Author Archives: 0x0FFF

Hadoop on Remote Storage

The question regarding running Hadoop on a remote storage rises again and again by many independent developers, enterprise users and vendors. And there are still many discussions in community, with completely opposite opinions. I’d like to state here my personal view on this complex problem.

Hadoop elephant balancing on the shared storage ball

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The Story of Online Data Warehouse

The faster your data warehousing solution runs, the higher would be the business demand related to the speed of new data availability in their reports. Over the last time I’ve seen a number of attempts to build up a cool thing called “online DWH” – a data warehouse that is almost in sync with data sources and has its data marts and reports dynamically updated as new data flows into it. This is a very great and powerful thing, but unfortunately its implementation is not as straightforward as the business wants it to be.

Rocket_Tortoise

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Spark Architecture: Shuffle

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.

Spark Shuffle Design

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.

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Spark DataFrames are faster, aren’t they?

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