I use several loadtesting tools (Loadrunner, JMeter, NeoLoad) to performance test different applications. Im wondering if it is possible to monitor all layers of an application stack so for example. Say i have the following data chain.
Loadbalancer <-x-> Application Server <-x-> RMI <-x-> Java Application <-x-> MQ <-x-> Legacy application <-x-> Database
Where i have marked the x in the chain i am interested in monitoring, for example avg responsetimes.
Obviously we could simply create a wrapper on all endpoints which would gather the statistics for us and maybe we could import it into loadrunner or other loadtesting tools and sideline hem with the tools inbuilt performance statistics, but maybe there is tools/applications which already does this?
If not, how should we proceed, in order to gather this kind of statistics?
The standard for this was supposed to be Application Response Measurement (ARM). It was a cross language set of APIs that did just what you were looking for. The issue is that the products that implement this spec all tend to be big, expensive "enterprise" level monitoring tools. Think multi-week installs, consultants, more infrastructure and lots of buzzwords.
Still, if this is a mission critical app with a mission critical budget, this may be what you need. But you may be able to build your own that does just enough without too much effort. A quick search turns up at least one open source ARM implementation if you still want to use that API.
Another option is to simply to have transactions you can run against each tier of the system to check general responsiveness. For example you can have a static web page on the LB, a no-op tx on the app server, a "hello" servlet on the Java app, put a message directly on the queue, etc. During a performance / load test, these could be hit directly by the load testing tool or you could write a wrapper servlet / application call that does this as a single HTTP (RMI?) call. Running these a few times a minute won't add too much load to the system, but it should help you pinpoint which tier is slower. The nice thing about this approach is that it also works in production, just watch out for security issues.
For single user kind of test, where you know you have problem (e.g. this tx is "slow"), I have also had pretty good luck with network tracing. It's very tedious, but when you aren't sure what tier is slow, starting up a network trace on a few machines and running a single tx usually gives a good idea of what the system is doing.
I have handled this decomposition a number of ways in the past. The first is at a very low level using protocol analyzer dumped data to find the time points where a conversation leaves tier X and enters tier Y. The second method is through the use of log examination for the various tiers. Something that can make your examination quite usefule in this case is a common log server for all of your components (syslog, Rsyslog, etc....) and a nice log parsing tool, such as the freely available Microsoft Logparser. The third method utilization of the audit trail for an application stored in the database. You may find this when working on enterprise services bus style applications which have a consumer/producer model and a bus to pass information rather than a direct connection. The audit trails I have seen are typically stored in a database and allow the tracking of an individual transaction through the entire application infrastructure. Your Load balancer, as a network device, may be out of the hunt on this one.
Note, if you go the protocol analyzer or log route, then be sure and synchronize all of your source information devices to a common time server. Having one of your collectors (analyzer, app log) off on a time stamp basis can really be a hair pulling experience when you get into the analysis phase.
As to how you move from your collected data into LoadRunner, that part is very mechanical. The Analysis program supports an interface to import external datapoints. The format is very specific and is documented in both help and the online docs. This import process works very well, as I often have to use it for collection of statistics from hosts which I do not have direct monitoring access to, but which need to be included as a part of the monitored test infrastructure.
James Pulley
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I have come to know about opentracing and is even working on a POC with Jaeger and Spring. We have around 25+ micro services in production. I have read about it but is a bit confused as how it can be really used.
I'm thinking to use it as a troubleshooting tool to identify the root cause of a failure in the application. For this, we can search for httpStatus codes, custom tags, traceIds and application logs in JaegerUI. Also, we can find areas of bottlenecks/slowness by monitoring the traces.
What are the other usages?
Jaeger has a request sampler and I think we should not sample every request in Prod as it may have adverse impact. Is this true?
If yes, then why and what can be the impact on the application? I guess it can't be really used for troubleshooting in this case as we won't have data on every request.
What sampling configuration is recommended for Prod?
Also, how a tool like Jaeger is different from APM tools and where does it fit in? I mean you can do something similar with APM tools as well. For e.g., one can drill through a service's transaction and jump to corresponding request to other service in AppDynamics. Alerts can be put on slow transactions. One can also capture request headers/body so that they can be searched upon, etc.
There's a lot of different questions here, and some of them don't have answers without more information about your specific setup, but I'll try to give you a good overview.
Why Tracing?
You've already intuited that there are a lot of similarities between "APM" and "tracing" - the differences are fairly minimal. Distributed Tracing is a superset of capabilities marketed as APM (application performance monitoring) and RUM (real user monitoring), as it allows you to capture performance information about the work being done in your services to handle a single, logical request both at a per-service level, and at the level of an entire request (or transaction) from client to DB and back.
Trace data, like other forms of telemetry, can be aggregated and analyzed in different ways - for example, unsampled trace data can be used to generate RED (rate, error, duration) metrics for a given API endpoint or function call. Conventionally, trace data is annotated (tagged) with properties about a request or the underlying infrastructure handling a request (things like a customer identifier, or the host name of the server handling a request, or the DB partition being accessed for a given query) that allows for powerful exploratory queries in a tool like Jaeger or a commercial tracing tool.
Sampling
The overall performance impact of generating traces varies. In general, tracing libraries are designed to be fairly lightweight - although there are a lot of factors that influence this overhead, such as the amount of attributes on a span, the log events attached to it, and the request rate of a service. Companies like Google will aggressively sample due to their scale, but to be honest, sampling is more beneficial to consider from a long-term storage perspective rather than an up-front overhead perspective.
While the additional overhead per-request to create a span and transmit it to your tracing backend might be small, the cost to store trace data over time can quickly become prohibitive. In addition, most traces from most systems aren't terribly interesting. This is why dynamic and tail-based sampling approaches have become more popular. These systems move the sampling decision from an individual service layer to some external process, such as the OpenTelemetry Collector, which can analyze an entire trace and determine if it should be sampled in or out based on user-defined criteria. You could, for example, ensure that any trace where an error occurred is sampled in, while 'baseline' traces are sampled at a rate of 1%, in order to preserve important error information while giving you an idea of steady-state performance.
Proprietary APM vs. OSS
One important distinction between something like AppDynamics or New Relic and tools like Jaeger is that Jaeger does not rely on proprietary instrumentation agents in order to generate trace data. Jaeger supports OpenTelemetry, allowing you to use open source tools like the OpenTelemetry Java Automatic Instrumentation libraries, which will automatically generate spans for many popular Java frameworks and libraries, such as Spring. In addition, since OpenTelemetry is available in multiple languages with a shared data format and trace context format, you can guarantee that your traces will work properly in a polyglot environment (so, if you have Node.JS or Golang services in addition to your Java services, you could use OpenTelemetry for each language, and trace context propagation would work seamlessly between all of them).
Even more advantageous, though, is that your instrumentation is decoupled from a specific vendor or tool. You can instrument your service with OpenTelemetry and then send data to one - or more - analysis tools, both commercial and open source. This frees you from vendor lock-in, and allows you to select the best tool for the job.
If you'd like to learn more about OpenTelemetry, observability, and other topics I wrote a longer series that you can find here (look for the other 'OpenTelemetry 101' posts).
I'm wondering if there are any utilities/patterns/paradigms/standards for monitoring React applications in production.
I've seen a lot of documentation about React performance debugging that recommends the Chrome Dev Tools (which are great, but aren't a passive way to monitor end user performance)
How could I log data to know how long users are waiting for components to mount or render?
The only thing I've thought of so far is creating a Loggable[Pure]Component that extends React.[Pure]Component whose constructor, componentWillMount/Update, and componentDidMount/Update methods log render/mount times to a server. Then, components I want to monitor can extend these components and, if need be, call super() in the lifecycle methods before doing their own work. To specifically know which components these metrics go to, I'd have to expose a method in the Loggable[Pure]Component class that does something silly like setUniqueId and then each derived class would have to call it in the constructor.
This all seems terrible and I'm very much hoping there are some things people out there have implemented, but I haven't found anything thus far.
I would have a look at some APM tools, they handle the frontend monitoring, and the backend monitoring as well. They all support react, and folks use these all the time for that use case. It really depends on your goals in the monitoring, are you doing this for fun? Do you have a startup? Are you working for a large enterprise? There are 3 major players in this market.
AppDynamics - Enterprise APM, handles the most complex apps. Unified product offering delivered SaaS or on-premises. Has deep database, server, and other monitoring.
Dynatrace - Enterprise APM, handles complex apps well. Fragmented portfolio, but the SaaS product is good. The SaaS product has limited depth in some ways. Handles server and cloud infrastructure monitoring well.
New Relic - Easy and cheap(er than others), not as in-depth as some other options. Tends to be popular with small companies. Does a good job monitoring cloud infrastructure services.
These products all do what you are looking for, but it depends on your goals with the data and how you plan to analyze it.
If you want something free and less functional there are ways to do this with open source, but you'll have to stand up and manage a pretty complex stack. Here is one option.
Check out boomerang, which can log/extract the metrics you are looking for, it doesn't "understand" react, but it should work. This data can be posted to many different systems. The best suited is likely the ELK stack (open source log analytics, and more). Here is one of several examples which marries these two together to provide analysis of the browser performance https://github.com/naukri-engineering/NewMonk
I was wondering if there is a tool to keep track of application performance. What I have in mind is a tool that will listen for updates and register performance metrics published by an application. i.e. time to serve a request, time a certain operation took to finish. And this tool would then aggregate the data and measure performance trends.
If you want to measure your application from outside, then you can use RRDtool to collect the data.
You can use slamd for webapp written in Java.
For Django use hotshot.
Search for profiler + your language, framework
Take a look at HP SiteScope. It's ability to drive the system with a Web User Script, to monitor the metrics on the backend, even to the extent of creation of custom shell scripts and database queries, plus the ability to add logic for report/alert against these combined data sets appears to be what you need.
Other mechanisms that you might consider would be a roll your own service using CURL to push information in, queries to the systems involved to pull metrics or database information and then your own interface for alerting and reporting.
Then it becomes a cost question, can you roll the level of functionality for less money than you can purchase an already existing solution on the open market.
Ref:
HP SiteScope Wiki Page
I have been tasked with looking for a performance testing solution for one of our Java applications running on a Weblogic server. The requirement is to record production requests (both GET and POST including POST data) and then run these requests in a performance test environment with a copy of the production database.
The reasons for using production requests instead of a test script are:
It is a large application with no existing test scripts so it would be a a large amount of work to write scripts to cover the entire application.
Some performance issues only appear when users do a number of actions in a particular order.
To test using actual user interaction with the system not an estimation at how the users may interact with the system. We all know that users will do things we have not thought of.
I want to be able to fix performance issues and rerun the requests against the fixed code before releasing to production.
I have looked at using JMeters Access Log Sampler with server access logs however the access logs do not contain POST data and the access log sampler only looks at the request URL so it cannot simulate users submitting form data.
I have also looked at using the JMeter HTTP Proxy Server however this can record the actions of only one user and requires the user to configure their browser to use the proxy. This same limitation exist with Tsung and The Grinder.
I have looked at using Wireshark and TCReplay but recording at the packet level is excessive and will not give any useful reports at a request level.
Is there a better way to analyze production performance considering I need to be able to test fixes before releasing to production?
That is going to be a hard ask. I work with Visual Studio Test Edition to load test my applications and we are only able to "estimate" the users activity on the site.
It is possible to look at the logs and gather information on the likelyhood of certain paths through your app. You can then look at the production database to look at the likely values entered in any post requests. From that you will have to make load tests that approach the useage patterns of your production site.
With any current tools I don't think it is possible to record and playback actual user interation.
It is possible to alter your web app so that is records and logs every request and post against session and datetime. This custom logging could be then used to generate load test requests against a test website. This would be some serious code change to your existing site and would likely have performance impacts.
That said, I have worked with web apps that do this level of logging and the ability to analyse the exact series of page posts/requests that caused an error is quite valuable to a developer.
So in summary: It is possible, but I have not heard of any off the shelf tools that do it.
Please check out this Whitepaper by Impetus Technologies on this page.. http://www.impetus.com/plabs/sandstorm.html
Honestly, I'm not sure the task you're being asked to do is even possible, let alone a good idea. Depending on how complex the application's backend is, and how perfect you can recreate the state (ie: all the way down to external SOA services or the time/clock), it may not be possible to make those GET and POST requests reproduce the same behavior.
That said, performance testing against production data is always great, but it usually requires application-specific knowledge that will stress said data. Simply repeating HTTP GETs and POSTs will almost certainly not yield useful results.
Good luck!
I would suggest the following to get the production requests and simulate the accurate workload:
1) Use coremetrics: CoreMetrics provides such solutions using which you can know the application usage patterns. This would help in coming up with an accurate workload model. This model can then be converted into test scripts and executed against a masked copy of production database. This will provide you accurate results about the application performance in realtime.
2) Another option would be creating a small utility using AOP (Aspect oriented apporach) so that it can trace all the requests and corresponding method traces. This would help in identifying the production usage pattern and in turn accurate simulation of workload. AOP frameworks such as AspectJ can be used. This would not require any changes in code. The instrumentation can be done on the fly. The other benefit would be that thi cna only be enabled for a specific time window and then it can be turned off.
Regards,
batterywalam
What can be the various performance testing scenarios to be considered for a website with huge traffic? Is there any way to identify the elements of the code which are adversely affecting the site performance?
Please provide something similar to checklist of generalised scenarios to be tested to ensure proper performance testing.
It would be good to start with some load testing tools like JMeter or PushToTest and start running it against your web application. JMeter simulates HTTP traffic and loads the server that way. You can do that as well as load test AJAX parts of your application with PushToTest because it can use Selenium Scripts.
If you don't have the resources (computers to run load tests) you can always use a service like BrowserMob to run the scripts against a web accessible server.
It sounds like you need more of a test plan than a suggestion of tools to use. In performance testing, it is best to look at the users of the application -
How many will use the application on a light day? How many will use the app on a heavy day?
What type of users make up your user population?
What transactions will each of these user types perform?
Using this information, you can identify the major transactions and come up with different user levels (e.g. 10, 25, 50, 100) and percentages of user types (30% user A, 50% user B, ...) to test these transactions with. Time each of these transactions for each test you execute and examine how the transaction times change as compared to your user levels.
After gathering some metrics, since you should be able to narrow transactions to individual pieces of code, you will be able to know where to focus your code improvements. If you still need to narrow things down further, finer tests within each transaction can be created to provide more granular results.
Concurrency will kill you here, as you need to test your maximum projected concurrent users + wiggling room hitting the database, website, and any other web service simultaneously. It really depends on the technologies you're using, but if you have a large interaction of different web technologies, you may want to check out Neoload. I've had nothing but success with this web stress tool, and the support is top notch if you need to emulate specific, complicated behavior (such as mocking AMF traffic, or using responses from web pages to dictate request behavior.)
If you have a DB layer then this should be the initial focus of your attention, once the system is stable (i.e. no memory leaks or other resource issues). If the DB is not the bottle neck (or not relevant) then you need to correlate CPU/Memory/Disk IO and Network traffic with the increasing load and increasing response times. This gives you an idea of capacity and correlation (but not cause) to resource usage.
To find the cause of a given issue with resources you need to establish a Six Sigma style project where you define the problem and perform root case analysis in order to pin point the piece of code (or resource configuration) that is the bottleneck. Once you have done this a couple of times in your environment, you will notice patterns of workload, resource usage and counter measures (solutions) that will guide you in your future performance testing 'projects'.
To choose correct performance scenarios you need to go through the next basic checklist:
High priority scenarios from the business logic perspective. For example: login/order transactions, etc.
Mostly used scenarios by end users. Here you may need information from monitoring tools like NewRelic, etc.
Search / filtering functionality (if applicable) - Scenarios which involve different user roles/permissions
Performance test is a comparison test either with the previous release of the same application or with the existing players in the market.
Case 1- Existing application
1)Carry out the test for the same scenarios as covered before to get a clear picture on the response of the application before and after the upgrade.
2)If you need to dig deeper you can get back to the database team to understand which functionalities are getting more requests. Also ask them on the total number of requests on an average on any particular day so that you can take a call on what user load and time duration to be given for the test.
Case 2- New Application
1) Look for existing market players and design your test as per the critical functions of the rival product (for e.g. Gmail might support many functions what what is being used often is launch ->login ->compose mail -> inbox ->outbox).
2) Any time you can get back to your clients on what they suppose to be business critical scenarios or scenarios that will be used more often..