smt-e2e-dataflow-debugging

Testing & Quality

Debugs logical errors and data discrepancies in Dataflow templates by launching jobs via Terraform and comparing source (e.g. Cloud SQL) vs destination (e.g. Spanner) data. Use ONLY when the pipeline launches and runs to completion (terminal state) but exhibits data discrepancies or logical issues. Do NOT use for debugging template startup/runtime crashes or staging/building new templates.

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Skill: Dataflow Template Logical Error Debugging

Trigger

/smt-e2e-dataflow-debugging <FILE_NAME>.tfvars

Scope

This skill is STRICTLY restricted to testing the following templates:

gcs-spanner-dv
sourcedb-to-spanner
datastream-to-spanner
spanner-to-sourcedb

Goal

To debug logical errors and data discrepancies by comparing source data (e.g., Cloud SQL) with destination data (e.g., Spanner).

Prerequisites

The Dataflow job must be able to launch and run to a terminal state (Succeeded, Failed) using the provided .tfvars file. This skill is NOT for debugging startup/runtime crashes.
terraform CLI installed and configured.
gcloud CLI installed and authenticated (gcloud auth login).
mvn (Maven) and a compatible JDK installed.
git installed.
Access to the source database (e.g., Cloud SQL) instance, database, and credentials.
Access to the destination Spanner instance and database.
Appropriate IAM permissions for Dataflow, Cloud SQL, Spanner, GCS, and Cloud Logging.
Database client tools installed (e.g., psql, mysql client, or willingness to use gcloud sql connect).

Variables

<FILE_NAME>.tfvars: The Terraform variables file.
<YOUR_PROJECT_ID>: The target Google Cloud Project ID.
<YOUR_REGION>: The Google Cloud region for the Dataflow job.
<JOB_ID>: The Dataflow Job ID.
<JOB_NAME>: The name of the Dataflow job.
Cloud SQL Connection Info (Extracted from .tfvars):
- <CSQL_INSTANCE>: Cloud SQL instance name.
- <CSQL_DATABASE>: Cloud SQL database name.
- <CSQL_USER>: Cloud SQL username.
- <CSQL_PASSWORD>: Cloud SQL password (if applicable).
- <CSQL_PROJECT>: Project of the Cloud SQL instance.
Spanner Connection Info (Extracted from .tfvars):
- <SPANNER_INSTANCE>: Spanner instance name.
- <SPANNER_DATABASE>: Spanner database name.
- <SPANNER_PROJECT>: Project of the Spanner instance.
<MAVEN_MODULE_PATH>: The relative path to the template's maven module (e.g. v2/sourcedb-to-spanner).
<TEMPLATE_NAME>: The name of the template.
<YOUR_STAGING_BUCKET>: Cloud Storage bucket for staging.

Global Agent Rules

Fail-Fast Protocol: If any executed terminal command returns an error or non-zero exit code, STOP IMMEDIATELY. Output the error to the user and ask for intervention. Do not attempt autonomous retries.
Data Privacy: Ensure no real PII, credentials, or production connection details are exposed in logs or command outputs.

Workflow Phases

Phase 1: Deployment & Job Monitoring

Deploy the Dataflow Job:
- Navigate to the directory containing <FILE_NAME>.tfvars.
- Initialize Terraform:
```
terraform init
```
- Launch the job:
```
terraform apply --var-file=<FILE_NAME>.tfvars -auto-approve
```
Monitor Status: Wait for the job to reach a terminal state (Succeeded or Failed). Monitor status via Google Cloud Console or gcloud.

Phase 2: Log Collection & Initial Analysis

Retrieve Job IDs: Infer <JOB_ID> and <JOB_NAME> from the Terraform output or by listing active jobs:
```
gcloud dataflow jobs list --project=<YOUR_PROJECT_ID> --region=<YOUR_REGION>
```

Inspect Job Logs: Retrieve job message logs to identify warnings or non-obvious issues:

gcloud logging read 'resource.type="dataflow_step" AND resource.labels.job_id="<JOB_ID>" AND logName=~"projects/.*/logs/dataflow.googleapis.com%2Fjob-message"' \
  --project=<YOUR_PROJECT_ID> \
  --limit=200 \
  --format="table(timestamp, textPayload, severity)" --order=asc

Inspect Worker Logs: Check worker execution logs for specific exceptions or stack traces:

gcloud logging read 'resource.type="dataflow_step" AND logName=~"projects/.*/logs/dataflow.googleapis.com%2Fworker" AND resource.labels.job_id="<JOB_ID>"' \
  --project=<YOUR_PROJECT_ID> \
  --limit=500 \
  --format="table(timestamp, jsonPayload.message, severity)" --order=asc

Phase 3: Data Inspection & Validation

Inspect Source Data (Cloud SQL): Connect to the Cloud SQL instance and query source tables:

# For PostgreSQL
gcloud sql connect <CSQL_INSTANCE> --user=<CSQL_USER> --project=<CSQL_PROJECT>
# For MySQL
gcloud sql connect <CSQL_INSTANCE> --user=<CSQL_USER> --project=<CSQL_PROJECT>

Execute validation queries:

SELECT COUNT(*) FROM your_source_table;
SELECT * FROM your_source_table LIMIT 10;

Inspect Destination Data (Spanner): Execute SQL queries on the destination Spanner database:

gcloud spanner databases execute-sql <SPANNER_DATABASE> \
  --instance=<SPANNER_INSTANCE> \
  --project=<SPANNER_PROJECT> \
  --sql="SELECT COUNT(*) FROM your_destination_table"

gcloud spanner databases execute-sql <SPANNER_DATABASE> \
  --instance=<SPANNER_INSTANCE> \
  --project=<SPANNER_PROJECT> \
  --sql="SELECT * FROM your_destination_table LIMIT 10"

Phase 4: Discrepancy Verification & Resolution

Analyze Differences:
- Row Counts: Do source and destination row counts match?
- Schema Mapping: Are column types and names mapped correctly?
- Value Assertions: Check NULL values, string encoding, timestamps, and precision.
Code Correction: Locate the transformation logic in the .java files under the GoogleCloudPlatform/DataflowTemplates repository and fix the bug.

Re-stage Template: Rebuild and upload the updated Flex Template:

mvn clean package -PtemplatesStage -DskipTests \
  -DprojectId="<YOUR_PROJECT_ID>" \
  -DbucketName="<YOUR_STAGING_BUCKET>" \
  -DstagePrefix="templates" \
  -DtemplateName="<TEMPLATE_NAME>" \
  -pl <MAVEN_MODULE_PATH> -am

Phase 5: Re-testing & Iteration

Clean Destination Table: Delete destination records to prepare for a clean run:

gcloud spanner databases execute-sql <SPANNER_DATABASE> \
  --instance=<SPANNER_INSTANCE> \
  --project=<SPANNER_PROJECT> \
  --sql="DELETE FROM your_destination_table WHERE true"

Re-run Job: Deploy the job again with the newly built template (Phase 1) and verify the fix.

Important Considerations

Idempotency: Ensure clean test tables before re-running to avoid duplicate count errors.
Data Volume: Limit queries to subsets when working with large volumes.
Terraform State: Avoid modifying resources manually outside of Terraform to prevent state drift.