The twenty-first century has been dominated by the need for large scale data processing, marking the birth of big data platforms such as Cosmos. This paper describes the evolution of the exabyte-scale Cosmos big data platform at Microsoft; our journey right from scale and reliability all the way to efficiency and usability, and our next steps towards improving security, compliance, and support for heterogeneous analytics scenarios. We discuss how the evolution of Cosmos parallels the evolution of the big data field, and how the changes in the Cosmos workloads over time parallel the changing requirements of users across industry.

Consider a systematic linear code where some (local) parity symbols depend on few prescribed symbols, whereas other (heavy) parity symbols may depend on all data symbols. Such codes have been studied recently in the context of erasure coding for data storage, where the local parities facilitate fast recovery of any single symbol when it is erased, whereas the heavy parities provide tolerance to a large number of simultaneous erasures. A code as above is maximally recoverable, if it corrects all erasure patterns, which are information theoretically correctable given the prescribed dependence relations between data symbols and parity symbols. In this paper, we present explicit families of maximally recoverable codes with locality. We also initiate the general study of the tradeoff between maximal recoverability and alphabet size.

Companies providing cloud-scale services have an increasing need to store and analyze massive data sets such as search logs and click streams. For cost and performance reasons, processing is typically done on large clusters of shared-nothing commodity machines. It is imperative to develop a programming model that hides the complexity of the underlying system but provides flexibility by allowing users to extend functionality to meet a variety of requirements. In this paper, we present a new declarative and extensible scripting language, SCOPE (Structured Computations Optimized for Parallel Execution), targeted for this type of massive data analysis. The language is designed for ease of use with no explicit parallelism, while being amenable to efficient parallel execution on large clusters. SCOPE borrows several features from SQL. Data is modeled as sets of rows composed of typed columns. The select statement is retained with inner joins, outer joins, and aggregation allowed. Users can easily define their own functions and implement their own versions of operators: extractors (parsing and constructing rows from a file), processors (row-wise processing), reducers (group-wise processing), and combiners (combining rows from two inputs). SCOPE supports nesting of expressions but also allows a computation to be specified as a series of steps, in a manner often preferred by programmers. We also describe how scripts are compiled into efficient, parallel execution plans and executed on large clusters.

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