Vivace: Exact Temporal OLAP over Interval Histories via Independent Serverless Execution

2026-06-11

AI Summary

This work addresses the challenges of executing temporal OLAP queries in serverless architectures, where computation partitioning often leads to missing state and incorrect aggregations—particularly compromising correctness for non-trivial aggregations such as duration-weighted sums or cumulative threshold computations. To resolve this, the authors propose a novel approach that replicates overlapping time intervals across partitions during a pre-query phase and merges partial results at query time according to operator semantics, guaranteeing output identical to that of single-process execution. This is the first solution to simultaneously ensure both temporal state completeness and correctness of complex aggregations in serverless environments. Experimental evaluation on real-world datasets demonstrates up to 82% lower latency and 84% reduced cost compared to SQL baselines, while maintaining generality and high efficiency.

Abstract

Temporal online analytical processing (OLAP) analyzes past states of data whose values change over time. Such histories are naturally stored as interval histories, in which each row records the period during which a value remained valid. Because temporal analyses typically arrive in infrequent, intermittent bursts, serverless execution that launches functions only at query time offers a cost advantage over always-on clusters. Splitting a computation that a single process performs as a whole across independent serverless functions, however, breaks correctness in two ways. A function may not receive the rows that determine the state of its time range, and naively summing partial results yields incorrect answers for duration-weighted and cumulative-threshold queries. Existing SQL engines and serverless analytics do not address both problems together. This paper presents Vivace, a serverless system for exact temporal OLAP over interval histories. Vivace resolves the two problems in separate stages. Before any query arrives, a pre-query layout step partitions the interval history, replicating boundary-crossing intervals so each function computes its range completely from a single file. At query time, a merge step combines partial results under operator-specific rules. Associative aggregates merge intermediate values, and ranking re-orders candidates within each time range. We prove that this partitioned execution matches single-process computation up to canonical form. Evaluated on AWS Lambda with real-world datasets, Vivace reduces latency and monetary cost by up to 82% and 84%, respectively, against an equivalent SQL baseline that queries the history directly, demonstrating robust generality and efficiency.

Problem

• Temporal OLAP
• Interval Histories
• Serverless Execution
• Query Correctness
• Duration-weighted Queries

Innovation

• Temporal OLAP
• Serverless Computing
• Interval Histories
• Exact Aggregation
• Partitioned Execution