Service flow metrics
When you select a service within service flow, the path to the service—beginning with the service that triggered the flow—is highlighted in blue (see image below). Additionally, the arrows constituting the path are enriched with two metrics. The first metric shows the percentage of requests initiated by the source service that involve calling the target service. The second metric indicates the average number of calls to the target service that were included in each request.
Apart from response time contribution, you can also view the Average response time and the number of Requests initiated by each service included in service flow. Just click a service to expand it. Once clicked, you can view further details like Avg. time spent in called services and the number of Failed requests (see image above, bottom right).
Each service also shows the Response time contribution, which is calculated using the following formula:
The displayed value for the response time contribution may differ from the actual mathematical result as the calculations are based on underlying data and then rounded off afterwards.
In the service flow example above these are marked as follows:
- 1—Average response time service
- 2—Call share
- 3—Call per request
- 4—Average response time overall
Let's assume that your service flow indicates these request values for your service:
- Average response time =
- Call percentage =
- Calls per request =
- Response time contribution = (Average response time) x (Call percentage) x (Calls per request)
- Response time contribution =
In this case, the response time contribution =
If you click Filter service flow on the service, the view will filter down to show only the requests that call that service. The call percentage will then rise to
100%. This set of requests is a subset of what you examine and, because it contributes to every request in that new view, the contribution will be higher.
The top tile side of the side pane shows the details of the service requests that triggered the service flow. At the image below it's
easyTravel Customer Frontend.
The Passing transactions tab shows the throughput of the service and its performance—response time, number of requests and more. Click Show more to view further analysis options. The analysis includes all the requests, originating from the service.
The Infrastructure tab shows that
easyTravel Customer Frontend runs on two hosts and how the throughput is distributed between them. Click Apply as filter to filter Service flow—only calls originating from the selected host will be shown.
When you select a service within Service flow, the bottom tile appears, showing the details of the selected service. At the image below it's
Now the top tile shows details in context of the selected service. We can see that the count of calls which originate from
easyTravel Customer Frontend and subsequently call
easyTravel-Business is 8,490 calls out of 127,000. Click Filter service flow to show only call originating from
easyTravel Customer Frontend and subsequently hitting
easyTravel-Business. Further analysis options also focus on these calls.
The bottom tile shows details of calls to
easyTravel-Business which originated from
easyTravel Customer Frontend. Further analysis options also focus on these calls.
In between tiles you can see the list of all intermediate services and/or proxies between
easyTravel Customer Frontend and
easyTravel-Business. Click it to expand each section and view its details.
Further analysis options
- Distributed traces
Analyze the detailed method-level chain of calls.
- Analyze backtrace
Explore the sequence of service calls that led up to the specific service request.
- View response time
View how the response time is distributed along different functions of the service (for example, database usage, code execution, etc.)
- Analyze outliers
View the response time distribution of requests to the service within a specific timeframe.
You can specify additional filtering options to narrow down the scope of the analysis. See Service flow filtering to learn how. All these filters will also be applied to further analysis.