Cost-based Query Optimization Research Articles

Budget-aware Query Tuning: An AutoML Perspective

Modern database systems rely on cost-based query optimizers to come up with good execution plans for input queries. Such query optimizers rely on cost models to estimate the costs of candidate query execution plans. A cost model represents a function from a set of cost units to query execution cost, where each cost unit specifies the unit cost of executing a certain type of query processing operation (such as table scan or join). These cost units are traditionally viewed as constants, whose values only depend on the platform configuration where the database system runs on top of but are invariant for queries processed by the database system. In this paper, we challenge this classic view by thinking of these cost units as variables instead. We show that, by varying the cost-unit values one can obtain query plans that significantly outperform the default query plans returned by the query optimizer when viewing the cost units as constants. We term this cost-unit tuning process 'query tuning' (QT) and show that it is similar to the well-known hyper-parameter optimization (HPO) problem in AutoML. As a result, any state-of-the-art HPO technologies can be applied to QT. We study the QT problem in the context of anytime tuning, which is desirable in practice by constraining the total time spent on QT within a given budget-we call this problem budgetaware query tuning. We further extend our study from tuning a single query to tuning a workload with multiple queries, and we call this generalized problem budgetaware workload tuning (WT), which aims for minimizing the execution time of the entire workload. WT is more challenging as one needs to further prioritize individual query tuning within the given time budget. We propose solutions to both QT and WT and experimental evaluation using both benchmark and real workloads demonstrates the efficacy of our proposed solutions.

Identifying the Root Causes of DBMS Suboptimality

The query optimization phase within a database management system (DBMS) ostensibly finds the fastest query execution plan from a potentially large set of enumerated plans, all of which correctly compute the same result of the specified query. Sometimes the cost-based optimizer selects a slower plan, for a variety of reasons. Previous work has focused on increasing the performance of specific components, often a single operator, within an individual DBMS. However, that does not address the fundamental question: from where does this suboptimality arise, across DBMSes generally? In particular, the contribution of each of many possible factors to DBMS suboptimality is currently unknown. To identify the root causes of DBMS suboptimality, we first introduce the notion of empirical suboptimality of a query plan chosen by the DBMS, indicated by the existence of a query plan that performs more efficiently than the chosen plan, for the same query. A crucial aspect is that this can be measured externally to the DBMS, and thus does not require access to its source code. We then propose a novel predictive model to explain the relationship between various factors in query optimization and empirical suboptimality. Our model associates suboptimality with the factors of complexity of the schema, of the underlying data on which the query is evaluated, of the query itself, and of the DBMS optimizer. The model also characterizes concomitant interactions among these factors. This model induces a number of specific hypotheses that were tested on multiple DBMSes. We performed a series of experiments that examined the plans for thousands of queries run on four popular DBMSes. We tested the model on over a million of these query executions, using correlational analysis, regression analysis, and causal analysis, specifically Structural Equation Modeling (SEM). We observed that the dependent construct of empirical suboptimality prevalence correlates positively with nine specific constructs characterizing four identified factors that explain in concert much of the variance of suboptimality of two extensive benchmarks, across these disparate DBMSes. This predictive model shows that it is the common aspects of these DBMSes that predict suboptimality, not the particulars embedded in the inordinate complexity of each of these DBMSes. This paper thus provides a new methodology to study mature query optimizers, identifies underlying DBMS-independent causes for the observed suboptimality, and quantifies the relative contribution of each of these causes to the observed suboptimality. This work thus provides a roadmap for fundamental improvements of cost-based query optimizers.

RelJoin: Relative-cost-based selection of distributed join methods for query plan optimization

Selecting appropriate distributed join methods for logical join operations in a query plan is crucial for the performance of data-intensive scalable computing (DISC). Different network communication patterns in the data exchange phase generate varying network communication workloads and significantly affect the distributed join performance. However, most cost-based query optimizers focus on the local computing cost and do not precisely model the network communication cost. We propose a cost model for various distributed join methods to optimize join queries in DISC platforms. Our method precisely measures the network and local computing workloads in different execution phases, using information on the size and cardinality statistics of datasets and cluster join parallelism. Our cost model reveals the importance of the relative size of the joining datasets. We implement an efficient distributed join selection strategy, known as RelJoin in SparkSQL, which is an industry-prevalent distributed data processing framework. RelJoin uses runtime adaptive statistics for accurate cost estimation and selects optimal distributed join methods for logical joins to optimize the physical query plan. The evaluation results on the TPC-DS benchmark show that RelJoin performs best in 62 of the 97 queries and can reduce the average query time by 21% compared with other strategies.1

FASTgres: Making Learned Query Optimizer Hinting Effective

The traditional and well-established cost-based query optimizer approach enumerates different execution plans for each query, assesses each plan with costs, and selects the plan that promises the lowest costs for execution. However, the optimal execution plan is not always selected. To steer the optimizer in the right direction, many query optimizers provide configuration parameters called query optimizer hints. These hints can be set for every single query separately. To show the great potential of these hints for the optimization of analytical queries, we present results of a comprehensive and in-depth evaluation using three benchmarks and two different versions of the open-source database system PostgreSQL. In particular, we highlight that query optimizer hinting is a non-trivial challenge. To solve this challenge, we propose FASTgres , a learning-based context-aware classification strategy for hint set prediction. Compared to related work, FASTgres provides transparent and direct hint set predictions with consistent performance improvements. In our end-to-end evaluation, we demonstrate that FASTgres effectively reduces benchmark runtimes by a factor of up to 3.25x with only steering the cost-based optimizer.

Efficient Sorting, Duplicate Removal, Grouping, and Aggregation

Database query processing requires algorithms for duplicate removal, grouping, and aggregation. Three algorithms exist: in-stream aggregation is most efficient by far but requires sorted input; sort-based aggregation relies on external merge sort; and hash aggregation relies on an in-memory hash table plus hash partitioning to temporary storage. Cost-based query optimization chooses which algorithm to use based on several factors, including the sort order of the input, input and output sizes, and the need for sorted output. For example, hash-based aggregation is ideal for output smaller than the available memory (e.g., Query 1 of TPC-H), whereas sorting the entire input and aggregating after sorting are preferable when both aggregation input and output are large and the output needs to be sorted for a subsequent operation such as a merge join. Unfortunately, the size information required for a sound choice is often inaccurate or unavailable during query optimization, leading to sub-optimal algorithm choices. In response, this article introduces a new algorithm for sort-based duplicate removal, grouping, and aggregation. The new algorithm always performs at least as well as both traditional hash-based and traditional sort-based algorithms. It can serve as a system’s only aggregation algorithm for unsorted inputs, thus preventing erroneous algorithm choices. Furthermore, the new algorithm produces sorted output that can speed up subsequent operations. Google’s F1 Query uses the new algorithm in production workloads that aggregate petabytes of data every day.

Join cardinality estimation by combining operator-level deep neural networks

Join cardinality estimation is fundamental to cost-based query optimizers. The state-of-the-art deep learning based join cardinality estimation methods do not fully take the structure of a query plan into account and lack of data information about joins, causing significant performance degradation when the number of joins in a query increases. In this paper, we propose CAPE, a join cardinality estimation method combining operator-level deep neural networks. CAPE introduces two operator-level deep neural networks for selection operators and join operators, as well as an output deep neural network that maps the intermediate representations to join cardinality estimates. Given the query plan rooted at a join operator, CAPE generates a plan-level deep neural network by combining operator-level deep neural networks. In this way, CAPE can handle arbitrary query plans with simple operator-level deep neural networks rather than a single complicated model. In addition, we introduce join-crossing sampling information to detect join-crossing correlations. Experiments are conducted on a dataset constructed from the IMDb dataset, and the experimental results show that CAPE is significantly better than the state-of-the-art methods.

Cost-Based Query Optimization in Centralized Relational Databases

Query optimization is the most significant factor for any centralized relational database management system (RDBMS) that reduces the total execution time of a query. Query optimization is the process of executing a SQL (Structured Query Language) query in relational databases to determine the most efficient way to execute a given query by considering the possible query plans. The goal of query optimization is to optimize the given query for the sake of efficiency. Cost-based query optimization compares different strategies based on relative costs (amount of time that the query needs to run) and selects and executes one that minimizes the cost. The cost of a strategy is just an estimate based on how many estimated CPU and I/O resources that the query will use. In this paper, cost is considered by counting number of disk accesses for each query plan because disk access tends to be the dominant cost in query processing for centralized relational databases.

A review of different cost-based distributed query optimizers

The paper narrates the review of cost-based query optimizers designed using database strategies, deterministic, stochastic, hybrid and energy efficiency-based techniques. It was endowed that earlier authors have used a different database and deterministic strategy like indexing, query filtering, normalization, query graph, tableau, exhaustive enumeration, query graph and dynamic programming to optimize queries. However, these techniques are not pertinent to the optimization of serpentine database queries. Nonetheless, it can be resourcefully optimized by using divergent individual and hybrid nature-inspired computing techniques. Research divulges that the hybrid approach was and remains effective to unravel the query optimization problem. Moreover, notable work is effectuated to optimize data retrieval queries only; however, little work is carried out to optimize write, delete and update queries. Additionally, energy-efficient query optimization is an emanate area. The copious amount of energy can be defended by using energy-efficient query optimizers. The extensive publication trend of distributed query optimizers has also examined that can be of enormous concern for the researchers who want to publish their article and to pursue their research in this domain area. It is ascertained that momentous volume of query optimization work has been effectuated using genetic algorithm followed by swarm particle optimization. Additionally, the researcher has to use and analyze the performance of different emerging evolutionary techniques (Ant Lion Optimization, Whale Optimization, Monkey Search, Dolphin Echolocation, Chaotic Swarming) in designing cost-based query optimizer.

Bubble execution

Enabling interactive data exploration at cloud scale requires minimizing end-to-end query execution latency, while guaranteeing fault tolerance, and query execution under resource-constraints. Typically, such a query execution involves orchestrating the execution of hundreds or thousands of related tasks on cloud scale clusters. Without any resource constraints, all query tasks can be scheduled to execute simultaneously (gang scheduling) while connected tasks stream data between them. When the data size referenced by a query increases, gang scheduling may be resource-wasteful or un-satisfiable with a limited, per-query resource budget. This paper introduces B ubble E xecution , a new query processing framework for interactive workloads at cloud scale, that balances cost-based query optimization, fault tolerance, optimal resource management, and execution orchestration. Bubble execution involves dividing a query execution graph into a collection of query sub-graphs (bubbles), and scheduling them within a per-query resource budget. The query operators (tasks) inside a bubble stream data between them while fault tolerance is handled by persisting temporary results at bubble boundaries. Our implementation enhances our JetScope service, for interactive workloads, deployed in production clusters at Microsoft. Experiments with TPC-H queries show that bubble execution can reduce resource usage significantly in the presence of failures while maintaining performance competitive with gang execution.

CostFed: Cost-Based Query Optimization for SPARQL Endpoint Federation

The runtime optimization of federated SPARQL query engines is of central importance to ensure the usability of the Web of Data in real-world applications. The efficient selection of sources (SPARQL endpoints in our case) as well as the generation of optimized query plans belong to the most important optimization steps in this respect. This paper presents CostFed, an index-assisted federation engine for federated SPARQL query processing. CostFed makes use of statistical information collected from endpoints to perform efficient source selection and cost-based query planning. In contrast to the state of the art, it relies on a non-linear model for the estimation of the selectivity of joins. Therewith, it is able to generate better plans than the state-of-the-art federation engines. Our experiments on the FedBench benchmark shows that CostFed is 3 to 121 times faster than the current federation engines.

Query optimization for dynamic imputation

Missing values are common in data analysis and present a usability challenge. Users are forced to pick between removing tuples with missing values or creating a cleaned version of their data by applying a relatively expensive imputation strategy. Our system, ImputeDB, incorporates imputation into a cost-based query optimizer, performing necessary imputations on-the-fly for each query. This allows users to immediately explore their data, while the system picks the optimal placement of imputation operations. We evaluate this approach on three real-world survey-based datasets. Our experiments show that our query plans execute between 10 and 140 times faster than first imputing the base tables. Furthermore, we show that the query results from on-the-fly imputation differ from the traditional base-table imputation approach by 0--8%. Finally, we show that while dropping tuples with missing values that fail query constraints discards 6--78% of the data, on-the-fly imputation loses only 0--21%.

CrowdOp: Query Optimization for Declarative Crowdsourcing Systems

We study the query optimization problem in declarative crowdsourcing systems. Declarative crowdsourcing is designed to hide the complexities and relieve the user of the burden of dealing with the crowd. The user is only required to submit an SQL-like query and the system takes the responsibility of compiling the query, generating the execution plan and evaluating in the crowdsourcing marketplace. A given query can have many alternative execution plans and the difference in crowdsourcing cost between the best and the worst plans may be several orders of magnitude. Therefore, as in relational database systems, query optimization is important to crowdsourcing systems that provide declarative query interfaces. In this paper, we propose CrowdOp , a cost-based query optimization approach for declarative crowdsourcing systems. CrowdOp considers both cost and latency in query optimization objectives and generates query plans that provide a good balance between the cost and latency. We develop efficient algorithms in the CrowdOp for optimizing three types of queries: selection queries, join queries, and complex selection-join queries. We validate our approach via extensive experiments by simulation as well as with the real crowd on Amazon Mechanical Turk.

Proximal operator of quotient functions with application to a feasibility problem in query optimization

In this paper we determine the proximity functions of the sum and the maximum of componentwise (reciprocal) quotients of positive vectors. For the sum of quotients, denoted by Q1, the proximity function is just a componentwise shrinkage function which we call q-shrinkage. This is similar to the proximity function of the ℓ1-norm which is given by componentwise soft shrinkage. For the maximum of quotients Q∞, the proximal function can be computed by first order primal–dual methods involving epigraphical projections.The proximity functions of Qν, ν=1,∞ are applied to solve convex problems of the form argminxQν(Axb) subject to x≥0, 1⊤x≤1. Such problems are of interest in selectivity estimation for cost-based query optimizers in database management systems.

Cost-based Query Optimization for Xpath

In this article, we address the issues which are related to the cost-based XML query optimization for XPath. Specially, we focus on the issue of how to determine the execution order for a given XPath expression according to the cost models. The main impact factor that dominates the execution order is the size of the intermediate results during the evaluation of queries. The hierarchy encoding scheme and the value-encoding histogram are introduced to support the size estimation of the path expressions. Two cost models are proposed to describe the costs of different types of join operations in the path expressions. A heuristic-based dynamic programming approach is proposed to determine the optimal execution tree. The primary experimental results demonstrate the validity of our approaches.

Cost-Based Query Optimization via AI Planning

In this paper we revisit the problem of generating query plans using AI automated planning with a view to leveraging significant recent advances in state-of-the-art planning techniques. Our efforts focus on the specific problem of cost-based join-order optimization for conjunctive relational queries, a critical component of production-quality query optimizers. We characterize the general query-planning problem as a delete-free planning problem, and query plan optimization as a context-sensitive cost-optimal planning problem. We propose algorithms that generate high-quality query plans, guaranteeing optimality under certain conditions. Our approach is general, supporting the use of a broad suite of domain-independent and domain-specific optimization criteria. Experimental results demonstrate the effectiveness of AI planning techniques for query plan generation and optimization.

Counter strike

Finding the optimal execution order of join operations is a crucial task of today's cost-based query optimizers. There are two approaches to identify the best plan: bottom-up and top-down join enumeration. But only the top-down approach allows for branch-and-bound pruning, which can improve compile time by several orders of magnitude while still preserving optimality. For both optimization strategies, efficient enumeration algorithms have been published. However, there are two severe limitations for the top-down approach: The published algorithms can handle only (1) simple (binary) join predicates and (2) inner joins. Since real queries may contain complex join predicates involving more than two relations, and outer joins as well as other non-inner joins, efficient top-down join enumeration cannot be used in practice yet. We develop a novel top-down join enumeration algorithm that overcomes these two limitations. Furthermore, we show that our new algorithm is competitive when compared to the state of the art in bottom-up processing even without playing out its advantage by making use of its branch-and-bound pruning capabilities.

Query optimization over crowdsourced data

Deco is a comprehensive system for answering declarative queries posed over stored relational data together with data obtained on-demand from the crowd. In this paper we describe Deco's cost-based query optimizer, building on Deco's data model, query language, and query execution engine presented earlier. Deco's objective in query optimization is to find the best query plan to answer a query, in terms of estimated monetary cost. Deco's query semantics and plan execution strategies require several fundamental changes to traditional query optimization. Novel techniques incorporated into Deco's query optimizer include a cost model distinguishing between "free" existing data versus paid new data, a cardinality estimation algorithm coping with changes to the database state during query execution, and a plan enumeration algorithm maximizing reuse of common subplans in a setting that makes reuse challenging. We experimentally evaluate Deco's query optimizer, focusing on the accuracy of cost estimation and the efficiency of plan enumeration.

An overview of the deco system

Deco is a comprehensive system for answering declarative queries posed over stored relational data together with data obtained on-demand from the crowd. In this overview paper, we describe Deco's data model, query language, and system prototype, summarizing material from earlier papers. Deco's data model was designed to be general, flexible, and principled. Deco's query language extends SQL with simple constructs necessary for crowdsourcing, and has a precise semantics for arbitrary queries. Deco's query execution engine and cost-based query optimizer incorporate many novel techniques to address the limitations of traditional query processing techniques in the crowdsourcing setting. Query processing is guided by the objective of minimizing monetary cost and reducing latency.

Similarity queries: their conceptual evaluation, transformations, and processing

Many application scenarios can significantly benefit from the identification and processing of similarities in the data. Even though some work has been done to extend the semantics of some operators, for example join and selection, to be aware of data similarities, there has not been much study on the role and implementation of similarity-aware operations as first-class database operators. Furthermore, very little work has addressed the problem of evaluating and optimizing queries that combine several similarity operations. The focus of this paper is the study of similarity queries that contain one or multiple first-class similarity database operators such as Similarity Selection, Similarity Join, and Similarity Group-by. Particularly, we analyze the implementation techniques of several similarity operators, introduce a consistent and comprehensive conceptual evaluation model for similarity queries, and present a rich set of transformation rules to extend cost-based query optimization to the case of similarity queries.

Towards Building a High Performance Spatial Query System for Large Scale Medical Imaging Data.

Support of high performance queries on large volumes of scientific spatial data is becoming increasingly important in many applications. This growth is driven by not only geospatial problems in numerous fields, but also emerging scientific applications that are increasingly data- and compute-intensive. For example, digital pathology imaging has become an emerging field during the past decade, where examination of high resolution images of human tissue specimens enables more effective diagnosis, prediction and treatment of diseases. Systematic analysis of large-scale pathology images generates tremendous amounts of spatially derived quantifications of micro-anatomic objects, such as nuclei, blood vessels, and tissue regions. Analytical pathology imaging provides high potential to support image based computer aided diagnosis. One major requirement for this is effective querying of such enormous amount of data with fast response, which is faced with two major challenges: the "big data" challenge and the high computation complexity. In this paper, we present our work towards building a high performance spatial query system for querying massive spatial data on MapReduce. Our framework takes an on demand index building approach for processing spatial queries and a partition-merge approach for building parallel spatial query pipelines, which fits nicely with the computing model of MapReduce. We demonstrate our framework on supporting multi-way spatial joins for algorithm evaluation and nearest neighbor queries for microanatomic objects. To reduce query response time, we propose cost based query optimization to mitigate the effect of data skew. Our experiments show that the framework can efficiently support complex analytical spatial queries on MapReduce.