One of the first use cases for publish/subscribe event driven computing was on a trading floor. If you consider the typical architecture of a trading floor, it comprises information sources from a variety of providers. These providers aggregate content from many sources and feed that information as a stream of subject-oriented feeds. For instance, a trader who focuses on the oil sector will subscribe to any information that’s relevant that will likely in the traders estimation have an impact on prices of oil securities. Each trader would have a different view on what affects oil securities or the type of trading they do; therefore, even though you may have 2,000 traders on your trading floor, it’s unlikely that two of them will be interested in the same set of information or how these are presented.
Building a trading floor using EDA architecture involves building a high-performance infrastructure consisting of a number of services that must be able to sustain data rates well in excess of 1,000 transactions/second. As explained in Figures 1 and 2, ultra high reliability and transactional semantics are needed throughout. Every process is provided in a cluster or set of clusters and usually an active/active method of fault tolerance is employed. Message broker (MB) is used for trades and things related to auditable entities. Topics are used to distribute market data. Systems are monitored using an activity monitor and metrics produced. Data also needs to be reliably sent to risk analysis, which computes credit limits and other limits the firm has for trading operations in real-time. Complex event processing is used to detect anomalous events, security events, or even opportunities.
WSO2 offers a full suite of open source components for both event-driven EDA and Web Services architectures to implement highly scalable and reliable enterprise grade solutions. It is typical to use both architectures in today’s enterprises. WSO2 is one of the only vendors that can deliver all components of both architectures.
WSO2 is also open source and built to be enterprise grade throughout.
In a high-frequency-trading application (HFT), specialized MBs are used to minimize latency to communicate to the stock exchanges directly. A bank of computers will pull market information directly from sources and high powered computers will calculate opportunities to trade. Such trading happens in an automated way because the timing has to be at the millisecond level to take advantage of opportunities.
Other applications are for macro analysis that usually involves complex ingestion of data from sources that aren’t readily available. A lot of effort is put into data cleansing and a columnar time-series database that understands the state of things as they were known (prior to being modified by data improvements). These are called as-of data and involve having persistent all variations of data and modifications so the time-series can be recreated as was known at a certain time. Apple uses such notions in its Time Machine technology where calculations involve running historical data through algorithms to determine if the calculations will produce a profit or are reliable.