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Using Data Indexes to Boost Performance and Minimize Footprint in Embedded Software

Are you using data indexes to boost performance in locating data objects in embedded software?  If so, the right index can enhance lookup speed logarithmically, while reducing RAM and CPU demands. While the B-Tree is the best known index, there are many others can be more efficient in specific circumstances, such as ingeospatial/mapping and telecom/networking applications. This Webinar examines less well-known indexes including T-Tree, Hash table, R-Tree, Patricia trie and others. It emphasizes index implementation methods that avoid data duplication, to minimize memory footprint.

eXtremeDB provides specialized indexes to facilitate particular types of database search and navigation.

  • B-Tree
  • Patricia Trie (ideal for managing IP addresses)
  • R-Tree (example using spatial coordinates)
  • KD-Tree (example using Query-by-example)
  • Trigram (using Trigram indexes for text searches)
  • Autoid indexes and unique Object Id searches
  • List indexes
Learn more about eXtremeDB indexes
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Visit the index page in our online documentation.

View short segments from the Webinar about these indexes:

  • Patricia trie

  • T-tree

  • B-tree

  • R-tree

  • Hash indexes

What makes eXtremeDB different?

Our extensive combination of features.  Learn about features designed to:

Maximize database reliability
Reduce memory usage
Develop a faster database

Related Resources

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Embedded Databases: Building In Always On High Availability
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Embedded Databases: Make or Break Technology Choices for High Performance Applications
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More than just IoT database Webinars, find our experts in respected journals

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Review a list of articles

Code Size vs. Memory Footprint And Why They Matter
The terms “code size” and “footprint” are often used interchangeably. But they are not the same—code size is a subset of footprint. In this article, McObject CEO Steve Graves explains the difference and relevance. He then describes some techniques to minimize footprint. Read more in Circuit Cellar

Data Indexes Boost Embedded Software’s Performance and Efficiency. This article in Military Embedded Systems explains the benefits of specialized database indexes such as R-Trees for mapping and navigation, and Patricia Tries for communications systems.

Comparing Optimistic and Pessimistic Concurrency
Usually (with rare exception), a database is a shared resource, meaning that it is used concurrently by two or more tasks. This leads us to the topic of concurrency control; i.e. how do we coordinate a tasks’ access to the database? This is part-and-parcel of providing a database management system that adheres to the ACID properties. 

Does Dr. Who Use An Embedded Database? This technical article in EPN explains how a type-safe API is used to bolster stability in an aerospace system.

Change Data Capture in Embedded Databases
Change Data Capture (CDC) is broadly defined as tracking changes in a database. The purposes of tracking changes are many and varied. CDC in embedded database systems can be implemented in several different ways that are sometimes invisible to applications, and ways that applications can exploit for data sharing, responding to events, and incremental back up. 

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We have been testing, improving on, and retesting our software from the beginning in 2001 in order to provide our clients with the best possible data management solutions. Read “Database Persistence, Without The Performance Penalty” and more.

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White paper: Exploring Code Size and Footprint

The terms ‘code size’ and ‘footprint’ are often used interchangeably. But they are not the same; code size is a subset of footprint. This paper will explain the differentiation and relevance, then proceed to describe some of the techniques employed within eXtremeDB to minimize footprint.

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A distributed database system is one in which the data belonging to a single logical database is distributed to two or more physical databases. Beyond that simple definition, there are a confusing number of possibilities for when, how, and why the data is distributed. Some are applicable to edge and/or fog computing, some others are applicable to fog and/or cloud  computing, and some are applicable across the entire spectrum of edge, fog and cloud computing.

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In-memory database systems (IMDSs) have changed the software landscape, enabling “smarter” embedded applications and sparking mergers and acquisitions involving the largest technology companies. But IMDSs’ popularity has sparked a flurry of products falsely claiming to be in-memory database systems. Understanding the distinction is critical to determining the performance, cost and ultimately the success or failure of a solution. This white paper examines specific products, seeking to answer the question, “is it really an in-memory database system?”