Like any other application testing, Big Data Testing Tutorial refers to the testing of the Bigdata applications. As we know that Bigdata deals with the storage and retrieval of the voluminous data involving large datasets and therefore, the Bigdata application testing cannot be conducted using traditional testing techniques. There are various tools, techniques, and frameworks that are available in the software industries to test the Bigdata applications which involve the testing of data creation, storage, retrieval, and analysis in terms of variety, volume, and velocity. In this tutorial, we are going to discuss in detail the following.
Big Data Testing Strategy
Big data testing relies more on data verification than testing the individual features of any software application. The data verification involves both functional as well as performance testing. The data verification is conducted on the processing of application data in terabytes size by using a commodity cluster, where the data processing can be of three types i.e. batch processing, real-time processing, and interactive processing.
Data quality testing is another important factor in Hadoop testing which can be considered as a part of database testing where a tester tests the various database characteristics such as accuracy, conformity, consistency, data completeness, duplication, validity, etc.
Testing of the Hadoop Applications
In Big Data Testing Tutorial, the testing of Bigdata applications can be divided into the following three steps.
Step 1: Validation of Data Staging, this step is also referred to as the pre-Hadoop stage which involves the following process validation.
- The data from different data sources such as weblogs, RDBMS, social media, etc. should be validated in order to make sure that the valid data has been pulled into the system.
- Data comparison between the data from the source and the same data loaded into the Hadoop system to ensure that the data is matching.
- Ensure that the required data has been loaded into the correct HDFS location.
- Data staging validation can be done by using tools such as Data meter, Talend, etc.
Step 2: Validation of “MapReduce”, in this step the actual business logic is verified that involves the validation at each node in the cluster ensuring the following things.
- MapReduce logic works efficiently and correctly.
- Data segregation and aggregation rules are implemented correctly on the data.
- Proper key and value pairs are generated.
- Data validation post the completion of the MapReduce process.
Step 3: Output data Validation step, this is the final step of the Bigdata testing where the validation of the output data files is conducted. These output data files are ready to get moved out to an EDW (Enterprise Data Warehouse) or any other data management system as per the requirements. This step includes the following activities.
- Verification that the data transformation rules are applied correctly.
- To ensure that the data has successfully loaded into the target system without compromising the data integrity.
- Ensure no data corruption in the complete process by comparing the target data with the HDFS file system data.
Big Data Applications Architecture Testing
As we know the Hadoop system is well known to process the voluminous data in its entirety and therefore, in order to avoid any bottleneck in terms of resources and the system performance, a well-defined architecture testing is required. A successful architecture testing will definitely lead to a successful Big Data project. A Bigdata project requires a bare minimum Performance and Failover testing to ensure architectural testing. Like any other, non-functional or performance testing, the Bigdata performance testing deals with the testing of the memory utilization, data processing throughput, job completion time, CPU utilization, etc. On the other hand, the Failover testing deals to verify that the data processing occurs flawlessly in the event of one or more data nodes failure.
Big Data Applications Performance Testing
Bigdata performance Testing involves the following two main actions.
- Data ingestion and Throughout In this action, the Bigdata application tester verifies the speed at which the application is ingesting the data from various data sources. Testing involves the identification of various messages that the queue can process in a given time frame. It also comprises the action to measure how quickly data can be inserted into the target datastore e.g., the data insertion rate into a Cassandra and Mongo database.
- Data Processing: In this action, the Bigdata application tester verifies the speed at which the queries or map-reduce jobs are executed. It also comprises the data processing testing in isolation when the target datastore is populated within the data sets e.g., executing Map Reduce jobs on the target HDFS. The testing also involves the evaluation of the rate at which the message is indexed and consumed, query performance, MapReduce jobs, search, etc.
Big Data Applications Performance Testing Approach
- The Bigdata application testing is conducted with the following approach.
- Firstly, the setting of the Big data cluster is done which is actually getting performance tested.
- Next, the workloads are Identified and designed.
- Next, individual clients such as Custom Scripts are created.
- Finally, the tests are executed and the performance results are analyzed.
- Lastly, the configuration is optimized in order to procure the best result.
Parameters for Performance Testing
- The following parameters are verified as a part of Bigdata application performance testing.
- How data is persisted on different nodes?
- How many threads can accomplish write and read operations?
- Evaluation of the Map-reduce performance such as Sorts, merge, etc.
- The rate at which the Message queue is processing and its size.
- How large the commit log is permissible to expand?
- Procuring the values for query timeout, connection timeout, etc.
- How to tune the cache setting such as “row cache”, “key cache”, etc.?
- Setting up of JVM Parameters such as Heap size, Garbage collection algorithms, etc.
Big Data Applications Test Environment Needs
In Big Data Testing Tutorial, the test environment requires the following setup.
- Ample storage space to process voluminous data.
- Requires a cluster with distributed nodes and data.
- Ensuring the minimum CPU and memory utilization in order to maintain high performance.
Big Data Testing Tools used in Scenarios
The following tools can be used based on the Bigdata cluster.
| Bigdata Cluster | Bigdata Tools |
| NoSQL | Cassandra CouchDB DatabasesMongoDB HBase Redis ZooKeeper |
| MapReduce | Cascading Flume Kafka Hadoop Hive Oozie Pig MapR S4 |
| Processing | BigSheets Datameer Mechanical Turk R Yahoo! Pipes |
| Servers | Elastic EC2 Google App Engine Heroku |
| Storage | S3 HDFS |
Challenges faced during Big Data Testing
There are various challenges which are faced during the Bigdata application testing such as automation, creating the virtualization test scenarios, and preparing the large data sets. The testers are advised to use the appropriate tools as specified before and try to minimize the manual workaround in order to overcome the testing challenges and procuring the best test results.
Conclusion:
In Big Data Testing Tutorial, we discussed in detail the Bigdata application testing approaches, techniques, and challenges.
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Big Data Course Syllabus
| Tutorial | Introduction to BigData |
| Tutorial | Introduction to Hadoop Architecture, and Components |
| Tutorial | Hadoop installation on Windows |
| Tutorial | HDFS Read & Write Operation using Java API |
| Tutorial | Hadoop MapReduce |
| Tutorial | Hadoop MapReduce First Program |
| Tutorial | Hadoop MapReduce and Counter |
| Tutorial | Apache Sqoop |
| Tutorial | Apache Flume |
| Tutorial | Hadoop Pig |
| Tutorial | Apache Oozie |
| Tutorial | Big Data Testing |
