eG Monitoring
 

Measures reported by KBusinessTransTest

For any business-critical application, some transactions will always be considered key from the point of view user experience and business impact. For instance, in the case of a retail banking web application, fund transfers executed online are critical transactions that have to be tracked closely for delays / errors, as problems in the transaction will cost both consumers and the company dearly. Using the KBusinessTransTest, administrators can perform focused monitoring of such critical transactions alone.

For each transaction URL pattern configured for monitoring on a JVM node, this test reports the count of requests for that transaction pattern, and the count and percentage of transactions of that pattern that were slow / stalling / error-prone. Detailed diagnostics provided by the test highlight the slow / stalled / error transactions of a pattern, and pinpoint the precise reason why that key transaction slowed down / stalled / encountered errors - is it because of an inefficient database query? is it because of a processing bottleneck on the JVM node? or is it owing to slow remote service calls? This way, the test enables you to quickly detect inconsistencies in the performance of your critical business transactions and accurately isolate its root-cause, so that you can fix the issues well before users notice them.

Output of the test: One set of results for each URL pattern configured for monitoring

The measures made by this test are as follows:

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.
All_urls_avg_time Indicates the average time taken by the transactions of this pattern to complete execution. Msecs 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.
Healthy_trans_count Indicates the number of healthy transactions of this pattern. Number  
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.
Slow_urls_count Indicates the number of transactions of this pattern that were slow during the last measurement period. Number This measure will report the number of transactions with a response time higher than the configured SLOW TRANSACTION CUTOFF (MS).

A high value is a cause for concern, as too many slow transactions means that user experience with the web application is poor.
Slow_urls_avg_time Indicates the average time taken by the slow transactions of this pattern to execute. Msecs  
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.
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.
Error_urls_avg_time Indicates the average duration for which the transactions of this pattern were processed before an error condition was detected. Msecs The value of this measure will help you discern if error transactions were also slow.
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.
Stalled_urls_count Indicates the number of transactions of this pattern that were stalled during the last measurement period. Number This measure will report the number of transactions with a response time higher than the configured STALLED TRANSACTION CUTOFF (MS).

A high value is a cause for concern, as too many stalled transactions means that user experience with the web application is poor.
Stalled_urls_avg_time Indicates the average time taken by the stalled transactions of this pattern to execute. Msecs  
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.
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  
Slow_sql_avg_time Indicates the average execution time of the slow SQL queries that were run by the transactions of this pattern. Msecs 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. Deep-diving into the queries will reveal the slowest queries and their impact on the execution time of the transaction.

Cpu_time Indicates the average time for which transactions of this pattern were utilizing the CPU. Msecs Compare the value of this measure across transaction patterns to accurately identify the CPU-intensive transaction patterns.

For the Summary descriptor, this measure will report the average time for which all the transactions on the target JVM used the CPU.

Note:

This measure is reported only under the following circumstances:

  1. The ENABLE THREAD CPU MONITORING flag of this test is set to Yes;

  2. The target application server's JVM implementation supports CPU time monitoring of threads; to verify JVM support for CPU time monitoring, use the procedure described below:

    • Login to the target application server.

    • Open jconsole.

    • Switch to the MBeans tab page in the jconsole.

    • When the page appears, expand the java.lang node in the tree-structure of the left panel and click the Attributes sub-node. If you browse the list of attributes that then appear in the right panel, you will find the ThreadContentionMonitoringSupported attribute. If this attribute is set to yes, it means that the JVM supports contention monitoring. Likewise, look for the ThreadCpuTimeSupported attribute in that list, and once found, check its status. If this attribute is set to yes, it means that the JVM supports CPU time monitoring.
Block_time Indicates the average duration for which transactions of this pattern were blocked and could not execute. Msecs If the Avg response time for any transaction pattern is very high, you may want to check the value of this measure for that pattern. This will help you figure out whether/not prolonged blocking is causing transactions of that pattern to slow down or stall.

For the Summary descriptor, this measure will report the average time for which all the transactions on the target JVM were blocked.

Note:

This measure is reported only under the following circumstances:

  1. The ENABLE THREAD CONTENTION MONITORING flag of this test is set to Yes;

  2. The target application server's JVM implementation supports thread contention monitoring; to verify JVM support for thread contention monitoring, use the procedure described below:

    • Login to the target application server.

    • Open jconsole.

    • Switch to the MBeans tab page in the jconsole.

    • When the page appears, expand the java.lang node in the tree-structure of the left panel and click the Attributes sub-node. If you browse the list of attributes that then appear in the right panel, you will find the ThreadContentionMonitoringSupported attribute. If this attribute is set to yes, it means that the JVM supports contention monitoring. Likewise, look for the ThreadCpuTimeSupported attribute in that list, and once found, check its status. If this attribute is set to yes, it means that the JVM supports CPU time monitoring.
Wait_time Indicates the average duration for which transactions of this pattern were waiting before they resumed execution. Msecs If the Avg response time for any transaction pattern is very high, you may want to check the value of this measure for that pattern. This will help you figure out whether/not a very high waiting time is what is causing the transactions to slow down/stall.

For the Summary descriptor, this measure will report the average time for which all the transactions on the target JVM were waiting.

Note:

This measure is reported only under the following circumstances:

  1. The ENABLE THREAD CONTENTION MONITORING flag of this test is set to Yes;

  2. The target application server's JVM implementation supports thread contention monitoring; to verify JVM support for thread contention monitoring, use the procedure described below:

    • Login to the target application server.

    • Open jconsole.

    • Switch to the MBeans tab page in the jconsole.

    • When the page appears, expand the java.lang node in the tree-structure of the left panel and click the Attributes sub-node. If you browse the list of attributes that then appear in the right panel, you will find the ThreadContentionMonitoringSupported attribute. If this attribute is set to yes, it means that the JVM supports contention monitoring. Likewise, look for the ThreadCpuTimeSupported attribute in that list, and once found, check its status. If this attribute is set to yes, it means that the JVM supports CPU time monitoring.
Call_per_minutes Indicates the number of transactions of this pattern that are executed per minute. Number For the Summary descriptor, this measure will report the total number of transactions that were executed per minute. This is a good indicator of the transaction processing ability of the target application server.
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.

For the Summary descriptor, this measure will report the total number of error transactions that were executed per minute.