Layout Optimization for Distributed Relational Databases Using Machine Learning

A common problem when running Web-based applications is how to scale-up the database. The solution to this problem usually involves having a smart Database Administrator determine how to spread the database tables out amongst computers that will work in parallel. Laying out database tables across multiple machines so they can act together as a single efficient database is hard. Automated methods are needed to help eliminate the time required for database administrators to create optimal configurations. There are four operators that we consider that can create a search space of possible database layouts: 1) denormalizing, 2) horizontally partitioning, 3) vertically partitioning, and 4) fully replicating. Textbooks offer general advice that is useful for dealing with extreme cases - for instance you should fully replicate a table if the level of insert to selects is close to zero. But even this seemingly obvious statement is not necessarily one that will lead to a speed up once you take into account that some nodes might be a bottle neck. There can be complex interactions between the 4 different operators which make it even more difficult to predict what the best thing to do is. Instead of using best practices to do database layout, we need a system that collects empirical data on when these 4 different operators are effective. We have implemented a state based search technique to try different operators, and then we used the empirically measured data to see if any speed up occurred. We recognized that the costs of creating the physical database layout are potentially large, but it is necessary since we want to know the Ground Truth about what is effective and under what conditions. After creating a dataset where these four different operators have been applied to make different databases, we can employ machine learning to induce rules to help govern the physical design of the database across an arbitrary number of computer nodes. This learning process, in turn, would allow the database placement algorithm to get better over time as it trains over a set of examples. What this algorithm calls for is that it will try to learn 1) What is a good database layout for a particular application given a query workload? and 2) Can this algorithm automatically improve itself in making recommendations by using machine learned rules to try to generalize when it makes sense to apply each of these operators? There has been considerable research done in parallelizing databases where large amounts of data are shipped from one node to another to answer a single query. Sometimes the costs of shipping the data back and forth might be high, so in this work we assume that it might be more efficient to create a database layout where each query can be answered by a single node. To make this assumption requires that all the incoming query templates are known beforehand. This requirement can easily be satisfied in the case of a Web-based application due to the characteristic that users typically interact with the system through a web interface such as web forms. In this case, unseen queries are not necessarily answerable, without first possibly reconstructing the data on a single machine. Prior knowledge of these exact query templates allows us to select the best possible database table placements across multiple nodes. But in the case of trying to improve the efficiency of a Web-based application, a web site provider might feel that they are willing to suffer the inconvenience of not being able to answer an arbitrary query, if they are in turn provided with a system that runs more efficiently

10381 Summary and Abstracts Collection -- Robust Query Processing

Dagstuhl seminar 10381 on robust query processing (held 19.09.10 - 24.09.10) brought together a diverse set of researchers and practitioners with a broad range of expertise for the purpose of fostering discussion and collaboration regarding causes, opportunities, and solutions for achieving robust query processing. The seminar strove to build a unified view across the loosely-coupled system components responsible for the various stages of database query processing. Participants were chosen for their experience with database query processing and, where possible, their prior work in academic research or in product development towards robustness in database query processing. In order to pave the way to motivate, measure, and protect future advances in robust query processing, seminar 10381 focused on developing tests for measuring the robustness of query processing. In these proceedings, we first review the seminar topics, goals, and results, then present abstracts or notes of some of the seminar break-out sessions. We also include, as an appendix, the robust query processing reading list that was collected and distributed to participants before the seminar began, as well as summaries of a few of those papers that were contributed by some participants

Graph Processing in Main-Memory Column Stores

Evermore, novel and traditional business applications leverage the advantages of a graph data model, such as the offered schema flexibility and an explicit representation of relationships between entities. As a consequence, companies are confronted with the challenge of storing, manipulating, and querying terabytes of graph data for enterprise-critical applications. Although these business applications operate on graph-structured data, they still require direct access to the relational data and typically rely on an RDBMS to keep a single source of truth and access. Existing solutions performing graph operations on business-critical data either use a combination of SQL and application logic or employ a graph data management system. For the first approach, relying solely on SQL results in poor execution performance caused by the functional mismatch between typical graph operations and the relational algebra. To the worse, graph algorithms expose a tremendous variety in structure and functionality caused by their often domain-specific implementations and therefore can be hardly integrated into a database management system other than with custom coding. Since the majority of these enterprise-critical applications exclusively run on relational DBMSs, employing a specialized system for storing and processing graph data is typically not sensible. Besides the maintenance overhead for keeping the systems in sync, combining graph and relational operations is hard to realize as it requires data transfer across system boundaries. A basic ingredient of graph queries and algorithms are traversal operations and are a fundamental component of any database management system that aims at storing, manipulating, and querying graph data. Well-established graph traversal algorithms are standalone implementations relying on optimized data structures. The integration of graph traversals as an operator into a database management system requires a tight integration into the existing database environment and a development of new components, such as a graph topology-aware optimizer and accompanying graph statistics, graph-specific secondary index structures to speedup traversals, and an accompanying graph query language. In this thesis, we introduce and describe GRAPHITE, a hybrid graph-relational data management system. GRAPHITE is a performance-oriented graph data management system as part of an RDBMS allowing to seamlessly combine processing of graph data with relational data in the same system. We propose a columnar storage representation for graph data to leverage the already existing and mature data management and query processing infrastructure of relational database management systems. At the core of GRAPHITE we propose an execution engine solely based on set operations and graph traversals. Our design is driven by the observation that different graph topologies expose different algorithmic requirements to the design of a graph traversal operator. We derive two graph traversal implementations targeting the most common graph topologies and demonstrate how graph-specific statistics can be leveraged to select the optimal physical traversal operator. To accelerate graph traversals, we devise a set of graph-specific, updateable secondary index structures to improve the performance of vertex neighborhood expansion. Finally, we introduce a domain-specific language with an intuitive programming model to extend graph traversals with custom application logic at runtime. We use the LLVM compiler framework to generate efficient code that tightly integrates the user-specified application logic with our highly optimized built-in graph traversal operators. Our experimental evaluation shows that GRAPHITE can outperform native graph management systems by several orders of magnitude while providing all the features of an RDBMS, such as transaction support, backup and recovery, security and user management, effectively providing a promising alternative to specialized graph management systems that lack many of these features and require expensive data replication and maintenance processes

Adaptive Management of Multimodel Data and Heterogeneous Workloads

Data management systems are facing a growing demand for a tighter integration of heterogeneous data from different applications and sources for both operational and analytical purposes in real-time. However, the vast diversification of the data management landscape has led to a situation where there is a trade-off between high operational performance and a tight integration of data. The difference between the growth of data volume and the growth of computational power demands a new approach for managing multimodel data and handling heterogeneous workloads. With PolyDBMS we present a novel class of database management systems, bridging the gap between multimodel database and polystore systems. This new kind of database system combines the operational capabilities of traditional database systems with the flexibility of polystore systems. This includes support for data modifications, transactions, and schema changes at runtime. With native support for multiple data models and query languages, a PolyDBMS presents a holistic solution for the management of heterogeneous data. This does not only enable a tight integration of data across different applications, it also allows a more efficient usage of resources. By leveraging and combining highly optimized database systems as storage and execution engines, this novel class of database system takes advantage of decades of database systems research and development. In this thesis, we present the conceptual foundations and models for building a PolyDBMS. This includes a holistic model for maintaining and querying multiple data models in one logical schema that enables cross-model queries. With the PolyAlgebra, we present a solution for representing queries based on one or multiple data models while preserving their semantics. Furthermore, we introduce a concept for the adaptive planning and decomposition of queries across heterogeneous database systems with different capabilities and features. The conceptual contributions presented in this thesis materialize in Polypheny-DB, the first implementation of a PolyDBMS. Supporting the relational, document, and labeled property graph data model, Polypheny-DB is a suitable solution for structured, semi-structured, and unstructured data. This is complemented by an extensive type system that includes support for binary large objects. With support for multiple query languages, industry standard query interfaces, and a rich set of domain-specific data stores and data sources, Polypheny-DB offers a flexibility unmatched by existing data management solutions

The Third NASA Goddard Conference on Mass Storage Systems and Technologies

This report contains copies of nearly all of the technical papers and viewgraphs presented at the Goddard Conference on Mass Storage Systems and Technologies held in October 1993. The conference served as an informational exchange forum for topics primarily relating to the ingestion and management of massive amounts of data and the attendant problems involved. Discussion topics include the necessary use of computers in the solution of today's infinitely complex problems, the need for greatly increased storage densities in both optical and magnetic recording media, currently popular storage media and magnetic media storage risk factors, data archiving standards including a talk on the current status of the IEEE Storage Systems Reference Model (RM). Additional topics addressed System performance, data storage system concepts, communications technologies, data distribution systems, data compression, and error detection and correction

Scalable Integration View Computation and Maintenance with Parallel, Adaptive and Grouping Techniques

Materialized integration views constructed by integrating data from multiple distributed data sources help to achieve better access, reliable performance, and high availability for a wide range of applications. In this dissertation, we propose parallel, adaptive, and grouping techniques to address scalability challenges in high-performance integration view computation and maintenance due to increasingly large data sources and high rates of source updates. State-of-the-art parallel integration view computation makes the common assumption that the maximal pipelined parallelism leads to superior performance. We instead propose segmented bushy parallel processing that combines pipelined parallelism with alternate forms of parallelism to achieve an overall more effective strategy. Experimental studies conducted over a cluster of high-performance PCs confirm that the proposed strategy has an on average of 50\% improvement in terms of total processing time in comparison to existing solutions. Run-time adaptation becomes critical for parallel integration view computation due to its long running and memory intensive nature. We investigate two types of state level adaptations, namely, state spill and state relocation, to address the run-time memory shortage. We propose lazy-disk and active-disk approaches that integrate both adaptations to maximize run-time query throughput in a memory constrained environment. We also propose global throughput-oriented state adaptation strategies for computation plans with multiple state intensive operators. Extensive experiments confirm the effectiveness of our proposed adaptation solutions. Once results have been computed and materialized, it\u27s typically more efficient to maintain them incrementally instead of full recomputation. However, state-of-the-art incremental view maintenance require O($n^2$) maintenance queries with n being the number of data sources that the view is defined upon. Moreover, they do not exploit view definitions and data source processing capabilities to further improve view maintenance performance. We propose novel grouping maintenance algorithms that dramatically reduce the number of maintenance queries to (O(n)). A cost-based view maintenance framework has been proposed to generate optimized maintenance plans tuned to particular environmental settings. Extensive experimental studies verify the effectiveness of our maintenance algorithms as well as the maintenance framework

Business strategy and information systems alignment : a study of the use of enterprise architectures in Australian Government

This thesis investigates the use of Enterprise Architectures ("the logical structuring and classification of descriptive representations of an enterprise") as enablers of alignment between business strategy and information systems in public sector agencies. The scope of this study has been shaped by Australian government policies that have set firm directions for the delivery of community products and services in the electronic domain. Foundation management and information systems theories, empirical studies and public management literature -have been used extensively in grounding this research study. A substantial body of literature has been reviewed, and this study positioned in the context of these prior literary works. In particular, the principal alignment theories have been adopted and the research model developed from the published works of eminent management and information systems researchers. The primary research question asks whether Enterprise Architectures are enablers of business strategy and information systems alignment, and if so, what are the associated alignment enabling processes? The study's four research themes are: (i) Enterprise Architecture frameworks and methods; (ii) architectural completeness; (iii) the social aspects of alignment (management support, business planning style, business plan communications); and (iv) the formal high level alignment mechanisms used by public agencies. The study has used an exploratory qualitative case_study research method that includes semi-structured and unstructured interviews, archival research and document discovery, public announcement and presentation information, organisational observations, and system demonstrations for the collection and triangulation of data. The case studies at four government agencies are presented as metastories of how Enterprise Architectures and other alignment mechanisms are used within the contextual frame of each public organisation. The research shows that Enterprise Architectures can be enablers of alignment within a public organization environment. Architectures possess the ability to define and describe the states of the agency business and technology domains, and the intimate domain relationships and processes that inform the agency's state of alignment. Changes in the agencies or their operating environments are reflected in the architecture and its subsequent evolutionary changes (such as new business requiring new supporting information systems and technology). Enterprise Architectures were considered as important enablers of alignment with each agency dedicating specialist corporate resources for architecture development and maintenance. The case studies showed that the origin (either internally developed or commercially acquired) of the agency Enterprise Architecture was not necessarily important for the enabling of alignment. However, organizations would do well to concentrate their resources on developing and implementing architectures that accurately represent and integrate the agency business and technology domains. The research used an architectural requirements framework, adapted from an International Standard (ISO 15704), to gauge architecture completeness. The study found that substantially complete architectures integrated the business and information systems entities, included the necessary components (such as the governance frameworks) to achieve strategic alignment, and offered opportunities for agency alignment. Architectures that were deficient in their business, technology or managerial orientations could display reduced clarity of the business and technology states, placing the organisations at risk of misalignment. The case research allowed the comparison of centralised and decentralised agency business structures and information systems, allowing explanations to be developed for the longer architecture implementation periods, and reduced architecture completeness at the decentralised agencies. In particular, the research findings point to the non-uniform application of decentralised resources, and the reduced corporate visibility of decentralised systems, as reasons for long architecture implementation periods, reduced completeness, and impaired alignment. The case studies identified that architectures develop and evolve over time and possess specific characteristics that assist the alignment process. Architectures acted as focal points for business entities and processes that are enabled by the supporting information systems. Architectures provided a mechanism for information systems and technology governance that jointly support business and information systems requirements. Architectures enabled agency information structuring and sharing for the support of business operations. Architectures supported the reuse of systems and technologies for the delivery of business strategies and plans. Other characteristics, such as using architecture as a corporate philosophy, were agency-specific and reflected the agency's culture, people, business capabilities, and corporate history. The detailed examination of management support, business planning styles and business plan communications, showed that the social aspects of alignment were important. In particular the study showed that executive managers must support business and technical directions through demonstrable understanding of the important business and information systems issues, and cohesive decision-making that is built on sound relationships between business and technically oriented executives. The case studies also showed that business plans that are horizontally and vertically integrated, and are well communicated and understood by stakeholders, assisted the enabling of alignment. Finally, the study uncovered several formal alignment mechanisms (such as corporate boards, agency plans, balanced score cards) that were consistent with alignment and governance theory and government management literature. The findings of the case research placed this study of alignment in a process or system frame, while empirically demonstrating that alignment is a continuous and dynamic process that combines several enabling mechanisms. The study showed that any research or conceptual analysis of alignment should consider the alignment mechanisms to operate in combination with each other. Future directions for alignment and architecture research were also described

Runtime Systems for Persistent Memories

Emerging persistent memory (PM) technologies promise the performance of DRAM with the durability of disk. However, several challenges remain in existing hardware, programming, and software systems that inhibit wide-scale PM adoption. This thesis focuses on building efficient mechanisms that span hardware and operating systems, and programming languages for integrating PMs in future systems. First, this thesis proposes a mechanism to solve low-endurance problem in PMs. PMs suffer from limited write endurance---PM cells can be written only 10^7-10^9 times before they wear out. Without any wear management, PM lifetime might be as low as 1.1 months. This thesis presents Kevlar, an OS-based wear-management technique for PM, that requires no new hardware. Kevlar uses existing virtual memory mechanisms to remap pages, enabling it to perform both wear leveling---shuffling pages in PM to even wear; and wear reduction---transparently migrating heavily written pages to DRAM. Crucially, Kevlar avoids the need for hardware support to track wear at fine grain. It relies on a novel wear-estimation technique that builds upon Intel's Precise Event Based Sampling to approximately track processor cache contents via a software-maintained Bloom filter and estimate write-back rates at fine grain. Second, this thesis proposes a persistency model for high-level languages to enable integration of PMs in to future programming systems. Prior works extend language memory models with a persistency model prescribing semantics for updates to PM. These approaches require high-overhead mechanisms, are restricted to certain synchronization constructs, provide incomplete semantics, and/or may recover to state that cannot arise in fault-free program execution. This thesis argues for persistency semantics that guarantee failure atomicity of synchronization-free regions (SFRs) --- program regions delimited by synchronization operations. The proposed approach provides clear semantics for the PM state that recovery code may observe and extends C++11's "sequential consistency for data-race-free" guarantee to post-failure recovery code. To this end, this thesis investigates two designs for failure-atomic SFRs that vary in performance and the degree to which commit of persistent state may lag execution. Finally, this thesis proposes StrandWeaver, a hardware persistency model that minimally constrains ordering on PM operations. Several language-level persistency models have emerged recently to aid programming recoverable data structures in PM. The language-level persistency models are built upon hardware primitives that impose stricter ordering constraints on PM operations than the persistency models require. StrandWeaver manages PM order within a strand, a logically independent sequence of PM operations within a thread. PM operations that lie on separate strands are unordered and may drain concurrently to PM. StrandWeaver implements primitives under strand persistency to allow programmers to improve concurrency and relax ordering constraints on updates as they drain to PM. Furthermore, StrandWeaver proposes mechanisms that map persistency semantics in high-level language persistency models to the primitives implemented by StrandWeaver.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155100/1/vgogte_1.pd