

RTI Data Distribution Service leads the industry in performance and scalability. These benchmark results demonstrate that RTI provides an order-of-magnitude advantage over most other messaging and integration middleware.
These benchmarks were conducted with following configuration:
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These graphs show the average one-way latency in microseconds for publish/subscribe messaging. Latency was measured by having the consumer (DDS DataReader) echo messages back to the producer (DDS DataWriter). This allowed roundtrip latency to be measured on the sending machine, avoiding time synchronization issues. The roundtrip latency was divided in half to get the one-way latency that is shown.
This data shows that latency remains consistently low as message size increases.
These two graphs are based on the same data as the previous latency graphs for Gigabit Ethernet and InfiniBand. It shows the same average latency (middle line) but adds jitter—the variation in latency from message to message. A system is more deterministic if it exhibits lower jitter.
The two blue series show the minimum measured latency and the 99 percentile latency (the latency below which 99% of the samples fell). Even at larger message sizes, the variation between minimum and 99% latency remains consistently low. This shows that RTI Data Distribution Service exhibits very low jitter and very high determinism, making it suitable for time- and mission-critical applications.
This graph shows sustainable one-to-one (point-to-point) publish/subscribe throughput in terms of network bandwidth (megabits per second). It was measured between a single producing and consuming thread, each using a single Gigabit Ethernet or InfiniBand port, and over a single DDS topic.
Accounting for Ethernet, IP and UDP overhead, the maximum bandwidth available for message data (and metadata) is slightly over 950 megabits on 1Gb Ethernet. As can be seen, RTI Data Distribution Service is able to fully utilize all of this available bandwidth when sending messages larger than 128 bytes—meaning throughput is limited by the network and not by the CPU or RTI Data Distribution Service. This throughput is 25x higher than most other messaging and integration middleware.
Because RTI Data Distribution Service uses true peer-to-peer messaging—with no centralized or per-node Enterprise Service Bus (ESB), message broker, server or daemon processes—it does not impose any inherent limit on aggregate messaging capacity. It is limited only by the network infrastructure. So, in practice, RTI can deliver orders of magnitude higher capacity than other solutions. (See the topic and capacity scalability benchmark for an illustration of this.)
These graphs present the same throughput data in terms of the message rate (measured in messages per second), showing that with message size of 256 bytes, RTI can support over 2,000,000 messages per second per-producer, per-consumer and per-topic on InfiniBand with VMA, and over 450,000 messages per second on 1Gb Ethernet.
Throughput's impact on latency is an essential measure of a messaging solution's suitability for time-critical applications. For many applications, latency is most critical when message rates (and throughput) increase.
Enterprise messaging middleware typically queues messages (or blocks producers) when volume exceeds capacity. In contrast, RTI Data Distribution Service is designed for real-time applications in which the consequences of excessive latency could be catastrophic.
This graph shows RTI's sustainable average latency as a function of message rate for 256-byte messages. Average latency remains low, staying under 250 microseconds with 1Gb Ethernet and under 120 microseconds with InfiniBand and VMA, even near network saturation. With most other messaging middleware, latency jumps to several milliseconds or more during bursts—if they can be handled at all.
This graph is based on the same data as the previous, with measures of jitter added. The dashed red lines indicate standard deviation and the blue line show the latency below which 99% of the samples fell. It illustrates that RTI's real-time performance is still well-bounded even under extreme load.
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