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Introduction - The Global Database Edge Cache

PolyScale makes databases fast, everywhere.

PolyScale is a Platform as a Service that makes data-driven apps faster by simplifying global data distribution and caching. PolyScale caches database data locally to users reducing latency, accelerating read performance and scaling throughput.

Using PolyScale's global network (Points of Presence), applications use local database query-compute and storage, reducing latency, accelerating read performance and scaling throughput.

What Is PolyScale?

At its core, PolyScale is a global database cache as a service. Using PolyScale, database data can be distributed and cached, seamlessly scaling your current database without altering transactional semantics.

PolyScale's global network proxies and caches native database wire protocols. It transparently connects to your current database and requires no code or infrastructure changes. Queries are inspected and reads (SQL SELECT) can be cached geographically close to the requesting origin for accelerated performance. All other traffic (INSERT, UPDATE and DELETE) seamlessly pass through to the source database.

PolyScale provides a plug and play approach to global data distribution and scaling.

The Data Distribution and Scaling Challenge

With the adoption of globally replicated delivery (CDN) and distributed computing frameworks (Edge serverless), global latencies continue to be reduced. Users expect low latency performance no matter where they are or how complex the underlying requirements may be.

Today, it is relatively simple and inexpensive to deploy both static assets and business logic locally to users, pushing latencies lower. This paradigm shift however poses challenges at the data tier in that storing consistent data in a distributed manner implies additional operational overhead, cost, and strains monolithic databases.

Data latencies are typically reduced for users by scaling the database (for example adding read-replicas), or by implementing application level caching using in-memory solutions such as Redis. Both of these solutions come with ongoing costs. Distributed databases can help, but the distributed aspect makes them complex to deploy and maintain.

What Problems Does It Solve?

  • Regional Latency - Make data-driven apps blisteringly fast, no matter where the audience resides.
  • Read Query Performance - Execute any database query in single digit milliseconds, while also reducing performance variability.
  • High Scalability & Availability - Scale to thousands of concurrent connections with multi-region failover.
  • Reduced DB Workloads & Costs - Decouple reads to free up resources for writes, reduce infrastructure costs.