I’d start with a bold statement: Hadoop is rapidly losing the momentum. We can see it from the following Google Trends chart:Continue reading
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.
Here is the video of my talk on Modern Data Architecture from Java Day Kiev 2015
The slides are available here: Modern Data Architecture – JD Kiev v05
This is the talk I made on Java Day Kiev 2015. It was a great conference after all
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.
Previously I have described the “legacy” model of memory management in this article about Spark Architecture almost one year ago. Also I have written an article on Spark Shuffle implementations that briefly touches memory management topic as well.
This article describes new memory management model used in Apache Spark starting version 1.6.0, which is implemented as UnifiedMemoryManager.
Today I had a great talk at the Hadoop User Group Ireland meetup in Dublin, and it was an adapted and refactored version of the article on the same subject, MPP vs Hadoop. Here are the slides:
Feel free to comment and share your opinion on this subject
Finally I have translated my talk from Highload++ 2015 conference in Moscow into English, so now you can enjoy the fresh information about the Apache HAWQ internals!
If you’d like to download the slides, you can find them here: HAWQ Architecture HL++ 2015 Moscow
Here are the slides for the talk I just gave at JavaDay Kiev about the architecture of Apache Spark, its internals like memory management and shuffle implementation:
If you’d like to download the slides, you can find them here: Spark Architecture – JD Kiev v04
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.