This paper describes TidyFS, a simple and small distributed file system that provides the abstractions necessary for data parallel computations on clusters. In recent years there has been an explosion of interest in computing using clusters of commodity, shared nothing computers. Frequently the primary I/O workload for such clusters is generated by a distributed execution engine such as MapReduce, Hadoop or Dryad, and is high-throughput, sequential, and read-mostly. Other large-scale distributed file systems have emerged to meet these workloads, notably the Google File System (GFS) and the Hadoop Distributed File System (HDFS). TidyFS differs from these earlier systems mostly by being simpler. The system avoids complex replication protocols and read/write code paths by exploiting properties of the workload such as the absence of concurrent writes to a file by multiple clients, and the existence of end-to-end fault tolerance in the execution engine. We describe the design of TidyFS and report some of our experiences operating the system over the past year for a community of a few dozen users. We note some advantages that stem from the system’s simplicity and also enumerate lessons learned from our design choices that point out areas for future development.