McObject LLC

Name: McObject LLC
Address: 33309 1st Way So, Federal Way, Federal Way, WA, 98003, US
Telephone: +14258888505

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• ARF mitigates connectivity issues
• An 8KB or LESS Edge Client
• The only HARD real-time DBMS
• In-memory and/or persistent
• Pipelining for the lowest possible latency
• Multiple indexes including ANN & Patricia trie
• 99.999% HA
www.McObject.com

10/14/2026

7 reasons to learn more about eXtremeDB
1) Active Replication Fabric™ to mitigate connectivity issues
2) An 8KB or LESS IoT Edge Client
3) The only COTS *hard* real-time option
4) Support for shared data in Asymmetric Multiprocessing (AMP) configurations
5) Platform independent interoperability (that’s really 2, but oh well)
6) Record setting time series data processing
7) Pipelining for the lowest possible latency

www.McObject.com

10/13/2026

Review the paper, “Optimizing Robotic Precision Unleashing Real-Time Performance With Advanced Foundational Software Solutions”. This QNX white paper explores the challenges of latency and jitter in robotic systems and the importance of deterministic behavior in safety- and mission-critical applications. It describes how real-time operating systems use predictable scheduling, deterministic interrupt handling, and microkernel-based architectures to improve the precision, reliability, and responsiveness of robotic control systems.

Read the paper: https://t.ly/QNX_Robotics

Learn more about eXtremeDB/rt, the one and only COTS deterministic *hard* real-time database management system, and a QNX partner https://t.ly/Se6bH

10/12/2026

Only eXtremeDB offers support for shared data in Asymmetric Multiprocessing (AMP) configurations, as well as the only COTS *hard* real-time database management option, making it ideal for mixed criticality systems. Small footprint eXtremeDB also offers platform independent interoperability.
Learn more https://t.ly/YMhJI

10/06/2026

Embedded data management is changing.

Devices are collecting more information, making more local decisions, and retaining more state. At the same time, engineering teams are still expected to control power, memory, storage cost, and long-term reliability.

That is why database evaluation should go beyond API features and benchmark speed. The storage stack also needs to be judged by predictable recovery, memory footprint, physical write volume, and behavior as the device fills.

At McObject, we build eXtremeDB for intelligent devices, embedded systems, and demanding data-driven applications. Our focus is helping engineering teams manage data without treating device constraints as an afterthought.

If embedded storage architecture is becoming a challenge in your product, I would be glad to compare notes.

Email us at [email protected] or learn more at McObject.com

10/05/2026

Five questions to ask before choosing the storage architecture for an embedded product:
1. What is the real write pattern—sequential, random, bursty, or continuous?
2. What must remain consistent after sudden power loss?
3. How does performance change as flash utilization rises (e.g. 40% full to 80% full)?
4. How much physical NAND traffic does each logical transaction create?
5. What endurance margin remains at the end of the required service life?

Benchmarking only throughput on a mostly empty device answers a very small part of the problem. A useful evaluation should include transaction behavior, cache size, garbage collection, recovery, and realistic fill levels.

The earlier these questions are answered, the more freedom the engineering team has to improve reliability and control hardware cost.

What would you add to this checklist?

10/02/2026

In a safety-critical embedded device, persistent data is part of product behavior—not an afterthought.

A medical controller, industrial system, or transportation device may need to preserve configuration, alarms, events, counters, and diagnostic history through power loss and unexpected resets. That makes transactional storage essential.

It also creates a design tension: every protection mechanism can add writes, while the device still has strict limits on flash capacity, power, space, and service life.

The goal is not simply to write less. It is to preserve the right data reliably, recover predictably, and avoid unnecessary work in the storage stack.

For long-life products, that balance should be tested under realistic utilization—not only on an empty device in the lab.

What persistent data is most critical in your system?

10/01/2026

Three embedded storage architectures can support the same application and still generate very different NAND workloads.

1. Database over a flash filesystem
2. Database over an FTL-provided block device
3. Database with integrated flash management

Each approach can be valid. The important question is where transaction management, mapping, garbage collection, and recovery work are performed—and whether multiple layers repeat similar work.

The difference becomes especially important when the workload contains frequent small updates, the device must survive power interruption, and flash utilization (the percent of available storage capacity used) increases over time.

Architecture decisions made early can influence flash capacity, endurance margin, performance consistency, and total system cost for years.

Which architecture does your current embedded product use?

09/30/2026

When an embedded device begins running out of storage headroom, which response does your team consider first?

A. Increase flash capacity
B. Tune the filesystem or FTL
C. Reduce the application write rate
D. Revisit the database architecture

There is no universal answer. The right choice depends on the workload, utilization, transaction size, cache behavior, recovery requirements, and the expected service life of the device.
But one lesson appears repeatedly: adding more flash can hide the symptom without addressing the write amplifi
cation underneath it. Measuring the entire storage stack usually reveals more options.

Vote and share the factor that most influences your decision.

09/29/2026

Write amplification is easy to overlook because applications see logical writes, while the flash device absorbs the physical work.
A transaction may update only a small amount of application data. Underneath, the system may also write recovery information, metadata, mapping changes, and relocated pages created by garbage collection and/or wear leveling.

That is why a write amplification factor of 3 matters: every 1 MB of logical data results in 3 MB programmed to NAND. The consequences are practical, not academic:
• Faster consumption of flash endurance
• More spare capacity needed for garbage collection
• Higher storage cost
• More uncertainty late in the product lifecycle

The database, filesystem, FTL, cache, and transaction model all influence the result. Storage architecture deserves to be evaluated as a complete stack.

What part of the stack has created the biggest surprise in your projects?

09/28/2026

When does embedded data need a database rather than another custom file or in-memory structure?

The answer is usually not based on data volume alone. A database becomes valuable when the application needs several of these capabilities at the same time:
• Multiple records that must remain consistent
• Fast lookup through indexes or queries
• Concurrent access from different tasks
• Predictable recovery after interrupted updates
• Schema evolution across a long product life
• A reliable way to manage increasing data complexity

Hand-built storage can look simpler at the beginning. But as requirements grow, application code often starts recreating transactions, indexing, locking, recovery, and migration logic one feature at a time.

The right question is not “Can we store this without a database?” It is “What data-management behavior will the product need over its full lifetime?”

Which requirement caused your team to move from custom storage to a database?

Address

33309 1st Way So, Federal Way
Federal Way, WA
98003

Telephone

+14258888505

Website

http://www.mcobject.com/

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