Track Service Reuse Metrics

December 24, 2011

Service driven systematic reuse takes conscious design decisions, governance, and disciplined execution – project after project. In order to sustain long running efforts such as service orientation, it is critical to track, report, and get buy-in from senior management in the organization. So what metrics are useful? Here are a few:

  • Total number of service operations reused in a time period
  • Total effort saved due to systematic reuse in a time period
  • Number of new service consumers in a time period
  • Number of new consumer integrations in a time period (this includes integrations from both new and existing consumer
  • Service integrations across transports/interface points (for instance, the service operation could be accessed SOAP over HTTP, or as SOAP over JMS, or REST, etc.)

What metrics do your teams track?

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Capture Service Metrics

October 22, 2010

Metrics are very useful to understand service usage, volume trends (growth/decline) – leading to improving performance/capacity planning,  diversity of user base, etc.

A service mediation layer can initialize and persist metrics and can help efforts to mine that data to generate reports using the data. What specific metrics can be captured? Here are a few attributes:

incoming time, outgoing time (or publication time) , transport (whether service was invoked via HTTP, JMS, or some other transport), requesting host name/queue name etc. Additionally, if a request processing resulted in an error – error details including stack trace can be captured. Finally, service/operation-specific metrics can be captured – if you don’t have demanding reporting requirements,  these attributes can be stored as a set of name/value pairs.

In a follow up post, will elaborate on how specific metrics can be captured throughout service processing.


Visualize Service Metrics

November 24, 2009

Business applications often utilize log files and databases to capture metrics about usage and error patterns. However, analysis and pattern detecting becomes challenging with additional data and system complexity.  Tree map visualization of application metrics could greatly aid rapid view of system state, error analysis, trends, and remedial actions.  A tree map visualization can be generated using metrics from a database.  It can generate useful views and present information of use to both business stakeholders and the system support team.

Service View – a treemap of metrics organized by service invoked on your SOA platform. The idea is to provide a comprehensive view of the metrics captured and answer questions such as the following: which services are being invoked in the system? Which services receive the highest volume of invocations? How does the volume of service invocations compare with each other? What proportion of invocations in each service was successful and what proportion ended in generating errors?

Transport View – a treemap of metrics organized by kind of transport used to invoke requests in your SOA platform. This is especially useful for systems support staff who need to quickly assess the system-wide impact of messaging providers and their non-availability. The idea is to provide a transport-level view of the metrics captured and answer questions such as the following: what are the transports used by clients when invoking services in your SOA paltform? Which transport mechanism is being used to process the bulk of requests? How does volume of invocations via a transport compare with one another? Which transport is having a higher proportion of errors? Are the failing invocations using reliable transports or are they using unreliable ones?

Status Code View – a treemap of metrics organized by kind of status codes that service requests returned. This is very useful for both development staff and systems support staff. This view displays a bird’s-eye view of the metrics with respect to return codes and answer questions such as the following: what service codes are being returned to clients? What proportion of codes are success codes and erroneous codes? What is the volume of error codes with respect to each other? In summary, this view provides a sense of how the system is performing as a whole – what matters most is whether the platform can provide good response to clients and this is a succinct visualization that answers that.

Are there additional opportunities for visualizing metrics using data in your services layer?

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