Scalable XML Query Processing using Parallel Pushdown Transducers

In online social networking, network monitoring and financial applications, there is a need to query high rate streams of XML data, but methods for executing individual XPath queries on streaming XML data have not kept pace with multicore CPUs. For data-parallel processing, a single XML stream is typically split into well-formed fragments, which are then processed independently. Such an approach, however, introduces a sequential bottleneck and suffers from low cache locality, limiting its scalability across CPU cores.

We describe a data-parallel approach for the processing of streaming XPath queries based on pushdown transducers. Our approach permits XML data to be split into arbitrarily-sized chunks, with each chunk processed by a parallel automaton instance. Since chunks may be malformed, our automata consider all possible starting states for XML elements and build mappings from starting to finishing states. These mappings can be constructed independently for each chunk by different CPU cores. For streaming queries from the XPathMark benchmark, we show a processing throughput of 2.5 GB/s, with near linear scaling up to 64 CPU cores.

40th International Conference on Very Large Data Bases (VLDB)
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