Papyrus Application Scalability

Many Papyrus Platform installations have recently grown substantially and therefore means of application scalability have become a common subject. The principal scalability of the Papyrus Platform is unlimited due to its peer-to-peer cluster design but it is restricted by the synchronicity needs of the application. Simply reading or displaying documents or content from storage has no limitation, but keeping multiple write accesses in sync creates scalability issues. Clearly, when the number of users is doubled from 1000 to 2000 users then it does require monitoring and if necessary scaling the HW. When the number of documents or process tasks is doubled from 1000 to 2000 per hour that also requires consideration ow that load can be safely spread across server nodes.

Scaling Papyrus Platform applications is very simple compared to for example three-tiered Web/Java/SQL/SOA application clusters. A more detailed discussion of Java Application scalability I have posted on my ‘Real World’ blog.

User complaints that the ‘application is slow’ without any details being measured are not helpful but have to be taken seriously. Often the user has no means to understand that the processing requirements for similar looking documents can differ substantially. The document might be simple for the user but the backend process can be fairly complex. We propose that proactive monitoring is established. The Papyrus Platform has all the means for such monitoring available.  Simple dashboards can be created and summary reports are available.

Scalability is not only about tuning or maintaining an acceptable response time for a growing number of users. It is unreasonable to expect that the system will handle growth in users and transactions automatically. No system does. Papyrus has many load balancing and tuning options and many are set either by default or by the system. We found that some automatic functions had to be changed for weak networks or when growth was not gradual but for example many users, nodes or new documents and processes were added at once. That is related to the automatic version control and deployment of the Papyrus Platform.

Document application are a complicated conglomerate of GUI, process, and rule execution threads that read and write data from a number of service interfaces or databases. The performance of the SOA backend service interfaces or the database is much more relevant to the scalability than the user front end. Using common database or transaction measurements the Papyrus Platform executes millions of transaction per hour. That is however irrelevant. The question is how that translates into user experience!

The following measurements are used to define the quality of scalability:

1) IRT – Initial Response Time: from request to first usable feedback

2) TRT – Total Response Time: from request to completed function

3) SRP – Service Processing Time: the time the request is actively being processed

4) SQT – Service Queuing Time: the time the request waits for processing

5) ATT – Average Transaction Throughput: transactions performed in a time window

These values can not be taken from the system itself but the measurement of user experience has to be defined in the application. In a multi-tiered Java application that is practically impossible. A screen refresh may or may not be connected to a previous user entry. Papyrus assigns a JOB-ID to each user data entry and enables the tracing through all the functions and servers. A JOB-ID will now trigger the five above measurement points and thus enable a real-world measurement. It is planned to provide a ‘User Experience Dashboard’ for each user that will display a kind of ‘VitalSigns’ statistics for real-time feedback.

The Papyrus Platform uses the Application Performance Analyzer or APA to measure those values and relate them to the general statistical data about CPU, I/O and RAM usage.

APA Tuning and Monitoring

APA offers a unique level of insight across all application functions from the user click on the desktop or portal to the final display. Next to the elapsed time measurements, the measurement of resource usage is the key to understanding and tuning. How much CPU, RAM, disk I/O and network bandwidth is consumed per transaction and in total has to be known for tuning.

Web/Java/SOA applications have substantial overhead for sticky-load-balancing, transaction-safe Java caching and database clustering, and parsing and validating the XML data for SOA data communication. Rather than the immense complexity of clustering multi-layered caches – with multiple conversion from tables to cache pages to objects and reverse – the Papyrus Platform collapses the horizontal structures and work with a purely (vertical) object model from definition to storage. The proxy replication mechanism of the Papyrus Platform uses a partitioned caching concept where there is always a unique data owner and each server node uses a replicated copy that is either pull or push updated. The objects do not have to be de/serialized as they are cached/replicated/binary-stored as is. The same object caching mechanism works transparently for all objects regardless whether they are populated via Web services, external databases or from the Papyrus objectspace.

As a consequence of this future proof design the Papyrus Platform can scale linearly as servers and nodes are added to the application clusters. It is however important to segment the database properly. Papyrus provides transparent object access and search across any number of nodes without any changes to an application. it is important to understand that even with a powerful system such as Papyrus the application has to be defined scalable as much as the system.


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