In recent years, the business world has undergone rapid changes, setting new benchmarks across industries. Efficiency plays a crucial role in driving these innovations that are reshaping modern enterprises. The MySQL open-source relational database exemplifies this trend.
Its reliability, security, performance, flexibility, scalability & extensive community support have made MySQL highly efficient & transformed many businesses. Today, leading global companies including Facebook, Twitter, Netflix, YouTube, Uber, Airbnb, eBay, Booking.com, Bank of America & NASA rely on MySQL for their database needs.
Moreover, as organizations grapple with exponentially increasing data volumes, MySQL has emerged as the database of choice. For example, IDC predicts that total global data (the DataSphere) will grow massively from 59 zettabytes in 2020 to 175 zettabytes by 2025. They also forecast that by 2025, nearly 50% of all data will be stored in public cloud environments & 30% of data will be produced & consumed in real-time.
With its proven efficiency, MySQL is well positioned to manage these vast & rapidly growing data stores.
Challenges in Current Data & Analytics Solutions
However, alongside efficiency, MySQL users face challenges extracting, transforming & loading (ETL) data into dedicated databases for analytics, reporting & optimization. This invites errors, risks, delays, overhead & complexity, diminishing MySQL’s advantages.
It hinders real-time transactions, reporting & analytics on a single database. Running online transaction processing (OLTP) & online analytical processing (OLAP) workloads on the same MySQL database in real-time is key for businesses today.
MySQL is optimized for OLTP, not analytic processing (OLAP). Organizations needing efficient analytics must move data to another database. This introduces complexity & costs:
– Applications must define complex logic to extract pertinent data from MySQL
– Extracted data must be securely transported across networks, consuming bandwidth & adding latency
– External databases must be manually synced with MySQL, risking stale analytics data
– Added costs & overhead of managing multiple databases for OLTP & analytics
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The Oracle MySQL Database Service comes with HeatWave.
The Oracle MySQL Database Service with HeatWave is a powerful solution that enables enterprises to leverage MySQL, one of the world’s most popular databases, for both OLTP & OLAP workloads in real-time. HeatWave is an in-memory query accelerator that overcomes limitations of traditional data warehouses that rely on periodic ETL batch jobs. Together, Oracle’s managed MySQL services & HeatWave on Oracle Cloud Infrastructure deliver up to 400x faster performance compared to standard MySQL.
Moreover, Oracle MySQL Database Service provides 1100x greater query speed at one-third the cost versus AWS Aurora & Redshift. When combined with HeatWave’s parallel processing, the price-performance ratio is 8100x better than RDS. No other major cloud provider like Azure, AWS, or Google offers this level of performance at this price point.
The MySQL Database Service is also highly compatible with OLTP applications like social, ecommerce, fintech, & SaaS as well as analytics apps like Oracle Analytics Cloud, Tableau, Qlik, & Looker, significantly outperforming other database services.
As a cloud-native service on OCI, Oracle MySQL Database Service integrates seamlessly with other OCI offerings like Oracle Analytics Cloud for visualization, Cloud Guard for security, & Container Engine for Kubernetes. It stays current with the latest MySQL updates.
For enterprises wanting to remain on-premises, Oracle MySQL Database Service enables replication to OCI to leverage HeatWave analytics while shutting it down when not needed.
Extensive use of ML & AI in areas like provisioning & query scheduling further automate & optimize the service.
MySQL Database Service with HeatWave
Oracle identified several key requirements for improving MySQL’s analytics capabilities, including keeping data within MySQL, eliminating ETLs, no application changes, superior performance to MySQL an& d data warehouses, no impact on transaction speed, unified analytics processes, superior scalability, leveraging commodity infrastructure, & lower costs than alternatives.
After massive R&D investments, Oracle launched MySQL Database Service with HeatWave, exceeding those objectives. HeatWave is a fully integrated MySQL analytics engine that achieves orders-of-magnitude better performance than MySQL & data warehouses, while keeping data in MySQL, requiring no ETLs or application changes.
It scales seamlessly without slowing transactions, uses commodity hardware & software, & costs less than alternatives. HeatWave is available across all Oracle Cloud data centers & Cloud@Customer. Through deep innovation, Oracle delivered the first complete MySQL analytics solution.
The Design of HeatWave Architecture
The HeatWave Architecture has three key design choices that lead to impressive performance & cost benefits:
1. An innovative in-memory columnar analytics engine optimized for scalability & speed by implementing cutting-edge algorithms.
2. Tailored for optimal use on Oracle Cloud Infrastructure.
3. The Heatwave engine utilizes a columnar in-memory structure that enables vectorized processing, resulting in excellent query performance. The data is encrypted & compressed before loading into memory. This compressed & optimized in-memory format is used for both numeric & string data, significantly accelerating performance & reducing memory footprint for lower customer costs.
The primary emphasis in the design of the Heatwave engine is on the extensive partitioning of data across a cluster of HeatWave nodes, allowing for parallel processing of the partitioned data on each node. This allows for high cache hits for analytic operations & exceptional scalability between nodes.
Each HeatWave node in a cluster & each core inside a node can process the partitioned data in parallel, including parallel scans, group-by, joins, aggregation, & top-k processing. HeatWave has implemented state-of-the-art algorithms for distributed in-memory analytic processing. Joins within a partition are processed instantly using vectorized build & probe join kernels.
Highly optimized network communication between analytics nodes uses asynchronous batch I/Os. The algorithms overlap compute time with communication of data across nodes, achieving great scalability.
HeatWave Deployment Scenarios
HeatWave is an Oracle-managed analytics service that is only available on OCI. It significantly enhances the MySQL database by natively integrating analytics capabilities. As a result, customers using MySQL can easily perform analytics by simply enabling HeatWave.
A HeatWave instance consists of a MySQL Database Service (MDS) instance & multiple analytics nodes clustered together. When enabled, a HeatWave server is installed on the MDS node which handles cluster management, loading data into the analytics nodes’ memory, query scheduling, & & execution. Existing MySQL applications & tools using standard connectors like ODBC/JDBC work seamlessly with HeatWave.
HeatWave is also fully compatible with MySQL syntax, so current SQL-based applications & tools function without any query adjustments.
Analytics data is stored in a hybrid columnar compressed format in the memory of HeatWave nodes. The number of nodes needed is determined by the amount of analytic data, compression ratio achieved, & query characteristics. The Auto Provisioning advisor has the capability to automatically assess the node requirements.
Currently, HeatWave supports up to 24 nodes per cluster with approximately 10TB processing capacity. This is the max data amount that can be loaded into the nodes’ memory at once. There are no limits on the MySQL database’s storage, so users can choose which tables/columns to load into HeatWave’s memory. Unneeded tables can be removed to free up space.
HeatWave enables running massive transactional & analytical workloads simultaneously by executing transactions on the MySQL node while transparently propagating updates to the HeatWave cluster for analytics. This allows concurrent OLTP & real-time OLAP workloads in one platform.
For on-premises MySQL users, HeatWave’s hybrid deployment model replicates data to HeatWave without ETL, enabling analytics while meeting compliance/regulatory requirements.
MySQL Analytics in its native form
MySQL Database Service & HeatWave are integrated to provide a unified platform for transactional & analytical workloads. HeatWave is implemented as a pluggable MySQL storage engine, abstracting the underlying storage details from users. As a result, both HeatWave & MySQL can be managed through the same interfaces like the console, CLI, & APIs.
Since HeatWave is an in-memory engine, data persists in the MySQL InnoDB storage. This enables managing analytical data similar to transactional data in MySQL.
Users & applications access HeatWave via the MySQL database node in the cluster, using standard connectors & tools. HeatWave supports similar ACID guarantees & ANSI SQL standards as MySQL, including diverse data types. So existing applications can leverage HeatWave without any code changes, enabling seamless integration.
When a query is submitted to MySQL, the optimizer determines if it should be processed by HeatWave or MySQL based on the functions used & estimated processing time. The query is then executed on the chosen system, with results returned to MySQL & the user.
HeatWave data is persisted in MySQL InnoDB. Any updates are automatically propagated to HeatWave’s memory in real-time using an efficient change propagation algorithm, providing access to latest data.
Key Highlights:
- Unified Database Service: HeatWave integrates transaction processing, real-time analytics across data warehouses & data lakes, & machine learning within a single MySQL Database, eliminating the complexity & inefficiency of using separate systems.
- Exceptional Performance: By utilizing in-memory hybrid columnar processing & a massively parallel architecture, HeatWave delivers unmatched query performance & scalability.
- Advanced Machine Learning: HeatWave AutoML enables the creation, training, deployment, & explanation of machine learning models directly within the database, saving time & reducing expenses.
- Generative AI & Vector Store: HeatWave’s support for generative AI & vector store enhances natural language interactions & provides more precise insights using proprietary data.
- Real-Time Elasticity: HeatWave provides the flexibility to scale out data management & adjust cluster sizes in real-time without downtime, ensuring consistent performance & cost efficiency.
- Hybrid Cloud Capabilities: HeatWave supports hybrid deployments, allowing on-premises OLTP systems to utilize cloud-based OLAP analytics without the need for ETL, addressing compliance & regulatory requirements.
Conclusion
The Oracle MySQL Database Service coupled with HeatWave is unmatched in solving complex queries & performing analytics on MySQL data. Competitor offerings like AWS Redshift (with or without AQUA optimization), Amazon Aurora, Snowflake, Azure Synapse, & Google BigQuery cannot match HeatWave’s real-time analytics performance & capabilities.
Organizations using MySQL on-premises or in the cloud that need reporting & analytics should strongly consider Oracle’s MySQL Database Service with HeatWave. It delivers much faster performance, greater scalability, & lower costs than any other combination of a MySQL database & data warehouse.
HeatWave has fundamentally improved MySQL analytics. This advancement makes AWS Aurora, Redshift (with or without AQUA), Azure Synapse, & Google BigQuery completely outmatched.
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