INTERPRETING THE DATA PARALLEL ANALYSIS WITH SAWZALL PDF

Download Google Scholar Copy Bibtex Abstract Very large data sets often have a flat but regular structure and span multiple disks and machines. Examples include telephone call records, network logs, and web document repositories. These large data sets are not amenable to study using traditional database techniques, if only because they can be too large to fit in a single relational database. On the other hand, many of the analyses done on them can be expressed using simple, easily distributed computations: filtering, aggregation, extraction of statistics, and so on. We present a system for automating such analyses. A filtering phase, in which a query is expressed using a new programming language, emits data to an aggregation phase.

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Download Google Scholar Copy Bibtex Abstract Very large data sets often have a flat but regular structure and span multiple disks and machines. Examples include telephone call records, network logs, and web document repositories.

These large data sets are not amenable to study using traditional database techniques, if only because they can be too large to fit in a single relational database.

On the other hand, many of the analyses done on them can be expressed using simple, easily distributed computations: filtering, aggregation, extraction of statistics, and so on.

We present a system for automating such analyses. A filtering phase, in which a query is expressed using a new programming language, emits data to an aggregation phase.

Both phases are distributed over hundreds or even thousands of computers. The results are then collated and saved to a file. The design -- including the separation into two phases, the form of the programming language, and the properties of the aggregators -- exploits the parallelism inherent in having data and computation distributed across many machines. Animation: The paper references this movie showing how the distribution of requests to google.

Research Areas Distributed Systems and Parallel Computing Learn more about how we do research We maintain a portfolio of research projects, providing individuals and teams the freedom to emphasize specific types of work.

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Interpreting the Data: Parallel Analysis with Sawzall

Accepted30 Dec Abstract Very large data sets often have a flat but regular structure and span multiple disks and machines. Examples include telephone call records, network logs, and web document repositories. These large data sets are not amenable to study using traditional database techniques, if only because they can be too large to fit in a single relational database. On the other hand, many of the analyses done on them can be expressed using simple, easily distributed computations: filtering, aggregation, extraction of statistics, and so on. We present a system for automating such analyses. A filtering phase, in which a query is expressed using a new procedural programming language, emits data to an aggregation phase.

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