Issue in Implement Datastream and Dataflow for analytics

[NishinThattil]

I was adding a new source in Datastream whose base db is in Postgres I’ve setup the pipeline where the destination is through cloud storage and then the dataflow job sends the data to BQ because we wanted to logs separately. When I’m backfilling the data/when the new updates are coming it’s flowing into the staging dataset but not the main dataset, the table is created but with 0 data, essentially the merge operation is not happening. Any idea why this could be happening?

[ms4446]

The issue with the merge operation not happening as expected could be due to several reasons:

Possible Causes

1. BigQuery Schema Mismatch: Differences in schema between the PostgreSQL source, the intermediate Cloud Storage representation, and the BigQuery target can lead to merge failures. BigQuery is case-sensitive, and discrepancies in column names, order, or constraints (nullable/required) can cause issues.

2. Data Type Errors: Mismatches in data types across the source, Cloud Storage, and BigQuery can prevent successful merges. Pay special attention to date, timestamp, and numeric types, ensuring compatibility across systems.

3. MERGE Statement Configuration: Errors in the syntax or logic of a custom MERGE statement within your Dataflow job can obstruct the merging process. The statement must accurately reflect your data handling strategy, including how duplicates are managed and when to perform updates versus inserts.

4. Incorrect Permissions: The service account used by Dataflow and/or Datastream must have sufficient permissions in BigQuery. Ensure it has at least bigquery.dataEditor and bigquery.jobUser roles, applied at the correct levels (dataset and project, respectively).

5. Error Handling: Inadequate error handling or logging within the Dataflow job can obscure the root cause of merge failures. Enhancing logging can illuminate specific issues occurring during the merge process.

6. Timeouts: Operations that take too long can time out, especially with large datasets or complex merge operations. Adjusting timeout settings may be necessary to accommodate your data's needs.

Below are some troubleshooting steps that might help resolve the issue:

1. Verify Schemas: Ensure complete alignment in schema names, column order, and constraints between your PostgreSQL source, Cloud Storage, and BigQuery. Remember that BigQuery is case-sensitive.

2. Data Type Check: Confirm that data types are consistent and compatible across your PostgreSQL source, Cloud Storage, and the BigQuery target. Special attention should be given to how numeric, date, and timestamp types are handled.

3. MERGE Statement Review: Examine your custom MERGE statement for syntax accuracy and logical correctness. The conditions in the ON clause are crucial for identifying matches correctly. Test the logic with a subset of data directly in BigQuery if possible.

4. Permissions: Double-check the permissions of the service accounts for Dataflow and Datastream. Ensure they have the necessary BigQuery roles (bigquery.dataEditor and bigquery.jobUser) at the appropriate scope.

5. Error Logging: Implement or enhance logging within your Dataflow job to capture detailed error information. Consider using Google Cloud's monitoring tools like Stackdriver for better visibility into the process.

6. Timeout Settings: If you suspect timeouts are an issue, increase the relevant timeout values for your Datastream and Dataflow jobs. This might involve adjusting parameters like streamingEngineTimeout in Dataflow or timeoutMs in BigQuery jobs.

Additional Tips

• Test with a Small Sample: Begin troubleshooting with a smaller data sample to quickly identify and isolate errors.

• Validate Source Data: Check a few records from your PostgreSQL source to ensure data quality and format meet your expectations.