Skill: Implement Source in SourceDb to Spanner Template

Overview

This skill provides a step-by-step procedure for adding support for a new database source connector to the v2/sourcedb-to-spanner forward migration template. It details prerequisites, scope boundaries, type mapping requirements, connector implementation, factory registration, unit testing, and smoke testing procedures.

Prerequisites

[!CRITICAL] MANDATORY PREREQUISITE GATE: Before running code searches, inspecting files, or executing unit tests, you MUST verify if the user provided the inputs below. If any required input is missing, STOP IMMEDIATELY and ask the user for clarification before proceeding further.

1. Datatype Mapping File: Ask the user to provide or point to the Datatype Mapping Matrix for the new source database, defining the mappings between source database datatypes and Spanner (GoogleSQL and PostgreSQL dialects) datatypes or UnifiedMappingProvider.Type.
2. Test Setup Details: Ask the user for the test environment details required for smoke testing, including:
   • Source Database Instance details (host/instance name, port, database name, credentials/connection method, and schema/namespace if applicable).
   • Target Spanner Instance & Database details.

Architectural Boundaries & Code Scope

All implementation for a new source connector MUST be strictly confined to:

1. Source Connector Package: v2/sourcedb-to-spanner/src/main/java/com/google/cloud/teleport/v2/source/<source_type>/
   • Implementation of ISrcToSpSourceConnector (<Source>SrcToSpSourceConnector.java) or subclassing AbstractJdbcSrcToSpSourceConnector (for JDBC sources).
   • Dialect adapter, value mapping provider, and config defaults classes in package <source_type> (e.g., <Source>ConfigDefaults.java).
2. Connector Factory: SourceConnectorFactory.java (v2/sourcedb-to-spanner/src/main/java/com/google/cloud/teleport/v2/source/SourceConnectorFactory.java)
   • Dynamic connector lookup in getSourceConnectorByDialect(), getSourceConnectorBySourceType(), and getSourceJdbcConnectorByDialect().
3. Pipeline Options: SourceDbToSpannerOptions.java (v2/sourcedb-to-spanner/src/main/java/com/google/cloud/teleport/v2/options/SourceDbToSpannerOptions.java)
   • Source dialect constant definition (e.g., String <SOURCE>_SOURCE_DIALECT = "<SOURCE_DIALECT_NAME>";) and enum registration in @TemplateParameter.Enum.
4. Shared Core Registries & Configs: v2/spanner-common
   • Registries and constant files in spanner-common (e.g. Constants.java / SourceConstants.java for public static final String <SOURCE>_SOURCE_TYPE = "<source_type>";).

Datatype Mapping Matrix Requirements

Consult the Datatype Mapping Matrix provided in the prerequisites to verify correct datatype conversion between the source database datatypes and UnifiedMappingProvider.Type / Spanner target datatypes. Ensure proper alignment for character, numeric, temporal, binary, boolean, JSON, and any other dialect-specific datatypes as specified in the mapping file.

Step-by-Step Implementation Workflow

Step 0: Mandatory Prerequisite Gate

1. Verify Inputs: Inspect the request for the required inputs. If the Datatype Mapping Matrix and Test Setup Details are not provided in the user request, DO NOT run any inspection or execution tools. Stop and ask the user for the missing details first.

Step 1: Implement <Source>SrcToSpSourceConnector

Implement ISrcToSpSourceConnector (or extend AbstractJdbcSrcToSpSourceConnector for JDBC sources) in com.google.cloud.teleport.v2.source.<source_type>:

1. Define type mappings between source datatypes and UnifiedMappingProvider.Type in getTypeMapping(). Ensure all the types mentioned in the datatype mapping are covered.
2. For sources which have a beam IO library, use that library.For JDBC supported sources, implement the relevant JDBC connector methods similar to existing JDBC sources.
3. Configure connection parameters and dialect-specific setup to work with Spanner target databases (GoogleSQL and PostgreSQL dialects).
4. For the types marked as primary key supported in the data type mapping ensure an implementation for the splitter is provided.

Step 2: Implement Source IO Wrapper Config Defaults and Schema Discovery

1. Create source configuration defaults (e.g. <Source>ConfigDefaults.java), dialect adapter, and value mappings provider in package com.google.cloud.teleport.v2.source.<source_type>.
2. Configure schema discovery, fetch size, connection pooling, and uniform partitioning parameters appropriate for the source database.

Step 3: Register Source in Options, Constants & Factory

1. Add the source dialect constant in SourceDbToSpannerOptions.java and update @TemplateParameter.Enum:

   String <SOURCE>_SOURCE_DIALECT = "<SOURCE_DIALECT_NAME>";
   

2. Add the source type constant in Constants.java / SourceConstants.java in v2/spanner-common:

   public static final String <SOURCE>_SOURCE_TYPE = "<source_type>";
   

3. Register the connector in SourceConnectorFactory.java:
   • Update getSourceConnectorByDialect(...)
   • Update getSourceConnectorBySourceType(...)
   • Update getSourceJdbcConnectorByDialect(...) (for JDBC sources)

Step 4: Unit Testing & Verification

Execute Maven unit tests for the template:

mvn test -pl v2/sourcedb-to-spanner \
  -Dtest=<Source>SrcToSpSourceConnectorTest,SourceConnectorFactoryTest

All unit tests must pass with BUILD SUCCESS.

Step 5: Mandatory Live Smoke Testing & Verification

[!CRITICAL] MANDATORY EXECUTION REQUIREMENT: Immediately after unit tests pass, you MUST AUTOMATICALLY PROCEED to execute live end-to-end smoke testing using the test environment details provided in the prerequisites. Do NOT stop, pause, or declare completion after unit testing without running the live smoke tests.

[!IMPORTANT] WORKER MACHINE TYPE DIRECTIVE: When submitting the Dataflow job for live smoke testing, you MUST explicitly specify a worker machine type of the correct size with at least 4 vCPUs (e.g., --worker-machine-type=n2-standard-4). Omitting this parameter will cause Dataflow job launch validation to fail with a machine specification policy violation.

1. Environment Setup:
   • Connect to the source database instance and target Spanner database instance configured in the test setup.
2. Populate Source Data:
   • Insert test rows covering all supported datatypes into the source database.
3. Verify Replication / Migration Flow:
   • Launch the sourcedb-to-spanner Dataflow pipeline.
   • Query the target Spanner database tables to confirm that source rows are accurately migrated to destination Spanner tables.
4. Retry Loop on Failure:
   • If any record fails to migrate or produces data discrepancies in Spanner, inspect error logs, modify connector, IO wrapper, or mapping code, rebuild, and re-test until all operations pass cleanly.