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Performance comparison of HeatWave with MySQL Database, Amazon Redshift, and Amazon Aurora


Setup configuration

HeatWave MySQL Database AWS Redshift AWS Aurora
Instance shape E3 E3 Dc2.8xlarge db.r5.24xlarge
Cluster size 10 + 1 MDS 1 4 1

1. Common setup

  • The workload is derived from the TPC's TPC-H benchmark*
  • Generate TPC-H data using the TPC-H data generation tool
  • Provision and configure the target service
  • Create TPC-H schema on the target service instance
  • Import TPC-H data generated to the target service instance
  • Run queries derived from TPC-H to test the performance
  • For best performance numbers, always do multiple runs of the query and ignore the first (cold) run
  • You can always do a explain plan to make sure that you get the best expected plan

2. HeatWave specific setup

  • Use optimal encodings for the columns that will be loaded into HeatWave. 5 of the string columns In the TPC-H schema are VARLEN encoded while others are DICTIONARY encoded
  • Use custom data placement for the tables that will be loaded into HeatWave. For LINEITEM table, l_orderkey is used as the data placement key. For the other tables, primary keys are used as the data placement key.
  • Mark the tables as offloadable and load them into HeatWave
  • For each query, force offload to HeatWave using the hint (set_var(use_secondary_engine=forced))
  • A straight_join hint is required for certain queries to get the optimal query plan for HeatWave
  • Reference HeatWave GitHub for specific setup details

3. MySQL Database specific setup

  • Use a large enough innodb_buffer_pool size (e.g. 450G for a 512G DRAM)
  • Tune innodb_sort_buffer_size, max_heap_table_size, tmp_table_size
  • Make sure the larger tables are partitioned for faster load
  • A straight_join hint can be used if the query plan looks sub-optimal

4. AWS Redshift specific setup

  • Determine the best shape and cluster size for the experiments (in our experiments, we got the best results when we had 1TB of uncompressed data per dc2.8xlarge node)
  • For efficient ingest, follow the guidelines for enhanced VPC routing
  • Use the default parameters as specified by the Amazon documentation
  • Make sure that the sort keys and distribution keys for each table are optimal for queries
  • Use the scripts provided by awslabs

5. AWS Aurora specific setup

  • Use the largest shape possible so that as much of the data can fit into the buffer cache as possible
  • For the 1TB and 4TB TPC-H datasets, use the db.r5.24xlarge shapes
  • Set the innodb_buffer_pool size to 630G
  • Other settings that were modified from their default value in our experiments (innodb_max_purge_lag = 1000000; innodb_max_purge_lag_delay=300000; innodb_sort_buffer_size=67108864; lock_wait_timeout =86400; max_binlog_cache_size= 4294967296; max_heap_table_size=103079215104; tmp_table_size=103079215104)
  • Set aurora_disable_hash_join = 0 and aurora_parallel_query = ON to use parallel query
  • Follow the best practices for aurora database configuration for any other tuning
  • For parallel query to work, make sure that none of the tables are partitioned
  • A straight_join hint can be used if the query plan looks sub-optimal

6. Results


4TB TPC-H

HeatWave AWS Redshift AWS Aurora
Instance shape E3 Dc2.8xlarge db.r5.24xlarge
Cluster size 10 + 1 MDS 4 1
Geo-mean result 7.3 seconds 19.7 seconds 2.5 hours
Annual cost USD$37,022 USD$110,560 USD$129,336

400GB TPC-H

HeatWave MySQL Database
Instance shape E3 E3
Cluster size (this small config is for testing purpose only—minimum supported is 2+1) 1 + 1 MDS 1
Geo-mean result 4.2 seconds 1700 seconds
Annual cost USD$6,483 USD$3,386

*Disclaimer: Benchmark queries are derived from the TPC-H benchmark, but results are not comparable to published TPC-H benchmark results since they do not comply with the TPC-H specification.