Guaranteeing the \~O(AGM/OUT) Runtime for Uniform Sampling and OUT Size Estimation over Joins

We propose a new method for estimating the number of answers OUT of a small join query Q in a large database D, and for uniform sampling over joins. Our method is the first to satisfy all the following statements. - Support arbitrary Q, which can be either acyclic or cyclic, and contain binary and non-binary relations. - Guarantee an arbitrary small error with a high probability always in \~O(AGM/OUT) time, where AGM is the AGM bound OUT (an upper bound of OUT), and \~O hides the polylogarithmic factor of input size. We also explain previous join size estimators in a unified framework. All methods including ours rely on certain indexes on relations in D, which take linear time to build offline. Additionally, we extend our method using generalized hypertree decompositions (GHDs) to achieve a lower complexity than \~O(AGM/OUT) when OUT is small, and present optimization techniques for improving estimation efficiency and accuracy.Comment: 19 page

FactorJoin: A New Cardinality Estimation Framework for Join Queries

Cardinality estimation is one of the most fundamental and challenging problems in query optimization. Neither classical nor learning-based methods yield satisfactory performance when estimating the cardinality of the join queries. They either rely on simplified assumptions leading to ineffective cardinality estimates or build large models to understand the data distributions, leading to long planning times and a lack of generalizability across queries. In this paper, we propose a new framework FactorJoin for estimating join queries. FactorJoin combines the idea behind the classical join-histogram method to efficiently handle joins with the learning-based methods to accurately capture attribute correlation. Specifically, FactorJoin scans every table in a DB and builds single-table conditional distributions during an offline preparation phase. When a join query comes, FactorJoin translates it into a factor graph model over the learned distributions to effectively and efficiently estimate its cardinality. Unlike existing learning-based methods, FactorJoin does not need to de-normalize joins upfront or require executed query workloads to train the model. Since it only relies on single-table statistics, FactorJoin has small space overhead and is extremely easy to train and maintain. In our evaluation, FactorJoin can produce more effective estimates than the previous state-of-the-art learning-based methods, with 40x less estimation latency, 100x smaller model size, and 100x faster training speed at comparable or better accuracy. In addition, FactorJoin can estimate 10,000 sub-plan queries within one second to optimize the query plan, which is very close to the traditional cardinality estimators in commercial DBMS.Comment: Paper accepted by SIGMOD 202

Streaming Weighted Sampling over Join Queries

Join queries are a fundamental database tool, capturing a range of tasks that involve linking heterogeneous data sources. However, with massive table sizes, it is often impractical to keep these in memory, and we can only take one or few streaming passes over them. Moreover, building out the full join result (e.g., linking heterogeneous data sources along quasi-identifiers) can lead to a combinatorial explosion of results due to many-to-many links. Random sampling is a natural tool to boil this oversized result down to a representative subset with well-understood statistical properties, but turns out to be a challenging task due to the combinatorial nature of the sampling domain. Existing techniques in the literature focus solely on the setting with tabular data residing in main memory, and do not address aspects such as stream operation, weighted sampling and more general join operators that are urgently needed in a modern data processing context. The main contribution of this work is to meet these needs with more lightweight practical approaches. First, a bijection between the sampling problem and a graph problem is introduced to support weighted sampling and common join operators. Second, the sampling techniques are refined to minimise the number of streaming passes. Third, techniques are presented to deal with very large tables under limited memory. Finally, the proposed techniques are compared to existing approaches that rely on database indices and the results indicate substantial memory savings, reduced runtimes for ad-hoc queries and competitive amortised runtimes

A New Framework for Join Product Skew

Different types of data skew can result in load imbalance in the context of parallel joins under the shared nothing architecture. We study one important type of skew, join product skew (JPS). A static approach based on frequency classes is proposed which takes for granted the data distribution of join attribute values. It comes from the observation that the join selectivity can be expressed as a sum of products of frequencies of the join attribute values. As a consequence, an appropriate assignment of join sub-tasks, that takes into consideration the magnitude of the frequency products can alleviate the join product skew. Motivated by the aforementioned remark, we propose an algorithm, called Handling Join Product Skew (HJPS), to handle join product skew