GigaOm: Hadoop days are numbered… Are they?
Interesting article at GigaOm: http://bit.ly/OINpfr I won’t repeat the main points – but basically it says that since Hadoop is disk/ETL/batch based it won’t fit for real time processing of frequently changing data. Author correctly points out that real time processing (i.e. perceptual real time meaning sub-second to few seconds response time) is becoming a HUGE trend that’s impossible to ignore. He points to Google that moved away from Hadoop MapReduce-like approach towards massively distributed in-memory platform for its various projects like Precolator and Dremel…
So, What’s New?!
The wide spread confusion about Hadoop’s role and its applicability is becoming alarming… Hadoop was never designed to process anything in real time or process live streaming data or process anything that’s rapidly changing. Hadoop’s core is HDFS technology – a highly scalable distributed file system that works on spinning disks and supports effective batch storing and accessing data. It is an excellent data warehouse technology that scales to petabytes of data on commodity hardware. And Hadoop does an excellent job at this.
Now, Hadoop eco-system also has MapReduce (and various satellite projects like Pig, Hive, etc.). Hadoop’s implementation of MapReduce (as well as Pig, Hive, HBase, etc.) “suffers” from exactly the same limitation – it works over HDFS and therefore is architecturally a batch & disk oriented. Let me repeat it again – Hadoop MapReduce was never meant to processing anything in real time or work on live streaming data. Period, end of story. It was designed to work over datasets stored in disk-based HDFS – and it does so very well.
Are They Really Numbered?
I don’t see anything on the horizon that would displace Hadoop HDFS. There’s a clear business use case & demand for massive disk-based storage on petabyte/exabyte scale – and Hadoop HDFS is a clear industry choice today. Hadoop HDFS is here to stay for a long time…
But as Gartner’s Merv Adrian says the Big Data has two sides to its coin: storage and processing. Hadoop HDFS provides excellent storage technology but its processing side isn’t as shiny. As I (and many others) have mentioned Hadoop MapReduce is bound to live by limitations of HDFS – batch & offline oriented disk-based processing. Some companies will be content with that limitations (and for many it is just fine). Others – will follow Facebook, Google and Twitter in moving away from disk-based, offline processing towards real time in-memory data platforms.
What is very important to understand is that move to in-memory processing isn’t about the raw speed only (although the RAM access is up to 10,000,000 times faster than disk). What’s more important is that when you keep your working set in memory it enables a complete new family of algorithms that you can employ. Incremental indexing (Google’s Precolator, GridGain’s Data Grid), streaming MapReduce/CEP (GridGain’s Compute Grid, Twitter’s Storm), etc. – all of these are not something that Hadoop engineers just didn’t know about – it is rather something that is largely enabled by in-memory technology.
Naturally, in-memory based technologies don’t invalidate the need for Hadoop HDFS, the proverbial data warehouse. In many cases (but not all) HDFS can happily coexist with something like GridGain that provides native upstream and downstream integration with HDFS enabling you to do streaming MapReduce/CEP processing on data in HDFS – among many other things.
To sum up my thoughts I believe and hope that Hadoop HDFS is there to stay and we’ll see more and more companies moving away from disk-based processing towards all kinds of in-memory based technologies.