Google Research

Synchronization Schemas

  • Rajeev Alur
  • Phillip Hilliard
  • Zachary G. Ives
  • Konstantinos Kallas
  • Konstantinos Mamouras
  • Filip Niksic
  • Caleb Stanford
  • Val Tannen
  • Anton Xue
PODS'21: Proceedings of the 40th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems, Virtual Event, China, June 20-25, 2021, ACM, pp. 1-18

Abstract

We present a type-theoretic framework for data stream processing for real-time decision making, where the desired computation involves a mix of sequential computation, such as smoothing and detection of peaks and surges, and naturally parallel computation, such as relational operations, key-based partitioning, and map-reduce. Our framework unifies sequential (ordered) and relational (unordered) data models. In particular, we define synchronization schemas as types, and series-parallel streams (SPS) as objects of these types. A synchronization schema imposes a hierarchical structure over relational types that succinctly captures ordering and synchronization requirements among different kinds of data items. Series-parallel streams naturally model objects such as relations, sequences, sequences of relations, sets of streams indexed by key values, time-based and event-based windows, and more complex structures obtained by nesting of these. We introduce series-parallel stream transformers (SPST) as a domain-specific language for modular specification of deterministic transformations over such streams. SPSTs provably specify only monotonic transformations allowing streamability, have a modular structure that can be exploited for correct parallel implementation, and are composable allowing specification of complex queries as a pipeline of transformations.

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