Google Processes Over 20 Terabytes of Data Per Day

The Google Operating System blog has presented some amazing facts concerning Google indexing processes. Using massively parallel processing, Google indexes some 20 Terabytes or 20,000 Gigabytes of data each and every day.

I can easily remember a time when a 1 Gigabyte hard drive seem huge, how times have changed. Check out Google Reveals New MapReduce Stats:

An updated version of Google's paper aboutMapReduce (available at ACM and mirrored here) provides new information about Google's scale. MapReduce is a software framework used by Google to "support parallel computations over large (...) data sets on unreliable clusters of computers". Google uses it for indexing the web and computing PageRank, for processing geographic information in Google Maps, clustering news articles, machine translation, Google Trends etc.

The input data for some of the MapReduce jobs run in September 2007 was 403,152 TB (terabytes), the average number of machines allocated for a MapReduce job was 394, while the average completion time was 6 minutes and a half. The paper mentions that Google's indexing system processes more than 20 TB of raw data. Since 2003, when MapReduce was built, the indexing system progressed from 8 MapReduce operations to a much bigger number today.

Niall Kennedy calculates that the average MapReduce job runs across a $1 million hardware infrastructure, assuming that Google still uses the same cluster configurations from 2004: two 2 GHz Intel Xeon processors with Hyper-Threading enabled, 4 GB of memory, two 160 GB IDE hard drives and a gigabit Ethernet link.

Greg Linden notices that Google's infrastructure is an important competitive advantage. "Anyone at Google can process terabytes of data. And they can get their results back in about 10 minutes, so they can iterate on it and try something else if they didn't get what they wanted the first time."

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