TAG | BTM
Everyday in our life we rely on services provided by other people. Making a phone call, getting a car fixed, or ordering a pizza – and yet we want those things to happen as quickly as possible, because time often means money. If you take your car to a Mercedes or BMW dealer, you will understand this point better than anyone. Our productivity (and often happiness) is therefore controlled, everyday, by different organizations and people. When things slow down or don’t happen we get upset, frustrated, and sometimes rant on twitter like these folk:
If your application today has SOA design principles, is heavily distributed and relies on 3rd party service providers, then you’ve probably become frustrated at some point when your application slows down or crashes. The problem is this: your end user experience and quality of service (QoS) is only as good as the QoS of your service providers. So, unless you monitor QoS you can’t measure QoS–and if you can’t measure QoS, you can’t manage your service providers and your end user experience. For example, take a look at this customer e-commerce application which has 7 JVM’s, 1 database and 7 external web service providers:
This customer recently had a slowdown with their e-commerce production application. After a few minutes browsing AppDynamics, they successfully identified that one of their web service providers was having latency issues (AppDynamics automatically baselines performance and flags deviations for each web service provider as shown in the above screenshot). The customer called their service provider, and sure enough the service provider admitted to having issues. A few hours later the service provider called back and said “we fixed the problem, everything should be back to normal”–yet the customer could clearly see latency issues still occurring in AppDynamics. So they sent their service provider a screenshot showing the evidence. The service provider then checked again, and called back a few minutes later saying “Yes, sorry a few customers are still being impacted.” Without this level of visibility, many organizations are simply blind to how external service providers impact their end user experience and business.
Being able to troubleshoot slow performance in minutes is helpful, but what about being able to report the exact service level you receive–say, from each of your service providers over a period of time? Did your service improve over time or did it regress? How many outages or severity 1 incidents did your service providers cause this week for your application?
Take the below screenshot from AppDynamics, which plots the maximum response time for five different web services consumed by an application over the last week. You can see that three out of the five web services (denoted by pink, blue and turquoise lines) consistently deliver sub-second response times and provide a great service level. However, the other two web services (red and green lines) show performance spikes with response times of between 14 and 22 seconds. The green web service in particular is very inconsistent and shows several performance spikes in two days.
Below is the response time of another web service (PayPal) for a customer application over the last 3 months. Notice the spikes in response time and look at the deviation between average and maximum response time over the time period. What’s impressive is that despite the occasional service blip, the PayPal service has slowly improved by 14% from 450 milliseconds to around 385 milliseconds. It’s also been very stable the last few weeks, along with having a consistent service (small deviation from average and maximum response time).
If your application relies on one or more 3rd party web services, you should periodically check and report what level of service you are receiving each week. That way, you can truly understand your service provider QoS and its impact on your end user experience and application performance. You can also keep your service providers honest, with complete visibility of whether QoS is improving or degrading over time as service outages occur and are fixed.
The next time you experience a slow down or outage in your application, you should first check external web services before you start to troubleshoot your own. The last thing you want to be doing is debugging your own code, when it could be someone else’s service and code that is causing the issue. Using AppDynamics it’s possible to monitor, measure, and manage the QoS from each of your web service providers. You can get started right now by downloading AppDynamics Lite (our free edition) for a single JVM or IIS web server, or you can request a 30-day trial of AppDynamics Pro (our commercial edition) for Java or .NET applications with multiple JVMs and IIS web servers.Link to this post:
Last week I flew into Las Vegas for #Interop fully suited and booted in my big blue costume (no joke). I’d been invited to speak in a vendor debate on User eXperience (UX): Monitor the Application or the Network? NetScout represented the Network, AppDynamics (and me) represented the Application, and “Compuware dynaTrace Gomez” sat on the fence representing both. Moderating was Jim Frey from EMA, who did a great job introducing the subject, asking the questions and keeping the debate flowing.
At the start each vendor gave their usual intro and company pitch, followed by their own definition on what User Experience is.
Defining User Experience
So at this point you’d probably expect me to blabber on about how application code and agents are critical for monitoring the UX? Wrong. For me, users experience “Business Transactions”–they don’t experience applications, infrastructure, or networks. When a user complains, they normally say something like “I can’t Login” or “My checkout timed out.” I can honestly say I’ve never heard them say – ”The CPU utilization on your machine is too high” or “I don’t think you have enough memory allocated.”
Now think about that from a monitoring perspective. Do most organizations today monitor business transactions? Or do they monitor application infrastructure and networks? The truth is the latter, normally with several toolsets. So the question “Monitor the Application or the Network?” is really the wrong question for me. Unless you monitor business transactions, you are never going to understand what your end users actually experience.
Monitoring Business Transactions
So how do you monitor business transactions? The reality is that both Application and Network monitoring tools are capable, but most solutions have been designed not to–just so they provide a more technical view for application developers and network engineers. This is wrong, very wrong and a primary reason why IT never sees what the end user sees or complains about. Today, SOA means applications are more complex and distributed, meaning a single business transaction could traverse multiple applications that potentially share services and infrastructure. If your monitoring solution doesn’t have business transaction context, you’re basically blind to how application infrastructure is impacting your UX.
The debate then switched to how monitoring the UX differs from an application and network perspective. Simply put, application monitoring relies on agents, while network monitoring relies on sniffing network traffic passively. My point here was that you can either monitor user experience with the network or you can manage it with the application. For example, with network monitoring you only see business transactions and the application infrastructure, because you’re monitoring at the network layer. In contrast, with application monitoring you see business transactions, application infrastructure, and the application logic (hence why it’s called application monitoring).
Monitor or Manage the UX?
Both application and network monitoring can identify and isolate UX degradation, because they see how a business transaction executes across the application infrastructure. However, you can only manage UX if you can understand what’s causing the degradation. To do this you need deep visibility into the application run-time and logic (code). Operations telling a Development team that their JVM is responsible for a user experience issue is a bit like Fedex telling a customer their package is lost somewhere in Alaska. Identifying and Isolating pain is useful, but one could argue it’s pointless without being able to manage and resolve the pain (through finding the root cause).
Netscout made the point that with network monitoring you can identify common bottlenecks in the network that are responsible for degrading the UX. I have no doubt you could, but if you look at the most common reason for UX issues, it’s related to change–and if you look at what changes the most, it’s application logic. Why? Because Development and Operations teams want to be agile, so their applications and business remains competitive in the marketplace. Agile release cycles means application logic (code) constantly changes. It’s therefore not unusual for an application to change several times a week, and that’s before you count hotfixes and patches. So if applications change more than the network, then one could argue it’s more effective for monitoring and managing the end user experience.
UX and Web Applications
We then debated which monitoring concept was better for web-based applications. Obviously, network monitoring is able to monitor the UX by sniffing HTTP packets passively, so it’s possible to get granular visibility on QoS in the network and application. However, the recent adoption of Web 2.0 technologies (ajax, GWT, Dojo) means application logic is now moving from the application server to the users browser. This means browser processing time becomes a critical part of the UX. Unfortunately, Network monitoring solutions can’t monitor browser processing latency (because they monitor the network), unlike application monitoring solutions that can use techniques like client-side instrumentation or web-page injection to obtain browser latency for the UX.
The C Word
We then got to the Cloud and which made more sense for monitoring UX. Well, network monitoring solutions are normally hardware appliances which plug direct into a network tap or span port. I’ve never asked, but I’d imagine the guys in Seattle (Amazon) and Redmond (Windows Azure) probably wouldn’t let you wheel a network monitoring appliance into their data-centre. More importantly, why would you need to if you’re already paying someone else to manage your infrastructure and network for you? Moving to the Cloud is about agility, and letting someone else deal with the hardware and pipes so you can focus on making your application and business competitive. It’s actually very easy for application monitoring solutions to monitor UX in the cloud. Agents can piggy back with application code libraries when they’re deployed to the cloud, or cloud providers can embed and provision vendor agents as part of their server builds and provisioning process.
What’s interesting also is that Cloud is highlighting a trend towards DevOps (or NoOps for a few organizations) where Operations become more focused on applications vs infrastructure. As the network and infrastructure becomes abstracted in the Public Cloud, then the focus naturally shifts to the application and deployment of code. For private clouds you’ll still have network Ops and Engineering teams that build and support the Cloud platform, but they wouldn’t be the people who care about user experience. Those people would be the Line of Business or application owners which the UX impacts.
In reality most organizations today already monitor the application infrastructure and network. However, if you want to start monitoring the true UX, you should monitor what your users experience, and that is business transactions. If you can’t see your users’ business transactions, you can’t manage their experience.
What are your thoughts on this?
I did have an hour spare at #Interop after my debate to meet and greet our competitors, before flying back to AppDynamics HQ. It was nice to see many of them meet and greet the APM Caped Crusader.
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It’s a bittersweet feeling when End Users, Operations, Developers and many Businesses suffer application performance pain. Outages cost the business money, but sometimes they cost people their jobs–which is truly unfortunate. However, when people solve performance issues, they become overnight heroes with a great sense of achievement, pride, and obviously relief.
To explain the complexity of managing application performance, imagine your application is 100 haystacks that represent tiers, and somewhere a needle is hurting your end user experience. It’s your job to find the needle as quickly as possible! The problem is, each haystack has over half a million pieces of hay, and they each represent lines of code in your application. It’s therefore no surprise that organizations can take days or weeks to find the root cause of performance issues in large, complex, distributed production environments.
End User Experience Monitoring, Application Mapping and Transaction profiling will help you identify unhappy users, slow business transactions, and problematic haystacks (tiers) in your application, but they won’t find needles. To do this, you’ll need x-ray visibility inside haystacks to see which pieces of hay (lines of code) are holding the needle (root cause) that is hurting your end users. This X-Ray visibility is known as “Deep Diagnostics” in application monitoring terms, and it represents the difference between isolating performance issues and resolving them.
For example, AppDynamics has great End User Monitoring, Business Transaction Monitoring, Application Flow Maps and very cool analytics all integrated into a single product. They all look and sound great (honestly they do), but they only identify and isolate performance issues to an application tier. This is largely what Business Transaction Management (BTM) and Network Performance Management (NPM) solutions do today. They’ll tell you what and where a business transaction slows down, but they won’t tell you the root cause so you can resolve the issues.
Why Deep Diagnostics for Production Monitoring Matters
A key reason why AppDynamics has become very successful in just a few years is because our Deep Diagnostics, behavioral learning, and analytics technology is 18 months ahead of the nearest vendor. A bold claim? Perhaps, but it’s backed up by bold customer case studies such as Edmunds.com and Karavel, who compared us against some of the top vendors in the application performance management (APM) market in 2011. Yes, End User Monitoring, Application Mapping and Transaction Profiling are important–but these capabilities will only help you isolate performance pain, not resolve it.
AppDynamics has the ability to instantly show the complete code execution and timing of slow user requests or business transactions for any Java or .NET application, in production, with incredibly small overhead and no configuration. We basically give customers a metal detector and X-Ray vision to help them find needles in haystacks. Locating the exact line of code responsible for a performance issue means Operations and Developers solve business pain faster, and this is a key reason why AppDynamics technology is disrupting the market.
Below is a small collection of needles that customers found using AppDynamics in production. The simple fact is that complete code visibility allows customers to troubleshoot in minutes as opposed to days and weeks. Monitoring with blind spots and configuring instrumentation are a thing of the past with AppDynamics.
Needle #1 – Slow SQL Statement
Pain: Key Business Transaction with 5 sec response times
Root Cause: Slow JDBC query with full-table scan
Needle #2 – Slice of Death in Cassandra
Industry: SaaS Provider
Pain: Key Business Transaction with 2.5 sec response times
Root Cause: Slow Thrift query in Cassandra
Needle #3 – Slow & Chatty Web Service Calls
Pain: Several Business Transactions with 2.5 min response times
Root Cause: Excessive Web Service Invocation (5+ per trx)
Needle #4 -Extreme XML processing
Pain: Key Business Transaction with 17 sec response times
Root Cause: XML serialization over the wire.
Needle #5 – Mail Server Connectivity
Pain: Key Business Transaction with 20 sec response times
Root Cause: Slow Mail Server Connectivity
Pain: Several Business Transactions with 30+ sec response times
Root Cause: Querying too much data
Needle #7 – Slow Security 3rd Party Framework
Pain: All Business Transactions with > 3 sec response times
Root Cause: Slow 3rd party code
Needle #8 – Excessive SQL Queries
Pain: Key Business Transactions with 2 min response times
Root Cause: Thousands of SQL queries per transaction
Needle #9 – Commit Happy
Pain: Several Business Transactions with 25+ sec response times
Root Cause: Unnecessary use of commits and transaction management.
Needle #10 – Locking under Concurrency
Pain: Several Business Transactions with 5+ sec response times
Root Cause: Non-Thread safe cache forces locking for read/write consistency
Industry: SaaS Provider
Pain: Key Business Transaction with 2+ min response times
Root Cause: Slow 3rd Party code
Industry: Financial Services
Pain: Several Business Transactions with 7+ sec response times
Root Cause: DB Connection Pool Exhaustion caused by excessive connection pool invocation & queries
Pain: Several Business Transactions with 50+ sec response times
Root Cause: Cache Sizing & Configuration
If you want to manage and troubleshoot application performance in production, you should seriously consider AppDynamics. We’re the fastest growing on-premise and SaaS based APM vendor in the market right now. You can download our free product AppDynamics Lite or take a free 30-day trial of AppDynamics Pro – our commercial product.
Now go find those needles that are hurting your end users!
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Imagine you’re an operations guy and you’ve just received a phone call or alert notifying you that the application your responsible for is running slow. You bring up your console, check all related processes, and notice one java.exe process isn’t using any CPU but the other Java processes are. The average sys admin at this point would just kill and restart the Java process, cross their fingers, and hope everything returns back to normal (this actually does work most of the time). An experienced sys admin might perform a kill -3 on the Java process, capture a thread dump, and pass this back to dev for analysis. Now suppose your application returns back to normal–end users stop complaining, you pat yourself on the back and beat your chest, and basically resume what you were doing before you were rudely interrupted.
The story I’ve just told may seem contrived, but I’ve witnessed it several times with customers over the years. The stark reality is that no one in operations has the time or visibility to figure out the real business impact behind issues like this. Therefore, little pressure is applied to development to investigate data like thread dumps so that root causes can be found and production slowdowns can be avoided again in future. It’s true restarting a JVM or CLR will solve a fair few issues in production, but it’s only a temporary fix over the real problems that exist within the application logic and configuration.
Now imagine for one minute that operations could actually figure out the business impact of production issues, along with identifying the root cause, and communicate this information to Dev so problems could be fixed rapidly. Sounds too good to be true, right? Well, a few weeks ago an AppDynamics customer did just that and the story they told was quite compelling.
Code Deadlock in a distributed E-Commerce Application
The customer application in question was a busy e-commerce retail website in the US. The architecture was heavily distributed with several hundred application tiers that included JVMs, LDAP servers, CMS server, message queues, databases and 3rd party web services. Here is a quick glimpse of what that architecture looked like from a high level:
Detecting Code Deadlock
If we look at the AppDynamics problem pane (right) as the customer saw things, it shows the severity of their issues. During the day the application was experiencing just over 4,000 business transactions per minute, which works out at just under 1 million transactions a day. Approximately 2.5% of these transactions were impacted by the slowdown, which was the result of the 92 code deadlocks you see here that occurred during peak hours.
AppDynamics is able to dynamically baseline the performance of every business transaction type before classifying each execution as normal, slow, very slow or stalled depending on its deviation from its unique performance baseline. This is critical for understanding the true business impact of every issue or slowdown because operations can immediately see how many user requests were impacted relative to the total requests being processed by the application.
From this pane, operations were able to drill down into the 92 code deadlocks and see the events that took place as each code deadlock occurred. As you can see from the screenshot (below left), the sys admins during the slowdown kept restarting the JVMs (as shown) to try and make the issues go away. Unfortunately, this didn’t work given that the application was experiencing high concurrency under peak load.
By drilling into each Code Deadlock event, operations were able to analyze the various thread contentions and locate the root cause of the issue. The root cause of the slowdown turned out to be an application cache which wasn’t thread-safe. If you look at the screenshot below, showing the final execution of the threads in deadlock accessing the cache, you can see that one thread was trying to remove an item, another was trying to get an item, and the last thread was trying to put an item. 3 threads were trying to do a put, get and remove at the same time! This caused a deadlock to occur on cache access, thus causing the related JVM to hang until those threads were released via a restart.
Analyzing Thread Dumps
Below you can see the thread dump that AppDynamics collected for one of the code deadlocks, which clearly shows where each thread was deadlocked. By copying the full thread dumps to clipboard, operations were able to see the full stack trace of each thread, thus identifying which business transactions, classes, and methods were responsible for cache access.
The root cause for this production slowdown may have been identified and passed to dev for resolution, but the most compelling conclusion from this customer story was related to them identifying the real business impact that occurred. The application was clearly running slow, but what did the end user experience during the slowdown and what impact would this have had on the business?
What was the Actual Business Impact?
The screenshot below shows all business transactions that were executing on the e-commerce web application during the five hour window before, during, and after the slowdown occurred.
Here are some hard hitting facts for the two most important business transactions inside this e-commerce application:
- 46,463 Checkouts processed
- 482 returned an error, 1325 were slow, 576 were very slow and 111 stalled.
- 3,956 Payments processed
- 12 returned an error, 242 were slow, 96 were very slow and 79 stalled
Error – transaction failed with an exception. Slow – the business transaction deviated from its baseline by more than 3 standard deviations. Very Slow – the business transaction deviated from its baseline by more than 4 standard deviations. Stalled – the transaction timed out.
If you take these raw facts and assume the average revenue per order is $100, then the potential revenue risk/impact of this slowdown was easily into six digits when you consider the end user experience for checkout and payment. Even if you take the 482 Errors and 111 Stalls relating to the Checkout business transaction alone – this still equates to around $60,000 of revenue at risk. And that’s a fairly conservative estimate!
If you add up all the errors, slow, very slow and stalls you see in the screenshot above, you start to picture how serious this issue was in production. The harsh reality is that incidents like this happen everyday in production environments, but no one has visibility into the true business impact of them, meaning little pressure is applied to development to fix “glitches.”
Agile isn’t about Change, It’s about Results
If development teams want to be truly agile, they need to forget about constant change and focus on what impact their releases has on the business. The next time your application slows down or crashes in production, ask yourself one question: “What impact did that just have on the business?” I guarantee just thinking about that answer will make you feel cold. If development teams found out more often the real business impact of their work, they’d learn pretty quickly how fast, reliable and robust their application code really is.
I’m pleased to say no developers were injured or fired during the making of this real-life customer story; they were simply educated on what impact their non-thread safe cache had on the business. Failure is OK–that’s how we learn and build better applications.
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