Shared query execution can reduce resource consumption by sharing common sub-expressions across concurrent queries. We show that this is not always the case when regularly querying a dataset under change. Depending on latency goals, how eagerly to incrementally process the new data differs. Naively sharing the execution of queries with different latency goals will push the whole shared plan to meet the lowest latency goal and execute more eagerly than each participating query. The overhead introduced by the eager execution can even offset the benefit of shared query execution. We propose an optimization framework iShare to exploit the benefit of shared execution and avoid the overhead of eager execution. iShare judiciously shares queries with different latency goals and selectively executes parts of the share plan lazily. iShare can significantly reduce resource consumption compared to eagerly executing share plans from the state-of-the-art multi-query optimizer or approaches that execute queries separately.

Relational query processing is an ideal candidate for serverless computation with its stateless, idempotent, and short-lived properties. However, current serverless offerings for query processing neither provide millisecond-based pricing nor allow users to optimize the cost of their queries. To have tradeoffs between the cost and performance of their queries, users are limited to using traditional serverful approaches, which we demonstrate to be 50% slower at approximately the same cost as serverless approaches. We propose a model that will allow service providers to dynamically provision clusters to achieve their users' desired time-cost tradeoffs.