Data-intensive distributed file systems are emerging as a key component of large scale Internet services and cloud computing platforms. They are designed from the ground up and are tuned for specific application workloads. Leading examples, such as the Google File System, Hadoop distributed file system (HDFS) and Amazon S3, are defining this new purpose-built paradigm. It is tempting to classify file systems for large clusters into two disjoint categories, those for Internet services and those

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... or high performance computing. In this paper we compare and contrast parallel file systems, developed for high performance computing, and data-intensive distributed file systems, developed for Internet services. Using PVFS as a representative for parallel file systems and HDFS as a representative for Internet services file systems, we configure a parallel file system into a data-intensive Internet services stack, Hadoop, and test performance with microbenchmarks and macrobenchmarks running on a 4,000 core Internet services cluster, Yahoo!'s M45. Once a number of configuration issues such as stripe unit sizes and application buffering sizes are dealt with, issues of replication, data layout and data-guided function shipping are found to be different, but supportable in parallel file systems. Performance of Hadoop applications storing data in an appropriately configured PVFS are comparable to those using a purpose built HDFS.