After having a basic understanding about Spark and SparkSQL, I came back to my system. The high level design of the system remains the same as I described two months ago. It is a client-server model, but the server is changed from the Spark server to the SparkSQL server. I spent roughly two weeks for some coding … Continue reading [SysDeg] Worksharing Framework and its design – Part 2: Communication method
The paper of SparkSQL provides a very nice figure about SparkSQL data flow. I’ve had experiences on Apache Pig for more than one year so I realized that it is better to put them all together. I created a new figure that includes the data flow of Hive, Pig, and SparkSQL. I know a little … Continue reading [Arch] SparkSQL Internals – Part 2: SparkSQL Data Flow
I assume that you’ve already read these documents about SparkSQL. Things that you should keep in your mind: DataFrame API: where relational processing meets procedural processing. Catalyst: extensible query optimizer which works on trees and rules, provides lazy optimization and is easy to extend/add a new rule. In this post, I will introduce to you … Continue reading [Arch] SparkSQL Internals – Part 1: SQLContext
Maybe you still remember the draft design of the system I proposed here. The reason why I delayed posting the part-2, which mostly focuses on technical details, because Spark is new for me so I need time to dig more into it. However, the design won’t be changed so much, I think. Come back to … Continue reading [Sysdeg] Moving to SparkSQL, why not?
I did write the Spark deploy mode in this post too, but I realized that it would be too long, so I decided to split it into two posts. I suggest you to go to this to have a good and general view of Spark in the ecosystem. In this post, I will focus on … Continue reading [Arch] Spark job submission breakdown