3,335 research outputs found
From Cooperative Scans to Predictive Buffer Management
In analytical applications, database systems often need to sustain workloads
with multiple concurrent scans hitting the same table. The Cooperative Scans
(CScans) framework, which introduces an Active Buffer Manager (ABM) component
into the database architecture, has been the most effective and elaborate
response to this problem, and was initially developed in the X100 research
prototype. We now report on the the experiences of integrating Cooperative
Scans into its industrial-strength successor, the Vectorwise database product.
During this implementation we invented a simpler optimization of concurrent
scan buffer management, called Predictive Buffer Management (PBM). PBM is based
on the observation that in a workload with long-running scans, the buffer
manager has quite a bit of information on the workload in the immediate future,
such that an approximation of the ideal OPT algorithm becomes feasible. In the
evaluation on both synthetic benchmarks as well as a TPC-H throughput run we
compare the benefits of naive buffer management (LRU) versus CScans, PBM and
OPT; showing that PBM achieves benefits close to Cooperative Scans, while
incurring much lower architectural impact.Comment: VLDB201
Optimizing I/O for Big Array Analytics
Big array analytics is becoming indispensable in answering important
scientific and business questions. Most analysis tasks consist of multiple
steps, each making one or multiple passes over the arrays to be analyzed and
generating intermediate results. In the big data setting, I/O optimization is a
key to efficient analytics. In this paper, we develop a framework and
techniques for capturing a broad range of analysis tasks expressible in
nested-loop forms, representing them in a declarative way, and optimizing their
I/O by identifying sharing opportunities. Experiment results show that our
optimizer is capable of finding execution plans that exploit nontrivial I/O
sharing opportunities with significant savings.Comment: VLDB201
MODIS information, data and control system (MIDACS) operations concepts
The MODIS Information, Data, and Control System (MIDACS) Operations Concepts Document provides a basis for the mutual understanding between the users and the designers of the MIDACS, including the requirements, operating environment, external interfaces, and development plan. In defining the concepts and scope of the system, how the MIDACS will operate as an element of the Earth Observing System (EOS) within the EosDIS environment is described. This version follows an earlier release of a preliminary draft version. The individual operations concepts for planning and scheduling, control and monitoring, data acquisition and processing, calibration and validation, data archive and distribution, and user access do not yet fully represent the requirements of the data system needed to achieve the scientific objectives of the MODIS instruments and science teams. The teams are not yet formed; however, it is possible to develop the operations concepts based on the present concept of EosDIS, the level 1 and level 2 Functional Requirements Documents, and through interviews and meetings with key members of the scientific community. The operations concepts were exercised through the application of representative scenarios
Pipelining in multi-query optimization
AbstractDatabase systems frequently have to execute a set of related queries, which share several common subexpressions. Multi-query optimization exploits this, by finding evaluation plans that share common results. Current approaches to multi-query optimization assume that common subexpressions are materialized. Significant performance benefits can be had if common subexpressions are pipelined to their uses, without being materialized. However, plans with pipelining may not always be realizable with limited buffer space, as we show. We present a general model for schedules with pipelining, and present a necessary and sufficient condition for determining validity of a schedule under our model. We show that finding a valid schedule with minimum cost is NP-hard. We present a greedy heuristic for finding good schedules. Finally, we present a performance study that shows the benefit of our algorithms on batches of queries from the TPCD benchmark
Forecasting the cost of processing multi-join queries via hashing for main-memory databases (Extended version)
Database management systems (DBMSs) carefully optimize complex multi-join
queries to avoid expensive disk I/O. As servers today feature tens or hundreds
of gigabytes of RAM, a significant fraction of many analytic databases becomes
memory-resident. Even after careful tuning for an in-memory environment, a
linear disk I/O model such as the one implemented in PostgreSQL may make query
response time predictions that are up to 2X slower than the optimal multi-join
query plan over memory-resident data. This paper introduces a memory I/O cost
model to identify good evaluation strategies for complex query plans with
multiple hash-based equi-joins over memory-resident data. The proposed cost
model is carefully validated for accuracy using three different systems,
including an Amazon EC2 instance, to control for hardware-specific differences.
Prior work in parallel query evaluation has advocated right-deep and bushy
trees for multi-join queries due to their greater parallelization and
pipelining potential. A surprising finding is that the conventional wisdom from
shared-nothing disk-based systems does not directly apply to the modern
shared-everything memory hierarchy. As corroborated by our model, the
performance gap between the optimal left-deep and right-deep query plan can
grow to about 10X as the number of joins in the query increases.Comment: 15 pages, 8 figures, extended version of the paper to appear in
SoCC'1
Geoprocessing Optimization in Grids
Geoprocessing is commonly used in solving problems across disciplines which feature geospatial data and/or phenomena. Geoprocessing requires specialized algorithms and more recently, due to large volumes of geospatial databases and complex geoprocessing operations, it has become data- and/or compute-intensive. The conventional approach, which is predominately based on centralized computing solutions, is unable to handle geoprocessing efficiently. To that end, there is a need for developing distributed geoprocessing solutions by taking advantage of existing and emerging advanced techniques and high-performance computing and communications resources. As an emerging new computing paradigm, grid computing offers a novel approach for integrating distributed computing resources and supporting collaboration across networks, making it suitable for geoprocessing. Although there have been research efforts applying grid computing in the geospatial domain, there is currently a void in the literature for a general geoprocessing optimization. In this research, a new optimization technique for geoprocessing in grid systems, Geoprocessing Optimization in Grids (GOG), is designed and developed. The objective of GOG is to reduce overall response time with a reasonable cost. To meet this objective, GOG contains a set of algorithms, including a resource selection algorithm and a parallelism processing algorithm, to speed up query execution. GOG is validated by comparing its optimization time and estimated costs of generated execution plans with two existing optimization techniques. A proof of concept based on an application in air quality control is developed to demonstrate the advantages of GOG
A Survey on the Evolution of Stream Processing Systems
Stream processing has been an active research field for more than 20 years,
but it is now witnessing its prime time due to recent successful efforts by the
research community and numerous worldwide open-source communities. This survey
provides a comprehensive overview of fundamental aspects of stream processing
systems and their evolution in the functional areas of out-of-order data
management, state management, fault tolerance, high availability, load
management, elasticity, and reconfiguration. We review noteworthy past research
findings, outline the similarities and differences between early ('00-'10) and
modern ('11-'18) streaming systems, and discuss recent trends and open
problems.Comment: 34 pages, 15 figures, 5 table
Rank-aware, Approximate Query Processing on the Semantic Web
Search over the Semantic Web corpus frequently leads to queries having large result sets. So, in order to discover relevant data elements, users must rely on ranking techniques to sort results according to their relevance. At the same time, applications oftentimes deal with information needs, which do not require complete and exact results. In this thesis, we face the problem of how to process queries over Web data in an approximate and rank-aware fashion
Automatic Parallelization of Database Queries
Although automatic parallelization of conventional language programs is now widely accepted, relatively little emphasis has been placed on automatic parallelization of database query programs (sometimes referred to as “multiple queries” ). In this paper, we discuss the unique problems associated with automatic parallelization of database programs. From this discussion, we derive a complete approach to automatic parallelization of database programs. Beside integrating a number of existing techniques, our approach relies heavily on several new concepts, including the concepts of “algorithm-level” analysis and hybrid static/dynamic scheduling
The Family of MapReduce and Large Scale Data Processing Systems
In the last two decades, the continuous increase of computational power has
produced an overwhelming flow of data which has called for a paradigm shift in
the computing architecture and large scale data processing mechanisms.
MapReduce is a simple and powerful programming model that enables easy
development of scalable parallel applications to process vast amounts of data
on large clusters of commodity machines. It isolates the application from the
details of running a distributed program such as issues on data distribution,
scheduling and fault tolerance. However, the original implementation of the
MapReduce framework had some limitations that have been tackled by many
research efforts in several followup works after its introduction. This article
provides a comprehensive survey for a family of approaches and mechanisms of
large scale data processing mechanisms that have been implemented based on the
original idea of the MapReduce framework and are currently gaining a lot of
momentum in both research and industrial communities. We also cover a set of
introduced systems that have been implemented to provide declarative
programming interfaces on top of the MapReduce framework. In addition, we
review several large scale data processing systems that resemble some of the
ideas of the MapReduce framework for different purposes and application
scenarios. Finally, we discuss some of the future research directions for
implementing the next generation of MapReduce-like solutions.Comment: arXiv admin note: text overlap with arXiv:1105.4252 by other author
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