MySQL HeatWave on OCI vs. Amazon Redshift with AQUA
HeatWave is a high-performance, highly parallel, in-memory query accelerator that boosts MySQL performance by orders of magnitude for analytics and mixed workloads. According to a performance comparison with publicly available benchmarking code from Oracle, enabling anyone to run the benchmarks, Oracle MySQL HeatWave delivers 13X better price performance than Amazon Redshift with AQUA. MySQL HeatWave is the only service that enables customers to run OLTP and OLAP workloads directly from their MySQL database.
Benefits of MySQL HeatWave over Amazon Redshift with AQUA:
- Performance: MySQL HeatWave is 6.8X faster than Amazon Redshift with AQUA.
- Cost savings: MySQL HeatWave is half the cost of Amazon Redshift.
- Single database for OLTP and OLAP workloads: MySQL HeatWave eliminates the risk, complexity, and cost of using two different databases.
- Machine learning-powered automation: MySQL AutoPilot uses advanced machine-learning techniques to automate HeatWave, further improving performance and scalability and making it easier to use.
- Real-time analytics without any modification to existing applications: No changes are required to existing applications to run OLTP and real-time analytics workloads simultaneously within a single MySQL database.
- In-database machine learning: HeatWave AutoML provides the ability to build, train, deploy, and explain machine learning models within MySQL HeatWave, eliminating the need to move data to a separate machine learning service.
1. MySQL HeatWave is 6.8X faster than Amazon Redshift with AQUA
HeatWave is an in-memory query accelerator architected for performance and scalability. MySQL HeatWave costs less and is faster than competing cloud database services, including Amazon Redshift with AQUA. This has been demonstrated by multiple standard industry benchmarks such as TPC-H, TPC-DS, CH-benCHmark—and on real-world customer workloads. For a 10 TB TPC-H analytics workload, MySQL HeatWave is 6.8X faster than Amazon Redshift with AQUA.
See the performance details and learn more about the benchmark configuration
Capability and Evidence |
MySQL HeatWave |
Amazon Redshift |
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Can customers obtain the best performance with minimum cost?HeatWave implements state-of-the-art algorithms for distributed in-memory analytic processing, which provide very high performance for queries. With 10 TB TPC-H data, MySQL HeatWave is 6.8X faster and half the cost of an Amazon Redshift with AQUA cluster with 8 ra3.4xlarge nodes. Redshift users can increase the size of their cluster to improve performance but will incur additional costs. |
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Can customers get the best query performance as their queries get more complex and data keeps growing?With MySQL Autopilot – Auto Query Plan Improvement, HeatWave automatically learns various statistics from the execution of queries. It then improves the execution plan of future queries without human intervention. Amazon Redshift with AQUA only maintains table statistics, it does not track query runtimes to optimize overall query performance. |
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"We believe that the MySQL Database Service with HeatWave offers the best value bar none across the entire MySQL DB market."
—Ron Westfall, Senior Analyst & Research Director, Futurum Research
2. MySQL HeatWave is half the cost of Amazon Redshift with AQUA
Compared to Amazon Redshift with AQUA discounted upfront 1-year reserved pricing, MySQL HeatWave is half the cost and provides 13X better price-performance based on a 10 TB TPC-H data set.
Capability and Evidence |
MySQL HeatWave |
Amazon Redshift |
|---|---|---|
Does the database service enable customers to expand or downsize their cluster to any number of nodes nondisruptively?Customers can expand or downsize their HeatWave cluster to any number of nodes nondisruptively. During the resizing operation, HeatWave remains online for queries, DMLs, or load operations—there is no downtime. As part of the resizing, data is automatically downloaded from object storage, automatically repartitioned among all available cluster nodes, and becomes immediately available for queries. With elastic resize, the Redshift cluster is unavailable for four to eight minutes of the resize period. There are several limitations to consider, and the elastic resize of the Redshift cluster can cause data skew between nodes from an uneven distribution of data slices—which can severely downgrade the performance of queries. |
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Pablo Lemos, Cofounder and CTO of Tetris.co, describes how MySQL HeatWave dramatically reduced the company’s Amazon Aurora and Redshift costs by more than 50% while delivering real-time insights and enabling the company's expansion.
3. Single database for OLTP and OLAP workloads
MySQL HeatWave, with its integrated in-memory query accelerator, is the only service that enables database administrators and application developers to run OLTP and OLAP workloads directly from their MySQL database.
Capability and Evidence |
MySQL HeatWave |
Amazon Redshift |
|---|---|---|
Can customers run both OLTP and OLAP workloads using a single database service?MySQL HeatWave is the only service that enables customers to run both OLTP and OLAP workloads in MySQL– without the need to extract, transform, and load (ETL) data to a separate database for analytics. No changes to existing applications are necessary. Amazon Redshift with AQUA is a forked version of PostgreSQL without OLTP capabilities. Customers cannot run OLTP workloads directly on Redshift; they require a different database and an ETL process to load data into Redshift for OLAP processing. This increases complexity and costs. Security and compliance risks also increase as data moves between data stores. |
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Do customers get real-time analytics?Changes operated by MySQL transactions are propagated in real-time to HeatWave and become immediately available for analytics queries, enabling real-time analytics. Amazon Redshift with AQUA users load data from their transactional applications into Redshift through an ETL process. By the time data is available in Redshift, it’s already stale, so customers don’t get real-time analytics. |
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Can customers eliminate the complexity, cost, and risk of the ETL process?MySQL HeatWave provides a unified platform for running transactional and analytics workloads in MySQL. This eliminates the need for the complex, time-consuming, expensive ETL and integration required by a separate analytics database. Amazon Web Services does not provide a single, unified service for OLTP and OLAP workloads. Amazon Redshift with AQUA processes only OLAP workloads. Customers are required to move all data from MySQL/OLTP data sources through an ETL process to Redshift, which increases complexity, risks and costs. |
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"Open source developers who have not yet moved to MySQL Database Service with HeatWave are running out of reasons not to give it a try. Not only has Oracle simplified their lives with a unified OLTP and OLAP MySQL service, it has eliminated the need for a separate analytical database or data warehouse and ETLs between them. Plus, now it has delivered unparalleled performance and cost/performance."
—Marc Staimer, Senior Analyst, Wikibon
4. Machine learning-powered automation
MySQL Autopilot automates important and challenging aspects of achieving high query performance at scale. MySQL Autopilot is available for free to MySQL HeatWave customers.
Capability and Evidence |
MySQL HeatWave |
Amazon Redshift |
|---|---|---|
Do customers benefit from built-in machine learning automation for auto provisioning?MySQL Autopilot Auto Provisioning predicts the number of HeatWave nodes required for running a workload by adaptive sampling of table data on which analytics is required. Customers no longer need to manually estimate the optimal size of their cluster. Amazon Redshift with AQUA does not have the equivalent capability for auto provisioning. Redshift uses a static approach to recommend cluster shape and size to customers. Customers need to manually test the recommendation for their workload, increasing cost and development time. |
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Do customers benefit from built-in machine learning automation for auto query plan improvement?MySQL Autopilot Auto Query Plan Improvement learns various statistics from the execution of queries and improves the execution plan of future queries. This means that the performance of the system improves as more queries are run. Amazon Redshift with AQUA is missing the equivalent capabilities of auto query plan improvement. Redshift requires DBAs to manually maintain table statistics to try and achieve better query execution plans. |
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"For MySQL database developers, this is a game changer. Usually, developers scale MySQL through labor-intensive sharding schemes, often resulting in complex application SQL logic, and exposing the system to multiple levels of human error. With HeatWave, this is not an issue. MySQL developers also run into constant challenges configuring the database for maximum efficiency and coding the SQL for the best performance. With HeatWave, these are no longer concerns."
—Carl Olofson, Research Vice President, Data Management Software, IDC
5. Real-time analytics for existing applications without any modification
Customers running MySQL applications can immediately take advantage of HeatWave for real-time analytics, without making any changes to their applications.
Capability and Evidence |
MySQL HeatWave |
Amazon Redshift |
|---|---|---|
Can customers run transactions and get real-time analytics in the same database without making changes to existing applications?Existing MySQL applications work with HeatWave, without any changes, to add real-time analytics. HeatWave is designed as a MySQL pluggable storage engine, which completely shields all the low-level implementation details from customers. Consequently, they can manage both the MySQL database and HeatWave with the same management tools including the OCI console, REST APIs, and command line interface. Amazon Redshift with AQUA is not designed for mixed workloads. Customers cannot run OLTP workloads directly on Redshift and require an ETL process to load data from MySQL or another source into Redshift for OLAP processing. |
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"The MySQL HeatWave technology is by far the best in the market now...Wikibon strongly recommends that enterprise IT departments set a three-year plan to eliminate separate OLAP databases and ETL from MySQL transactional databases."
—David Floyer, CTO, Wikibon
6. MySQL HeatWave eliminates the need for a separate machine learning service
With HeatWave AutoML, customers can build, train, deploy, and explain machine learning models within MySQL HeatWave.
Capability and Evidence |
MySQL HeatWave |
Amazon Redshift |
|---|---|---|
Does the database service provide in-database machine learning?With HeatWave AutoML, developers and data analysts can build, train, deploy, and explain machine learning models within MySQL HeatWave.Amazon Redshift lacks the equivalent in-database machine learning capability. As a result, customers must move their data to a separate machine learning service (e.g., Amazon SageMaker) to build and train machine learning models. Security and compliance risks increase as data moves between systems. |
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Are all machine learning models explainable, so users can understand and explain what happens in the models from input to output?All models created by HeatWave AutoML are explainable, which improves their reliability, fairness, trust, and regulatory compliance.All models in Amazon Redshift ML are not explainable, which can reduce trust in them, increase the risk of bias, and create difficulties for regulatory compliance. |
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Is the machine learning lifecycle automated?HeatWave AutoML fully automates the machine learning lifecycle, including algorithm selection, intelligent data sampling, feature selection, and hyperparameter tuning—saving customers significant time and effort. HeatWave AutoML enables developers and data analysts to build machine learning models using familiar SQL commands; they don’t have to learn new tools and languages.Amazon Redshift ML does not automate many elements of the machine learning lifecycle and requires data science expertise to influence the performance, accuracy, and cost of the machine learning models’ training. |
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Can customers benefit from the performance of in-database machine learning?Benchmarks demonstrate that, on average, HeatWave AutoML trains models up to 25X faster than Amazon Redshift ML while producing more accurate results. |
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Can customers benefit from the cost-savings of in-database machine learning?Benchmarks demonstrate that, on average, the cost of HeatWave AutoML is 1% of the cost of Amazon Redshift ML. |
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