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OrderFlow Technical Architecture

Realtime Despatch Software Ltd

Document Version: 4.0.6

Document Built: 2019-10-22

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Non-functional Aspects

This section deals with some of the non-functional elements of the OrderFlow architecture, in particular in areas of monitoring, performance and scalability.

Monitoring

OrderFlow's performance and health status can be closely monitored from the desktop performance dashboard, which shows requests that take a long time to serve, and other performance metrics.

Statistics on the performance of the system are automatically recorded in fifteen minute segments. These statistics cover both the time taken to serve different HTTP requests over the web as well as the time taken to call data layer methods.

In addition, key performance indicators such as CPU and memory utilisation are monitored in all of our hosted environments, to allow for early warning of performance related issues.

Performance

At Realtime Despatch we are very mindful of the importance of application performance in ensuring a rapid application response times and a good experience for OrderFlow users. We address this issue by both developing features to target application performance, and tuning environments to make the best use of the available hardware and software.

Application Features

OrderFlow includes a number of features which help the application to scale well even in hardware constrained environments.

  • a large amount of cacheing, used to reduce the amount of unnecessary work that the system needs to do to carry out its functions. Cacheing on the system is used in many places, from data layer entities right through to the output of dashboard reports that appear on the user interface.
  • built-in limits to the amount of data that can be returned through searches and reports.
  • fine-tuned use of database indexes to ensure that queries run as efficiently as possible.
  • advanced data archiving features, allowing for ageing data to be exported and removed from the system in an orderly fashion, helping to ensure that data volumes do not grow unnecessarily.
  • an advanced scheduling capability that allows most long running operations to run in the background, in some cases at times when the application is less heavily used.
Tuning Strategies

The Java Virtual Machine (JVM) in which OrderFlow runs can be tuned to provide the right amount of heap and non-heap memory for the needs of the environment.

Tuning it also possible at the database level, particularly to ensure that the available system memory is used effectively. Appropriate use of memory settings at the database level is very helpful in ensuring that the database engine can make the most effective use of cacheing, making sure that data access is fast.

Scalability

The strategies described in the previous section allow a very simple hardware configuration to be used to support all but the largest environments. This allows the application to scale vertically to accommodate increasing performance requirements.

Beyond a certain point, some horizontal scaling is necessary. The exact strategies that will be employed in a particular environment will depend on the bottlenecks that need to be addressed, and may involve actions at the application as well as database layer.

The main strategy used on the application tier is clustering. On the data tier, various strategies can be employed.

Clustering

OrderFlow can be configured to run in a clustered configuration, in which multiple instances of the OrderFlow application are run in a single load balanced environment. This allows the load on the application server to be spread over multiple JVM processes or even physical servers.

Communication between the nodes, where required, is achieved using the Hazelcast clustering technology. This allows, for example:

  • for caches to be kept consistent across the cluster
  • for process level locking to occur across all nodes in the cluster rather than just a single node

Note that application tier clustering requires the use of a load balancer configured to support 'sticky sessions', with each request within a user session directed to the same JVM throughout the lifetime of that session.

Data Scaling

A number of strategies can be employed to scale the data tier in an OrderFlow environment.

  • the main database and the blob database can be split into separate physical environments.
  • database replication can be used with a master/slave configuration, in which all non-transactional requests (such as for reporting data) can be routed to one of multiple read-only replicas.

An option that has yet to be explored is the use of a clustered database server, which offers horizontal scalability within the database server itself. This could potentially address the need for extreme cases where write operations to the database need to be clustered across multiple physical servers.

Testing

Although not strictly an architectural aspect, it is worth mentioning the approach to testing OrderFlow's functionality.

From the bottom up, testing is crucial delivering a quality system that is robust and performant. OrderFlow's developers include three main layers of testing when delivering functionality, which are expanded upon in the following sections.

All tests are automated, and are run regularly to ensure that OrderFlow remains fully-functional and 'ready to build' at short notice.

Unit Testing

Adopting a Test-Driven approach to development, most logical code is covered by Unit Tests, using the JUnit testing framework. This enforces thorough consideration of all aspects of new features, and protects against errors being introduced by changes to existing features. Unit tests not only form a comprehensive regression test suite, but also serve to directly document the code in the form of readable tests.

Integration Testing

Since unit tests cannot efficiently cover interaction between different classes, the unit tests are complemented by a suite of Integration Test, which cover this interaction more effectively and realistically.

Using the Impala dynamic modular productivity framework, sections of OrderFlow can be loaded in order to test co-operative areas of functionality. This provides a high degree of confidence in the behaviour of the real system, as it runs the actual code that is deployed in a production system.

Integration tests not only give functional assurance; they also prove that the system is structured correctly, and provide robust regression checks to ensure that no changes have had inadvertent side-effects in unexpected parts of the system.

Graphical User Interface Testing

To give an extra layer of confidence in the system's behaviour, and to cover parts of the user interface layer that may not have been comprehensively covered, OrderFlow has a suite of automated Graphical User Interface (GUI) tests. These exercise OrderFlow's user interfaces in the way that end users are expected to, and so again provide a level of functional and regression testing that unit and integration tests cannot provide.

We use Selenium to automate user interface testing from web browsers.