As a general computing engine, Spark can process data from various data management/storage systems, including HDFS, Hive, Cassandra and Kafka. For flexibility and high throughput, Spark defines the Data Source API, which is an abstraction of the storage layer. The Data Source API has two requirements.
1) Generality: support reading/writing most data management/storage systems.
2) Flexibility: customize and optimize the read and write paths for different systems based on their capabilities.
Data Source API V2 is one of the most important features coming with Spark 2.3. This talk will dive into the design and implementation of Data Source API V2, with comparison to the Data Source API V1. We also demonstrate how to implement a file-based data source using the Data Source API V2 for showing its generality and flexibility.
Session hashtag: #DDSAIS12
Wenchen Fan is a software engineer at Databricks, working on Spark Core and Spark SQL. He mainly focuses on the Apache Spark open source community, leading the discussion and reviews of many features/fixes in Spark. He is a Spark committer and a Spark PMC member.
Gengliang Wang is a software engineer in Databricks. He is an active Spark contributor and his main interest is on Spark SQL. Previously, he worked on building backend web services in Linkedin and Hulu.