Configuring workloads using JMeter – Defined Percentage Probability

Here is the usecase: I have 3 scenarios named A, B and C which are to be load tested with 6, 3 and 1 threads respectively.

These 3 scenarios have 7 use cases (T1 to T7)and  are to be executed using defined percentages as shown below:

Screen Shot 2017-03-15 at 8.03.31 AM

How do we configure it in JMeter? Had it been just the users with 3 scenarios we would have configured them in ThreadGroup. But how abt T1 to T7?

First create 3 threads groups with desired no. of users as shown below: Screen Shot 2017-03-15 at 8.14.44 AM

Then under the thread group add Throughput controller which is under logical controller. Configure the percentage and add the request to the throughput controller as show below:Screen Shot 2017-03-15 at 8.19.54 AM

Hope this helps.

Application Performance and Antipatterns

  • Excessive Layering – Most of the underlying performance starts with the excessive layering antipattern. The application design has grown over the usage of controllers, commands and facades. In order to decouple each layer, the designers are adding facades at each of the tiers. Now, for every request at the web tier, the request call goes through multiple layers just to fetch the results. Imagine doing this for thousands of requests coming in and the load the JVM need to handle to process these requests. The number of objects that get created and destroyed when making these calls add to the memory overhead. This further limits the amount of requests that can be handled by each server node. Based on the size of the application, deployment model, the number of user’s, appropriate decision need to be taken to reduce the number of layers. E.g. if the entire application gets deployed in the same container, there is no need to create multiple layers of process beans, service beans(business beans), data access objects etc. Similarly, when developing an internet scale application, large number of layers start adding overheads to the request processing. Remember, large number of layers means large number of classes which effectively start impacting the overall application maintainability.
  • Round Tripping– With the advent of ORM mappings, Session/DAO objects, the programmer starts making calls to beans for every data. This leading to excessive calls between the layers. Another side issue is the number of method calls each layer start having to support this model. Worse case is, when the beans are web service based. Client tier making multiple web service calls within a single user request have a direct impact on the application performance. To reduce the round tripping, the application needs to handle or combine multiple requests at the business tier.
  • Overstuffed Session– Session object is a feature provided by the JEE container to track user session during the web site visit. The application start with the promise of putting very minimal information in the session but over a period of time, the session object keeps on growing. Too much of data or wrong kind of data is stuffed into the session object. Large data objects will mean that the objects placed in the session will linger on till the session object is destroyed. This impacts the number of user’s that can be served by the application server node. Further, I have seen, application using session clustering to support availability requirements but adding significant overheads to the network traffic and ability of application to handle higher number of users. To unstuff the session object, take an inventory of what all goes there, see what is necessary, what objects can be defaulted to request scope. For others, remove the objects from session when their usage is over.
  • Golden Hammer (Everything is a Service) – With the advent of SOA, there is tendency to expose the business services, which can be orchestrated into process services. In the older applications, one can observe similar pattern being implemented with EJBs. This pattern coupled with the bottom up design approach at times, means exposing each and every data entity as a business service. This kind of design might be working correctly functionally, but from the performance and maintenance point of view, it soon becomes a night mare. Every web service call adds overhead in terms of data serialization and deserialization. At times, the data(XML) being passed with web service calls is also huge leading to performance issues. The usage of services or ejb’s should to be evaluated from application usage perspective. Attention needs to be paid on the contract design.
  • Chatty Services – Another pattern observed is the way the service is implemented via multiple web service calls each of which is communicating a small piece of data. This results in explosion of web services and which leads to degradation of performance and unmaintainable code. Also, from the deployment perspective, the application starts running into problems. I have come across projects which have hundred plus services all getting crammed into a single deployment unit. When the application comes up, the base heap requirement is already in 2Gb range leaving not much space for application to run. If the application is having too many fine grained services, then it an indication towards the application of this antipattern.

Refer to :


Top J2EE application performance problems

What are the most common performance and scalability problems for a J2EE (Java EE) Web application? Here are the most common tips and problems found in real production systems.

  1. Bad Caching Strategy: It is rare that users require absolutely real time information. Simply refreshing HTML content with a 60 second cache can already dramatically reduce the load to the application server and most important the DB for a high traffic web site. Cache HTML segment for the home page and most visited pages.   Implement other caching strategy in the business service layer or the DB layer. For example, use Spring AOP to cache data returned from a business service or configure hibernate to cache DB query result.
  2. Missing DB indexes: After a new code push, indexes may be missing for the new SQL codes. The data query may be slow if the table is huge and the missing index forces a full table scan. Most development DB has a very small data set and therefore the problem is un-detected. Check the DB log or profile in production for long executed SQLs and add index if needed.
  3. Bad SQLs: The second most common DB performance problem is bad SQLs. Check the DB log or profile for long executed query. Most problems can be resolved by re-written the SQLs. Paid attentions to sub-query or SQLs with complicated joins. Occasionally, DB table tuning may be required.
  4. Too many fine grain calls to the service, data or the DB layer: Developers may use an iteration loop in retrieving a list of data. Each iteration may make a middle tier call which results in multiple SQL calls. If the list is long, the total DB requests can be huge. Developers should write a new service call and retrieve the list in a single DB call.
  5. All application server threads are waiting for the DB or external system connection: Web server has a limited number of threads. When a HTTP request is processed, a thread will exclusively dedicate to a request until it is completed. Hence, if an external system like DB is very slow, all web server threads may be waiting.   When this happens, the web server will pause all new incoming requests.   From a end user perspective, the system seems not responding. Add timeout logic when communicate with external system. Increasing the thread counts will only delay the problem and in some cases counter productive.
  6. SQLs retrieve too many rows of data: Do not retrive hundreds row of data to just display a few of them. Check the DB log or profile constantly for un-expected high usage of SQLs that retrieve a lot of rows .
  7. Do not use prepared statement for the DB: Always use prepared statement to avoid DB side SQL hard parsing.   SQL hard parsing causes a lot of DB scalability problem when DB requests increases.
  8. Lack or improper pagination of data: Implement pagination to display a long list of data. Do not retrieve all the data from the database and use the Java code to filter out the data. Always use the database for data filtering and pagination.
  9. Non-optimize connection pool configuration: The maximum / minimum pool size and the retaining policy of idling pool thread can significant impact an application performance. The web server will be idle waiting for a DB connection if the pool size is too low. The retaining policy is important since most DB pool creation code has very low concurrency and cannot handle a sudden surge of concurrent requests.
  10. Frequent garbage collection caused by memory leak: When memory is leaking, the Java JVM will perform frequent garbage collection (GC) even they cannot reclaim too many memory. Eventually, the web server spend most of the time executing the GC rather than processing HTTP requests.   Rebooting the server can temporarily release the problem but only stopping the leak can solve the problem.
  11. Do not process large amount of data at once: For request involving large amount of data, in particular batch process, sub-divide the large data set into chunk and process it separately. Otherwise, the request may deplete the Java heap or stack memory and crashes the JVM.
  12. Concurrency problems in the synchronization block: Code synchronization block carefully.   Use established library to manage system and application resources like DB connection pool. For system with concurrency problem, the CPU utilization remains low even significantly increase the traffic.
  13. Bad DB tuning: If DB response is slow regardless of SQLs, DB instance tuning is needed. Monitor the memory paging activity closely in identifying any memory mis-configuration. Also monitor the file I/O wait time and DB memory usage closely.
  14. Process data in batch: To reduce DB requests, combine DB requests together and process those in a single batch. Use SQL batch if necessary instead of large volume of small SQL requests.
  15. JMS or application deadlock: Avoid a cyclic loop in making JMS requests.   A request may send to Queue A which then send a message to Queue B and then again to Queue A. This circle loop will trigger deadlock in high volume requests.
  16. Bad Java heap configuration: Configure the maximum heap size, the minimum heap size, the young generation heap and the garbage collection algorithm correctly. The bigger is not the better and it is often depends on the application.
  17. Bad application server thread configuration: Too high of a thread count triggers high context switching overhead while low thread count causes low concurrency. Tuning it according to the application needs and behavior. Configure the connection pool thread count according to the amount of thread count.
  18. Internal bugs in the third party libraries or the application server: If new third party libraries are added to the application, monitor any concurrency and memory leak issue closely.
  19. Out of file descriptors: If the application does not close file or network resources correctly in particular within exception handling, the application may ran out of file descriptors and stop processing new requests.
  20. Infinite loop in the application code: An iteration loop may run into an infinite loop and trigger high CPU utilization.   It can be data sensitive and happen to a small set of traffic.   If the CPU utilization remains high during low traffic time, monitor the thread closely.
  21. Wrong firewall configuration: Some firewall configuration limits the amount of concurrent access from a single IP.   This can be problematic if a web server is connected to another DB server through a firewall. Verify the firewall configuration if the application achieves much higher concurrency if tested within in a local network.
  1. Bad TCP tuning: In-proper TCP tuning causes un-resonable high amount of socket waiting to be closed (TIME_WAIT).   New version of OS is usually tuned correctly for Web server. Make changes to the default TCP tuning parameters only if needed. Direct TCP programming may sometimes need special programming parameters for short but frequent TCP messages.

Types of OOM Error & Java Memory Leak Causes

Types of OOM:

  • java.lang.OutOfMemoryError: Java heap space
  • java.lang.OutOfMemoryError: PermGen space
  • java.lang.OutOfMemoryError: GC overhead limit exceeded
  • java.lang.OutOfMemoryError: unable to create new native thread
  • java.lang.OutOfMemoryError: nativeGetNewTLA
  • java.lang.OutOfMemoryError: Requested array size exceeds VM limit
  • java.lang.OutOfMemoryError: request <size> bytes for <reason>. Out of swap
  • java.lang.OutOfMemoryError: <reason> <stack trace> (Native method)
  • java.lang.OutOfMemoryError: Metaspace

Here are the typical cause of Java Memory Leak:

  • Do not close DB, file, socket, JMS resources and other external resources properly
  • Do not close resources properly when an exception is thrown
  • Keep adding objects to a cache or a hash map or hashtable, or vector or ArrayLIst without expiring the old one
  • Do not implement the hash and equal function correctly for the key to a cache
  • Session data is too large
  • Leak in third party library or the application server
  • In an infinite application code loop (likely cause for high cpu)
  • Leaking memory in the native code