Datastream to bigquery data flow template problem

[Gokulavasan]

I have a streaming job in datastream which streams the data from mysql to cloud storage in avro format and I have enabled pub/sub notifications and I am trying to create a dataflow job to continuously stream data to big query and  I used existing "Datastream to big query" dataflow  template, the problem I am facing is the data is not loaded into the final table or replica table but i can see the data in the staging table anyone know whats the issue there? 

[ms4446]

If the data is not loading into the final table or replica table in your Dataflow job, even though you can see the data in the staging table, there are a few possible reasons:

• Merge frequency: The Datastream to BigQuery template merges data from the staging table into the replica table at a frequency specified by the mergeFrequencyMinutes parameter. If this frequency is set too high, the Dataflow job may not be able to keep up with the incoming data, and some data may be lost.

• Merge query: The Datastream to BigQuery template uses a MERGE query to upsert data from the staging table into the replica table. If there is a problem with this query, the data may not be merged correctly.

• Permissions: The Dataflow job needs to have write permissions on the replica table and the staging table in order to merge data into it. If the job does not have the correct permissions, the data will not be loaded.

• Schema mismatch: Ensure that the schema defined in the Datastream matches the schema in BigQuery. Any mismatches can cause issues during the merge process.

• Dead-letter table: Check the dead-letter table in BigQuery, which captures records that failed to be inserted. This can provide insights into why certain records were not loaded.

• BigQuery load jobs: Check the BigQuery UI for any failed load jobs, which can provide insights into why the data wasn't loaded into the replica table.

• Template configuration: Ensure that the Dataflow template's parameters, like the BigQuery dataset name, table name, etc., are correctly configured.

To troubleshoot the issue, you can try the following:

1. Check the merge frequency and merge query. Make sure that the mergeFrequencyMinutesparameter is set to a value that is low enough for the Dataflow job to keep up with the incoming data, and that the MERGE query is correct and using the correct schema for the replica table.
2. Check the permissions. Make sure that the Dataflow job has write permissions on the replica table and the staging table.
3. Check the schema for mismatches. Ensure that the schema defined in the Datastream matches the schema in BigQuery.
4. Check the dead-letter table and BigQuery load jobs. If there are any failed records or load jobs,investigate the cause of the failure.
5. Check the template configuration. Make sure that the Dataflow template's parameters are correctly configured.

If you have checked all of the above and you are still having problems, you can contact Google Cloud support for assistance.

Additional tips:

• Check the Dataflow job logs for any errors.
• Make sure that the Datastream stream is running and that it is producing data.
• Make sure that the Pub/Sub subscription is configured correctly and that it is receiving notifications from the Datastream stream.
• Make sure that the BigQuery staging and replica tables are created correctly and that they have the correct schema.

You can also try running the Dataflow job in debug mode to step through the pipeline and inspect the data at each stage. However, keep in mind that doing so in a production environment can have performance implications.

[Gokulavasan]

I tried the following to trouble shoot but still there is no data in the final table

1. I changed the merge frequency minutes to 0 

2. Data flow has all the permissions as it created a table 

3. The final table schema is same as staging table schema

4. there is no data in the dead letter table as well

5.all the parameters are configured correctly

and 
the datastream is runnig and there is no errors in dataflow logs as well .
this is the info i got in the dataflow log

so what else can I do?

[Gokulavasan]

This is the warning message I got in the bigquery merge step, so is it necessary to have a primary key in my source table (my source table is a Mysql database which is configured to the datastream ) to perform merge operation?

[ms4446]

If you are having trouble loading data from Datastream to BigQuery using a Dataflow job, there are a few things you can check:

• Datastream stream logs: Check the Datastream stream logs for any errors, warnings, or unusual patterns.
• BigQuery job logs: Check the BigQuery job logs for any errors,especially if the Dataflow job is using batch loads into BigQuery.
• Debug mode: Running the Dataflow job in debug mode can be helpful for identifying unexpected behavior in the pipeline. However, it is important to note that doing so in a production environment can have performance implications.
• Dataflow region and BigQuery dataset region: For best performance and cost, ensure that the Dataflow job and BigQuery dataset are in the same region. However, Dataflow can write to BigQuery datasets in other regions, but it is not recommended.
• Partitioned tables: Dataflow does support writing to partitioned tables in BigQuery. Ensure that the partitioning column and type are correctly configured in both the Dataflow job and the BigQuery table.

If you have checked all of the above and you are still having trouble, please provide me with the following information:

• The name of your Dataflow job
• The name of your BigQuery dataset and table
• The Dataflow job logs
• The BigQuery job logs

Warning message:

The warning message you provided indicates that BigQuery is unable to retrieve the primary keys for the table Admin.Patient_Table in the stream projects/715481357886/locations/us-central1/streams/akumin-streaming. This is likely because the table does not have a primary key defined.

Resolution:

To resolve this issue, you can either:

• Add a primary key to the table Admin.Patient_Table in the MySQL database.
• Configure the Datastream stream to use a custom merge key.

Adding a primary key:

To add a primary key to the table, you can use the following SQL statement:

ALTER TABLE Admin.Patient_Table ADD CONSTRAINT pk_patient_table PRIMARY KEY (patient_id);

Updating the Datastream configuration:

Once you have added a primary key to the table, you will need to update the Datastream stream to use the new primary key. You can do this by editing the stream configuration and setting the mergeKey property to the name of the primary key column.

Using a custom merge key:

If you choose to configure the Datastream stream to use a custom merge key, you can do so by editing the stream configuration and setting the mergeKey property to a comma-separated list of column names. The columns in the merge key must be unique and must be present in both the source table and the destination table.

Benefits of a primary key:

A primary key is not strictly necessary to perform a merge operation in BigQuery, but it is highly recommended. Without a primary key, BigQuery will use a combination of all columns in the table to determine which rows to update and which rows to insert. This can be inefficient and can lead to unexpected results.

Having a primary key in your source table also has the following benefits:

• It can improve the performance of merge operations in BigQuery.
• It can help to prevent duplicate data from being inserted into the destination table.
• It can make it easier to troubleshoot problems with merge operations.

Overall, I recommend adding a primary key to your source table if you are planning to use the Datastream to BigQuery template to load data into BigQuery.

[ms4446 · Accepted solution]

If you are having trouble loading data from Datastream to BigQuery using a Dataflow job, there are a few things you can check:

• Datastream stream logs: Check the Datastream stream logs for any errors, warnings, or unusual patterns. For example, you may see errors indicating that the Datastream stream is unable to connect to the MySQL database,or that it is unable to write data to Cloud Storage.
• BigQuery job logs: Check the BigQuery job logs for any errors,especially if the Dataflow job is using batch loads into BigQuery.For example, you may see errors indicating that the BigQuery table does not exist, or that the Dataflow job does not have permission to write to the BigQuery table.
• Debug mode: Running the Dataflow job in debug mode can be helpful for identifying unexpected behavior in the pipeline. However, it is important to note that doing so in a production environment can have performance implications.
• Dataflow region and BigQuery dataset region: For best performance and cost, ensure that the Dataflow job and BigQuery dataset are in the same region. However, Dataflow can write to BigQuery datasets in other regions, but it is not recommended.
• Partitioned tables: Dataflow does support writing to partitioned tables in BigQuery. Ensure that the partitioning column and type are correctly configured in both the Dataflow job and the BigQuery table.

If you have checked all of the above and you are still having trouble, please provide me with the following information:

• The name of your Dataflow job
• The name of your BigQuery dataset and table
• The Dataflow job logs
• The BigQuery job logs

Redacting sensitive information: When sharing logs, please redact or remove any sensitive information, such as passwords, database connection strings, and personally identifiable information (PII). It is also best to share logs directly with support or trusted individuals, rather than in public forums.