Ranked Enumeration of Join Queries with Projections (2201.05566v2)

Abstract: Join query evaluation with ordering is a fundamental data processing task in relational database management systems. SQL and custom graph query languages such as Cypher offer this functionality by allowing users to specify the order via the ORDER BY clause. In many scenarios, the users also want to see the first $k$ results quickly (expressed by the LIMIT clause), but the value of $k$ is not predetermined as user queries are arriving in an online fashion. Recent work has made considerable progress in identifying optimal algorithms for ranked enumeration of join queries that do not contain any projections. In this paper, we initiate the study of the problem of enumerating results in ranked order for queries with projections. Our main result shows that for any acyclic query, it is possible to obtain a near-linear (in the size of the database) delay algorithm after only a linear time preprocessing step for two important ranking functions: sum and lexicographic ordering. For a practical subset of acyclic queries known as star queries, we show an even stronger result that allows a user to obtain a smooth tradeoff between faster answering time guarantees using more preprocessing time. Our results are also extensible to queries containing cycles and unions. We also perform a comprehensive experimental evaluation to demonstrate that our algorithms, which are simple to implement, improve up to three orders of magnitude in the running time over state-of-the-art algorithms implemented within open-source RDBMS and specialized graph databases.

• The paper introduces novel algorithms that enable efficient ranked enumeration of join queries with projections by optimizing join trees and ranking functions.
• It develops advanced techniques for both sum and lexicographic ranking, reducing computational overhead through simplified value comparisons.
• Extensive evaluations reveal up to three orders of magnitude performance gains over traditional DBMS, optimizing preprocessing, space, and enumeration delay trade-offs.

Ranked Enumeration of Join Queries with Projections

The paper "Ranked Enumeration of Join Queries with Projections" (2201.05566) introduces novel algorithms for the efficient enumeration of join query results with projections, ranked according to specific ordering criteria. This work addresses the challenge of handling join queries in the presence of projections and provides algorithms with significant performance improvements over traditional database management systems (DBMS).

Acyclic Join Queries and Ranked Enumeration

The paper extends the paper of ranked enumeration to include acyclic join queries with projections. Two prominent ranking functions are considered: the sum ranking function and the lexicographic ranking function. The authors demonstrate that with a preprocessing phase of linear complexity relative to the database size, it is possible to achieve near-linear delay for the enumeration of query results.

A key algorithmic development is the utilization of join trees, which are leveraged to organize the hierarchical processing of queries. The preprocessing step constructs priority queues at each node of the join tree to facilitate efficient ranked enumeration during the query execution phase.

Figure 1: Illustration of join tree for a join-project query $Q = \pi_{A, E}(...).$

Advanced Algorithmic Techniques for Lexicographic Order

The paper explores a specialized approach for handling lexicographic ranking, recognizing that such an ordering implies local consistency across the query plan's subtree structures. This insight leads to an optimized algorithm that bypasses the need for complex priority queue operations in favor of simpler value comparisons during enumeration. Consequently, lexicographic ranking is achieved with a reduced delay, improving performance relative to sum-based rankings.

Handling Star Queries and Optimal Trade-offs

For a specific class of queries known as star queries, the authors develop an algorithm providing a flexible trade-off between preprocessing time, space requirements, and enumeration delay. This trade-off is significant in practical applications where resources are constrained, allowing users to optimize based on available computational resources.

Figure 2: Examples of GHD and \fhw. The leftmost is the minimal GHD of a cycle join.

Evaluation and Experimental Results

The research findings are validated through extensive empirical evaluation, demonstrating that the proposed algorithms outperform existing implementations in open-source relational DBMS and specialized graph databases by up to three orders of magnitude. This performance boost is attributed to the avoidance of materializing intermediate query results and the efficient handling of ranking constraints during query execution.

The evaluation reveals that for small values of $k$ (where $k$ is the number of top-ranked results desired), the algorithm's performance is near-instantaneous, offering significant improvements over traditional methods which involve sorting and duplicate elimination steps after computing the full join.

Conclusion and Implications

The paper concludes by underscoring the theoretical and practical implications of the work. By extending ranked enumeration to include queries with projections, this research broadens the applicability of optimized query processing techniques within modern data management systems.

Future work may focus on further refining these algorithms to handle more complex query structures, including cyclic queries, and enhancing integration with distributed database systems for improved scalability. Furthermore, exploring adaptive strategies that tune preprocessing based on query and data characteristics could lead to further advancements in processing efficiency.