RTI Connext DDS

One-to-Many Throughput Scalability

This graph shows the efficiency of RTI's reliable multicast protocol for one-to-many publish/subscribe messaging and real-time data distribution. A single producing thread was used to send a stream of 200-byte messages to up to 888 consumers, each running on a dedicated core with four consumers per NIC. Going from one to 888 subscribers had less than a 13% impact on maximum throughput for the topic.

This demonstrates that Connext DDS is highly scalable in terms of the number of consumers that can be supported on a given topic. The number of consumers has no significant impact on a topic's sustainable throughput. (RTI also provides flexible and automatic handling of slow consumers for cases in which some subscribers cannot keep up with the message rate.)

Note that this benchmark was conducted on different hardware than the others, using Intel Xeon processors.

Impact of Throughput on One-to-Many Latency and Scalability

This graph shows that, when there are multiple subscribers to the same topic, RTI's reliable multicast protocol maintains low latency in addition to high throughput (as was illustrated in the one-to-many scalability benchmark). The red series show the impact of latency on throughput with one consumer subscribed to the topic, the green with 20 and the blue with 40. Latency at a given throughput varies very little as the number of consumers increases.

What variation there is between 20 and 40 subscribers can mainly be attributed to NIC and operating system contention, since there were two subscribers per NIC in the 40-subscriber benchmark but one per NIC in the others.

Topic and Capacity Scalability

This benchmark illustrates the effect that the number of topics has on latency, throughput and system capacity.

The green series shows the relationship between throughput and latency when there is a single topic with one producer and one consumer (this is the same data as in the Impact of Throughput on Latency graph). The red series shows the relationship between throughput and latency when there are eight topics, each with a single producer and consumer. Messages per second is the aggregate across all eight topics. The load was equally balanced across all eight topics and latency is averaged over all eight consumers.

These results show that capacity scales proportionally with the number of topics—without impacting latency. Peak throughput with one topic was 563,937 MPS and with eight topics it was almost exactly eight-times this: 4,505,730 MPS. In both cases, average latency was about 275 microseconds.

This illustrates that:

• systems can be partitioned across multiple topics to achieve extraordinarily high aggregate throughput while maintaining very low latency and
• new topics can be added to a system without impacting latency or throughput on other topics (attributable to RTI's peer-to-peer architecture with no centralized brokers or ESBs as bottlenecks).

To simplify system design and achieve a similar effect, RTI also allows a single topic to be partitioned across multiple multicast groups. This allows per-topic throughput to exceed the capacity of a consumer or network port. Consumers that do not want all messages or that cannot sustain the topic's aggregate throughput can use a filter to specify the messages they want—corresponding to a DDS ContentFilteredTopic or JMS Message Selector. The filter determines the multicast addresses that are subscribed. IGMP snooping is supported so that undesired messages can be filtered by the switch.

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