We study the problem of optimizing one-time and continuous subgraph queries using the new worst-case optimal join plans. Worst-case optimal plans evaluate queries by matching one query vertex at a time using multiway intersections. The core problem in optimizing worst-case optimal plans is to pick an ordering of the query vertices to match. We make two main contributions: 1. A cost-based dynamic programming optimizer for one-time queries that (i) picks efficient query vertex orderings for worst-case optimal plans and (ii) generates hybrid plans that mix traditional binary joins with worst-case optimal style multiway intersections. In addition to our optimizer, we describe an adaptive technique that changes the query vertex orderings of the worst-case optimal subplans during query execution for more efficient query evaluation. The plan space of our one-time optimizer contains plans that are not in the plan spaces based on tree decompositions from prior work. 2. A cost-based greedy optimizer for continuous queries that builds on the delta subgraph query framework. Given a set of continuous queries, our optimizer decomposes these queries into multiple delta subgraph queries, picks a plan for each delta query, and generates a single combined plan that evaluates all of the queries. Our combined plans share computations across operators of the plans for the delta queries if the operators perform the same intersections. To increase the amount of computation shared, we describe an additional optimization that shares partial intersections across operators. Our optimizers use a new cost metric for worst-case optimal plans called intersection-cost . When generating hybrid plans, our dynamic programming optimizer for one-time queries combines intersection-cost with the cost of binary joins. We demonstrate the effectiveness of our plans, adaptive technique, and partial intersection sharing optimization through extensive experiments. Our optimizers are integrated into GraphflowDB.

中文翻译：

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使用最坏情况最优连接优化一次性和连续子图查询

我们研究了使用新的最坏情况最优连接计划优化一次性和连续子图查询的问题。最坏情况的最优计划通过使用多路交叉点一次匹配一个查询顶点来评估查询。优化最坏情况最优计划的核心问题是选择要匹配的查询顶点的顺序。我们做出了两个主要贡献： 1. 一种基于成本的一次性查询动态规划优化器，它 (i) 为最坏情况的最优计划选择有效的查询顶点排序，以及 (ii) 生成混合计划将传统的二元连接与最坏情况的最佳样式多路交叉点混合在一起。除了我们的优化器，我们描述了一个自适应技术这会在查询执行期间更改最坏情况最佳子计划的查询顶点顺序，以实现更有效的查询评估。我们的一次性优化器的计划空间包含不在计划空间中的计划，这些计划基于先前工作的树分解。2. 基于增量子图查询框架的连续查询的基于成本的贪心优化器。给定一组连续查询，我们的优化器将这些查询分解为多个增量子图查询，为每个增量查询选择一个计划，并生成一个评估所有查询的组合计划。如果操作员执行相同的交集，我们的组合计划将在 delta 查询计划的操作员之间共享计算。为了增加共享的计算量，我们描述了一个额外的优化，它在运营商之间共享部分交叉点。我们的优化器使用一种新的成本指标来衡量最坏情况的最优计划，称为交叉成本. 在生成混合计划时，我们的一次性查询动态规划优化器将交集成本与二元连接的成本结合起来。我们通过广泛的实验证明了我们的计划、自适应技术和部分交叉口共享优化的有效性。我们的优化器已集成到 GraphflowDB 中。