What is Big Data Testing?

BigData testing is defined as testing of Bigdata applications. Big data is a collection of large datasets that cannot be processed using traditional computing techniques. Testing of these datasets involves various tools, techniques, and frameworks to process. Big data relates to data creation, storage, retrieval and analysis that is remarkable in terms of volume, variety, and velocity.

Big Data Testing Strategy

Testing Big Data application is more verification of its data processing rather than testing the individual features of the software product. When it comes to Big data testing, performance and functional testing are the keys.

In Big data testing, QA engineers verify the successful processing of terabytes of data using commodity cluster and other supportive components. It demands a high level of testing skills as the processing is very fast. Processing may be of three types

Along with this, data quality is also an important factor in Hadoop testing. Before testing the application, it is necessary to check the quality of data and should be considered as a part of database testing. It involves checking various characteristics like conformity, accuracy, duplication, consistency, validity, data completeness, etc.

How to test Hadoop Applications

The following figure gives a high-level overview of phases in Testing Big Data Applications

Big Data Testing can be broadly divided into three steps

Step 1: Data Staging Validation

The first step of big data testing also referred as pre-Hadoop stage involves process validation.

Data from various source like RDBMS, weblogs, social media, etc. should be validated to make sure that correct data is pulled into the system

Comparing source data with the data pushed into the Hadoop system to make sure they match

Verify the right data is extracted and loaded into the correct HDFS location

Step 2: “MapReduce” Validation

The second step is a validation of “MapReduce”. In this stage, the tester verifies the business logic validation on every node and then validating them after running against multiple nodes, ensuring that the

Map Reduce process works correctly

Data aggregation or segregation rules are implemented on the data

Key value pairs are generated

Validating the data after the Map-Reduce process

Step 3: Output Validation Phase

The final or third stage of Big Data testing is the output validation process. The output data files are generated and ready to be moved to an EDW (Enterprise Data Warehouse) or any other system based on the requirement.

Activities in the third stage include

To check the transformation rules are correctly applied

To check the data integrity and successful data load into the target system

To check that there is no data corruption by comparing the target data with the HDFS file system data

Architecture Testing

Hadoop processes very large volumes of data and is highly resource intensive. Hence, architectural testing is crucial to ensure the success of your Big Data project. A poorly or improper designed system may lead to performance degradation, and the system could fail to meet the requirement. At least, Performance and Failover test services should be done in a Hadoop environment.

Performance testing includes testing of job completion time, memory utilization, data throughput, and similar system metrics. While the motive of Failover test service is to verify that data processing occurs seamlessly in case of failure of data nodes

Performance Testing

Performance Testing for Big Data includes two main action

Data ingestion and Throughout: In this stage, the tester verifies how the fast system can consume data from various data source. Testing involves identifying a different message that the queue can process in a given time frame. It also includes how quickly data can be inserted into the underlying data store for example insertion rate into a Mongo and Cassandra database.

In this stage, the tester verifies how the fast system can consume data from various data source. Testing involves identifying a different message that the queue can process in a given time frame. It also includes how quickly data can be inserted into the underlying data store for example insertion rate into a Mongo and Cassandra database. Data Processing : It involves verifying the speed with which the queries or map reduce jobs are executed. It also includes testing the data processing in isolation when the underlying data store is populated within the data sets. For example, running Map Reduce jobs on the underlying HDFS

: It involves verifying the speed with which the queries or map reduce jobs are executed. It also includes testing the data processing in isolation when the underlying data store is populated within the data sets. For example, running Map Reduce jobs on the underlying HDFS Sub-Component Performance: These systems are made up of multiple components, and it is essential to test each of these components in isolation. For example, how quickly the message is indexed and consumed, MapReduce jobs, query performance, search, etc.

Performance Testing Approach

Performance testing for big data application involves testing of huge volumes of structured and unstructured data, and it requires a specific testing approach to test such massive data.

Performance Testing is executed in this order

The process begins with the setting of the Big data cluster which is to be tested for performance Identify and design corresponding workloads Prepare individual clients (Custom Scripts are created) Execute the test and analyzes the result (If objectives are not met then tune the component and re-execute) Optimum Configuration

Parameters for Performance Testing

Various parameters to be verified for performance testing are

Data Storage: How data is stored in different nodes

How data is stored in different nodes Commit logs: How large the commit log is allowed to grow

How large the commit log is allowed to grow Concurrency: How many threads can perform write and read operation

How many threads can perform write and read operation Caching : Tune the cache setting “row cache” and “key cache.”

: Tune the cache setting “row cache” and “key cache.” Timeouts : Values for connection timeout, query timeout, etc.

: Values for connection timeout, query timeout, etc. JVM Parameters: Heap size, GC collection algorithms, etc.

Heap size, GC collection algorithms, etc. Map reduce performance: Sorts, merge, etc.

Sorts, merge, etc. Message queue: Message rate, size, etc.

Test Environment Needs

Test Environment needs to depend on the type of application you are testing. For Big data testing, the test environment should encompass

It should have enough space for storage and process a large amount of data

It should have a cluster with distributed nodes and data

It should have minimum CPU and memory utilization to keep performance high

Big data Testing Vs. Traditional database Testing

Properties Traditional database testing Big data testing Data Tester work with structured data Tester works with both structured as well as unstructured data Testing approach is well defined and time-tested The testing approach requires focused R&D efforts Tester has the option of “Sampling” strategy doing manually or “Exhaustive Verification” strategy by the automation tool “Sampling” strategy in Big data is a challenge Infrastructure It does not require a special test environment as the file size is limited It requires a special test environment due to large data size and files (HDFS) Validation Tools Tester uses either the Excel-based macros or UI based automation tools No defined tools, the range is vast from programming tools like MapReduce to HIVEQL Testing Tools can be used with basic operating knowledge and less training. It requires a specific set of skills and training to operate a testing tool. Also, the tools are in their nascent stage and over time it may come up with new features.

Tools used in Big Data Scenarios

Big Data Cluster Big Data Tools NoSQL CouchDB, DatabasesMongoDB, Cassandra, Redis, ZooKeeper, HBase MapReduce Hadoop, Hive, Pig, Cascading, Oozie, Kafka, S4, MapR, Flume Storage S3, HDFS ( Hadoop Distributed File System) Servers Elastic, Heroku, Elastic, Google App Engine, EC2 Processing R, Yahoo! Pipes, Mechanical Turk, BigSheets, Datameer

Challenges in Big Data Testing

Automation Automation testing for Big data requires someone with technical expertise. Also, automated tools are not equipped to handle unexpected problems that arise during testing

Virtualization It is one of the integral phases of testing. Virtual machine latency creates timing problems in real time big data testing. Also managing images in Big data is a hassle.

Large Dataset Need to verify more data and need to do it faster Need to automate the testing effort Need to be able to test across different platform



Performance testing challenges

Diverse set of technologies: Each sub-component belongs to different technology and requires testing in isolation

Each sub-component belongs to different technology and requires testing in isolation Unavailability of specific tools: No single tool can perform the end-to-end testing. For example, NoSQL might not fit for message queues

No single tool can perform the end-to-end testing. For example, NoSQL might not fit for message queues Test Scripting: A high degree of scripting is needed to design test scenarios and test cases

A high degree of scripting is needed to design test scenarios and test cases Test environment: It needs a special test environment due to the large data size

It needs a special test environment due to the large data size Monitoring Solution: Limited solutions exists that can monitor the entire environment

Limited solutions exists that can monitor the entire environment Diagnostic Solution: a Custom solution is required to develop to drill down the performance bottleneck areas