MySQL HeatWave Database Service
One MySQL cloud database service for transactions, real-time analytics across data warehouses and data lakes, and machine learning (ML)—without the complexity, latency, risks, and cost of extract, transform, and load (ETL) duplication. Available on Oracle Cloud Infrastructure (OCI), Amazon Web Services (AWS), and Microsoft Azure.
Why choose MySQL HeatWave?
Simplicity of transactions, real-time analytics across data warehouses and data lakes, and ML in one cloud database service
Eliminate the cost and complexity of separate analytics database, lakehouse, ML, and ETL cloud services. Query data in MySQL, in object storage, or across both. Avoid the latency and security risks of data movement between data stores.
Unmatched performance and price-performance
MySQL HeatWave is 4.2X faster than Amazon Redshift at one-fifth the cost, 3.3X faster than Snowflake at one-eighth the cost, and 1,400X faster than Amazon Aurora at half the cost. The price-performance of MySQL HeatWave Lakehouse for query processing is 8X better than Redshift, 18X better than Databricks, 22X better than Snowflake, and 30X better than Google BigQuery.
Ready for the distributed cloud
Deploy MySQL HeatWave on OCI, AWS, Azure, or in your data center.
MySQL HeatWave customer successes on AWS and OCI
See more customer successesMySQL HeatWave customers significantly improve productivity while reducing costs, deliver a better customer experience, scale to onboard more clients, and accelerate time to market.
Digital agency from Germany consolidates data processing and analytics with MySQL HeatWave on AWS for 90X faster complex queries than RDS, doubling click-through rates for marketing campaigns with greater scalability and less administration.
The multicloud tech leader consolidated data processing and analytics with MySQL HeatWave on AWS for 20X faster query performance, more scalability, and less administration than MariaDB on RDS. All with no code changes for real-time reporting.
This medical device manufacturer consolidated data processing and analytics with MySQL HeatWave on AWS for 50X faster complex queries than RDS for real-time insights to improve diabetes self-monitoring.
This K-12 educational SaaS provider in Brazil achieves real-time analytics with 300X faster complex query execution at 85% lower cost than Google BigQuery while supporting three million users—all to enhance student performance.
The Brazilian metaverse startup migrated all its data to MySQL HeatWave from AWS EC2. Within 3 hours, it achieved 5X better database performance for an event with more than one million visitors with greater security and at the half the cost.
This Japanese video game company gained real-time insights by adding HeatWave to MySQL Database Service, helping it meet its goal of continuously improving joyful entertainment for customers around the world.
Migrate to MySQL HeatWave on OCI or AWS.
Featured MySQL HeatWave use cases
-
Achieve real-time marketing analytics
See how MySQL HeatWave enables digital marketing agency customers to send the right offer to the right prospect via the right channel at the right time—and provides real-time campaign performance analytics to make the best decisions.
Learn more about real-time marketing analytics with MySQL HeatWave
-
Scale startups while reducing costs
Discover why numerous fast-growing, cloud native organizations migrate to MySQL HeatWave to overcome their growing pains—improving performance, scalability, security, and productivity while reducing costs.
MySQL HeatWave: A game changer for developers
One MySQL cloud database service for OLTP and OLAP
MySQL HeatWave is the only service that enables developers and database administrators to run OLTP and OLAP workloads directly from MySQL Database.
Eliminate ETL
Eliminate the complex, time-consuming, expensive ETL process and integration with a separate analytics database.
Deliver real-time analytics
Analytics queries always access the most up-to-date data as updates from transactions automatically replicate in real time to the HeatWave analytics cluster. There’s no need to index the data before running analytics queries.
Real-time analytics on JSON documents
Developers and DBAs can take advantage of HeatWave for real-time analytics on JSON documents stored in MySQL Database, accelerating analytics queries by orders of magnitude on the documents.
Improve security
Data at rest and in transit between MySQL Database and the nodes of the HeatWave cluster is always encrypted. There’s no risk of data being compromised during ETL since data isn’t transferred between databases.
No changes to MySQL applications
HeatWave is a native MySQL solution. Current MySQL applications work without changes.
Use existing business intelligence (BI) and data visualization tools
HeatWave supports the same BI and data visualization tools as MySQL Database, including Oracle Analytics Cloud, Tableau, and Looker.
Available in public clouds and your data center
Deploy MySQL HeatWave on OCI, AWS, or Azure. Replicate data from on-premises OLTP applications to MySQL HeatWave to get near real-time analytics without ETL. You can also use MySQL HeatWave in your data center with OCI Dedicated Region.
High performance, in-memory query accelerator
HeatWave is an in-memory, massively parallel, hybrid columnar query-processing engine. It implements state-of-the-art algorithms for distributed query processing that provide very high performance.
Architected for massive scale and performance
HeatWave massively partitions data across a cluster of nodes, which can be operated in parallel. This provides excellent internodal scalability. Each node within a cluster and each core within a node can process partitioned data in parallel. HeatWave has an intelligent query scheduler that overlaps computation with network communication tasks to achieve very high scalability across thousands of cores.
Optimized for the cloud
Query processing in HeatWave has been optimized for commodity servers in the cloud. The sizes of the partitions have been optimized to fit the cache of the underlying shapes. The overlap of computation with communication is optimized for the network bandwidth available. Various analytics processing primitives use the hardware instructions of the underlying virtual machines (VMs).
Optimized for high transaction rates and connections
Oracle MySQL Autopilot improves the performance of the MySQL HeatWave Thread Pool, providing a mechanism to optimally use hardware resources for better performance. As a result, MySQL HeatWave delivers higher throughput for OLTP workloads and prevents the throughput from dropping at high levels of transactions and concurrency.
MySQL Autopilot: Built-in machine learning–powered automation
MySQL Autopilot provides workload-aware, machine learning–powered automation. It improves performance and scalability without requiring database tuning expertise, increases the productivity of developers and DBAs, and helps eliminate human errors. MySQL Autopilot automates many of the most important and often challenging aspects of achieving high query performance at scale—including provisioning, data loading, query execution, and failure handling. MySQL Autopilot is available at no additional charge for MySQL HeatWave customers.
MySQL Autopilot provides numerous capabilities for both HeatWave and OLTP, including
- Auto provisioning predicts the number of HeatWave nodes required for running a workload by adaptive sampling of table data on which analytics is required. This means developers and DBAs no longer need to manually estimate the optimal size of their cluster.
- Auto thread pooling lets the database service process more transactions for a given hardware configuration, delivering higher throughput for OLTP workloads and preventing it from dropping at high levels of transactions and concurrency.
- Auto shape prediction continuously monitors the OLTP workload, including throughput and buffer pool hit rate, to recommend the right compute shape at any given time—allowing customers to always get the best price-performance.
- Auto encoding determines the optimal representation of columns being loaded into HeatWave, taking the queries into consideration. This optimal representation provides the best query performance and minimizes the size of the cluster to minimize costs.
- Auto query plan improvement learns various statistics from the execution of queries and improves the execution plan of future queries. This improves the performance of the system as more queries are run.
- Adaptive query optimization uses various statistics to adjust data structures and system resources after query execution has started—independently optimizing query execution for each node based on actual data distribution at runtime. This helps improve the performance of ad hoc queries by up to 25%.
- Auto data placement predicts the column on which tables should be partitioned in memory to achieve the best performance for queries. It also predicts the expected gain in query performance with the new column recommendation. This minimizes data movement across nodes due to suboptimal choices that can be made by operators when manually selecting the column.
- Auto compression determines the optimal compression algorithm for each column, which improves load and query performance with faster data compression and decompression. By reducing memory usage, customers can cut costs by up to 25%.
- Indexing (limited availability) automatically determines the indexes that customers should create or drop from their tables to optimize OLTP throughput, using machine learning to make a prediction based on individual application workloads. That helps customers eliminate the time-consuming tasks of creating optimal indexes for their OLTP workloads and maintaining those over time as workloads evolve.
In-database machine learning with AutoML
HeatWave AutoML includes everything users need to build, train, deploy, and explain machine learning models within MySQL HeatWave, at no additional cost.
No need for a separate machine learning service
With in-database machine learning in MySQL HeatWave, customers don’t need to move data to a separate machine learning service. They can easily and securely apply machine learning training, inference, and explanation to data stored both inside MySQL and in the object store with HeatWave Lakehouse. As a result, they can accelerate ML initiatives, increase security, and reduce costs.
Save time and effort with machine learning lifecycle automation
HeatWave AutoML automates the machine learning lifecycle, including algorithm selection, intelligent data sampling for model training, feature selection, and hyperparameter optimization—saving data analysts and data scientists significant time and effort. Aspects of the machine learning pipeline can be customized, including algorithm selection, feature selection, and hyperparameter optimization. HeatWave AutoML supports anomaly detection, forecasting, classification, regression, and recommender system tasks, including on text columns.
Recommender system for personalized recommendations
By considering both implicit feedback (past purchases, browsing behavior, and so forth) and explicit feedback (ratings, likes, and so forth), the HeatWave AutoML recommender system can generate personalized recommendations. Analysts, for instance, can predict items that a user will like, users who will like a specific item, and ratings that items will receive. They can also, given a user, obtain a list of similar users, and given a specific item, obtain a list of similar items.
Interactive MySQL HeatWave AutoML console
The interactive console lets business analysts build, train, run, and explain ML models using a visual interface—without using SQL commands or any coding. The console also makes it easy to explore what-if scenarios to evaluate business assumptions—for example, “How would investing 30% more in paid social media advertising affect both revenue and profit?”
Faster, less expensive, and more accurate than Redshift ML
Benchmarks demonstrate that, on average, HeatWave AutoML produces more accurate results than Amazon Redshift ML, trains models up to 25X faster at 1% of the cost, and scales as more nodes are added.
Explainable ML models
All the models trained by HeatWave AutoML are explainable. HeatWave AutoML delivers predictions with an explanation of the results, helping organizations with regulatory compliance, fairness, repeatability, causality, and trust.
Use current skills
Developers and data analysts can build machine learning models using familiar SQL commands; they don’t have to learn new tools and languages. Additionally, HeatWave AutoML is integrated with popular notebooks such as Jupyter and Apache Zeppelin.
Generative AI and vector store
Currently in private preview, the vector store will enable customers to leverage the power of large language models (LLMs) with their proprietary data to get answers that are more accurate than using models which have been trained on public data only. With generative AI and vector store capabilities, customers can interact with MySQL HeatWave in natural language and efficiently search documents in various file formats in HeatWave Lakehouse.
Fully managed database service
Improve productivity by automating time-consuming tasks such as high-availability management, patching, upgrades, and backup with a fully managed database service. Accelerate application development with instant provisioning of resources.
Built, managed, and supported by the MySQL engineering team
Developers can deliver modern, cloud native database applications with immediate access to the latest features from the MySQL team. MySQL security patches are automatically applied to limit exposure to security vulnerabilities. MySQL HeatWave is 100% compatible with on-premises MySQL for a seamless transition to the cloud without changes to applications.
MySQL HeatWave interactive console: Manage resources, run queries, and monitor performance
Developers and DBAs can easily create and manage MySQL Database and HeatWave nodes. Within the console, they can access MySQL Autopilot capabilities, such as auto-provisioning, to determine the optimal configuration of their HeatWave cluster. They can view and administer the tables loaded in MySQL HeatWave as well as rapidly build and run queries.
The console also lets developers and DBAs monitor the performance of the MySQL Database node and the HeatWave cluster. They can monitor the use of various hardware resources and diverse query execution metrics.
Advanced security
Advanced security features let customers implement additional security measures to protect data throughout its lifecycle and help comply with regulatory requirements.
Asymmetric encryption with key generation and digital signatures
Server-side asymmetric encryption enables developers and DBAs to increase the protection of confidential data using both public and private keys. They can also implement digital signatures to confirm the identity of people signing documents. Developers can encrypt data without modifying current applications. They get the tools they need for encryption, key generation, and digital signatures.
Hide your data
Data masking and deidentification hides and replaces real data values with substitutes (selective masking, random data substitution, blurring, and other functions are available). With data masking and deidentification in MySQL HeatWave, customers reduce the risk of a data breach by hiding sensitive data, which can then be used in nonproduction systems, such as development and test environments. These data masking functions are available when queries are executed on the MySQL Database node or the HeatWave cluster.
Block unauthorized database activities
The MySQL HeatWave database firewall monitors database threats, automatically creates an allowlist of approved SQL statements, and blocks unauthorized database activity. It provides real-time protection against database-specific attacks, such as SQL injections.
Faster than Amazon and Snowflake at a fraction of the cost
MySQL HeatWave is faster and less expensive, as demonstrated by multiple standard industry benchmarks, including TPC-H, TPC-DS, and CH-benCHmark.
Analytics: Snowflake, Amazon Redshift, Google BigQuery, Azure Synapse, and Amazon Aurora are slower and more expensive
- Snowflake: 3.3X slower; 8X more expensive
- Amazon Redshift: 4.2X slower; 5X more expensive
- Google BigQuery: 5.6X slower; 5X more expensive
- Azure Synapse: 3X slower; 5X more expensive
- Amazon Aurora: 1,400X slower; 2X more expensive
Mixed workloads: Amazon Aurora is slower and more expensive
Most real-world applications have a mix of OLTP and complex OLAP queries. For such workloads, MySQL HeatWave is much faster and costs a fraction of Amazon Aurora. Using the industry standard CH-benCHmark on a 100 GB dataset for OLAP queries, Amazon Aurora is 18X slower, provides 110X less throughput, and is 2.4X more expensive than MySQL HeatWave. For OLTP queries, Amazon Aurora has the same performance as MySQL HeatWave and is 2.4X more expensive.
Real-time elasticity
Real-time elasticity enables customers to increase or decrease the size of their HeatWave cluster by any number of nodes without incurring any downtime or read-only time.
Consistent high performance, even at peak times, and reduced costs with no downtime
The resizing operation takes only a few minutes, during which time HeatWave remains online, available for all operations. Once resized, data is downloaded from object storage, automatically rebalanced among all available cluster nodes, and becomes immediately available for queries. As a result, customers benefit from consistently high performance, even at peak times, and lower costs by downsizing their HeatWave cluster when appropriate—without incurring any downtime or read-only time.
With efficient data reloading from object storage, customers can also pause and resume their HeatWave cluster to reduce costs.
No overprovisioned instances
Customers can expand or reduce their HeatWave cluster to any number of nodes. They aren’t constrained to overprovisioned and costly instances forced by rigid sizing models offered by other cloud database providers. With HeatWave customers pay only for the exact resources they use.
MySQL HeatWave Lakehouse
MySQL HeatWave includes MySQL HeatWave Lakehouse, letting users query half a petabyte of data in object storage—in a variety of file formats, such as CSV, Parquet, Avro, and export files from other databases. The query processing is done entirely in the HeatWave engine, enabling customers to take advantage of HeatWave for non-MySQL workloads in addition to MySQL-compatible workloads. With HeatWave Lakehouse, MySQL HeatWave provides one cloud database service for transaction processing, real-time analytics across data warehouses and data lakes, and machine learning—without ETL across cloud services.
Faster and less expensive than Snowflake, Amazon Redshift, Databricks, and Google BigQuery
As demonstrated by a 500 TB TPC-H benchmark, the query performance of MySQL HeatWave Lakehouse is
- 9X faster than Amazon Redshift, delivering 8X better price-performance
- 17X faster than Databricks, delivering 18X better price-performance
- 17X faster than Snowflake, delivering 22X better price-performance
- 36X faster than Google BigQuery, delivering 30X better price-performance
The data load performance of MySQL HeatWave Lakehouse is
- 2X faster than Snowflake, delivering 3X better price-performance
- 6X faster than Databricks, delivering 6X better price-performance
- 8X faster than Google BigQuery, delivering 7X better price-performance
- 9X faster than Amazon Redshift, delivering 8X better price-performance
Fast lakehouse analytics and machine learning on all data
Customers can query data in various formats in object storage, transactional data in MySQL databases, or a combination of both using standard SQL commands. Querying the data in object storage is as fast as querying the databases, as demonstrated by a 10 TB TPC-H benchmark.
With HeatWave AutoML, customers can use data in object storage, the database, or both to automatically build, train, deploy, and explain ML models—without moving the data to a separate ML cloud service.
Scale-out architecture for data management and query processing
HeatWave’s massively partitioned architecture enables a scale-out architecture for MySQL HeatWave Lakehouse. Query processing and data management operations, such as loading/reloading data, scale with the size of data. Customers can query up to half a petabyte of data in object storage with MySQL HeatWave Lakehouse without copying it to the MySQL database. The HeatWave cluster scales to 512 nodes.
Increase performance and save time with machine learning–powered automation
MySQL Autopilot capabilities, such as auto provisioning, auto query plan improvement, and auto parallel loading, have been enhanced for MySQL HeatWave Lakehouse, further reducing database administration overhead and improving performance. New MySQL Autopilot capabilities are also available for MySQL HeatWave Lakehouse.
- Auto schema inference automatically infers the mapping of file data to the corresponding schema definition for all supported file types, including CSV. As a result, customers don’t need to manually define and update the schema mapping of files, saving time and effort.
- Adaptive data sampling intelligently samples the files in object storage to derive information used by MySQL Autopilot to make predictions for automation. Using adaptive data sampling, MySQL Autopilot can scan and make predictions, such as schema mapping on a 400 TB file in less than one minute.
- Adaptive data flow lets MySQL HeatWave Lakehouse dynamically adapt to the performance of the underlying object store in any region to improve overall performance, price-performance, and availability.
Key capabilities |
Available on OCI |
Available on AWS |
|---|---|---|
| Fully managed service | yes |
yes |
| OLTP and OLAP in MySQL | yes |
yes |
| Query acceleration for analytics and mixed workloads | yes |
yes |
| Data compression | yes |
yes |
| Machine learning–powered automation (MySQL Autopilot for HeatWave and OLTP)* | yes |
yes |
| Advanced security* | yes |
yes |
| In-database machine learning (HeatWave AutoML) | yes |
yes |
| Scale-out data management | yes |
yes |
| Interactive query and data management console | Coming soon | yes |
| Performance and workload monitoring from the console | Coming soon | yes |
| Interactive MySQL HeatWave AutoML console | Coming soon | yes |
| Adding HeatWave to any MySQL shape | Coming soon | yes |
| MySQL HeatWave Lakehouse | yes |
Limited availability |
* Auto thread pooling and auto shape prediction in MySQL Autopilot as well as the MySQL HeatWave database firewall will be available soon on OCI.
MySQL HeatWave: Architected for performance and scalability
MySQL HeatWave Lakehouse delivers the best performance and price-performance
As demonstrated by a 500 TB TPC-H benchmark, the query performance of MySQL HeatWave Lakehouse is
- 9X faster than Amazon Redshift, delivering 8X better price-performance
- 17X faster than Databricks, delivering 18X better price-performance
- 17X faster than Snowflake, delivering 22X better price-performance
- 36X faster than Google BigQuery, delivering 30X better price-performance
See the performance details and learn more about the benchmark setup configuration
MySQL HeatWave vs. Amazon Aurora, Amazon Redshift, and Snowflake
MySQL HeatWave helps Tetris.io triple revenues
Read the dbInsight case study to see how MySQL HeatWave enabled an advertising technology company to triple revenues and increase customer retention rates.
MySQL HeatWave scorches AWS on its own cloud
Discover why, according to Futurum Research, “a robust HeatWave warning clearly remains in effect across the cloud database landscape.”
A game changer for machine learning capabilities
Find out why according to IDC, HeatWave AutoML is "a game changer for application developers and a broad range of data analysts and scientists."
Enormous MySQL HeatWave TCO advantages
In this in-depth analysis, Wikibon discusses the TCO advantages of MySQL HeatWave over its competitors, praising it as an “unprecedented breakthrough in query processing and machine learning.”
See what top industry analysts are saying about MySQL HeatWave
IDC
“MySQL HeatWave on AWS is a very compelling solution not just for analytics but also for OLTP and mixed workloads, as may be seen in publicly available benchmarks. For any developers working with MySQL on AWS, Oracle has just dropped a big productivity boost on your doorstep without the big price tag.”
NAND Research
“HeatWave is the only cloud data lakehouse service to query data in object storage and in the database at the same speed, which has never been achieved before...Oracle simplifies the experience for users, removing the need to keep multiple copies of data across multiple object stores, paying for data movement and pipelines, all while shuffling critical data around.”
Wikibon
“MySQL HeatWave, now with Lakehouse, may be the most significant open source cloud database innovation in the last decade….MySQL HeatWave just took a giant leap by increasing the scale-out processing by a factor of 8x to 512 nodes. The ability of HeatWave to load and query data on such a massive number of nodes in parallel is the first in the industry. Expect it to spur a market focus on much lower cost/performance, accelerated innovation, and increased competition.”
Moor Insights & Strategy
“Oracle introduced MySQL HeatWave and they did send shockwaves because they named and shamed basically every database company out there and my favorite is what they talked about with Snowflake….You can spend $80K on HeatWave and that would cost you $420K to run on Snowflake.”
Introducing Vector Store and Generative AI in MySQL HeatWave
Nipun Agarwal, Oracle Senior Vice President, MySQL HeatWave Development
With support for Generative AI, users can interact with MySQL HeatWave in natural language. Both the user queries and the response from the system are generated in natural language using a Large Language Model (LLM).
MySQL HeatWave pricing
MySQL HeatWave
Product |
Comparison price (/vCPU)* |
Unit price |
Unit |
MySQL Database—Standard - AMD E4 - Compute |
OCPU per hour |
||
MySQL Database—Standard - AMD E4 - Memory |
Gigabyte per hour |
||
MySQL Database—Standard - Intel X9 - Compute |
OCPU per hour |
||
MySQL Database—Standard - Intel X9 - Memory |
Gigabyte per hour |
||
MySQL Database—Optimized - Intel X9 - Compute |
OCPU per hour |
||
MySQL Database—Optimized - Intel X9 - Memory |
Gigabyte per hour |
||
MySQL Database—Storage |
Gigabyte storage capacity per month |
||
MySQL Database—Backup Storage |
Gigabyte storage capacity per month |
||
HeatWave—Standard |
Node per hour |
||
MySQL Database for HeatWave—Standard |
Node per hour |
||
MySQL Database for HeatWave—Bare Metal Standard |
Node per hour |
||
Oracle Cloud Infrastructure - HeatWave |
HeatWave capacity per hour |
||
Oracle Cloud Infrastructure - HeatWave - Storage |
Gigabyte storage capacity per month |
- *To make it easier to compare pricing across cloud service providers, Oracle web pages show both vCPU (virtual CPUs) prices and OCPU (Oracle CPU) prices for products with compute-based pricing. The products themselves, provisioning in the portal, billing, etc. continue to use OCPU (Oracle CPU) units. OCPUs represent physical CPU cores. Most CPU architectures, including x86, execute two threads per physical core, so 1 OCPU is the equivalent of 2 vCPUs for x86-based compute. The per-hour OCPU rate customers are billed at is therefore twice the vCPU price since they receive two vCPUs of compute power for each OCPU, unless it's a sub-core instance such as preemptible instances. Additional details supporting the difference between OCPU vs. vCPU can be accessed here.
Small configuration
SCENARIO
A marketing agency wants to analyze advertising campaign performance in real-time. 1 TB of data.
SPECS
- 1 MySQL Database node (VM) - 16 OCPU (32vCPUs) and 512 GB memory
- 2 HeatWave nodes – 512 GB memory
- MySQL Database Storage – 1 TB
ESTIMATED MONTHLY COST
US$ 829.24
Medium configuration
SCENARIO
A telecommunications company wants to analyze its customers’ communication patterns in real-time. 10 TB of data.
SPECS
- 1 MySQL Database node (BM) - 128 OCPU (256vCPUs) and 2048 GB memory
- 13 HeatWave nodes – 512 GB memory
- MySQL Database Storage – 10 TB
ESTIMATED MONTHLY COST
US$ 5,467.53
Large configuration
SCENARIO
An automotive company wants to obtain real-time telemetry analytics. 30 TB of data.
SPECS
- 1 MySQL Database node (BM) - 128 OCPU (256vCPUs) and 2048 GB memory
- 38 HeatWave nodes – 512 GB memory
- MySQL Database Storage – 30 TB
ESTIMATED MONTHLY COST
US$ 12,844.49
Currently available from North America, Europe, Japan, and India.
ECPU (Elastic CPU) per hour is defined as a combination of the total CPU hours used by MySQL Database and a measure of work done by the MySQL Database and HeatWave. HeatWave capacity per hour is defined as a unit of 16 gigabyte memory hours allocated in MySQL HeatWave.
MySQL HeatWave on AWS
Product |
Unit price |
Unit |
HeatWave—AWS |
HeatWave capacity per hour |
|
MySQL Database—AWS—ECPU |
ECPU per hour |
|
MySQL Database—AWS—storage |
Gigabyte storage capacity per month |
|
MySQL Database—AWS—backup storage |
Gigabyte storage capacity per month |
|
MySQL Database—AWS—outbound data transfer—inter AWS region |
Gigabyte of data transferred |
|
MySQL Database—AWS—outbound data transfer—to internet |
Gigabyte of data transferred |
Small configuration
SCENARIO
A municipality is launching a new application to conduct various surveys and wants to run real-time analytics on the data. 50GB of data.
SPECS
- 1 MySQL Database node - 1 ECPU (2 vCPUs) and 16 GB memory
- 2 HeatWave nodes – 16 GB memory
- MySQL Database Storage – 50 GB
ESTIMATED MONTHLY COST
US$ 116
Medium configuration
SCENARIO
A marketing agency wants to analyze advertising campaign performance in real-time. 1 TB of data.
SPECS
- MySQL Database node - 4 ECPUS (8 vCPUs) and 64 GB memory
- 3 HeatWave nodes – 256 GB memory
- MySQL Database Storage – 1 TB
ESTIMATED MONTHLY COST
US$ 2,028
Large configuration
SCENARIO
A telecommunications company wants to analyze its customers’ communication patterns in real-time. 10 TB of data.
SPECS
- 1 MySQL Database node - 16 ECPUS (32 vCPUs) and 256 GB memory
- 25 HeatWave nodes – 256 GB memory
- MySQL Database Storage – 10 TB
ESTIMATED MONTHLY COST
US$ 16,486
MySQL resources
Documentation
HeatWave is a massively parallel, high performance, in-memory query accelerator that increases MySQL performance by orders of magnitude for analytics and mixed workloads—without any changes to existing applications.
Customer community
Join the conversation by visiting the MySQL HeatWave customer forum.
Cloud learning
Get the most out of MySQL HeatWave with a MySQL learning subscription.
Support and services
Get 24/7 access to MySQL support with My Oracle Support.
MySQL HeatWave related products
The Oracle Playbook series
We've compiled the secrets to our people, process, and systems strategy. And we want to share it with you.
Get started with MySQL HeatWave
Try MySQL HeatWave for free
Explore with $300 in free credits.
Contact us
Interested in learning more? Contact one of our experts.