Performance testing is a practice that measures system attributes such as speed, responsiveness, and storage. It allows you to enhance retrieval and processing capabilities for any kind of system. That’s why this is one of the most common forms of testing before sending the application to market: because it tackles fundamental issues stemming from bad design.
You can use performance testing in any application you’re working on but it’s especially useful for big data applications. Let’s see why.
Why You Need Performance Testing
1. Response time problems
Response time is the time it takes for an application to provide a result after initial input. At times, large input or output data can cause network blockages. This is especially true if you use MapReduce jobs.
A high replication rate may also cause response time issues. That might create problems in the network, such as bottlenecks. You can always trust in performance testing to identify these issues.
2. Load time problems
Load time is the time it takes for the application to start. In big data applications, decompression and compression cycles may cause the application and services to take more time than usual to start. Performance testing is essential to detect this problem.
3.Memory problems
Circular memory can fill the buffer, resulting in issues with scaling and loading. It can also lead to performance degradation due to data swapping. Those problems can cause your application to crash, so you need to carry out some performance testing to make sure you’re not suffering from them.
What types of metrics should you measure in performance testing?
These depend on who’s doing the test. A typical QA testing company would focus on metrics such as
- Data storage and memory usage
- Bandwidth
- Processor usage, concurrency, and cache
- Time-related parameters such as page faults/sec, CPU interrupts/sec, insertion rate, timeouts, etc
- Message queue and performance
- MapReduce performance indicators.
Key components of Big data performance testing
1. Ingestion
Data Ingestion is the process through which your application obtains and imports data for use or storage in a database. So, performance testing takes a look at how the application gets that data. Various data sources can be used for input, i.e., incoming data, warehoused data, etc.
This test also identifies which queue system you should use to process a message efficiently. It also takes into account the peak data and how the collected data is sorted into a datastore via a database.
2. Processing
Data processing is one of the biggest components of performance testing. For instance, it tests for speed of execution for MapReduce jobs and the processing time for message indexing. You should do data proofing for MapReduce to generate clean data for testing.
You need to use data points to aggregate and cluster different data and build a data profile. Through that, you can test the overall process and framework. Aspects like consumption time and query performance should be tested both as a part of the system and in isolation.
3. Analytics
Once the data is processed, you can use it to get insights. Companies use this data to find patterns and correlations between different factors. Through the analytics section of performance testing, you can test algorithms, throughput, and thread counts. You can also test database locking and unlocking after operations.
Methodology for Performance testing
Here are the steps you should follow for big data performance testing:
- You have to set up the big data application you want to test. That also means identifying your environment and understanding the hardware and network criteria. You also need to set up a cluster for testing.
- You should identify workloads and design them according to your application. You can also recognize the acceptance criteria for your tests.
- You need to prepare individual clients using custom scripts for different file systems.
- After that, you can perform the actual performance test for factors defined in setup and workload step.
- Once you have the output, analyze it thoroughly. Once that’s done, you can retune and recalibrate to identify the missing points and rerun the test to gain more insights. Keep performing test iterations until you have the optimum solution that factors in your hardware and software requirements without bottlenecking your CPU.
Conclusion
If you’re facing problems with capacity expansion, unpredictable performance issues, and poor improvement, you should do performance testing for your system. It’s typically done for client server-based applications.
You should keep factors like data insertion rate and message queue performance in mind while performing this test. You should use parameters such as bandwidth, data and memory usage, and MapReduce indicators as metrics for testing.
Performance testing does have it’s challenges. You need controlling resolutions and a scripting language. You also need specialized hardware for big data processing to keep up with the increasing warehouse data. The way the data is structured inside the data center and how it’s archived changes how you can interact with it. Management systems and data marts allow you to archive data and use it as per your speed and memory parameters set by you.
Performance testing is a guarantee against product failures and it’s capable of maintaining your clients’ trust in your brand. Sometimes, you may find it hard to search for a scalable and adaptable testing method that’s cost-effective and consistent. But once you determine one, managing the performance test is easy.
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