MySQL HeatWave increases ease-of-use for
customers with vector store, AutoML and Lakehouse enhancements,
JSON, and JavaScript support
LAS
VEGAS, Sept. 20, 2023 /PRNewswire/ -- Oracle
CloudWorld -- Oracle today announced significant
enhancements to MySQL HeatWave, including support for vector store,
generative AI, new in-database machine learning features, MySQL
Autopilot enhancements, new HeatWave Lakehouse capabilities,
support for JavaScript, acceleration of JSON queries, and support
for new analytic operators. 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.
"Today's enhancements to MySQL HeatWave are another significant
step on our journey to address pressing customer data, analytics,
and AI issues," said Edward Screven,
chief corporate architect, Oracle. "We've previously added
real-time analytics with the best price-performance in the
industry, automated machine learning, lakehouse, and multicloud
capabilities to HeatWave. Now vector store and generative AI bring
the power of LLMs to customers, providing them with an intuitive
way to interact with data in their enterprise and get the accurate
answers that they need for their business."
For customers looking to perform analytics, transaction
processing, machine learning, and generative AI across a variety of
data types and sources, additional capabilities have been added to
MySQL HeatWave—for both MySQL-compatible and non-MySQL
workloads.
Generative AI and vector store (private preview)
The vector store ingests documents in a variety of formats such as
PDF and stores them as embeddings generated via an encoder model.
For a given user query, the vector store identifies the most
similar documents by performing a similarity search over the stored
embeddings and the embedded query. These documents are used to
augment the prompt given to the LLM so that it provides a more
contextual answer.
MySQL HeatWave AutoML
MySQL HeatWave provides
in-database machine learning with a fully automated pipeline for
training models. 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 inside MySQL HeatWave. The following new capabilities have
been added:
- Support for HeatWave Lakehouse: Customers can now
leverage HeatWave AutoML for training, inference, and explanations
on data in object storage in addition to data in the MySQL
database—and use a much wider set of data for machine
learning.
- Text column support: Enables customers to perform
machine learning tasks - anomaly detection, forecasting,
classification, regression, and recommender system - on text
columns, further broadening the corpus of data on which customers
can leverage HeatWave AutoML.
- Enhanced recommender system: With support for Bayesian
Personalized Ranking (BPR), HeatWave AutoML can now consider both
implicit feedback (past purchases, browsing behavior) and explicit
feedback (ratings, likes) to generate personalized recommendations.
As an example, analysts can predict items a user will like, users
who will like a specific item, and ratings items will receive.
- Training Progress monitor: Customers can now monitor the
progress of the model training with HeatWave AutoML, allowing them
to better manage resources.
MySQL Autopilot
MySQL Autopilot is a
built-in capability of MySQL HeatWave that uses machine
learning-powered automation to help improve performance and
scalability without requiring database tuning expertise. It learns
from the execution of queries to improve the execution plan of
future queries. The latest enhancements to MySQL Autopilot
include:
- MySQL Autopilot indexing (limited availability): Helps
customers eliminate the time-consuming tasks of creating optimal
indexes for their OLTP workloads and maintaining those over time as
workloads evolve. MySQL Autopilot automatically determines the
indexes customers should create or drop from their tables to
optimize their OLTP throughput, using machine learning to make a
prediction based on individual application workloads. In addition,
Autopilot indexing predicts the expected improvement with the
recommended indexes without creating those indexes and without
incurring compute or storage overhead on the users' tenancy.
- Auto compression: Helps customers determine 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 percent.
- Adaptive query execution: Helps customers optimize the
execution plan of a query after the query has started to execute,
improving the performance of ad hoc queries by up to 25
percent. It uses information obtained from the partial
execution of the query to adjust data structures and system
resources and then independently optimizes query execution for each
HeatWave node based on actual data distribution at run
time.
- Auto load and unload: Autopilot automatically loads the
columns being used in an application workload to HeatWave and
automatically unload tables that were never or rarely queried. This
helps free up memory and reduce costs for customers, without having
to manually perform this task.
Additional MySQL HeatWave Enhancements
- JavaScript support (limited availability): Enables
customers to write stored procedures and functions in JavaScript
and execute them inside MySQL HeatWave. This makes it easier for
developers to write rich application logic in JavaScript and get
high performance by executing the program inside the MySQL
database. The performance of JavaScript applications is improved
since data is not transferred from the database to the client and
code is just-in-time (JIT) compiled in the GraalVM runtime.
- JSON acceleration: Developers and DBAs can now take
advantage of HeatWave for real-time analytics on JSON documents
stored in the MySQL database, accelerating queries by orders of
magnitude.
- New analytic operators: With support for new analytic
operators including CUBE, Hyper Log Log, Qualify, and Table sample,
customers can migrate more workloads to MySQL HeatWave.
- Bulk ingest into MySQL HeatWave: Support for parallel
building of index sub-trees while bulk loading data from CSV files
helps customers achieve a 10X improvement in data ingestion
performance over Amazon Aurora. As a result, data can be queried
sooner and the system resources used for loading data are freed up
much faster, lowering costs for customers.
"The MySQL HeatWave engineering team is clearly doubling down on
AI and machine learning innovation," said Steve McDowell, principal analyst and founding
partner, NAND Research. "Not only can customers now train ML models
on data both in the database and in object storage with full
automation, but with the new generative AI and vector store
capabilities they'll be able to interact with HeatWave in natural
language, and they'll receive accurate answers for their own
business purposes only—based on their own enterprise data in
addition to publicly available data. The flexibility to use
whichever LLMs organizations prefer continues to demonstrate the
open and collaborative approach of the MySQL HeatWave engineering
team."
MySQL HeatWave is the only cloud service that provides
transaction processing, real-time analytics, machine learning, data
lake querying, and machine learning-powered automation within a
single MySQL database service. A core part of Oracle's distributed
cloud strategy, MySQL HeatWave is available natively on OCI and
Amazon Web Services, as part of the Oracle Database Service for
Azure, and in customers' data centers with OCI Dedicated
Region.
Additional Resources
- Read the MySQL HeatWave technical blog
- Discover the MySQL HeatWave migration program
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