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Home > How DBAs are Using SolarWinds DPM and Marginalia to reduce MTTR


HOW DBAS ARE USING SOLARWINDS DPM AND MARGINALIA TO REDUCE MTTR

By Ewen Fortune
September 2, 2022 | Database Insights and Best Practices

Database performance is critical to business revenue. Slow and non-responsive
databases can result in hundreds of thousands or millions of dollars in lost
revenue from poor customer experience and downtime. High expectations in
performance require IT infrastructure to function at full speed. In 2006, an
Amazon study found that every 100ms in added page load time cost them 1% in
sales. The lost revenue at that time would have been around $107 million; today,
the financial hit would total closer to $4 billion. Similarly, a Google study
showed that a 500 ms increase in search display time reduced revenue by 20
percent*. Their full findings are shown in this table. Speed Impact study
“Microseconds make millions”** “Results showed that a mere 0.1s change in load
time can influence every step of the user journey, ultimately increasing
conversion rates. Conversions grew by 8% for retail sites and by 10% for Travel
sites on average.” Google/55/Deloitte, EMEA and U.S., Speed Impact study, Oct.
2019–Nov. 2019 More recently, in the PageSpeed Impact study from Google notes,
data show that fast response times are crucially important for the entire
customer journey, affecting user experience and revenue. In trying to identify
the cause of a performance issue, most of the time is spent determining which
queries are at fault and where they came. Typically, the amount of time fixing
the problem is a small percentage of the overall troubleshooting effort. With
large web applications such as the Shopify platform with builds of millions of
lines of code*** and managed by hundreds of engineers, finding which deploy
introduced the code and who is responsible for maintaining that code is a
sizeable portion of the time spent to repair. To pinpoint which queries have
been subject to change, we can look at changes in the workload compared to
normal operation. This screenshot shows the Compare feature used in the
SolarWinds® Database Performance Monitor Profiler to bubble up the groups of
queries that have changed most over the past hour. The challenge now becomes
where this query came from, a question DPM Profiler can also assist in solving.
In addition to visualizing meta data such as User, Schema, and origin data, it
also supports the display of query comments, which can communicate additional
information about the query. Given the size and complexity of today's
applications, manually annotating queries may not be feasible, and this is where
solutions such as the Marginalia gem, created by 37signals, can really help to
reduce the level of confusion. Marginalia ships with :application, :controller,
and :action enabled by default. In addition, implementation is provided for:
 * :line (for file and line number calling query). :line supports a
   configuration by setting a regexp in Marginalia::Comment.lines_to_ignore to
   exclude parts of the stacktrace from inclusion in the line comment.
 * :controller_with_namespace to include the full classname (including
   namespace) of the controller.
 * :job to include the classname of the ActiveJob being performed.
 * :hostname to include Socket.gethostname.
 * :pid to include current process id.
 * :db_host to include the configured database hostname.
 * :socket to include the configured database socket.
 * :database to include the configured database name.

To install, simply include it in your Gemfile. -- # Gemfile gem 'marginalia' --
This powerful Gem can automatically prepend or append comments which indicate
where in the application a particular query originated. SELECT `accounts`.* FROM
`accounts` WHERE `accounts`.`queenbee_id` = 1234567890 LIMIT 1
/*application:BCX,controller:project_imports,action:show*/ This means queries
can carry the Origin data such as the application, controller and function,
which is calling or generating the query, right down to the line of code and
author of the call. At this point, within a few clicks we have both the
problematic queries, the code responsible for those queries and the author of
the code itself. In some cases, we may need to perform more work to boil down
the candidate queries to the ones most relevant to the case in hand. Here too,
SolarWinds DPM can help and has the facility to parse comments included with a
query which contains key-value pairs and convert those into tags to associate
with query digests. This means we can use those same tags generated from the
Marginalia comment filter information in the Profiler, meaning we can see the
change in queries for a specific Application, Controller and View. During
diagnosis we can also use the key value pairs from the comments to understand
which applications, controllers and views may be affected, by grouping by tag.
Identifiers in the form of key value pairs embedded in comments such as those
provided by Marginalia reduce the total amount of data to be reviewed and can
indicate the source code and developer responsible for the query. As illustrated
by the functionality examples and screenshots, using SolarWinds DPM and
Marginalia together enables teams using Rails to perform root cause analysis
more efficiently, fix database problems more rapidly, and support service level
agreements to ensure the consistent optimal customer experience.
*http://robotics.stanford.edu/people/ronnyk/2007IEEEComputerOnlineExperiments.pdf
**Google/55/Deloitte, EMEA and U.S., Speed Impact study, Oct. 2019–Nov. 2019 ***
https://shopify.engineering/shopify-monolith ****
https://signalvnoise.com/posts/3130-tech-note-mysql-query-comments-in-rails
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Ewen Fortune
Ewen is a database geek with passion for performance and reliability. Having
spent several years at Percona, he joined VividCortex, which was acquired by
SolarWinds…
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