The popularity of the Web and Internet commerce provides many extremely large datasets from which information can be gleaned by data mining. This book focuses on practical algorithms that have been used to solve key problems in data mining and which can be used on even the largest datasets. It begins with a discussion of the map-reduce framework, an important tool for parallelizing algorithms automatically. The authors explain the tricks of locality-sensitive hashing and stream processing algorithms for mining data that arrives too fast for exhaustive processing. The PageRank idea and related tricks for organizing the Web are covered next. Other chapters cover the problems of finding frequent itemsets and clustering. The final chapters cover two applications: recommendation systems and Web advertising, each vital in e-commerce. Written by two authorities in database and Web technologies, this book is essential reading for students and practitioners alike.

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Mining of Massive Datasets

As computation continues to move into the cloud, the computing platform of interest no longer resembles a pizza box or a refrigerator, but a warehouse full of computers. These new large datacenters are quite different from traditional hosting facilities of earlier times and cannot be viewed simply as a collection of co-located servers. Large portions of the hardware and software resources in these facilities must work in concert to efficiently deliver good levels of Internet service performance, something that can only be achieved by a holistic approach to their design and deployment. In other words, we must treat the datacenter itself as one massive warehouse-scale computer (WSC). We describe the architecture of WSCs, the main factors influencing their design, operation, and cost structure, and the characteristics of their software base. We hope it will be useful to architects and programmers of today's WSCs, as well as those of future many-core platforms which may one day implement the equivalent of today's WSCs on a single board. Table of Contents: Introduction / Workloads and Software Infrastructure / Hardware Building Blocks / Datacenter Basics / Energy and Power Efficiency / Modeling Costs / Dealing with Failures and Repairs / Closing Remarks

/pdf/the-datacenter-as-a-computer-an-introduction-to-the-design-nu43k2m3iz.pdf

The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines

We present Mesos, a platform for sharing commodity clusters between multiple diverse cluster computing frameworks, such as Hadoop and MPI. Sharing improves cluster utilization and avoids per-framework data replication. Mesos shares resources in a fine-grained manner, allowing frameworks to achieve data locality by taking turns reading data stored on each machine. To support the sophisticated schedulers of today's frameworks, Mesos introduces a distributed two-level scheduling mechanism called resource offers. Mesos decides how many resources to offer each framework, while frameworks decide which resources to accept and which computations to run on them. Our results show that Mesos can achieve near-optimal data locality when sharing the cluster among diverse frameworks, can scale to 50,000 (emulated) nodes, and is resilient to failures.

/pdf/mesos-a-platform-for-fine-grained-resource-sharing-in-the-1mr3h7jksr.pdf

Mesos: a platform for fine-grained resource sharing in the data center

This paper presents the design of a read-optimized relational DBMS that contrasts sharply with most current systems, which are write-optimized. Among the many differences in its design are: storage of data by column rather than by row, careful coding and packing of objects into storage including main memory during query processing, storing an overlapping collection of column-oriented projections, rather than the current fare of tables and indexes, a non-traditional implementation of transactions which includes high availability and snapshot isolation for read-only transactions, and the extensive use of bitmap indexes to complement B-tree structures.We present preliminary performance data on a subset of TPC-H and show that the system we are building, C-Store, is substantially faster than popular commercial products. Hence, the architecture looks very encouraging.

/pdf/c-store-a-column-oriented-dbms-3oddwwyeo0.pdf

C-store: a column-oriented DBMS

The computing world today is in the middle of a revolution: mobile clients and cloud computing have emerged as the dominant paradigms driving programming and hardware innovation today. The Fifth Edition of Computer Architecture focuses on this dramatic shift, exploring the ways in which software and technology in the "cloud" are accessed by cell phones, tablets, laptops, and other mobile computing devices. Each chapter includes two real-world examples, one mobile and one datacenter, to illustrate this revolutionary change. Updated to cover the mobile computing revolutionEmphasizes the two most important topics in architecture today: memory hierarchy and parallelism in all its forms.Develops common themes throughout each chapter: power, performance, cost, dependability, protection, programming models, and emerging trends ("What's Next")Includes three review appendices in the printed text. Additional reference appendices are available online.Includes updated Case Studies and completely new exercises.

Computer Architecture, Fifth Edition: A Quantitative Approach

As traditional and mission-critical relational database workloads migrate to the cloud in the form of Database-as-a-Service (DaaS), there is an increasing motivation to provide performance goals in Service Level Objectives (SLOs). Providing such performance goals is challenging for DaaS providers as they must balance the performance that they can deliver to tenants and the data center's operating costs. In general, aggressively aggregating tenants on each server reduces the operating costs but degrades performance for the tenants, and vice versa. In this paper, we present a framework that takes as input the tenant workloads, their performance SLOs, and the server hardware that is available to the DaaS provider, and outputs a cost-effective recipe that specifies how much hardware to provision and how to schedule the tenants on each hardware resource. We evaluate our method and show that it produces effective solutions that can reduce the costs for the DaaS provider while meeting performance goals.

/pdf/towards-multi-tenant-performance-slos-2wh8vylo8e.pdf

Towards Multi-tenant Performance SLOs

The high cost associated with powering servers has introduced new challenges in improving the energy efficiency of clusters running data processing jobs. Traditional high-performance servers are largely energy inefficient due to various factors such as the over-provisioning of resources. The increasing trend to replace traditional high-performance server nodes with low-power low-end nodes in clusters has recently been touted as a solution to the cluster energy problem. However, the key tacit assumption that drives such a solution is that the proportional scale-out of such low-power cluster nodes results in constant scaleup in performance. This paper studies the validity of such an assumption using measured price and performance results from a low-power Atom-based node and a traditional Xeon-based server and a number of published parallel scaleup results. Our results show that in most cases, computationally complex queries exhibit disproportionate scaleup characteristics which potentially makes scale-out with low-end nodes an expensive and lower performance solution.

/pdf/wimpy-node-clusters-what-about-non-wimpy-workloads-2ojshr9l78.pdf

Wimpy node clusters: what about non-wimpy workloads?

The present invention extends to methods, systems, and computer program products for optimizing queries of parallel databases. Queries can be partially optimized at an optimizer that is unaware of its use to optimize queries for parallel processing. The optimizer can produce a data structure (e.g., a SQL Server MEMO) that encapsulates a logical serial plan search space. The logical serial plan search space may not incorporate any notion of parallelism into the plan space itself. A parallel-aware optimizer can parallelize the logical serial plan search space by augmenting the data structure (e.g., transforming the SQL Server MEMO into a parallel MEMO). Augmentation can be with data movement operations that move data associated one or more compute nodes in a distributed architecture. Cost estimates can be calculated for the operations contained in the parallelized data structure. The parallel plan with the lowest estimated cost can be selected for the query.

Optimizing queries of parallel databases

In order to facilitate the requesting of desired information from a graph database that stores a graph, a system may translate an initial query into a query that can be executed against the graph database. In particular, using primitives, the system may translate a query associated with a type of database (such as a relational database) into the query. The graph may include nodes, edges between the nodes, and predicates to represent and store data with index-free adjacency. Moreover, the primitives may include: a rule based on edges in the graph that expresses a relational schema in the type of database, and information associated with a compound key that specifies a relationship between nodes, edges and predicates in the graph corresponding to a table in the type of database. Then, the system may execute the query against the graph database, and may receive a result that includes a subset of the graph.

Translating queries into graph queries using primitives

As traditional and mission-critical relational database workloads migrate to the cloud in the form of Database-as-a-Service (DaaS), there is an increasing motivation to provide performance goals in Service Level Objectives (SLOs). Providing such performance goals is challenging for DaaS providers as they must balance the performance that they can deliver to tenants and the data center’s operating costs. In general, aggressively aggregating tenants on each server reduces the operating costs but degrades performance for the tenants, and vice versa. In this paper, we present a framework that takes as input the tenant workloads, their performance SLOs, and the server hardware that is available to the DaaS provider, and outputs a cost-effective recipe that specifies how much hardware to provision and how to schedule the tenants on each hardware resource. We evaluate our method and show that it produces effective solutions that can reduce the costs for the DaaS provider while meeting performance goals.

Towards Multi-Tenant Performance SLOs

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