INTERPRETING THE DATA PARALLEL ANALYSIS WITH SAWZALL PDF

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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. 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.

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Journal overview. Special Issues. Article Sections On this page Abstract Copyright. CA, USA. Received 30 Dec Accepted 30 Dec Abstract Very large data sets often have a flat but regular structure and span multiple disks and machines. More related articles. Order printed copies Order Share Post. Related articles.

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Interpreting the Data: Parallel Analysis with Sawzall

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.

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