Microsoft Technology Licensing, LLC

Yongchul Kwon

Marc Todd Friedman

Wangchao Le

Haibo Lin

Raghunath Ramakrishnan

Zhicheng Yin

Jaliya Nishantha Ekanayake

Tao Guan

José A Blakeley

Jin Sun

Ming Li

Constructing a heavy hitter summary for query optimization. The heavy hitter summary is constructed by sampling each of multiple partitions of a dataset using a uniformed sampling rate. For each partition, performing a two-stage heavy hitter estimation process to determine whether an estimated frequency of a key of the sampled data units may be included in a partition-level heavy hitter summary. Constructing a partition-level heavy hitter summary for each partition of the dataset based on the keys determined via the two-stage process, and constructing a dataset-level heavy hitter summary based on the partition-level heavy hitter summary. The dataset-level heavy hitter summary may be used to optimize query trees.

Building heavy hitter summary for query optimization

Query optimization using of a query that is compiled into a query tree. The optimization is efficiently performed by using a distinct value estimation data structure (e.g., a KMV synopsis) that represents within an interval distinctness of values that are generated based on data within an interval, even if the resultant data from a subinterval is considered. Various candidate query trees are evaluated, with distinct value generation data structures being propagated for parent nodes based on the distinct value generation data structures of its child node(s). Propagation operations correlate to the operation represented by the parent node in the query tree. The optimizer uses the propagated distinct value estimation structure in order to evaluate the number of distinct values of data that would result from execution of the candidate query tree at least at the corresponding operations (and not just based on the distinct values of the input data).

Query optimization using propagated data distinctness

Aspects extend to methods, systems, and computer program products for optimally pipelining result sets with fault tolerance in distributed query execution. Distributed computing jobs are optimized by dividing the distributed computing jobs into one or more bubbles for execution. Each bubble can be independently executed, potentially in parallel with other bubbles, when resources to handle the bubble are available. Intra-bubble communication can be streamed between vertices within a bubble. Inter-bubble communication can be stored to durable storage. Bubbles provide a failure boundary for a job graph and re-executing a bubble along with storage of intermediate results in durable storage can be used to recover from failures. When a vertex inside a bubble fails, computation can resume by rescheduling the execution of the failed bubble from the durable inputs for that bubble. Durable storage provides a light-weight failover to handle non-deterministic behavior. Jobs can also leverage streaming to increase performance.

Optimizing pipelining result sets with fault tolerance in distributed query execution

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Yongchul Kwon