGIC ### Defining Fashion Product Quality Checks +# MAGIC Before defining rules manually, DQX can **automatically infer data quality rules** from the data profile. This is useful for bootstrapping a quality rule set for a new dataset. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC **Step 1**: Read raw data and instantiate DQX + +# COMMAND ---------- + +import yaml +from pprint import pprint + +from databricks.sdk import WorkspaceClient +from databricks.labs.dqx.profiler.profiler import DQProfiler +from databricks.labs.dqx.profiler.generator import DQGenerator +from databricks.labs.dqx.engine import DQEngine +from databricks.labs.dqx.config import WorkspaceFileChecksStorageConfig, TableChecksStorageConfig + +ws = WorkspaceClient() +dq_engine = DQEngine(ws) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC **Step 2**: Run DQProfiler to infer quality rules + +# COMMAND ---------- + +# DBTITLE 1,Profile Data and Infer Quality Rules +profiler = DQProfiler(ws) +summary_stats, profiles = profiler.profile(products_df) + +generator = DQGenerator(ws) +inferred_checks = generator.generate_dq_rules(profiles) + +print("=== Inferred DQ Checks ===\n") +for idx, check in enumerate(inferred_checks): + print(f"========Check {idx} ==========\n") + pprint(check) + +# COMMAND ---------- + +# DBTITLE 1,Save Inferred Rules to Workspace File +fashion_inferred_rules_yaml = f"{quality_rules_path}/fashion_inferred_dq_rules.yml" +dq_engine.save_checks(inferred_checks, config=WorkspaceFileChecksStorageConfig(location=fashion_inferred_rules_yaml)) +displayHTML(f'Fashion Inferred Quality Rules YAML') + +# COMMAND ---------- + +# DBTITLE 1,Save Inferred Rules to Delta Table +fashion_inferred_rules_table = f"{catalog}.{schema}.fashion_inferred_quality_rules" +dq_engine.save_checks(inferred_checks, config=TableChecksStorageConfig(location=fashion_inferred_rules_table, run_config_name="fashion_inferred")) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC **Step 3**: Apply Inferred Quality Rules to Input Data + +# COMMAND ---------- + +# Load checks from workspace file +inferred_quality_checks = dq_engine.load_checks(config=WorkspaceFileChecksStorageConfig(location=fashion_inferred_rules_yaml)) + +# Apply checks on input data +valid_inferred_df, quarantined_inferred_df = dq_engine.apply_checks_by_metadata_and_split(products_df, inferred_quality_checks) + +print("=== Products Quarantined by Inferred Rules ===") +display(quarantined_inferred_df) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC ### Bring Your Own Rule: Suspicious Pricing Pattern Detection # MAGIC -# MAGIC We define our industry-specific checks using a YAML configuration. This is the recommended approach for DQX as it separates rule definitions from logic. +# MAGIC This section demonstrates how to extend DQX with a **custom quality rule**. The workflow follows 3 steps: +# MAGIC 1. **Define** the custom rule function +# MAGIC 2. **Add** the rule to the YAML definition +# MAGIC 3. **Apply** the DQ rules on input data # MAGIC -# MAGIC We also define a **custom check function** for detecting suspicious pricing patterns common in retail product catalogs: -# MAGIC * **Perfectly round retail prices** (e.g., exactly $100.00, $200.00) which often indicate placeholder or draft pricing that was never finalized +# MAGIC For this demo, we define a custom pricing intelligence rule that flags suspicious pricing patterns common in retail product catalogs: +# MAGIC * **Perfectly round retail prices** (e.g., exactly \$100.00, \$200.00) which often indicate placeholder or draft pricing that was never finalized # MAGIC * **Extreme margin outliers** — margins above 95% typically indicate a data entry error (e.g., cost entered in the wrong currency) # MAGIC * **Negative margins** — unit cost exceeding retail price signals a pricing or cost error -# MAGIC -# MAGIC These patterns are red flags for merchandising and pricing compliance teams. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC #### Step 1: Define the Custom Rule Function # COMMAND ---------- @@ -238,6 +360,13 @@ def is_suspicious_pricing_pattern( # COMMAND ---------- +# MAGIC %md +# MAGIC #### Step 2: Add the Custom Rule to YAML Definition +# MAGIC +# MAGIC We define all quality checks — both built-in and custom — in a single YAML configuration. + +# COMMAND ---------- + import yaml # Define industry-specific checks in YAML format @@ -389,17 +518,13 @@ def is_suspicious_pricing_pattern( # COMMAND ---------- -# MAGIC %md -# MAGIC ### Setup `DQEngine` - -# COMMAND ---------- - -from databricks.labs.dqx.engine import DQEngine -from databricks.labs.dqx.config import TableChecksStorageConfig -from databricks.sdk import WorkspaceClient - -ws = WorkspaceClient() # auto-authenticated inside Databricks -dq_engine = DQEngine(ws) +# DBTITLE 1,Validate Checks +status = DQEngine.validate_checks( + checks, + custom_check_functions={'is_suspicious_pricing_pattern': is_suspicious_pricing_pattern} +) +print(status) +assert not status.has_errors # COMMAND ---------- @@ -441,9 +566,18 @@ def is_suspicious_pricing_pattern( # COMMAND ---------- +# DBTITLE 1,Save Rules to Workspace File (Alternative Storage) +# DQX also supports storing rules as workspace YAML files +fashion_rules_yaml = f"{quality_rules_path}/fashion_quality_rules.yml" +dq_engine.save_checks(built_in_checks, config=WorkspaceFileChecksStorageConfig(location=fashion_rules_yaml)) + +# Display a link to the saved checks file +displayHTML(f'Fashion Quality Rules YAML') + +# COMMAND ---------- + # MAGIC %md -# MAGIC -# MAGIC ### Apply Checks & Quarantine Invalid Products +# MAGIC ### Step 3: Apply Checks & Quarantine Invalid Products # MAGIC # MAGIC We'll use `apply_checks_by_metadata_and_split` to process the **loaded** checks. # MAGIC @@ -475,7 +609,7 @@ def is_suspicious_pricing_pattern( # COMMAND ---------- # MAGIC %md -# MAGIC #### ✅ Valid Products — Ready for Storefront +# MAGIC #### Valid Products — Ready for Storefront # MAGIC Products that pass all error-level checks. Warning-flagged products still appear here for downstream processing. # COMMAND ---------- @@ -485,9 +619,23 @@ def is_suspicious_pricing_pattern( # COMMAND ---------- # MAGIC %md -# MAGIC #### ❌ Invalid / Quarantined Products — Merchandising Review Queue +# MAGIC #### Invalid / Quarantined Products — Merchandising Review Queue # MAGIC Data quality issues are captured in the invalid DataFrame via the `_errors` and `_warnings` array columns mapping directly to our named checks. # COMMAND ---------- display(invalid_df) + +# COMMAND ---------- + +# DBTITLE 1,Persist Quarantine Table +quarantine_table = f"{catalog}.{schema}.fashion_products_quarantine" +invalid_df.write.mode("overwrite").saveAsTable(quarantine_table) +print(f"Quarantined products saved to {quarantine_table}") + +# COMMAND ---------- + +# MAGIC %md +# MAGIC ### Visualize Quality on Pre-Configured Dashboard +# MAGIC +# MAGIC When you deploy DQX as [`workspace tool`](https://databrickslabs.github.io/dqx/docs/installation/#dqx-installation-as-a-tool-in-a-databricks-workspace), it automatically generates a Quality Dashboard.
You can open the dashboard using Databricks CLI: `databricks labs dqx open-dashboards` diff --git a/demos/dqx_demo_industry/dqx_manufacturing_demo.py b/demos/dqx_demo_industry/dqx_manufacturing_demo.py index 1b255b698..47c19588b 100644 --- a/demos/dqx_demo_industry/dqx_manufacturing_demo.py +++ b/demos/dqx_demo_industry/dqx_manufacturing_demo.py @@ -79,6 +79,13 @@ # COMMAND ---------- +# MAGIC %md +# MAGIC ### Production Best Practice: Pin DQX Version +# MAGIC +# MAGIC To ensure consistent behavior and avoid unexpected issues from automatic upgrades, it is highly recommended to always pin DQX to a specific version in your production pipelines. + +# COMMAND ---------- + # MAGIC %md # MAGIC ### Sample Data Generation # MAGIC diff --git a/docs/dqx/docs/dev/contributing.mdx b/docs/dqx/docs/dev/contributing.mdx index 4b0f6847b..7b70bbe57 100644 --- a/docs/dqx/docs/dev/contributing.mdx +++ b/docs/dqx/docs/dev/contributing.mdx @@ -91,7 +91,7 @@ Please make sure the following items are set up correctly in your local git conf Please ensure the following DoDs are met before submitting your pull request: - [ ] **Code formatting** — Code is formatted consistently with the project style (`make fmt`). - [ ] **Linting & checks** — Code passes linting and static analysis (`make lint`). -- [ ] **Testing** — Unit, integration, e2e and performance tests cover changes and all tests pass (`make test`, `make integration`, `make e2e`, `make perf`). Follow the [test pyramid](https://martinfowler.com/articles/practical-test-pyramid.html) principles. Most changes should be covered with unit tests, supplemented by integration tests when necessary, and only rarely with end-to-end or performance tests. +- [ ] **Testing** — Unit, integration, and e2e tests cover changes and all tests pass (`make test`, `make integration`, `make e2e`). Performance tests (`make perf`) run nightly and do not need to pass before submitting a PR. Follow the [test pyramid](https://martinfowler.com/articles/practical-test-pyramid.html) principles. Most changes should be covered with unit tests, supplemented by integration tests when necessary, and only rarely with end-to-end or performance tests. - [ ] **Docstrings** — Public functions, classes, and modules include [Google Style Python Docstrings](https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html). Docstrings are clean and consistent ([guidance](#writing-docstrings)). - [ ] **Documentation** — If user-facing behavior changes, ensure docs are updated. See [docs authoring](/docs/dev/docs_authoring). - [ ] **Demos** — If applicable, update or create demos to showcase new features or changes. @@ -107,9 +107,8 @@ This is a good way to gather early feedback without needing to meet all Definiti * Draft PRs — Integration and end-to-end (e2e) tests are not run automatically on Draft PRs. These tests will only execute once the PR is marked as "Ready for Review". * Forked PRs — The following applies for PRs opened from forks (external contributors scenario): - * Project maintainers must approve Github workflows (e.g. formatting, unit testing) before they run. Project maintainers must also trigger integration, end-to-end (e2e) and performance tests. + * Project maintainers must approve Github workflows (e.g. formatting, unit testing) before they run. Project maintainers must also trigger integration and end-to-end (e2e) tests. * You can also run the tests locally using [Databricks Free Edition](https://www.databricks.com/learn/free-edition) with the exception of Lakebase tests, which require a standard workspace. When using Databricks Free you can ignore the Lakebase tests. - * The performance workflow cannot commit the benchmark baseline back to a fork branch. The baseline is instead uploaded as a workflow artifact — see [Updating Baseline and Reports](#updating-baseline-and-reports). In all cases, the full test suite is executed on code merged into `main` branch as part of the nightly CI/CD pipeline. @@ -118,22 +117,21 @@ In all cases, the full test suite is executed on code merged into `main` branch This section provides a step-by-step guide for setting up your local environment and dependencies for efficient development. -To start with, install the required python version on your computer (see `python=` field in [pyproject.toml](https://github.com/databrickslabs/dqx/blob/v0.13.0/pyproject.toml)). +To start with, install the required python version on your computer (see `requires-python` field in [pyproject.toml](https://github.com/databrickslabs/dqx/blob/v0.13.0/pyproject.toml)). On MacOSX, run: ``` # double check the required version in pyproject.toml brew install python@3.12 ``` -Install [Hatch](https://github.com/pypa/hatch), which is our build tool. +Install [uv](https://docs.astral.sh/uv/), which is our package manager and build tool. On MacOSX, this is achieved using the following: ```shell -brew install hatch +brew install uv ``` -Clone the repo and run the following command in your project’s root directory to create the default environment and install development dependencies: +Clone the repo and run the following command in your project’s root directory to install development dependencies: ```shell -hatch env create make dev ``` @@ -148,6 +146,25 @@ make lint make test ``` +### Managing dependencies + +The project uses `uv.lock` to pin exact dependency versions. The Makefile enforces `UV_FROZEN=1` by default, +which prevents `uv` from modifying the lock file during normal operations like `make dev`, `make fmt`, or `make test`. + +To add, remove, or upgrade dependencies, use the `lock-dependencies` target: +```shell +make lock-dependencies +``` + +This target: +1. Runs `uv lock` to resolve dependencies. +2. Regenerates `.build-constraints.txt` with pinned build tool hashes. +3. Normalizes registry URLs in `uv.lock` to public PyPI. + + +Always use `make` targets instead of running `uv` commands directly. The Makefile sets `UV_FROZEN=1` to protect the lock file — running `uv sync`, `uv lock`, or `uv add` directly bypasses this and may modify `uv.lock` with internal registry URLs. To update dependencies, use `make lock-dependencies`. + + ### Running integration tests and code coverage Integration tests and code coverage are run automatically when you create a Pull Request in Github (except forks). @@ -252,7 +269,7 @@ pip install git+https://github.com/your-username/dqx.git@feature_branch_name Alternatively, if you want to test before committing the changes, you can build the wheel package and upload it to the workspace manually: ```commandline # Create wheel inside dist/databricks_labs_dqx--py3-none-any.whl -hatch build +make build # Upload the wheel to the workspace and run pip install //databricks_labs_dqx--py3-none-any.whl @@ -328,21 +345,24 @@ Pre-release versions (e.g. v0.1.12-alpha) do not trigger the release pipeline, a ## Performance testing -Performance tests run automatically in two cases: -* On every Pull Request in GitHub. -* Nightly, on the main branch CI/CD pipeline. +Performance tests run automatically as part of the **nightly** CI/CD pipeline on the `main` branch. They do not run on every Pull Request. All performance tests are located in the `tests/perf` folder. The tests use the [pytest-benchmark](https://pytest-benchmark.readthedocs.io/en/latest/) package. Performance tests should focus on the critical parts of the codebase where speed and efficiency matter most, such as the check functions. +To run performance tests locally: +```shell +make perf +``` + ### Baseline Metrics Baseline performance results are stored in `tests/perf/.benchmarks/baseline.json`. These values are used to compare the performance of each new run against the baseline. The comparison allows 25% degradation before the test fails. -The baseline file is managed automatically for every PR: -* If `baseline.json` does not exist → it is created. To recreate it from scratch, delete the file in your PR. +The baseline file is managed automatically by the nightly workflow: +* If `baseline.json` does not exist → it is created. * If a test is removed → it is also removed from `baseline.json`. * If a new test is added → it is added to `baseline.json`. * If the same tests are present → the existing baseline values are kept (not overwritten). @@ -353,9 +373,9 @@ Whenever the baseline is updated, the benchmark report in the documentation is r ### Updating Baseline and Reports -Baseline and benchmark reports are committed automatically by the performance github workflow. -However, these commits are not GPG-signed because your private key is not available to GitHub. -Before merging a PR that updates the baseline or reports, you must manually sign and re-apply the commits using your local GPG key, as follows: +When the nightly workflow detects baseline changes, it automatically opens a PR with the updated baseline and benchmark report. +These commits are not GPG-signed because your private key is not available to GitHub. +Before merging, you must manually sign and re-apply the commits using your local GPG key: ```shell git reset --soft $(git merge-base origin/main HEAD) # point to the common ancestor of main and your branch #git reset --soft HEAD~ # alternatively, specify how many commits to go back @@ -363,27 +383,9 @@ git commit -S --reuse-message=ORIG_HEAD git push --force-with-lease origin HEAD ``` - -The baseline is also uploaded as a workflow artifact (`baseline-artifacts`) in the GitHub Actions run. -To apply it manually (relevant for forks where we don't auto-commit the baseline): -1. Download the `baseline-artifacts` artifact from the GitHub Actions run. -2. Copy `baseline.json` to `tests/perf/.benchmarks/baseline.json` in your branch. -3. Commit and GPG-sign it: `git commit -S -m "Add pytest-benchmark performance baseline"`. -4. Push to your fork branch. - - ## Troubleshooting -If you encounter any package dependency errors after `git pull`, run `make clean` - -### Resolving Hatch JSON TypeError - -If you encounter an error like: -```text -TypeError: the JSON object must be str, bytes or bytearray, not Sentinel -``` - -you can resolve it by downgrading the Click package to a compatible version that works with hatch: `pip install "click<8.3"` +If you encounter any package dependency errors after `git pull`, run `make clean` followed by `make dev`. ### Common fixes for `mypy` errors diff --git a/docs/dqx/package-lock.json b/docs/dqx/package-lock.json deleted file mode 100644 index 73160e68a..000000000 --- a/docs/dqx/package-lock.json +++ /dev/null @@ -1,19852 +0,0 @@ -{ - "name": "dqx", - "version": "0.0.0", - "lockfileVersion": 3, - "requires": true, - "packages": { - "": { - "name": "dqx", - "version": "0.0.0", - "dependencies": { - "@docusaurus/core": "^3.8.1", - "@docusaurus/preset-classic": "^3.8.1", - 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sha512-4LCcse/U2MHZ63HAJVE+v71o7yOdIe4cZ70Wpf8D/IyjDKYQLV5GD46B+hSTjJsvV5PztjvHoU580EftxjDZFQ== zwitch@^1.0.0: version "1.0.5" - resolved "https://registry.npmjs.org/zwitch/-/zwitch-1.0.5.tgz" integrity sha512-V50KMwwzqJV0NpZIZFwfOD5/lyny3WlSzRiXgA0G7VUnRlqttta1L6UQIHzd6EuBY/cHGfwTIck7w1yH6Q5zUw== zwitch@^2.0.0, zwitch@^2.0.4: version "2.0.4" - resolved "https://registry.npmjs.org/zwitch/-/zwitch-2.0.4.tgz" integrity sha512-bXE4cR/kVZhKZX/RjPEflHaKVhUVl85noU3v6b8apfQEc1x4A+zBxjZ4lN8LqGd6WZ3dl98pY4o717VFmoPp+A== diff --git a/pyproject.toml b/pyproject.toml index d97ac7b4f..b5e1b2c17 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -36,7 +36,7 @@ classifiers = [ ] # also update fallback dependencies in workflow installer when changing dependencies! dependencies = [ - "databricks-labs-blueprint>=0.9.1,<0.10", + "databricks-labs-blueprint>=0.9.1,<=0.12", "databricks-sdk~=0.73", "sqlalchemy>=2.0,<3.0", "PyYAML~=6.0.3", @@ -49,7 +49,7 @@ cli = [ # The databricks-connect includes pyspark, pandas and numpy. # On Windows, Build Tools for Visual Studio are required to compile numpy. # Spark in local mode is not supported with databricks-connect. - "databricks-connect~=15.4", + "databricks-connect>=15.4,<18", ] pii = [ "numpy<2.0,>=1.20", @@ -64,17 +64,18 @@ pii = [ # The models cannot be delcared as dependency here buecase PyPI does not support URL-based dependencies which would prevent releases. ] llm = [ - "dspy~=3.0.3", - "databricks-langchain>=0.1.0", - "langchain-core>=0.1.0" + "dspy~=3.1.3", + "litellm>=1.64.0,<=1.82.6", # pin dspy transitive dependency + "databricks-langchain>=0.1.0,<1.0", + "langchain-core>=0.1.0,<1.0", ] datacontract = [ - "datacontract-cli>=0.11.1" + "datacontract-cli>=0.11.1,<1.0", ] anomaly = [ - "mlflow[databricks]>=2.19.1", # Unity Catalog support + azure-cli auth type support (PR #14158) + "mlflow[databricks]>=2.19.1,<3.0", # Unity Catalog support + azure-cli auth type support "scikit-learn>=1.0,<2.0", - "cloudpickle>=2.0", # For serializing sklearn models in pandas UDFs + "cloudpickle>=3.0,<4.0", # For serializing sklearn models in pandas UDFs "shap>=0.42.0,<0.50", # For TreeSHAP-based feature contributions ] @@ -88,28 +89,40 @@ Source = "https://github.com/databrickslabs/dqx" [build-system] # The `hatch-fancy-pypi-readme` plugin is used to generate correct links # in README showed on PyPi -requires = ["hatchling>=1.9.0", "hatch-fancy-pypi-readme"] +requires = [ + "hatchling>=1.9.0,<2.0", + "hatch-fancy-pypi-readme~=25.1.0" +] build-backend = "hatchling.build" [tool.hatch.build] sources = ["src"] -include = ["src"] +include = ["src", "README.md", "LICENSE", "NOTICE"] [tool.hatch.version] path = "src/databricks/labs/dqx/__about__.py" -[tool.hatch.envs.default] -features = ["pii", "llm", "datacontract", "anomaly"] -dependencies = [ +[dependency-groups] +dev = [ + { include-group = "lint" }, + { include-group = "test" }, +] +lint = [ "black~=24.8.0", - "chispa~=0.10.1", - "coverage[toml]~=7.4.4", "databricks-labs-pylint~=0.5", - "databricks-labs-pytester~=0.7.4", "mypy~=1.9.0", "pylint~=3.3.1", "pylint-per-file-ignores~=1.3", "pylint-pytest==2.0.0a1", + "ruff~=0.3.4", + "types-PyYAML~=6.0.3", + "types-requests~=2.31.0", +] +test = [ + "pip~=26.0", # required by databricks-labs-blueprint WheelsV2 which calls `pip wheel`; remove after blueprint migrates to uv + "chispa~=0.10.1", + "coverage[toml]~=7.4.4", + "databricks-labs-pytester~=0.7.4", "pytest~=8.3.3", "pytest-cov~=4.1.0", "pytest-mock~=3.14.0", @@ -117,49 +130,26 @@ dependencies = [ "pytest-xdist~=3.5.0", "pytest-rerunfailures~=14.0", "pytest-benchmark~=5.1.0", - "ruff~=0.3.4", - "types-PyYAML~=6.0.3", - "types-requests~=2.31.0", - "databricks-connect~=17.3", # bundles spark 4.0.0 + "databricks-connect~=17.3 ; python_version >= '3.12'", # bundles spark 4.0.0 "dbldatagen~=0.4.0", "pyparsing~=3.2.3", "jmespath~=1.0.1", "psycopg2-binary~=2.9.10", - "fastapi>=0.119.0", + "fastapi>=0.119.0,<1.0", + "dbt-core~=1.11.6", + "dbt-databricks~=1.11.6", ] - -python="3.12" # must match the version required by databricks-connect and python version on the test clusters - -# store virtual env as the child of this folder. Helps VSCode (and PyCharm) to run better -path = ".venv" - -[tool.hatch.envs.default.scripts] -test = "pytest tests/unit/ -n 10 --cov --cov-report=xml:coverage-unit.xml --timeout 60 --durations 20" -test_coverage = "pytest tests/ -n 10 --ignore=tests/e2e --ignore=tests/perf --cov --cov-report=html --timeout 600 --durations 20" -# Note that integration tests are controlled by the acceptance action (e.g. running 10 tests in parallel): -# https://github.com/databrickslabs/sandbox/blob/main/acceptance/ecosystem/pytest_run.py -# This integration config is only used when running integration tests locally. -integration = "pytest tests/integration/ -n 10 --cov --cov-report=xml --timeout 1200 --durations 20" -e2e = "pytest tests/e2e/ -n 10 --cov --cov-report=xml --timeout 600 --durations 20" -perf = "pytest tests/perf/ -n 10 --cov --cov-report=xml --timeout 600 --durations 20" -anomaly = "pytest tests/integration_anomaly/ -v -n 10 --timeout 1200 --durations 20 --reruns 2 --reruns-delay 5" -fmt = ["black .", - "ruff check . --fix", - "mypy .", - "pylint --output-format=colorized -j 0 src tests"] -verify = ["black --check .", - "ruff check .", - "mypy .", - "pylint --output-format=colorized -j 0 src tests"] -update_github_urls = "python docs/dqx/update_github_urls.py" -combine_coverage = [ - "coverage combine", # combine binary coverage data files - "coverage xml -o coverage-integration.xml", # generate XML report - "coverage erase" # remove coverage data files +docs = [ + "pydoc-markdown>=4.8.2,<5.0", +] +yq = [ + "yq~=3.4.3", ] -[tool.hatch.envs.docs] -dependencies = ["pydoc-markdown>=4.8.2"] +[tool.uv] +required-version = "~=0.11.0" +exclude-newer = "7 days" +exclude-newer-package = { "databricks-sdk" = false, "databricks-connect" = false } [tool.pydoc-markdown] loaders = [ @@ -890,4 +880,4 @@ replacement = '[\1](https://github.com/databrickslabs/dqx/tree/main/\g<2>)' [[tool.hatch.metadata.hooks.fancy-pypi-readme.substitutions]] # replace relative image links with absolute links pattern = '' -replacement = '>' \ No newline at end of file +replacement = '>' diff --git a/tests/conftest.py b/tests/conftest.py index 9420beb7f..a32d4083e 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -38,6 +38,14 @@ logger = logging.getLogger(__name__) +@pytest.fixture(autouse=True, scope="session") +def _mlflow_env(): + """Ensure MLflow points at Databricks Unity Catalog when running against a workspace. + Uses setdefault so explicit env vars (e.g. in CI) take precedence.""" + os.environ.setdefault("MLFLOW_TRACKING_URI", "databricks") + os.environ.setdefault("MLFLOW_REGISTRY_URI", "databricks-uc") + + def get_schema_validation_rules(rules: list[dict[str, Any]]) -> list[dict[str, Any]]: """Return rules that are has_valid_schema schema_validation rules (for data contract tests).""" return [ @@ -69,7 +77,11 @@ def debug_env(monkeypatch, debug_env_name): conf = json.load(f) if debug_env_name in conf: for env_key, value in conf[debug_env_name].items(): - monkeypatch.setenv(env_key, str(value)) + value = str(value) + # Ensure DATABRICKS_HOST always has the https:// scheme + if env_key == "DATABRICKS_HOST" and value and not value.startswith("https://"): + value = f"https://{value}" + monkeypatch.setenv(env_key, value) return os.environ diff --git a/tests/e2e/.codegen.json b/tests/e2e/.codegen.json index a76875481..ff8becdc7 100644 --- a/tests/e2e/.codegen.json +++ b/tests/e2e/.codegen.json @@ -3,9 +3,9 @@ "src/databricks/labs/dqx/__about__.py": "__version__ = \"$VERSION\"" }, "toolchain": { - "required": ["python3", "hatch"], - "pre_setup": ["hatch env create"], + "required": ["make", "uv"], + "pre_setup": ["make dev"], "prepend_path": ".venv/bin", "acceptance_path": "tests/e2e" } -} \ No newline at end of file +} diff --git a/tests/e2e/conftest.py b/tests/e2e/conftest.py index c47df75aa..f70649d57 100644 --- a/tests/e2e/conftest.py +++ b/tests/e2e/conftest.py @@ -41,6 +41,9 @@ def _get_remote_url(branch: str) -> str | None: ).strip() if url.endswith(".git"): url = url[:-4] + # Convert SSH URLs to HTTPS so pip can use them (git+git@... is invalid) + if url.startswith("git@github.com:"): + url = url.replace("git@github.com:", "https://github.com/", 1) return url except Exception: return None diff --git a/tests/e2e/test_run_demos.py b/tests/e2e/test_run_demos.py index 94e9adc84..7bf768502 100644 --- a/tests/e2e/test_run_demos.py +++ b/tests/e2e/test_run_demos.py @@ -6,6 +6,9 @@ from pathlib import Path from uuid import uuid4 from tempfile import TemporaryDirectory + +import yaml + from databricks.sdk.service.workspace import ImportFormat from databricks.sdk.service.pipelines import NotebookLibrary, PipelinesEnvironment, PipelineLibrary from databricks.sdk.service.jobs import NotebookTask, PipelineTask, Task @@ -315,20 +318,26 @@ def test_run_dqx_streaming_demo_diy(ws, make_notebook, make_job, tmp_path, libra logging.info(f"Job run {run.run_id} completed successfully for dqx_streaming_demo") -def test_run_dqx_demo_asset_bundle(make_schema, make_random, library_ref): +def test_run_dqx_demo_asset_bundle(ws, make_schema, make_random, library_ref): cli_path = shutil.which("databricks") path = Path(__file__).parent.parent.parent / "demos" / "dqx_demo_asset_bundle" catalog = TEST_CATALOG schema = make_schema(catalog_name=catalog).name run_id = make_random(10).lower() + # Select the bundle target matching the workspace cloud + host = ws.config.host or "" + target = "azure" if "azuredatabricks.net" in host else "aws" + try: - subprocess.run([cli_path, "bundle", "validate"], check=True, capture_output=True, cwd=path) + subprocess.run([cli_path, "bundle", "validate", "-t", target], check=True, capture_output=True, cwd=path) subprocess.run( [ cli_path, "bundle", "deploy", + "-t", + target, f'--var="library_ref={library_ref}"', f'--var="demo_catalog={catalog}"', f'--var="demo_schema={schema}"', @@ -340,9 +349,13 @@ def test_run_dqx_demo_asset_bundle(make_schema, make_random, library_ref): capture_output=True, cwd=path, ) - subprocess.run([cli_path, "bundle", "run", "dqx_demo_job"], check=True, capture_output=True, cwd=path) + subprocess.run( + [cli_path, "bundle", "run", "-t", target, "dqx_demo_job"], check=True, capture_output=True, cwd=path + ) finally: - subprocess.run([cli_path, "bundle", "destroy", "--auto-approve"], check=True, capture_output=True, cwd=path) + subprocess.run( + [cli_path, "bundle", "destroy", "-t", target, "--auto-approve"], check=True, capture_output=True, cwd=path + ) def test_run_dqx_multi_table_demo(ws, make_notebook, make_schema, make_job, library_ref): @@ -480,41 +493,44 @@ def test_run_dqx_row_anomaly_detection_demo(ws, make_notebook, make_schema, make def test_dbt_demo(make_schema, make_random, library_ref, debug_env): + """Test the dbt demo project. dbt-databricks only supports token or M2M OAuth auth.""" catalog = TEST_CATALOG schema = make_schema(catalog_name=catalog).name project_dir = Path(__file__).parent.parent.parent / "demos" / "dqx_demo_dbt" - # Create a temporary directory for DBT profiles + token = debug_env.get("DATABRICKS_TOKEN") + client_id = debug_env.get("TOOLS_CLIENT_ID") + client_secret = debug_env.get("TOOLS_DATABRICKS_SECRET") + # dbt expects just the hostname, not a full URL + host = debug_env.get("DATABRICKS_HOST", "").replace("https://", "").rstrip("/") + workspace_id = debug_env.get("TEST_DEFAULT_WAREHOUSE_HTTP_PATH") + + # Build dbt profile: token auth or M2M OAuth + profile: dict[str, object] = { + "type": "databricks", + "host": host, + "http_path": workspace_id, + "catalog": catalog, + "schema": schema, + "threads": 1, + "connect_timeout": 30, + } + if client_id and client_secret: + profile["auth_type"] = "oauth" + profile["client_id"] = client_id + profile["client_secret"] = client_secret + else: + profile["token"] = token + + # Create a temporary directory for dbt profiles with TemporaryDirectory() as temp_dir: dbt_profiles_dir = Path(temp_dir) / "dbt" dbt_profiles_dir.mkdir(parents=True, exist_ok=True) - client_id = debug_env.get("TOOLS_CLIENT_ID") - client_secret = debug_env.get("TOOLS_DATABRICKS_SECRET") - token = debug_env.get("DATABRICKS_TOKEN") # for local execution - auth_type = "token" # for local execution - - if client_id and client_secret: # for CI execution - auth_type = "oauth" - - # Create the profiles.yml file - profiles_yml_content = f""" - dbt_demo: - target: ci - outputs: - ci: - type: databricks - host: "{debug_env.get("DATABRICKS_HOST")}" - http_path: "{debug_env.get("TEST_DEFAULT_WAREHOUSE_HTTP_PATH")}" - catalog: "{catalog}" - schema: "{schema}" - auth_type: {auth_type} - client_id: {client_id} - client_secret: {client_secret} - token: {token} - threads: 1 - connect_timeout: 30 - """ + profiles_yml_content = yaml.dump( + {"dbt_demo": {"target": "ci", "outputs": {"ci": profile}}}, + default_flow_style=False, + ) profiles_yml_path = dbt_profiles_dir / "profiles.yml" profiles_yml_path.write_text(profiles_yml_content.strip()) diff --git a/tests/integration/.codegen.json b/tests/integration/.codegen.json index bd11afde0..524dbcbd4 100644 --- a/tests/integration/.codegen.json +++ b/tests/integration/.codegen.json @@ -3,9 +3,9 @@ "src/databricks/labs/dqx/__about__.py": "__version__ = \"$VERSION\"" }, "toolchain": { - "required": ["python3", "hatch"], - "pre_setup": ["hatch env create"], + "required": ["make", "uv"], + "pre_setup": ["make dev"], "prepend_path": ".venv/bin", "acceptance_path": "tests/integration" } -} \ No newline at end of file +} diff --git a/tests/integration_anomaly/.codegen.json b/tests/integration_anomaly/.codegen.json new file mode 100644 index 000000000..139b61b53 --- /dev/null +++ b/tests/integration_anomaly/.codegen.json @@ -0,0 +1,11 @@ +{ + "version": { + "src/databricks/labs/dqx/__about__.py": "__version__ = \"$VERSION\"" + }, + "toolchain": { + "required": ["make", "uv"], + "pre_setup": ["make dev"], + "prepend_path": ".venv/bin", + "acceptance_path": "tests/integration_anomaly" + } +} diff --git a/tests/integration_anomaly/conftest.py b/tests/integration_anomaly/conftest.py index eca836ccf..3bf11fbcd 100644 --- a/tests/integration_anomaly/conftest.py +++ b/tests/integration_anomaly/conftest.py @@ -401,6 +401,25 @@ def _delete_mlflow_experiment_by_name(experiment_path: str) -> None: logger.warning(msg) +@pytest.fixture(autouse=True) +def _ensure_databricks_config_file(ws, tmp_path_factory): + """Ensure DATABRICKS_CONFIG_FILE points to a valid profile. + + MLflow requires a Databricks config profile to exist even when using SDK auth. + If DATABRICKS_CONFIG_FILE is already set and the file exists, this is a no-op. + Otherwise, create a dummy profile with the real host from the workspace client. + """ + config_file = os.environ.get("DATABRICKS_CONFIG_FILE") + if config_file and os.path.isfile(config_file): + return + + profile_dir = tmp_path_factory.mktemp("databricks_config") + profile_path = profile_dir / "databricks_profile" + host = ws.config.host + profile_path.write_text(f"[DEFAULT]\nhost = {host}\ntoken = dummy\n") + os.environ["DATABRICKS_CONFIG_FILE"] = str(profile_path) + + @pytest.fixture def mlflow_worker_experiment(ws): """Create one MLflow experiment per xdist worker (via module cache); reuse across tests. diff --git a/uv.lock b/uv.lock index 31c5c4bf5..bea658818 100644 --- a/uv.lock +++ b/uv.lock @@ -3,10 +3,39 @@ revision = 3 requires-python = ">=3.10" resolution-markers = [ "python_full_version >= '3.13' and sys_platform != 'win32'", + "python_full_version == '3.12.*' and sys_platform != 'win32'", "python_full_version >= '3.13' and sys_platform == 'win32'", - "python_full_version == '3.12.*'", - "python_full_version == '3.11.*'", - "python_full_version < '3.11'", + "python_full_version == '3.12.*' and sys_platform == 'win32'", + "python_full_version == '3.11.*' and sys_platform != 'win32'", + "python_full_version < '3.11' and sys_platform != 'win32'", + 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