Performance testing consists of testing of the duration to complete the job, utilization of memory, the throughput of data, and parallel system metrics. Any failover test services aim to confirm that data is processed seamlessly in any case of data node failure. Performance Testing of Big Data primarily consists of two functions. First, is Data ingestion whereas the second is Data Processing
The initial step in the validation, which engages in process verification. Data from a different source like social media, RDBMS, etc. are validated, so that accurate uploaded data to the system. We should then compare the data source with the uploaded data into HDFS to ensure that both of them match. Lastly, we should validate that the correct data has been pulled, and uploaded into specific HDFS. There are many tools available, e.g., Talend, Datameer, are mostly used for validation of data staging.
Method of testing the performance of the application constitutes of the validation of large amount of unstructured and structured data, which needs specific approaches in testing to validate such data.
Virtualization is an essential stage in testing Big Data. The Latency of virtual machine generates issues with timing. Management of images is not hassle-free too.
Query Surge is one of the solutions for Big Data testing. It ensures the quality of data quality and the shared data testing method that detects bad data while testing and provides an excellent view of the health of data. It makes sure that the data extracted from the sources stay intact on the target by examining and pinpointing the differences in the Big Data wherever necessary.
Following are the various types of tools available for Big Data Testing:
It involves validating the rate with which map-reduce tasks are performed. It also consists of data testing, which can be processed in separation when the primary store is full of data sets.
EX: Map-Reduce tasks running on a specific HDFS.
The Query Surge Agent is the architectural element that executes queries against Source and Target data sources and getting the results to Query Surge.
Big data is a combination of the varied technologies. Each of its sub-elements belongs to a different equipment and needs to be tested in isolation.
Following are some of the different challenges faced while validating Big Data:
Any Query Surge or a POC, only one agent is sufficient. For production deployment, it is dependent on several factors (Source/data source products / Target database / Hardware Source/ Targets are installed, the style of query scripting), which is best determined as we gain experience with Query Surge within our production environment.
Test Environment depends on the nature of application being tested. For testing Big data, the environment should cover:
Different parameters need to be confirmed while performance testing which is as follows:
In the case of processing of the significant amount of data, performance, and functional testing is the primary key to performance. Testing is a validation of the data processing capability of the project and not the examination of the typical software features.
In Hadoop, engineers authenticate the processing of quantum of data used by Hadoop cluster with supportive elements. Testing of Big data needs asks for extremely skilled professionals, as the handling is swift. Processing is three types namely Batch, Real Time, & Interactive.
Query Surge has its inbuilt database, embedded in it. We need to lever the licensing of a database so that deploying Query Surge does not affect the organization currently has decided to use its services.
Big Data me a vast collection of structured and unstructured data, which is very expive & is complicated to process by conventional database and software techniques. In many organizations, the volume of data is enormous, and it moves too fast in modern days and exceeds current processing capacity. Compilation of databases that are not being processed by conventional computing techniques, efficiently. Testing involves specialized tools, frameworks, and methods to handle these massive amounts of datasets. Examination of Big data is meant to the creation of data and its storage, retrieving of data and analysis them which is significant regarding its volume and variety of speed.
Along with processing capability, quality of data is an essential factor while testing big data. Before testing, it is obligatory to ensure the data quality, which will be the part of the examination of the database. It involves the inspection of various properties like conformity, perfection, repetition, reliability, validity, completeness of data, etc.
Query Surge Architecture consists of the following components:
Third and the last phase in the testing of bog data is the validation of output. Output files of the output are created & ready for being uploaded on EDW (warehouse at an enterprise level), or additional arrangements based on need.
The third stage consists of the following activities:
MapReduce is the second phase of the validation process of Big Data testing. This stage involves the developer to verify the validation of the logic of business on every single systemic node and validating the data after executing on all the nodes, determining that:
Challenges in testing are evident due to its scale. In testing of Big Data:
A conventional way of a testing database does not need specialized environments due to its limited size whereas in case of big data needs specific testing environment.
The developer validates how fast the system is consuming the data from different sources. Testing involves the identification process of multiple messages that are being processed by a queue within a specific frame of time. It also consists of how fast the data gets into a particular data store.
EX: the rate of insertion into Cassandra & Mongo database.
Systems designed with multiple elements for processing of a large amount of data needs to be tested with every single of these elements in isolation.
Ex:how quickly the message is being consumed & indexed, MapReduce jobs, search, query performances, etc.
This pattern of testing is to process a vast amount of data extremely resources intensive. That is why testing of the architectural is vital for the success of any Project on Big Data. A faulty planned system will lead to degradation of the performance, and the whole system might not meet the desired expectations of the organization. At least, failover and performance test services need proper performance in any Hadoop environment.