Apache CarbonData is an indexed columnar data format for fast analytics on big data platform, e.g. Apache Hadoop, Apache Spark, etc. Knoldus enable Huawei to work in collaboration with them to implement different functionalities or integration support with different technologies including presto and hive in CarbonData. The below diagram illustrates CarbonData file structure:

Huawei wants to explore a domain where backend, frontend, and continuos integration ensure backward compatibility of the older versions when the newer versions will be rolled out on a frequent basis. Knoldus worked along with Huawei Team to help CarbonData in becoming an Apache-licensed project from an incubating project.

Knoldus worked closely with the Huawei team and helped in building the crucial functionalities, some of which are listed below:

• Development of Dictionary Generation Tool for CarbonData.

• Pre-aggregate functionality to improve performance of aggregation queries.

• CarbonData integration with Presto, Hive, Flink, and S3 technologies.

• Setting up of continuous Integration via Jenkins.

• Creation of Performance Testing tool to do benchmarking.

• Achieving zero bugs with Automation Testing.

• Development of Apache CarbonData website and its maintenance.

• Automation of documentation from Git to website.

• Development and enhancement in core packages of CarbonData.

• Benchmarking CarbonData against available file formats like Parquet and ORC, against frameworks like Spark, Presto, and Impala and against different storage systems like Hadoop, S3 and Kudu.

Knoldus worked with Huawei to develop a file-format which is faster and efficient in processing and querying on big data. Now, Huawei clients able to speed up their system by utilizing the features of CarbonData.

Our team also developed a proprietary performance benchmarking tool for CarbonData. This benchmark tool tests the performance of the CarbonData in comparison with its competitors like Parquet and ORC Format. The key functionality supported by the Benchmark tool are as follows:

• Generating the TPCH benchmarking data depending on the cluster size driven by configuration.

• Defining workloads as a configuration for particular datasets.

• Loading the data into all the formats into the Hive Store like CarbonData, Parquet, and ORC.

• Configuration based Tuning for Spark that included parallelism settings as well as spark configuration based on different workloads.

• Executing the workloads and capturing the response time and results with respect to load for all the formats.

• Comparison of the results in all the formats.

• Generating an Excel report showing the comparison of the results as well as success and failures of test execution.

With the rapid development and concise code offered with Scala, Knoldus was able to get the system into production in 4 months. The alerts are routed to different buckets on the basis of rules defined and reach the consumers' mailbox in a matter of seconds as soon as the news is broken. The product is being heavily used as a part of the infrastructure.