Shared nothing parallel data
ow systems aim to bridge the
gap between MapReduce and RDBMSs by combining parallel execution
of second order functions with operator based optimizations. In parallel
systems, job latency is strongly affected by data shuffling and unbalanced
data across nodes, thus the degree of parallelism and the data partition-
ing functions must be carefully considered when choosing optimization
strategies. However, it is hard to make good optimization choices with-
out any information about the distribution of the data. We attempt to
overcome this challenge in shared nothing parallel data
ows by tracking
statistics of data sets during query runtime. We use data streaming algo-
rithms to track statistics so as to affect job latency as little as possible.
We discuss how collected statistics can potentially be used to improve
execution plans during runtime.
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