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The responsiveness of a transaction is the key determinant of user experience with that transaction; if response time increases, user experience deteriorates. To make users happy, a business transaction should be rapidly processed by each of the IIS/Kestrel web servers in its path. Processing bottlenecks on a single server can slowdown/stall an entire business transaction or can cause serious transaction errors. This in turn can badly scar the experience of users. To avoid this, administrators should promptly identify slow/stalled/errored transactions, isolate the web server on which the slowness/error occurred, and uncover what caused the aberration on that server - is it owing to SQL queries executed by the server? Or is it because of external calls - eg., HTTP calls, web service calls, etc. - made by that node? The DTCBtmTransTest helps with this!

This test runs on a BTM-enabled Kestrel/IIS web servers in an IT infrastructure and tracks all the transaction requests received by any web site that has been configured for monitoring on that server. The test then groups requests based on user-configured pattern specifications. For each transaction pattern, the test then computes and reports the average time taken by that server to respond to the transaction requests of that pattern to the configured web site. In the process, the test identifies the slow/stalled transactions of that pattern, and reports the count of such transactions and their responsiveness. Detailed diagnostics provided by the test accurately pinpoint the exact transaction URLs that are slow/stalled, the total round-trip time of each transaction, and also indicate when such transaction requests were received by that server. The slowest transaction in the group can thus be identified.

Moreover, to enable administrators to figure out if the slowness can be attributed to a bottleneck in SQL query processing, the test also reports the average time the transactions of each pattern took to execute SQL queries. If a majority of the queries are slow, then the test will instantly capture the same and notify administrators.

Additionally, the test promptly alerts administrators to error transactions of each pattern. To know which are the error transactions, the detailed diagnosis capability of the test can be used.

This way, the test effortlessly measures the performance of each transaction to a web site on a Kestrel/IIS web server, highlights transactions that are under-performing, and takes administrators close to the root-cause of poor transaction performance.

This test will report metrics only if the following pre-requisites are fulfilled:

Measurement	Description	Measurement Unit	Interpretation
All_urls_count	Indicates the total number of requests received for transactions of this pattern during the last measurement period.	Number	By comparing the value of this measure across transaction patterns, you can identify the most popular transaction patterns. Using the detailed diagnosis of this measure, you can then figure out which specific transactions of that pattern are most requested. For the Summary descriptor, this measure will reveal the total number of transaction requests received by the target web site during the last measurement period. This is a good indicator of the transaction workload on that web site.
All_urls_avg_time	Indicates the average time taken by the transactions of this pattern to complete execution.	Secs	Compare the value of this measure across patterns to isolate the type of transactions that were taking too long to execute. You can then use the detailed diagnosis of the All transactions measure of that group to know how much time each transaction in that group took to execute. This will lead you to the slowest transaction. For the Summary descriptor, this measure will reveal the average responsiveness of all the transaction requests received by the target web siteduring the last measurement period. An abnormally low value for this measure for the Summary descriptor could indicate a serious processing bottleneck on the target web site.
Healthy_trans_count	Indicates the number of healthy transactions of this pattern.	Number	By default, this measure will report the count of transactions with a response time less than 4000 milliseconds. You can change this default setting by modifying the thresholds of the Avg response time measure using the eG admin interface. For the Summary descriptor, this measure will report the total number of healthy transactions on the target website.
Healthy_percentage	Indicates what percentage of the total number of transactions of this pattern is healthy.	Percent	To know which are the healthy transactions, use the detailed diagnosis of this measure. For the Summary descriptor, this measure will report the overall percentage of healthy transactions on the target web site.
Slow_urls_count	Indicates the number of transactions of this pattern that were slow during the last measurement period.	Number	By default, this measure will report the number of transactions with a response time higher than 4000 milliseconds and lesser than 60000 milliseconds. You can change this default setting by modifying the thresholds of the Avg response time measure using the eG admin interface. A high value for this measure is a cause for concern, as too many slow transactions means that user experience with the web application is poor. For the Summary descriptor, this measure will report the total number of slow transactons on the target web site. This is a good indicator of the processing power of the target web site.
Slow_urls_avg_time	Indicates the average time taken by the slow transactions of this pattern to execute.	Secs	For the Summary descriptor, this measure will report the average response time of all the slow transactions on the target web site.
Slow_percentage	Indicates what percentage of the total number of transactions of this pattern is currently slow.	Percent	Use the detailed diagnosis of this measure to know which precise transactions of a pattern are slow. You can drill down from a slow transaction to know what is causing the slowness. For the Summary descriptor, this measure will report the overall percentage of slow transactions on the monitored web site.
Error_urls_count	Indicates the number of transactions of this pattern that experienced errors during the last measurement period.	Number	A high value is a cause for concern, as too many error transactions to a web application can significantly damage the user experience with that application. For the Summary descriptor, this measure will report the total number of error transactons on the target web site. This is a good indicator of how error-prone the target web site is.
Error_urls_avg_time	Indicates the average duration for which the transactions of this pattern were processed before an error condition was detected.	Secs	The value of this measure will help you discern if error transactions were also slow. For the Summary descriptor, this measure will report the average response time of all error transactions on the target web site.
Error_percentage	Indicates what percentage of the total number of transactions of this pattern is experiencing errors.	Percent	Use the detailed diagnosis of this measure to isolate the error transactions. You can even drill down from an error transaction in the detailed diagnosis to determine the cause of the error. For the Summary descriptor, this measure will report the overall percentage of transactions of this pattern on the target web site that is currently experiencing errors.
Stalled_urls_count	Indicates the number of transactions of this pattern that were stalled during the last measurement period.	Number	By default, this measure will report the number of transactions with a response time higher than 60000 milliseconds. You can change this default setting by modifying the thresholds of the Avg response time measure using the eG admin interface. A high value is a cause for concern, as too many stalled transactions means that user experience with the web application is poor. For the Summary descriptor, this measure will report the total number of stalled transactons on the target web site.
Stalled_urls_avg_time	Indicates the average time taken by the stalled transactions of this pattern to execute.	Secs	For the Summary descriptor, this measure will report the average response time of all stalled transactions on the target web site.
Stalled_percentage	Indicates what percentage of the total number of transactions of this pattern is stalling.	Percent	Use the detailed diagnosis of this measure to know which precise transactions of a pattern are stalled. You can drill down from a stalled transaction to know what is causing that transaction to stall. For the Summary descriptor, this measure will report the overall percentage of transactions of this pattern on the target web site that is stalling.
Slow_sql_count	Indicates the number of slow SQL queries that were executed by the transactions of this pattern during the last measurement period.	Number	For the Summary descriptor, this measure will report the total number of slow SQL queries executed by all transactions to the target web site.
Slow_sql_avg_time	Indicates the average execution time of the slow SQL queries that were run by the transactions of this pattern.	Secs	If there are too many slow transactions of a pattern, you may want to check the value of this measure for that pattern to figure out if query execution is slowing down the transactions. Use the detailed diagnosis of the Slow transactions measure to identify the precise slow transaction. Then, drill down from that slow transaction to confirm whether/not database queries have contributed to the slowness. Deepdiving into the queries will reveal the slowest queries and their impact on the execution time of the transaction.
Call_per_minutes	Indicates the number of transactions of this pattern that are executed per minute.	Number
Error_call_per_minutes	Indicates the number of error transactions of this pattern that are executed per minute.	Number	A very low value is desired for this measure. Compare the value of this measure across transaction patterns to find that pattern of transactions that is experiencing errors frequently.