Everyone around the internet is constantly talking about the bright future of Apache Spark. How cool it is, how innovative it is, how fast it is moving, how big its community is, how big the investments into it are, etc. But what is really hiding behind this enthusiasm of Spark adepts, and what is the real future of Apache Spark?
In this article I show you the real data and real trends, trying to be as agnostic and unbiased as possible. This article is not affiliated with any vendor.
Starting Apache Spark version 1.6.0, memory management model has changed. The old memory management model is implemented by StaticMemoryManager class, and now it is called “legacy”. “Legacy” mode is disabled by default, which means that running the same code on Spark 1.5.x and 1.6.0 would result in different behavior, be careful with that. For compatibility, you can enable the “legacy” model with spark.memory.useLegacyMode parameter, which is turned off by default.
This is my second article about Apache Spark architecture and today I will be more specific and tell you about the shuffle, one of the most interesting topics in the overall Spark design. The previous part was mostly about general Spark architecture and its memory management. It can be accessed here. The next one is about Spark memory management and it is available here.
What is the shuffle in general? Imagine that you have a list of phone call detail records in a table and you want to calculate amount of calls happened each day. This way you would set the “day” as your key, and for each record (i.e. for each call) you would emit “1” as a value. After this you would sum up values for each key, which would be an answer to your question – total amount of records for each day. But when you store the data across the cluster, how can you sum up the values for the same key stored on different machines? The only way to do so is to make all the values for the same key be on the same machine, after this you would be able to sum them up.
Recently Databricks announced availability of DataFrames in Spark , which gives you a great opportunity to write even simpler code that would execute faster, especially if you are heavy Python/R user. In this article I would go a bit deeper than the publicly available benchmark results to show you how it really works.
When you are completely ready to start your “big data” initiative with Hadoop, one of your first questions would be related to the cluster sizing. What is the right hardware to choose in terms of price/performance? How much hardware you need to handle your data and your workload? I will do my best to answer these questions in my article.