eG Monitoring
 

Measures reported by SPSiteAnalyticTest

Enterprises typically use SharePoint to create web sites and web applications. The success of the SharePoint platform therefore hinges on how happy users are when interacting with the web sites that it helped create. If the number of visitors to a web site keeps increasing, it is indicative of an increase in the web site’s popularity, which directly translates into ‘many happy users’! Likewise, if users to a web site constantly complain of slowness when browsing that web site, it indicates that user experience with the web site is unsatisfactory - meaning, ‘many unhappy users’. This in turn can hit user productivity badly, escalate troubleshooting time and costs of the enterprise, and adversely impact its revenues and reputation! To improve user experience with SharePoint sites and to build user confidence in the SharePoint platform, administrators should be able to quickly identify slow web sites and precisely pinpoint the reason for the slowness.

This is where the SPSiteAnalyticTest test helps! This test queries the SharePoint usage database at configured intervals and collects metrics on web site usage that is stored therein - this includes the web sites accessed, count of hits to each web site, users who browsed every site, the browsers that were used for web site access, web pages requested, the time taken for the requested pages to load, where page views spent time and how much, error responses returned, resources consumed, and many more. For each web site configured for monitoring, the test then reports the average time taken by that site to load pages. In the process, the test points administrators to slow web sites and also leads them to the probable source of the slownes - is it owing to a latent web front end? is it because of slow service calls? Or is it due to inefficient queries to the backend database?

Sometimes, poor user experience can be attributed to HTTP errors. This is why, this test instantly alerts administrators to HTTP error responses, thus ensuring their timely intervention and rapid resolution of the error conditions.

This way, the SPSiteAnalyticTest test enables administrators to detect web site slowness well before users notice, helps them promptly and accurately diagnose the source of the poor user experience with a web site, and thus ensures that they initiate measures to enhance user experience and pre-empt the damage that may be caused to revenue and reputation.

Note that this test will run only if a SharePoint Usage and Health Service application is created and is configured to collect usage and health data. To know how to create and configure this application, Click here.

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

The measures made by this test are as follows:

Measurement Description Measurement Unit Interpretation
Unique_users Indicates the number of unique users of this web site. Number The detailed diagnosis of this measure reveals the names of the unique users and the number of requests from each user to the web site being monitored. From this, you can identify those users who are actively using the web site.
Unique_visitors Indicates the number of unique visitors to this web site. Number SharePoint authenticated users and anonymous users (using IP address) are counted as visitors.

Compare the value of this measure across sites to identify the most popular SharePoint site.

You can use the detailed diagnosis of this measure to know who are the unique visitors to the web site and the number of requests from each visitor to the web site. This way, you can identify that visitor who visits the web site most frequently.
Unique_destinations Indicates the number of unique destinations of this site. Number To know the most popular destination URLs of this site, use the detailed diagnosis of this measure. Here, you will find the top-10 destinations in terms of the number of hits.
Unique_browsers Indicates the number of unique browsers used for accessing this site. Number To know which browsers are commonly used to access this web site, use the detailed diagnosis of this measure. Here, the unique browsers will be listed and the number of hits to the web site from each browser will be displayed alongside, so that you can instantly identify that browser that has been widely used to access the web site.
Unique_referrers Indicates the number of unique URLs external to this site (parent site is treated as external as well), from where the users navigated to this site. Number To know which referrer URL was responsible for the maximum hits to this web site, use the detailed diagnosis of this measure. The top-10 unique referrer URLs in terms of the number of hits they generated will be displayed as part of the detailed diagnostics.
Apdex_score Indicates the apdex score of this site. Number Apdex (Application Performance Index) is an open standard developed by an alliance of companies. It defines a standard method for reporting and comparing the performance of software applications in computing. Its purpose is to convert measurements into insights about user satisfaction, by specifying a uniform way to analyze and report on the degree to which measured performance meets user expectations.

The Apdex method converts many measurements into one number on a uniform scale of 0-to-1 (0 = no users satisfied, 1 = all users satisfied). The resulting Apdex score is a numerical measure of user satisfaction with the performance of enterprise applications. This metric can be used to report on any source of end-user performance measurements for which a performance objective has been defined.

The Apdex formula is:

Apdext = (Satisfied Count + Tolerating Count / 2) / Total Samples

This is nothing but the number of satisfied samples plus half of the tolerating samples plus none of the frustrated samples, divided by all the samples.

A score of 1.0 means all responses were satisfactory. A score of 0.0 means none of the responses were satisfactory. Tolerating responses half satisfy a user. For example, if all responses are tolerating, then the Apdex score would be 0.50.

Ideally therefore, the value of this measure should be 1.0. A value less than 1.0 indicates that the user experience with the web site has been less than satisfactory.
Satisfied_page_views Indicates the number of times pages in this web site were viewed without any slowness. Number A page view is considered to be slow when the average time taken to load that page exceeds the SLOW TRANSACTION CUTOFF configured for this test. If this SLOW TRANSACTION CUTOFF is not exceeded, then the page view is deemed to be ‘satisfactory’.

Ideally, the value of this measure should be high.

If the value of this measure is much lesser than the value of the Tolerating_page_views and the Frustrated_page_views, it is a clear indicator that the experience of the users of this web site is below-par. In such a case, use the detailed diagnosis of the Tolerating page views and Frustrated page views measures to know which pages are slow.
Tolerating_page_views Indicates the number of tolerating page views to this web site. Number If the Total_duration of a page exceeds the SLOW TRANSACTION CUTTOFF configuration of this test, but is less than 4 times the SLOW TRANSACTION CUTOFF (i.e., < 4 * SLOW TRANSACTION CUTOFF), then such a page view is considered to be a Tolerating page view.

Ideally, the value of this measure should be 0. A value higher than that of the Satisfied_page_views measure is a cause for concern, as it implies that the overall user experience from this browser is less than satisfactory. To know which pages are contributing to this sub-par experience, use the detailed diagnosis of this measure.
Frustrated_page_views Indicates the number of frustrated page views to this web site. Number If the Total_duration of a page is over 4 times the SLOW TRANSACTION CUTTOFF configuration of this test (i.e., > 4 * SLOW TRANSACTION CUTOFF), then such a page view is considered to be a Frustrated page view.

Ideally, the value of this measure should be 0. A value higher than that of the Satisfied_page_views measure is a cause for concern, as it implies that the experience of users using this browser has been less than satisfactory. To know which pages are contributing to this sub-par experience, use the detailed diagnosis of this measure.
Total_duration Indicates the average time taken by the pages in this web site to load completely. Secs This is the average interval between the time that a user initiates a request and the completion of the page load of the response in the user-s browser.

If the value of this measure is consistently high for a web site, there is reason to worry. This is because, it implies that the web site is slow in responding to requests. If this condition is allowed to persist, it can adversely impact user experience with the web site. You may want to check the Apdex_score in such circumstances to determine whether/not user experience has already been affected. Regardless, you should investigate the anomaly and quickly determine where the bottleneck lies – is it with the web front-end? is it owing to slow service calls? Or is it because of inefficient queries to the backend? - so that the problem can be fixed before users even notice any slowness! For that, you may want to compare the values of the Duration, Service_calls_duration, CPU_duration, IIS_latency, and Query_duration measures of this test.

If the Average front end time is the highest, it indicates that the problem is with the web site/web application front end - this can be attributed to a slowdown in page rendering or in DOM building. If the Average server connection time is the highest, it denotes that the network is the problem source. This in turn can be caused by TCP connection latencies and delays in domain look up. On the other hand, if the Average response available time measure registers the highest value, it indicates that the problem lies with the web site/web application backend - i.e., the web/web application server that is hosting the web site/web application being monitored.
Duration Indicates the average time in milliseconds it took for the web front end server to process the requests to this web site. Msecs If the Total_duration of a web site is abnormally high, then you can compare the value of this measure with that of the Service_calls_duration, CPU_duration, IIS_latency, and Query_duration measures of this test to know what exactly is delaying page loading - a slow front-end web server? inefficient queries to the backend database? or slow service calls?
Service_calls_duration Indicates the time taken by this web site to generate service calls. Msecs If the Avg page load time of a web site is abnormally high, then you can compare the value of this measure with that of the Duration, CPU_duration, IIS_latency, and Query_duration measures of this test to know what exactly is delaying page loading - a slow front-end web server? inefficient queries to the backend database? or slow service calls?
IIS_latency Indicates the average time requests to this web site took in the frontend web server after the requests were received by the frontend web server but before this web site began processing the requests. Msecs If the Total_duration of a web site is abnormally high, then you can compare the value of this measure with that of the Duration, CPU_duration, Service_calls_duration, and Query_duration measures of this test to know what exactly is delaying page loading - a slow front-end web server? inefficient queries to the backend database? or slow service calls?
CPU_duration Indicates the average time for which requests to this web site used the CPU. Msecs If the Total_duration of a web site is abnormally high, then you can compare the value of this measure with that of the Duration, IIS_latency, Service_calls_duration, and Query_duration measures of this test to know what exactly is delaying page loading - a slow front-end web server? inefficient queries to the backend database? or slow service calls?
SQL_logical_reads Indicates the total number of 8 kilobyte blocks that this web site read from storage on the back-end database server. Number  
CPU_mega_cycles Indicates the average number of CPU mega cycles spent processing the requests to this web site in the client application on the front end web server. Number  
Total_queries Indicates the total number of database queries generated for this site. Number  
Query_duration Indicates the average time taken for all backend database queries generated for this site. Msecs If the Total_duration of a web site is abnormally high, then you can compare the value of this measure with that of the Duration, IIS_latency, Service_calls_duration, and CPU_duration measures of this test to know what exactly is delaying page loading – a slow front-end web server? inefficient queries to the backend database? or slow service calls?
Bytes_consumed Indicates the average bytes of data downloaded by requests to this web site. KB  
GET_requests Indicates the number of GET requests to this web site. Number  
POST_requests Indicates the number of POST requests to this web site. Number  
OPTIONS_requests Indicates the number of OPTION request to this web site. Number  
Responses_300 Indicates the number of responses to requests to this web site with a status code in the 300-399 range. Number 300 responses could indicate page caching on the client browsers. Alternatively 300 responses could also indicate redirection of requests. A sudden change in this value could indicate a problem condition.
Errors_400 Indicates the number responses to requests to this web site that had a status code in the range 400-499. Number A high value indicates a number of missing/error pages.

Use the detailed diagnosis of this measure to know when each of the 400 errors occurred, which user experienced the error, when using what browser, from which machine. This information will greatly aid troubleshooting.
Errors_500 Indicates the number of responses to the requests to this web site that had a status code in the range 500-599. Number Since responses with a status code of 500-600 indicate server side processing errors, a high value reflects an error condition.

Use the detailed diagnosis of this measure to know when each of the 500 errors occurred, which user experienced the error, when using what browser, from which machine. This information will greatly aid troubleshooting.