Big Data Needs a Better Network
This article was originally written by Marten Terpstra for the Plexxi blog.
Earlier this week I had some interesting conversations with @davehusak. Where the conversation started early in the day with a discussion on overlay networks and what network functions are performed where and in what context, later in the afternoon the discussion moved to networking solutions (and specifically Plexxi solutions) for big data applications.
It’s easy to jump to Hadoop or similarly structured cluster computing applications (Spark, Storm, or a long list of others) as the definition of a big data application. With all its simplicity for the overall distribution of work, Hadoop is a fairly tough network problem to solve if you want to do anything more than “throw bandwidth at the problem”. And when you do throw bandwidth at the problem, the extreme burstiness of the traffic will still significantly drag down the performance of the overall solution. And for many, CPU cycles to reduce the data is not the biggest challenge, storage and movement of data throughout a big data cluster is the biggest pain point. Intel has done a fine job providing compute firepower that far outpaces the evolution of network capacity.
The network plays a significant factor in several stages of a Hadoop solution cycle. It starts with chopping the to-be-analyzed data into chunks and distributing it across the datanodes. Hadoop has a notion of a rack and it has some basic intelligence when placing data and jobs that work on that data. By default the data will be replicated 3 times across at least 2 racks, if racks have been defined. The data to be distributed is easily in the 100s of Gigabytes or even Terabytes, so triple that data is being moved throughout the Hadoop cluster to the datanodes.
Once distributed, the actual Map jobs are launched against that data, these are the tasks that take the data and perform a first pass mapping into (in its most basic form) <key, value> tuples. Again here there is an attempt to have jobs work on local data, where local can be defined as local to the server that has that chunk of data or local to the rack, in an attempt to avoid as much cross rack communication as possible. This is based on the assumption that cross rack communication is much more constrained and aggregated and therefore more prone to congestion and packet loss.
Once the mapper jobs complete their task, the results of the mapping exercise are sent to reducers. Reducers take the <key, value> information and essentially tally (reduce) the results. This transfer is the most taxing part of a Hadoop cycle on the network. Since most Hadoop mapping jobs run the same function on a similar sized dataset, that first set of mappers will all complete their task at about the same time and will all start sending their results to the same set of reducers, creating 1) a lot of traffic and 2) a lot of traffic to the same set of destinations. Depending on the amount of data, mappers and compute nodes, this cycle repeats (the next set of mapping jobs are fired off) and at the end of each cycle a very significant spike in network traffic appears. At the very end, all results are brought together for one last spike in traffic. Each one of these network events is a source of significant congestion.
Many variables contribute to the overall performance of the Hadoop solution. What is the relationship between the chunks of data and the amount of servers and jobs? How many reducers are used? Where are the reducers in relation to the mappers? Is the Map function compute heavy or I/O heavy? How aggressive is the speculative scheduling that allows the same data to be worked on by multiple mappers?
With that many variables that can be tuned, and with so many variables different from one analysis to the next, it is hard to imagine that a single network design or implementation provides the best supporting infrastructure. There are assumptions in Hadoop placement of data and jobs that can easily be altered. The basic concept of a rack can easily expanded into a multi layer locality definition. With the right tools in the network, the definition of a rack, or even the locality and closeness of nodes in a cluster or virtual cluster can be adjusted according to the analysis to be completed.
We tend to give our applications variables to tune its performance based on what the network provides. It is time that the network adjusts itself based on the application needs. In a clustered application like Hadoop, there is lots of knowledge and even some predictability of network traffic. Wouldn’t that make for a great opportunity to infuse the network with some of that knowledge and have it morph itself to provide the best possible service? And Hadoop is not unique, cluster compute framework almost all carefully track placement of data and compute jobs, which makes them all great candidates to share some of that information with a smart network.
There are far simpler big data needs and applications that can and should be supported by flexible networks. Storage networks are still often separated from data networks for performance reasons and fear of interference. If you could actually separate the various types of data with logically or even physically different paths in the same network, would you still?
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