This Dataflow error message indicates that the worker processes responsible for running your pipeline have crashed repeatedly, leading to job failure. This signifies a disruption in the communication between the worker process (running your pipeline code) and the Dataflow service, often due to a crash. The error indicates that a specific work item failed multiple times, likely related to reading data from a MySQL database. While the immediate cause is "SDK disconnect," the underlying issue leading to the crash needs further investigation. Here are some troubleshooting steps you can take:
-
Check Worker Logs:
- Navigate to the Dataflow job's Worker Logs tab in the Google Cloud Console.
- Look for error messages, stack traces, or exceptions around the time of the crash. Common issues include:
- Out of memory errors (OOM)
- Dependency conflicts
- Problems with MySQL connection or data
-
Check Diagnostics Tab:
- Review the Diagnostics tab for resource utilization (CPU, memory, disk).
- Look for spikes or anomalies that could have caused the worker to crash.
-
Verify MySQL Connectivity and Credentials:
- Ensure that the Dataflow worker service account has the necessary permissions to access your MySQL database.
- Double-check connection parameters (host, port, username, password) in your Dataflow code.
-
Review Dataflow Code:
- Examine the portion of your code that reads from the MySQL database (specifically the
JdbcIO.ReadAll operation).
- Check for potential issues such as:
- Incorrect queries or data types
- Inefficient data processing
- Handling large datasets
-
Increase Worker Resources:
- If the worker logs indicate memory or CPU issues, try increasing the worker machine type (e.g., from
n1-standard-1 to n1-standard-2).
-
Consider Batching or Windowing:
- If handling large volumes of data from MySQL, implement batching or windowing in your pipeline to process data in smaller chunks.
Additional Tips
- Retry Logic: Incorporate retry mechanisms to handle transient errors when connecting to MySQL.
- Isolate the Issue: Create a simplified Dataflow pipeline that only reads from MySQL to determine if the problem lies solely in database interaction.
- Community Resources: Search for similar issues on Stack Overflow and the Google Cloud Community forums.
Example Code Snippet (Checking for Errors)
pipeline
.apply("Read from MySQL", JdbcIO.<KV<Integer, String>>read()
.withDataSourceConfiguration(JdbcIO.DataSourceConfiguration.create("com.mysql.cj.jdbc.Driver", "jdbc:mysql://your-database-host:3306/your-database-name")
.withUsername("your-username")
.withPassword("your-password"))
.withQuery("select id, name from your_table")
.withCoder(KvCoder.of(BigEndianIntegerCoder.of(), StringUtf8Coder.of()))
.withRowMapper(new JdbcIO.RowMapper<KV<Integer, String>>() {
@Override
public KV<Integer, String> mapRow(ResultSet resultSet) throws Exception {
// Handle errors gracefully here, e.g., log exceptions
return KV.of(resultSet.getInt("id"), resultSet.getString("name"));
}
}))
// ... rest of your pipeline
Additional Considerations:
- Environment Setup: Ensure your Dataflow environment and dependencies are compatible with your Flex-Template.
- Flex-Template Debugging: When debugging Flex-Templates, ensure the Docker container has the necessary resources and configurations.
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