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The eXtremeDB Financial Edition DBMS Platform

eXtremeDB Financial Edition benefits from a highly optimized and scalable underlying embedded in-memory database system (IMDS) design with rich developer tools.

In-Memory Database System  (IMDS)

eXtremeDB Financial Edition stores data in main memory to eliminate performance-draining I/O, cache management, data transfer, and other sources of latency. (Persistent storage can be added for selected record types – for example, when managing historical data; but an IMDS is ideal real-time data).


eXtremeDB runs entirely within the application process, eliminating inter-process communication (IPC) between client and server modules.

Short execution path

A minimal code path speeds execution by reducing the number of CPU cycles required per database operation and increasing the likelihood that code needed for an operation is already in the CPU cache.


The 64-bit eXtremeDB platform is proven managing terabyte-plus databases with billions of rows entirely in memory; persistently stored data scales to OS file system limits.  Choose between transactions managers to scale predominantly read-only and predominantly read-write and/or clustered applications.


eXtremeDB Financial Edition provides numerous tools to boost developer productivity and maximize reliability and performance of real-time capital markets applications. These include:

Multiple APIs. For the lowest latency, applications typically access eXtremeDB Financial Edition using its fast, native navigational C/C++ API. The product’s SQL implementation includes an ODBC API; a more succinct and easier to use proprietary interface; and a type 3, version 4 JDBC driver. Also provided are native Java and C# (.NET) APIs.

Market data enhancements. Developers use eXtremeDB Financial Edition’s optional columnar layout to efficiently organize trades, quotes and other time series data, and vector-based statistical functions to accelerate financial calculations.

Choice of indexes. Supported indexes include standard B-trees, hash indexes, KD-trees for multi-dimensional data and Query-by-Example (QBE), custom indexes and more.

Performance monitoring interface. GUI-based performance monitoring enables users to zero in on key metrics when fine-tuning to reduce latency. Performance metrics are also provided via an API, so developers can build this capability into applications, with their own GUI. (See a screen shot of the performance monitor.)

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