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26
SkewTune: Mitigating Skew in MapReduce Applications
, 2012
"... We present an automatic skew mitigation approach for userdefined MapReduce programs and present SkewTune, a system that implements this approach as a dropin replacement for an existing MapReduce implementation. There are three key challenges: (a) require no extra input from the user yet work for al ..."
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Cited by 44 (6 self)
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We present an automatic skew mitigation approach for userdefined MapReduce programs and present SkewTune, a system that implements this approach as a dropin replacement for an existing MapReduce implementation. There are three key challenges: (a) require no extra input from the user yet work for all MapReduce applications, (b) be completely transparent, and (c) impose minimal overhead if there is no skew. The SkewTune approach addresses these challenges and works as follows: When a node in the cluster becomes idle, SkewTune identifies the task with the greatest expected remaining processing time. The unprocessed input data of this straggling task is then proactively repartitioned in a way that fully utilizes the nodes in the cluster and preserves the ordering of the input data so that the original output can be reconstructed by concatenation. We implement SkewTune as an extension to Hadoop and evaluate its effectiveness using several real applications. The results show that SkewTune can significantly reduce job runtime in the presence of skew and adds little to no overhead in the absence of skew.
Communication steps for parallel query processing.
 In Proceedings of the 32nd ACM Symposium on Principles of Database Systems, PODS,
, 2013
"... ABSTRACT We consider the problem of computing a relational query q on a large input database of size n, using a large number p of servers. The computation is performed in rounds, and each server can receive only O(n/p 1−ε ) bits of data, where ε ∈ [0, 1] is a parameter that controls replication. We ..."
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Cited by 22 (4 self)
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ABSTRACT We consider the problem of computing a relational query q on a large input database of size n, using a large number p of servers. The computation is performed in rounds, and each server can receive only O(n/p 1−ε ) bits of data, where ε ∈ [0, 1] is a parameter that controls replication. We examine how many global communication steps are needed to compute q. We establish both lower and upper bounds, in two settings. For a single round of communication, we give lower bounds in the strongest possible model, where arbitrary bits may be exchanged; we show that any algorithm requires ε ≥ 1−1/τ * , where τ * is the fractional vertex cover of the hypergraph of q. We also give an algorithm that matches the lower bound for a specific class of databases. For multiple rounds of communication, we present lower bounds in a model where routing decisions for a tuple are tuplebased. We show that for the class of treelike queries there exists a tradeoff between the number of rounds and the space exponent ε. The lower bounds for multiple rounds are the first of their kind. Our results also imply that transitive closure cannot be computed in O(1) rounds of communication.
Upper and Lower Bounds on the Cost of a MapReduce Computation
, 2013
"... In this paper we study the tradeoff between parallelism and communication cost in a mapreduce computation. For any problem that is not “embarrassingly parallel,” the finer we partition the work of the reducers so that more parallelism can be extracted, the greater will be the total communication be ..."
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Cited by 20 (1 self)
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In this paper we study the tradeoff between parallelism and communication cost in a mapreduce computation. For any problem that is not “embarrassingly parallel,” the finer we partition the work of the reducers so that more parallelism can be extracted, the greater will be the total communication between mappers and reducers. We introduce a model of problems that can be solved in a single round of mapreduce computation. This model enables a generic recipe for discovering lower bounds on communication cost as a function of the maximum number of inputs that can be assigned to one reducer. We use the model to analyze the tradeoff for three problems: finding pairs of strings at Hamming distance d, finding triangles and other patterns in a larger graph, and matrix multiplication. For finding strings of Hamming distance 1, we have upper and lower bounds that match exactly. For triangles and many other graphs, we have upper and lower bounds that are the same to within a constant factor. For the problem of matrix multiplication, we have matching upper and lower bounds for oneround mapreduce algorithms. We are also able to explore tworound mapreduce algorithms for matrix multiplication and show that these never have more communication, for a given reducer size, than the best oneround algorithm, and often have significantly less.
Distributed Data Management Using MapReduce
, 2013
"... MapReduce is a framework for processing and managing large scale data sets in a distributed cluster, which has been used for applications such as generating search indexes, document clustering, access log analysis, and various other forms of data analytics. MapReduce adopts a flexible computation mo ..."
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Cited by 19 (7 self)
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MapReduce is a framework for processing and managing large scale data sets in a distributed cluster, which has been used for applications such as generating search indexes, document clustering, access log analysis, and various other forms of data analytics. MapReduce adopts a flexible computation model with a simple interface consisting of map and reduce functions whose implementations can be customized by application developers. Since its introduction, a substantial amount of research efforts have been directed towards making it more usable and efficient for supporting databasecentric operations. In this paper we aim to provide a comprehensive review of a wide range of proposals and systems that focusing fundamentally on the support of distributed data management and processing using the MapReduce framework.
Minimal MapReduce algorithms
, 2013
"... MapReduce has become a dominant parallel computing paradigm for big data, i.e., colossal datasets at the scale of terabytes or higher. Ideally, a MapReduce system should achieve a high degree of load balancing among the participating machines, and minimize the space usage, CPU and I/O time, and netw ..."
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Cited by 16 (1 self)
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MapReduce has become a dominant parallel computing paradigm for big data, i.e., colossal datasets at the scale of terabytes or higher. Ideally, a MapReduce system should achieve a high degree of load balancing among the participating machines, and minimize the space usage, CPU and I/O time, and network transfer at each machine. Although these principles have guided the development of MapReduce algorithms, limited emphasis has been placed on enforcing serious constraints on the aforementioned metrics simultaneously. This paper presents the notion of minimal algorithm, that is, an algorithm that guarantees the best parallelization in multiple aspects at the same time, up to a small constant factor. We show the existence of elegant minimal algorithms for a set of fundamental database problems, and demonstrate their excellent performance with extensive experiments.
Optimizing Graph Algorithms on Pregellike Systems
, 2014
"... We study the problem of implementing graph algorithms efficiently on Pregellike systems, which can be surprisingly challenging. Standard graph algorithms in this setting can incur unnecessary inefficiencies such as slow convergence or high communication or computation cost, typically due to structu ..."
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Cited by 10 (2 self)
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We study the problem of implementing graph algorithms efficiently on Pregellike systems, which can be surprisingly challenging. Standard graph algorithms in this setting can incur unnecessary inefficiencies such as slow convergence or high communication or computation cost, typically due to structural properties of the input graphs such as large diameters or skew in component sizes. We describe several optimization techniques to address these inefficiencies. Our most general technique is based on the idea of performing some serial computation on a tiny fraction of the input graph, complementing Pregel’s vertexcentric parallelism. We base our study on thorough implementations of several fundamental graph algorithms, some of which have, to the best of our knowledge, not been implemented on Pregellike systems before. The algorithms and optimizations we describe are fully implemented in our opensource Pregel implementation. We present detailed experiments showing that our optimization techniques improve runtime significantly on a variety of very large graph datasets.
Parallel Skyline Queries
"... In this paper, we design and analyze parallel algorithms for skyline queries. The skyline of a multidimensional set consists of the points for which no other point exists that is at least as good along every dimension. As a framework for parallel computation, we use both the MP model proposed in (Ko ..."
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Cited by 9 (0 self)
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In this paper, we design and analyze parallel algorithms for skyline queries. The skyline of a multidimensional set consists of the points for which no other point exists that is at least as good along every dimension. As a framework for parallel computation, we use both the MP model proposed in (Koutris and Suciu, PODS 2011), which requires that the data is perfectly loadbalanced, and a variation of the model in (Afrati and Ullman, EDBT 2010), the GMP model, which demands weaker load balancing constraints. In addition to load balancing, we want to minimize the number of blocking steps, where all processors must wait and synchronize. We propose a 2step algorithm in the MP model for any dimension of the dataset, as well a 1step algorithm for the case of 2 and 3 dimensions. Moreover, we present a 1step algorithm in the GMP model for any number of dimensions.
On factorisation of provenance polynomials
 In TaPP
, 2011
"... Tracking and managing provenance information in databases has applications in incomplete information and probabilistic databases, query evaluation under bag semantics, view maintenance and update, debugging and ..."
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Cited by 9 (5 self)
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Tracking and managing provenance information in databases has applications in incomplete information and probabilistic databases, query evaluation under bag semantics, view maintenance and update, debugging and
When Distributed Computation is Communication Expensive
"... We consider a number of fundamental statistical and graph problems in the messagepassing model, where we have k machines (sites), each holding a piece of data, and the machines want to jointly solve a problem defined on the union of the k data sets. The communication is pointtopoint, and the goal ..."
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Cited by 9 (4 self)
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We consider a number of fundamental statistical and graph problems in the messagepassing model, where we have k machines (sites), each holding a piece of data, and the machines want to jointly solve a problem defined on the union of the k data sets. The communication is pointtopoint, and the goal is to minimize the total communication among the k machines. This model captures all pointtopoint distributed computational models with respect to minimizing communication costs. Our analysis shows that exact computation of many statistical and graph problems in this distributed setting requires a prohibitively large amount of communication, and often one cannot improve upon the communication of the simple protocol in which all machines send their data to a centralized server. Thus, in order to obtain protocols that are communicationefficient, one has to allow approximation, or investigate the distribution or layout of the data sets. 1
Making queries tractable on big data with preprocessing
 In VLDB
, 2013
"... A query class is traditionally considered tractable if there exists a polynomialtime (PTIME) algorithm to answer its queries. When it comes to big data, however, PTIME algorithms often become infeasible in practice. A traditional and effective approach to coping with this is to preprocess data off ..."
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Cited by 7 (3 self)
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A query class is traditionally considered tractable if there exists a polynomialtime (PTIME) algorithm to answer its queries. When it comes to big data, however, PTIME algorithms often become infeasible in practice. A traditional and effective approach to coping with this is to preprocess data offline, so that queries in the class can be subsequently evaluated on the data efficiently. This paper aims to provide a formal foundation for this approach in terms of computational complexity. (1) We propose a set of Πtractable queries, denoted by ΠT0Q, to characterize classes of queries that can be answered in parallel polylogarithmic time (NC) after PTIME preprocessing. (2) We show that several natural query classes are Πtractable and are feasible on big data. (3) We also study a set ΠTQ of query classes that can be effectively converted to Πtractable queries by refactorizing its data and queries for preprocessing. We introduce a form of NC reductions to characterize such conversions. (4) We show that a natural query class is complete for ΠTQ. (5) We also show that ΠT0Q ⊂ P unless P = NC, i.e., the set ΠT0Q of all Πtractable queries is properly contained in the set P of all PTIME queries. Nonetheless, ΠTQ = P, i.e., all PTIME query classes can be made Πtractable via proper refactorizations. This work is a step towards understanding the tractability of queries in the context of big data. 1.