Index optimization: By designing indexes properly, data retrieval speed can be accelerated. The appropriate type of index should be selected based on actual business requirements and query methods.
Query optimization: Try to avoid using complex query statements, reduce the amount of data and connections in the query, and avoid unnecessary sorting and aggregation operations.
Data partitioning: splitting data into multiple partitions according to specific rules can improve query efficiency and parallel processing capability.
Data compression can improve query performance by reducing storage space and I/O operations.
Update statistical information regularly for tables to ensure the optimizer can correctly choose execution plans.
Avoid full table scans: try to avoid scanning the entire table by using methods such as indexes or partitions to speed up queries.
Parallel processing: Utilizing the parallel processing capability of Teradata can enhance the efficiency of data processing.
Cache optimization: Setting the cache size and cache strategy properly can improve query hit rate and performance.
Reconstructing the data model, based on actual business needs and query methods, can enhance query performance.
Regular monitoring and optimization: Regularly monitor performance metrics of the system to promptly identify and resolve performance bottlenecks, continuously optimizing system performance.
