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Announcing SingleStore Self-Managed with IBM 8.5

By PETER CASLER posted Thu October 24, 2024 11:13 AM

  

The one Database for All Data-Intensive and Real Time Applications

With near ubiquitous connectivity driving high-velocity, high-volume data workloads, the SingleStore Self-Managed with IBM database enables companies to simplify their data architectures while delivering the ultra-fast speed and elastic scalability needed to create breakthrough experiences.

Designed for intelligent applications, SingleStore Self-Managed with IBM is a real-time data platform that can read, write and reason on petabyte-scale data in a few milliseconds and allows you to transact, analyze and contextualize data.

Our latest innovations underscore our focus on outcomes that matter to customers including speed, reliability and ease.

New in SingleStore Self-Managed with IBM 8.5:

Kai:

Kai is a fast, easy, and powerful API to drive up to 100x faster analytics on your MongoDB applications without any query changes, application migration or data transformations. 

An internal BSON type has been added for use by the Kai 1 MongoDB -compatible API. This dramatically improves compatibility with MongoDB when using the Kai interface.

Vector Search Enhancements:

Two important new features have been added to improve vector data processing and the performance of vector search:

1. Indexed approximate-nearest-neighbor (ANN) search:

Indexed ANN vector search facilitates the creation of large-scale semantic search and generative AI applications. Supported index types include inverted file (IVF), hierarchical navigable small world (HNSW), and variants of both based on product quantization (PQ) – a vector compression method.

2. A VECTOR data type:

The VECTOR type makes creating, testing, and debugging vector-based applications easier. New infix operators are available for DOT_PRODUCT (<*>) and EUCLIDEAN_DISTANCE (<->) to help shorten queries and make them more readable.

SQL Improvements:

  • INSERT ON DUPLICATE KEY now supports a DELETE, enabling streaming maintenance of aggregates, e.g. the total number of people currently viewing an online video stream.

  • Name length limits for tables, views, and columns have increased to 256 characters (from 64) for easier porting of apps from other database products such as Oracle and Microsoft SQL Server.

Programmability Improvements:

  • Extensions are supported. These are packages of PSQL and Wasm-based functions, which can now be installed as a unit with a single command rather than one at a time; this eases the management of logical extension packages and improves the sharing of extension code.

  • New table%rowtype and tablecolumn%type 1 notation is supported to allow easier creation and evolution of stored procedures and anonymous code blocks that need to declare variables with types that track table objects. This also makes it easier to port code from Oracle to SingleStore Self-Managed with IBM.

Monitoring Improvements:

  • Query history is now supported. This allows you to trace every occurrence of every query (above some runtime threshold you can set), including the query text, who ran it, when it ran, how long it took, and much more. This greatly simplifies application performance monitoring and tuning.

Performance Improvements:

  • ORDER BY/LIMIT queries now perform better under flexible parallelism.

  • More than a dozen query optimization improvements, including correlation statistics, LEFT JOIN optimizations, and multiple new query transformations.

SingleStore Self-Managed with IBM makes real-time decision making a reality:

The market is demanding databases be able to store more data, and access it with lower latency and higher throughput. This performance and data capacity requirement is often combined with a desire for flexible data access. These access patterns can range from low-latency, high-throughput writes for real-time data loading and deduplication to efficient batch loading and complex analytical queries over the same data.

Built on a modern, lock-free cloud-native architecture, SingleStore Self-Managed with IBM can process queries with extremely low latency, while scaling access to tens or hundreds of thousands of concurrent users. The ability to ingest millions of events per second with ACID transactions while running complex analytical queries on that data in real time means that as workloads grow, SingleStore Self-Managed with IBM can easily scale up to meet demand without complex application rewrites or data migrations.

Common Use Cases for SingleStore Self-Managed with IBM:

Building Gen AI apps with Simplicity: SingleStore Self-Managed with IBM offers a performant vector database together with an enterprise-grade data platform with in-built functions, delivering fast hybrid search that includes keyword match, vector similarity and semantic search with high recall to power modern generative AI applications.

Operation Analytics: SingleStore Self-Managed with IBM helps you deliver fast, scalable reporting and analytics across your operational data — including streaming, real-time and historical data. With SingleStore Self-Managed with IBM you can meet the toughest service-level agreements using distributed, lock-free ingestion and real-time query processing.

Dashboard Acceleration: SingleStore Self-Managed with IBM delivers fast data ingestion and extreme query performance for every data workload. Whether you are looking to speed up existing dashboards, or build new data applications and analytics, SingleStore Self-Managed with IBM provides the performance and scale for high concurrent-user access. 

Fraud and Anomalies: Most organizations are struggling to keep up with the influx of data or to access it when it matters. Ingesting and analyzing machine, sensor or application data delivers more value in real time. With SingleStore Self-Managed with IBM, you can continually monitor and detect fraud and anomalies.

IOT Analytics: As always-on devices and sensors proliferate, their data must be immediately ingested and actionable — and the infrastructure behind each IoT app is powered by a real-time database. SingleStore Self-Managed with IBM supports fast ingest and concurrent analytics for sensor systems, delivering instant and actionable insights.

The IBM and SingleStore partnership: Better together

The IBM and SingleStore partnership promises to be a value add and win for the customer. IBM enhances the SingleStoreDB experience through added service centralization, facilitated integrations, support and consultancy services, and specialized warranty programs.

Start building your modern data architecture today!

For more information you can visit our partnership page.

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