Bitmaps leveraging our graph database for mobile devices

Starting Guide

User Manual

Technical references

  • .Net
  • Java
  • C++
  • ObjectiveC
  • Python
  • openCypher

Sparksee technology

Small memory footprint that is easy to manage

The graph is represented through bitmap data structures that allow high compression rates.

Node adjacencies are represented by bitmaps to minimize their footprint.

Each value in the database is represented only once, avoiding unnecessary replication.

I/O efficiency retrieving data from the database

Each of the bitmaps is partitioned into chunks that fit into disk pages to improve I/O locality.

The number of times each data page is brought to memory is minimized with advanced I/O policies.

It provides direct access to OS buffers, avoiding the decoding and encoding of other solutions

Boosting performance at its maximum

Using bitmaps, operations are computed with binary logic instructions that simplify the execution in pipelined processors.

The C++ core avoids overhead execution and complex memory management, as opposed to Java based engines.

High concurrency and low latency query response times secure high performance even under stress conditions.

Wide compatibility to be user oriented

Full native indexing allows an extremely fast access to each of the graph data structures.

Natively available for .Net, C++, Python, Objective-C and Java, and for iOS and Android based mobile devices.

Low level API with direct access to the core engine functionalities.

Learn more about our patented bitmaps solution


 New way to represent graphs to encourage multilevel memories: Graph is split into smaller structures to favor the caching of significant parts Object identifiers are used to reduce memory requirements (OIDS) Specific structures to help in the navigation and traversal of edges Attributes are fully indexed to allow queries based on filters

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