There is a growing need for ad-hoc analysis of extremely large data sets, especially at internet companies where innovation critically depends on being able to analyze terabytes of data collected every day. Parallel database products, e.g., Teradata, offer a solution, but are usually prohibitively expensive at this scale. Besides, many of the people who analyze this data are entrenched procedural programmers, who find the declarative, SQL style to be unnatural. The success of the more procedural map-reduce programming model, and its associated scalable implementations on commodity hardware, is evidence of the above. However, the map-reduce paradigm is too low-level and rigid, and leads to a great deal of custom user code that is hard to maintain, and reuse. We describe a new language called Pig Latin that we have designed to fit in a sweet spot between the declarative style of SQL, and the low-level, procedural style of map-reduce. The accompanying system, Pig, is fully implemented, and compiles Pig Latin into physical plans that are executed over Hadoop, an open-source, map-reduce implementation. We give a few examples of how engineers at Yahoo! are using Pig to dramatically reduce the time required for the development and execution of their data analysis tasks, compared to using Hadoop directly. We also report on a novel debugging environment that comes integrated with Pig, that can lead to even higher productivity gains. Pig is an open-source, Apache-incubator project, and available for general use. 1.

Companies providing cloud-scale services have an increasing need to store and analyze massive data sets such as search logs and click streams. For cost and performance reasons, processing is typically done on large clusters of shared-nothing commodity machines. It is imperative to develop a programming model that hides the complexity of the underlying system but provides flexibility by allowing users to extend functionality to meet a variety of requirements. In this paper, we present a new declarative and extensible scripting language, SCOPE (Structured Computations Optimized for Parallel Execution), targeted for this type of massive data analysis. The language is designed for ease of use with no explicit parallelism, while being amenable to efficient parallel execution on large clusters. SCOPE borrows several features from SQL. Data is modeled as sets of rows composed of typed columns. The select statement is retained with inner joins, outer joins, and aggregation allowed. Users can easily define their own functions and implement their own versions of operators: extractors (parsing and constructing rows from a file), processors (row-wise processing), reducers (group-wise processing), and combiners (combining rows from two inputs). SCOPE supports nesting of expressions but also allows a computation to be specified as a series of steps, in a manner often preferred by programmers. We also describe how scripts are compiled into efficient, parallel execution plans and executed on large clusters. 1.

We describe PNUTS, a massively parallel and geographically distributed database system for Yahoo!’s web applications. PNUTS provides data storage organized as hashed or ordered tables, low latency for large numbers of concurrent requests including updates and queries, and novel per-record consistency guarantees. It is a hosted, centrally managed, and geographically distributed service, and utilizes automated load-balancing and failover to reduce operational complexity. The first version of the system is currently serving in production. We describe the motivation for PNUTS and the design and implementation of its table storage and replication layers, and then present experimental results.

While the use of MapReduce systems (such as Hadoop) for large scale data analysis has been widely recognized and studied, we have recently seen an explosion in the number of systems developed for cloud data serving. These newer systems address “cloud OLTP ” applications, though they typically do not support ACID transactions. Examples of systems proposed for cloud serving use include BigTable, PNUTS, Cassandra, HBase, Azure, CouchDB, SimpleDB, Voldemort, and many others. Further, they are being applied to a diverse range of applications that differ considerably from traditional (e.g., TPC-C like) serving workloads. The number of emerging cloud serving systems and the wide range of proposed applications, coupled with a lack of applesto-apples performance comparisons, makes it difficult to understand the tradeoffs between systems and the workloads for which they are suited. We present the Yahoo! Cloud Serving Benchmark (YCSB) framework, with the goal of facilitating performance comparisons of the new generation of cloud data serving systems. We define a core set of benchmarks and report results for four widely used systems: Cassandra, HBase, Yahoo!’s PNUTS, and a simple sharded MySQL implementation. We also hope to foster the development of additional cloud benchmark suites that represent other classes of applications by making our benchmark tool available via open source. In this regard, a key feature of the YCSB framework/tool is that it is extensible—it supports easy definition of new workloads, in addition to making it easy to benchmark new systems.

Cloud storage solutions promise high scalability and low cost. Existing solutions, however, differ in the degree of consistency they provide. Our experience using such systems indicates that there is a non-trivial trade-off between cost, consistency and availability. High consistency implies high cost per transaction and, in some situations, reduced availability. Low consistency is cheaper but it might result in higher operational cost because of, e.g., overselling of products in a Web shop. In this paper, we present a new transaction paradigm, that not only allows designers to define the consistency guarantees on the data instead at the transaction level, but also allows to automatically switch consistency guarantees at runtime. We present a number of techniques that let the system dynamically adapt the consistency level by monitoring the data and/or gathering temporal statistics of the data. We demonstrate the feasibility and potential of the ideas through extensive experiments on a first prototype implemented on Amazon’s S3 and running the TPC-W benchmark. Our experiments indicate that the adaptive strategies presented in the paper result in a significant reduction in response time and costs including the cost penalties of inconsistencies. 1.

We study the problem of bulk-inserting records into tables in a system that horizontally range-partitions data over a large cluster of shared-nothing machines. Each table partition contains a contiguous portion of the table’s key range, and must accept all records inserted into that range. Examples of such systems include BigTable [8] at Google, and PNUTS [15] at Yahoo! During bulk inserts into an existing table, if most of the inserted records end up going into a small number of data partitions, the obtained throughput may be very poor due to ineffective use of cluster parallelism. We propose a novel approach in which a planning phase is invoked before the actual insertions. By creating new partitions and intelligently distributing partitions across machines, the planning phase ensures that the insertion load will be well-balanced. Since there is a tradeoff between the cost of moving partitions and the resulting throughput gain, the planning phase must minimize the sum of partition movement time and insertion time. We show that this problem is a variation of NP-hard bin-packing, reduce it to a problem of packing vectors, and then give a solution with provable approximation guarantees. We evaluate our approach on a prototype system deployed on a cluster of 50 machines, and show that it yields significant improvements over more naïve techniques.

Though database technology has been remarkably successful in building a large market and adapting to the changes of the last three decades, its impact on the broader market of information management is surprisingly limited. If we were to design an information management system from scratch, based upon today’s requirements and hardware capabilities, would it look anything like today’s database systems? In this paper, we introduce Impliance, a next-generation information management system consisting of hardware and software components integrated to form an easy-to-administer appliance that can store, retrieve, and analyze all types of structured, semi-structured, and unstructured information. We first summarize the trends that will shape information management for the foreseeable future. Those trends imply three major requirements for Impliance: (1) to be able to store, manage, and uniformly query and transform all data, not just structured records; (2) to be able to scale out as the volume of this data grows; and (3) to be simple and robust in operation. We then describe four key ideas that are uniquely combined in Impliance to address these requirements, namely the ideas of: (a) integrating software and off-the-shelf hardware into a generic information appliance; (b) automatically discovering, organizing, and managing all data – unstructured as well as structured – in a uniform way; (c) achieving scale-out by exploiting simple, massive parallel processing, and (d) virtualizing compute and storage resources to unify, simplify, and streamline the management of Impliance. Impliance is an ambitious, long-term effort to define simpler, more robust, and more scalable information systems for tomorrow’s enterprises. 1.

Database systems serving cloud platforms must serve large numbers of applications (or tenants). In addition to managing tenants with small data footprints, different schemas, and variable load patterns, such multitenant data platforms must minimize their operating costs by efficient resource sharing. When deployed over a pay-per-use infrastructure, elastic scaling and load balancing, enabled by low cost live migration of tenant databases, is critical to tolerate load variations while minimizing operating cost. However, existing databases—relational databases and Key-Value stores alike—lack low cost live migration techniques, thus resulting in heavy performance impact during elastic scaling. We present Albatross, a technique for live migration in a multitenant database serving OLTP style workloads where the persistent database image is stored in a network attached storage. Albatross migrates the database cache and the state of active transactions to ensure minimal impact on transaction execution while allowing transactions active during migration to continue execution. It also guarantees serializability while ensuring correctness during failures. Our evaluation using two OLTP benchmarks shows that Albatross can migrate a live tenant database with no aborted transactions, negligible impact on transaction latency and throughput both during and after migration, and an unavailability window as low as 300 ms. 1.

Abstract — Requirements from new types of applications call for new database system solutions. Computational science applications performing distributed computations on Grid networks with requirements for efficient storage and query solutions are now emerging. For this purpose we have developed DASCOSA-DB, a P2P-based distributed database system, which in addition to providing location-transparent storage and querying, also includes novel features like efficient partial restart of queries and redistribution of query operators in the context of failure, dynamic refragmentation and allocation, and distributed semantic caching. In this demo, the novel features will be demonstrated, combined with a more general description of the architecture and demonstration of the distributed query processing capabilities. I.

We describe our early experience building and optimizing GOOG-411, a fully automated, voice-enabled, business finder. We show how taking an iterative approach to system development allows us to optimize the various components of the system, thereby progressively improving user-facing metrics. We show the contributions of different data sources to recognition accuracy. For business listing language models, we see a nearly linear performance increase with the logarithm of the amount of training data. To date, we have improved our correct accept rate by 25 % absolute, and increased our transfer rate by 35 % absolute. Index Terms — business finder, directory assistance, voice search, speech recognition. 1.