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Unique ID of table values is not unique anymore after merge every x-times

zeta_load
New Contributor II

I have two tables with unique IDs:

ID val ID val

1 10 1 10

2 11 2 10

3 13 3 13

I then merge those two tables so that it results in one table with only unique IDs. The logic is more or less irrelevant for my problem which is, that every 90/100 times the operation fails (without any error) and my resulting table has duplicate IDs. I persisted the table in hope that it would change something but it didn't.

Can someone please give me some reasons that can lead to a problem like that and also some solution addvice? I'm stuck here because the problem occurs very rarely. I'm using pyspark and a standard multinode cluster,

1 ACCEPTED SOLUTION

Accepted Solutions

Anonymous
Not applicable

@Lukas Goldschmied​ :

There are a few reasons why you might be experiencing this issue:

  1. Data Skew: Data skew is a common problem in distributed computing when one or more nodes in the cluster have more data to process than others. This can lead to longer processing times, timeouts, and other issues. In your case, if there are certain IDs that have significantly more rows than others, it could be causing the merge to fail.
  2. Memory Issues: When working with large datasets, memory issues can arise, causing the merge operation to fail. If the data is not properly partitioned or cached, it can cause the nodes to run out of memory, resulting in incomplete or failed operations.
  3. Cluster Configuration: The configuration of your cluster can also be a factor in the success of the merge operation. If your cluster is not properly configured or has insufficient resources, it can cause operations to fail.

Here are some potential solutions to try:

  1. Increase the number of partitions: Increasing the number of partitions can help distribute the workload more evenly across the cluster, reducing the likelihood of data skew. You can try increasing the number of partitions and see if that resolves the issue.
  2. Increase the amount of memory allocated to your cluster: Increasing the amount of memory allocated to your cluster can help reduce memory issues during the merge operation. You can try increasing the memory allocated to your cluster and see if that resolves the issue.
  3. Increase the timeout period: Increasing the timeout period can allow the merge operation to complete successfully. You can try increasing the timeout period and see if that resolves the issue.
  4. Use a different merge algorithm: Depending on the specific nature of your data and the merge operation, using a different merge algorithm may resolve the issue. You can try using a different merge algorithm and see if that resolves the issue.
  5. Check the cluster logs: You can check the logs of your cluster to see if there are any errors or warnings that may be related to the failed merge operation. This can help identify the root cause of the issue and guide your troubleshooting efforts.

View solution in original post

1 REPLY 1

Anonymous
Not applicable

@Lukas Goldschmied​ :

There are a few reasons why you might be experiencing this issue:

  1. Data Skew: Data skew is a common problem in distributed computing when one or more nodes in the cluster have more data to process than others. This can lead to longer processing times, timeouts, and other issues. In your case, if there are certain IDs that have significantly more rows than others, it could be causing the merge to fail.
  2. Memory Issues: When working with large datasets, memory issues can arise, causing the merge operation to fail. If the data is not properly partitioned or cached, it can cause the nodes to run out of memory, resulting in incomplete or failed operations.
  3. Cluster Configuration: The configuration of your cluster can also be a factor in the success of the merge operation. If your cluster is not properly configured or has insufficient resources, it can cause operations to fail.

Here are some potential solutions to try:

  1. Increase the number of partitions: Increasing the number of partitions can help distribute the workload more evenly across the cluster, reducing the likelihood of data skew. You can try increasing the number of partitions and see if that resolves the issue.
  2. Increase the amount of memory allocated to your cluster: Increasing the amount of memory allocated to your cluster can help reduce memory issues during the merge operation. You can try increasing the memory allocated to your cluster and see if that resolves the issue.
  3. Increase the timeout period: Increasing the timeout period can allow the merge operation to complete successfully. You can try increasing the timeout period and see if that resolves the issue.
  4. Use a different merge algorithm: Depending on the specific nature of your data and the merge operation, using a different merge algorithm may resolve the issue. You can try using a different merge algorithm and see if that resolves the issue.
  5. Check the cluster logs: You can check the logs of your cluster to see if there are any errors or warnings that may be related to the failed merge operation. This can help identify the root cause of the issue and guide your troubleshooting efforts.

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