performance tuning

Shambhu Rai 1,406 Reputation points

Hi Expert, if we have 100 thousand million records in target tableand wants use merge using with1000000 records what would be first approach which we should use before merge or after merge like if we cache source table then target table is taking time to load . if we cache target table also then cache taking time how we can make sure query will finish asap.

and finallly cache -- will load data in table or not?

i.e.  merge into  table1(100 thousand million records) using table2(1000000 ) records
Azure Databricks
Azure Databricks
An Apache Spark-based analytics platform optimized for Azure.
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  1. Amira Bedhiafi 12,481 Reputation points

    Performance tuning for large-scale data operations like the one you're describing requires a strategic approach. When dealing with a merge operation between a very large table and a smaller one there are several aspects to consider for optimizing performance.

    You didn't provide enough information, but I assume that these are the considerations you may need to know :

    • Are there common keys or columns used for joins? What is the distribution of these keys? This understanding can influence how you approach optimization.
    • If not already done, consider partitioning and clustering your large table based on the keys used for the merge operation. This can reduce the amount of data that needs to be scanned during the merge.
    • Caching can be effective, but it depends on the available memory. If you're caching a smaller table, ensure that the cluster has enough memory to hold this data. If caching is taking too long, it might not be beneficial.
    • If the smaller table can fit into memory, consider using a broadcast hint to broadcast the smaller table to each node. This can speed up the join process significantly.

    If you can provide more info we may help you.

    1 person found this answer helpful.