docs: Comprehensive SAP HANA Enterprise CDC documentation update #69105
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- Restructure documentation to follow Db2 connector model - Add comprehensive CDC setup guide with Python script - Include detailed trigger-based CDC implementation explanation - Add CDC prerequisites, configuration, and behavior sections - Improve data type mapping table formatting - Add configuration reference with database parameter - Fix typos and improve clarity throughout Co-Authored-By: ian.alton@airbyte.io <ian.alton@airbyte.io>
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AI-Generated Documentation UpdateThis PR was created by Devin (AI) based on a request from ian.alton@airbyte.io to update the SAP HANA Enterprise connector documentation with comprehensive CDC setup guidance. Review Notes:
Reviewer Actions:
Devin Session: https://app.devin.ai/sessions/275c29b177454508a5b15b7945240b3e You can also review and modify this work directly in the Devin webapp IDE at the session link above. |
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| - Python 3.7 or later | ||
| - pip (Python package installer) | ||
| - Install dependencies from requirements.txt (installs hdbcli) |
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🚫 [vale] reported by reviewdog 🐶
[Vale.Spelling] Did you really mean 'hdbcli'?
| - `_ab_trigger_operation_type`: Type of operation (INSERT, UPDATE, DELETE) | ||
| - `_ab_trigger_{column}_before`: Previous value for each source column (for UPDATE/DELETE) | ||
| - `_ab_trigger_{column}_after`: New value for each source column (for INSERT/UPDATE) | ||
| **Change tracking:** The connector tracks three types of operations: |
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[Google.Colons] ': T' should be in lowercase.
| ## Reference | ||
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| ### Configuration | ||
| | `BOOLEAN` | boolean | | |
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🚫 [vale] reported by reviewdog 🐶
[Vale.Spelling] Did you really mean 'boolean'?
| | `TEXT` | string | | | ||
| | `BINTEXT` | string | | | ||
| | `DATE` | date | | | ||
| | `TIME` | time_without_timezone | | |
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🚫 [vale] reported by reviewdog 🐶
[Vale.Spelling] Did you really mean 'time_without_timezone'?
| | `BINTEXT` | string | | | ||
| | `DATE` | date | | | ||
| | `TIME` | time_without_timezone | | | ||
| | `SECONDDATE` | timestamp_without_timezone | | |
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🚫 [vale] reported by reviewdog 🐶
[Vale.Spelling] Did you really mean 'timestamp_without_timezone'?
| | `DATE` | date | | | ||
| | `TIME` | time_without_timezone | | | ||
| | `SECONDDATE` | timestamp_without_timezone | | | ||
| | `TIMESTAMP` | timestamp_without_timezone | | |
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🚫 [vale] reported by reviewdog 🐶
[Vale.Spelling] Did you really mean 'timestamp_without_timezone'?
| | `password` | string | The password associated with the username. | | | ||
| | `database` | string | The name of the tenant database to connect to. This is required for multi-tenant SAP HANA systems. For single-tenant systems, this can be left empty. | | | ||
| | `schemas` | array | The list of schemas to sync from. Defaults to user. Case sensitive. | | | ||
| | `filters` | array | Inclusion filters for table selection per schema. If no filters are specified for a schema, all tables in that schema will be synced. | | |
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[Google.Will] Avoid using 'will'.
| | `checkpoint_target_interval_seconds` | integer | How often (in seconds) a stream should checkpoint, when possible. | `300` | | ||
| | `concurrency` | integer | Maximum number of concurrent queries to the database. | `1` | | ||
| | `check_privileges` | boolean | When enabled, the connector will query each table individually to check access privileges during schema discovery. | `true` | | ||
| | `check_privileges` | boolean | When enabled, the connector will query each table individually to check access privileges during schema discovery. In large schemas, this might cause schema discovery to take too long, in which case it might be advisable to disable this feature. | `true` | |
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🚫 [vale] reported by reviewdog 🐶
[Vale.Spelling] Did you really mean 'boolean'?
| | `checkpoint_target_interval_seconds` | integer | How often (in seconds) a stream should checkpoint, when possible. | `300` | | ||
| | `concurrency` | integer | Maximum number of concurrent queries to the database. | `1` | | ||
| | `check_privileges` | boolean | When enabled, the connector will query each table individually to check access privileges during schema discovery. | `true` | | ||
| | `check_privileges` | boolean | When enabled, the connector will query each table individually to check access privileges during schema discovery. In large schemas, this might cause schema discovery to take too long, in which case it might be advisable to disable this feature. | `true` | |
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[Google.Will] Avoid using 'will'.
| | `checkpoint_target_interval_seconds` | integer | How often (in seconds) a stream should checkpoint, when possible. | `300` | | ||
| | `concurrency` | integer | Maximum number of concurrent queries to the database. | `1` | | ||
| | `check_privileges` | boolean | When enabled, the connector will query each table individually to check access privileges during schema discovery. | `true` | | ||
| | `check_privileges` | boolean | When enabled, the connector will query each table individually to check access privileges during schema discovery. In large schemas, this might cause schema discovery to take too long, in which case it might be advisable to disable this feature. | `true` | |
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[Google.WordList] Use 'turn off' or 'off' instead of 'disable'.
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| ```python | ||
| # Copyright (c) 2025 Airbyte, Inc., all rights reserved. | ||
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| import argparse | ||
| import csv | ||
| import json | ||
| import os | ||
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| from hdbcli import dbapi | ||
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| # =============================================== | ||
| # command to run this script: | ||
| # python cdc_setup_sap_hana.py --host <HOST> --port <PORT> --user <USER> --password <PASSWORD> --schema <SOURCE_SCHEMA> | ||
| # =============================================== | ||
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| def get_connection(host, port, user, password, database=None): | ||
| """Establishes a connection to SAP HANA.""" | ||
| conn = dbapi.connect( | ||
| address=host, | ||
| port=port, | ||
| user=user, | ||
| password=password, | ||
| databaseName=database if database else None | ||
| ) | ||
| return conn | ||
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| def check_cdc_schema_exists(conn, cdc_schema): | ||
| """Checks if the CDC schema exists.""" | ||
| cursor = conn.cursor() | ||
| query = "SELECT COUNT(*) FROM SYS.SCHEMAS WHERE SCHEMA_NAME = ?" | ||
| count = 0 | ||
| try: | ||
| cursor.execute(query, (cdc_schema,)) | ||
| count = cursor.fetchone()[0] | ||
| except Exception as e: | ||
| print(f"Warning: Error checking if schema exists {cdc_schema}: {e}") | ||
| finally: | ||
| cursor.close() | ||
| return count > 0 | ||
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| def create_cdc_schema(conn, cdc_schema): | ||
| """Creates the CDC schema.""" | ||
| cursor = conn.cursor() | ||
| try: | ||
| cursor.execute(f'CREATE SCHEMA "{cdc_schema}"') | ||
| conn.commit() | ||
| print(f"Successfully created CDC schema {cdc_schema}") | ||
| except Exception as e: | ||
| print(f"Error creating CDC schema {cdc_schema}: {e}") | ||
| conn.rollback() | ||
| finally: | ||
| cursor.close() | ||
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| def check_cdc_table_exists(conn, cdc_schema, cdc_table): | ||
| """Checks if the specific CDC table exists.""" | ||
| cursor = conn.cursor() | ||
| query = "SELECT COUNT(*) FROM SYS.TABLES WHERE SCHEMA_NAME = ? AND TABLE_NAME = ?" | ||
| count = 0 | ||
| try: | ||
| cursor.execute(query, (cdc_schema, cdc_table)) | ||
| count = cursor.fetchone()[0] | ||
| except Exception as e: | ||
| print(f"Warning: Error checking if table exists {cdc_schema}.{cdc_table}: {e}") | ||
| finally: | ||
| cursor.close() | ||
| return count > 0 | ||
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| def create_cdc_table(conn, cdc_schema, cdc_table, columns_with_types): | ||
| """Creates the CDC table with before/after columns for each source column.""" | ||
| cursor = conn.cursor() | ||
| columns = [ | ||
| '"_ab_trigger_change_id" BIGINT GENERATED BY DEFAULT AS IDENTITY PRIMARY KEY', | ||
| '"_ab_trigger_change_time" TIMESTAMP', | ||
| '"_ab_trigger_operation_type" NVARCHAR(10)', | ||
| ] | ||
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| for col in columns_with_types: | ||
| col_name = col["name"] | ||
| data_type = col["type"] | ||
| safe_col_name = col_name.replace('"', '""') | ||
| columns.append(f'"_ab_trigger_{safe_col_name}_before" {data_type}') | ||
| columns.append(f'"_ab_trigger_{safe_col_name}_after" {data_type}') | ||
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| ddl = f'CREATE COLUMN TABLE "{cdc_schema}"."{cdc_table}" (\n {",\n ".join(columns)}\n)' | ||
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| try: | ||
| cursor.execute(ddl) | ||
| conn.commit() | ||
| print(f"Successfully created CDC table {cdc_schema}.{cdc_table}") | ||
| except Exception as e: | ||
| print(f"Error creating CDC table {cdc_schema}.{cdc_table}: {e}") | ||
| conn.rollback() | ||
| finally: | ||
| cursor.close() | ||
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| def get_table_columns_with_types(conn, schema, table): | ||
| """Gets column names and their full data types.""" | ||
| cursor = conn.cursor() | ||
| query = """ | ||
| SELECT COLUMN_NAME, DATA_TYPE_NAME, LENGTH, SCALE | ||
| FROM SYS.TABLE_COLUMNS | ||
| WHERE SCHEMA_NAME = ? AND TABLE_NAME = ? | ||
| ORDER BY POSITION | ||
| """ | ||
| columns = [] | ||
| try: | ||
| cursor.execute(query, (schema, table)) | ||
| for row in cursor.fetchall(): | ||
| col_name, data_type, length, scale = row | ||
| full_type = data_type | ||
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| # Handle type-specific attributes | ||
| if data_type in ["VARCHAR", "NVARCHAR", "CHAR", "NCHAR", "BINARY", "VARBINARY"]: | ||
| if length is not None: | ||
| full_type += f"({length})" | ||
| elif data_type in ["DECIMAL", "DEC"]: | ||
| if scale is not None and scale > 0: | ||
| full_type += f"({length},{scale})" | ||
| elif length is not None: | ||
| full_type += f"({length})" | ||
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| columns.append({"name": col_name, "type": full_type}) | ||
| except Exception as e: | ||
| print(f"Error getting columns for {schema}.{table}: {e}") | ||
| finally: | ||
| cursor.close() | ||
| return columns | ||
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| def check_trigger_exists(conn, schema_name, trigger_name): | ||
| """Checks if a trigger exists.""" | ||
| cursor = conn.cursor() | ||
| query = "SELECT COUNT(*) FROM SYS.TRIGGERS WHERE SCHEMA_NAME = ? AND TRIGGER_NAME = ?" | ||
| count = 0 | ||
| try: | ||
| cursor.execute(query, (schema_name, trigger_name)) | ||
| count = cursor.fetchone()[0] | ||
| except Exception as e: | ||
| print(f"Warning: Error checking trigger {schema_name}.{trigger_name}: {e}") | ||
| finally: | ||
| cursor.close() | ||
| return count > 0 | ||
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| def create_single_trigger(conn, recreate_trigger, operation_type, source_schema, source_table, cdc_schema, cdc_table, columns_with_types): | ||
| """Creates a single trigger for the specified operation.""" | ||
| trigger_name = f"TRG_{source_schema}_{source_table}_CDC_{operation_type[:3].upper()}" | ||
| if check_trigger_exists(conn, source_schema, trigger_name): | ||
| if recreate_trigger: | ||
| drop_trigger(conn, source_schema, trigger_name) | ||
| print(f'Dropped trigger "{source_schema}"."{trigger_name}"') | ||
| else: | ||
| print(f"Trigger {trigger_name} exists. Skipping.") | ||
| return | ||
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| columns = ['"_ab_trigger_change_time"', '"_ab_trigger_operation_type"'] | ||
| values = ["CURRENT_TIMESTAMP", f"'{operation_type}'"] | ||
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| if operation_type == "INSERT": | ||
| referencing = "REFERENCING NEW AS N" | ||
| elif operation_type == "UPDATE": | ||
| referencing = "REFERENCING OLD AS O NEW AS N" | ||
| elif operation_type == "DELETE": | ||
| referencing = "REFERENCING OLD AS O" | ||
| else: | ||
| print(f"Invalid operation type: {operation_type}") | ||
| return | ||
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| for col in columns_with_types: | ||
| col_name = col["name"] | ||
| safe_col = col_name.replace('"', '""') | ||
| if operation_type in ["INSERT", "UPDATE"]: | ||
| columns.append(f'"_ab_trigger_{safe_col}_after"') | ||
| values.append(f'N."{safe_col}"') | ||
| if operation_type in ["UPDATE", "DELETE"]: | ||
| columns.append(f'"_ab_trigger_{safe_col}_before"') | ||
| values.append(f'O."{safe_col}"') | ||
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| columns_str = ", ".join(columns) | ||
| values_str = ", ".join(values) | ||
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| ddl = f""" | ||
| CREATE TRIGGER "{source_schema}"."{trigger_name}" | ||
| AFTER {operation_type} ON "{source_schema}"."{source_table}" | ||
| {referencing} | ||
| FOR EACH ROW | ||
| BEGIN | ||
| INSERT INTO "{cdc_schema}"."{cdc_table}" ( | ||
| {columns_str} | ||
| ) | ||
| VALUES ( | ||
| {values_str} | ||
| ); | ||
| END | ||
| """ | ||
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| cursor = conn.cursor() | ||
| try: | ||
| cursor.execute(ddl) | ||
| conn.commit() | ||
| print(f"Created trigger {trigger_name}") | ||
| except Exception as e: | ||
| print(f"Error creating trigger {trigger_name}: {e}") | ||
| conn.rollback() | ||
| finally: | ||
| cursor.close() | ||
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| def drop_trigger(conn, schema_name, trigger_name): | ||
| """Drops a trigger.""" | ||
| cursor = conn.cursor() | ||
| query = f'DROP TRIGGER "{schema_name}"."{trigger_name}"' | ||
| try: | ||
| cursor.execute(query) | ||
| conn.commit() | ||
| except Exception as e: | ||
| print(f"Error dropping trigger {schema_name}.{trigger_name}: {e}") | ||
| conn.rollback() | ||
| finally: | ||
| cursor.close() | ||
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| def get_tables_from_schema(conn, schema): | ||
| """Retrieves tables from a schema.""" | ||
| cursor = conn.cursor() | ||
| query = "SELECT TABLE_NAME FROM SYS.TABLES WHERE SCHEMA_NAME = ? AND IS_USER_DEFINED_TYPE = 'FALSE'" | ||
| tables = [] | ||
| try: | ||
| cursor.execute(query, (schema,)) | ||
| tables = [{"schema": schema, "table": row[0]} for row in cursor.fetchall()] | ||
| except Exception as e: | ||
| print(f"Error fetching tables for schema {schema}: {e}") | ||
| finally: | ||
| cursor.close() | ||
| return tables | ||
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| def get_tables_from_file(input_file): | ||
| """Reads tables from CSV/JSON file.""" | ||
| tables = [] | ||
| ext = os.path.splitext(input_file)[1].lower() | ||
| try: | ||
| with open(input_file, "r", encoding="utf-8") as f: | ||
| if ext == ".csv": | ||
| reader = csv.DictReader(f) | ||
| for row in reader: | ||
| tables.append({"schema": row["schema"], "table": row["table"]}) | ||
| elif ext == ".json": | ||
| data = json.load(f) | ||
| tables = [{"schema": item["schema"], "table": item["table"]} for item in data] | ||
| except Exception as e: | ||
| print(f"Error reading input file: {e}") | ||
| exit(1) | ||
| return tables | ||
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| def main(): | ||
| parser = argparse.ArgumentParser(description="Create CDC triggers in SAP HANA") | ||
| group = parser.add_mutually_exclusive_group(required=True) | ||
| group.add_argument("--schema", help="Process all tables in a schema") | ||
| group.add_argument("--input-file", help="CSV/JSON file with tables") | ||
| parser.add_argument("--tables", nargs="+", help="List of table names to process (requires --schema)") | ||
| parser.add_argument("--host", required=True) | ||
| parser.add_argument("--port", required=True, type=int) | ||
| parser.add_argument("--user", required=True) | ||
| parser.add_argument("--password", required=True) | ||
| parser.add_argument("--database", help="Database name (for multi-tenant systems)") | ||
| parser.add_argument("--cdc-schema", default="_ab_cdc") | ||
| parser.add_argument("--recreate-triggers", action="store_true", default=False) | ||
| args = parser.parse_args() | ||
| print(args) | ||
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| try: | ||
| conn = get_connection(args.host, args.port, args.user, args.password, args.database) | ||
| except Exception as e: | ||
| print(f"Connection failed: {e}") | ||
| exit(1) | ||
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| # Ensure CDC schema exists | ||
| if not check_cdc_schema_exists(conn, args.cdc_schema): | ||
| create_cdc_schema(conn, args.cdc_schema) | ||
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| tables = [] | ||
| if args.schema and args.tables: | ||
| # Process specific tables in the given schema | ||
| tables = [{"schema": args.schema, "table": table} for table in args.tables] | ||
| elif args.schema: | ||
| # Process all tables in the schema | ||
| tables = get_tables_from_schema(conn, args.schema) | ||
| elif args.input_file: | ||
| # Process tables from the input file | ||
| tables = get_tables_from_file(args.input_file) | ||
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| for table in tables: | ||
| source_schema = table["schema"] | ||
| source_table = table["table"] | ||
| cdc_table = f"_ab_trigger_{source_schema}_{source_table}" | ||
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| columns = get_table_columns_with_types(conn, source_schema, source_table) | ||
| if not columns: | ||
| continue | ||
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| if not check_cdc_table_exists(conn, args.cdc_schema, cdc_table): | ||
| create_cdc_table(conn, args.cdc_schema, cdc_table, columns) | ||
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| for op in ["INSERT", "UPDATE", "DELETE"]: | ||
| create_single_trigger(conn, args.recreate_triggers, op, source_schema, source_table, args.cdc_schema, cdc_table, columns) | ||
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| print("done") | ||
| conn.close() | ||
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| if __name__ == "__main__": | ||
| main() | ||
| ``` |
There was a problem hiding this comment.
(aside) We should be very careful here. Devin authored this script on its own using Db2 as a model and SAP's trigger docs. It hasn't been tested.
The script is SAP HANA-specific - it uses:
SAP HANA's hdbcli Python library (not Db2's driver)
SAP HANA trigger syntax (REFERENCING NEW AS N, FOR EACH ROW)
SAP HANA system tables (SYS.SCHEMAS, SYS.TABLES, SYS.TRIGGERS)
Column type handling specific to SAP HANA (VARCHAR, DECIMAL, etc.)
If we already have one we know works, let's just use that.
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
|
Devin can you look at the MarkDownLint and Vale linting suggestions please? Most of these can probably be addressed by adjusting heading levels or using code font to avoid triggering spellcheck on technical terms. |
- Fix MD001 heading increment: Change h4 to h3 under Getting Started - Wrap technical terms in backticks to fix Vale spelling issues: - hdbcli (Python library) - boolean (data type) - time_without_timezone (data type) - timestamp_without_timezone (data type) Co-Authored-By: ian.alton@airbyte.io <ian.alton@airbyte.io>
Lint Fixes AppliedI've addressed all the MarkDownLint and Vale linting suggestions: MD001 Heading Increment Issue
Vale Spelling IssuesWrapped technical terms in backticks to prevent spellcheck false positives:
All changes are minimal and focused on addressing the lint suggestions without altering the documentation content or structure. |
|
Deploy preview for airbyte-docs ready! ✅ Preview Built with commit f49fedf. |
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What
Updates the SAP HANA Enterprise connector documentation to provide comprehensive CDC setup guidance and fix technical inaccuracies. This addresses the user request to improve CDC documentation accuracy and model the structure after the Db2 connector documentation.
Key improvements:
database,filters)Requested by ian.alton@airbyte.io - Session: https://app.devin.ai/sessions/275c29b177454508a5b15b7945240b3e
How
Restructured documentation following Db2 connector model:
Added Python CDC setup script (330 lines) to automate:
_ab_cdc)Documented CDC behavior including:
Fixed technical issues:
\(Yes/No\)→(Yes/No)BOOLEAN→boolean)Review guide
High Priority - Requires Testing
Python CDC Setup Script (
cdc_setup_sap_hana.pyin documentation)hdbclilibraryTrigger Creation Syntax
REFERENCING NEW AS N/OLD AS OsyntaxAFTER INSERT/UPDATE/DELETEvsBEFOREtimingMedium Priority - Technical Accuracy
CDC Behavior Documentation
Permission Requirements
CREATE SCHEMA,CREATE TABLE,CREATE TRIGGERprivileges are accurateSELECTandDELETEpermissions on tracking tablesConfiguration Parameters
databaseparameter - verify this is correct for multi-tenant systemsfiltersparameter - confirm this exists in connector specLow Priority - Editorial
User Impact
Positive:
Potential Negative:
Can this PR be safely reverted and rolled back?
This is a documentation-only change with no code modifications to the connector itself. Reverting would restore the previous (less comprehensive) documentation.
Note: The Python CDC setup script included in the documentation has not been tested on an actual SAP HANA database and should be validated before users rely on it for production setups.