Use the default parameters as specified by the Amazon documentation.For efficient data ingestion, follow the guidelines for enhanced VPC routing.In our experiments, we used ra3.4xlarge instance shape. Determine the best shape and cluster size for the experiments.You can also try without clustering and pick the scheme that gives better performance.For TPC-H, use clustering on ORDERS(o_orderdate) and LINEITEM(l_shipdate) tables.HeatWave GitHub for TPC-DS and CH-benCHmark.For detailed setup, reference the following:.A straight_join hint is required for certain queries to get the optimal query plan for HeatWave.For each query, force offload to HeatWave using the hint (set_var(use_secondary_engine=forced)).Mark the tables as offloadable and load them into HeatWave.Custom data placement is used for certain tables that will be loaded into HeatWave.Optimal encodings are used for certain columns that will be loaded into HeatWave.Note the OLTP and OLAP throughput and average latency as reported.Run 128 concurrent sessions of TPC-C (OLTP) and 4 concurrent sessions of CH-benCH (OLAP).Set incoming rate of TPC-C transactions at 500 per second (30K transactions per minute) and incoming rate of CH-benCH transactions at 4 per second (240 transactions per minute).Generate and load the 1000 W (100 GB) dataset to the target service instance.Create the mixed workload schema (TPC-C and CH-benCH) on the target service instance.Provision and configure the target service instance.The OLTPBench framework (with changes made to support HeatWave) was used to run the mixed workload benchmark.The workload is derived from CH-benCHmark.**.You can always do an explain plan to make sure that you get the best expected plan.For best performance numbers, always do multiple runs of the query and ignore the first (cold) run.Run queries derived from TPC-H or TPC-DS to test the performance.Import the data generated to the target service instance.Create the corresponding schema on the target service instance.Provision and configure the target service.Generate data using the corresponding data generation tool.The workload is derived from the TPC's TPC-H and TPC-DS benchmarks.*.Common setup for analytic (OLAP) workload test for TPC-H and TPC-DS The performance experiments use a wide variety of publicly known datasets for machine learning classification and regression problems.ฤก. The performance comparison encompasses a variety of benchmarks-TPC-H, TPC-DS, and CH-benCHmark with different dataset sizes (4 TB, 10 TB, and 30 TB) to validate the speedup provided by HeatWave. Several performance comparisons have been run and the results are presented below. Performance comparison of MySQL HeatWave on OCI with Snowflake, Amazon Redshift, Amazon Aurora, and Amazon RDS for MySQL
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