Abstract. Applications with very large databases, where data items are continuously appended, are becoming more and more common. Thus, the development of efficient workload-based data partitioning is one of the main requirements to offer good performance to most of those applications that have complex access patterns, e.g. scientific applications. However, the existing workload-based approaches, which are executed in a static way, cannot be applied to very large databases. In this paper, we propose DynPart, a dynamic partitioning algorithm for continuously growing databases. DynPart efficiently adapts the data partitioning to the arrival of new data elements by taking into account the affinity of new data with queries and fragments. In contrast to existing static approaches, our approach offers a constant execution time, no matter the size of the database, while obtaining very good partitioning efficiency. We validated our solution through experimentation over real-world data; the results show its effectiveness.
Abstract. Applications with very large databases, where data items are continuously appended, are becoming more and more common. Thus, the development of efficient data partitioning is one of the main requirements to yield good performance. In the case of applications that have complex access patterns, e.g. scientific applications, workload-based partitioning could be exploited. However, existing workload-based approaches, which work in a static way, cannot be applied to very large databases. In this paper, we propose DynPart and DynPartGroup, two dynamic partitioning algorithms for continuously growing databases. These algorithms efficiently adapt the data partitioning to the arrival of new data elements by taking into account the affinity of new data with queries and fragments. In contrast to existing static approaches, our approach offers constant execution time, no matter the size of the database, while obtaining very good partitioning efficiency. We validated our solution through experimentation over real-world data; the results show its effectiveness.
Nowadyas, we are witnessing the fast production of very large amount of data, particularly by the users of online systems on the Web. However, processing this big data is very challenging since both space and computational requirements are hard to satisfy. One solution for dealing with such requirements is to take advantage of parallel frameworks, such as MapReduce or Spark, that allow to make powerful computing and storage units on top of ordinary machines. Although these key-based frameworks have been praised for their high scalability and fault tolerance, they show poor performance in the case of data skew. There are important cases where a high percentage of processing in the reduce side ends up being done by only one node.In this paper, we present FP-Hadoop, a Hadoop-based system that renders the reduce side of MapReduce more parallel by efficiently tackling the problem of reduce data skew. FP-Hadoop introduces a new phase, denoted intermediate reduce (IR), where blocks of intermediate values are processed by intermediate reduce workers in parallel. With this approach, even when all intermediate values are associated to the same key, the main part of the reducing work can be performed in parallel taking benefit of the computing power of all available workers.We implemented a prototype of FP-Hadoop, and conducted extensive experiments over synthetic and real datasets. We achieved excellent performance gains compared to native Hadoop, e.g. more than 10 times in reduce time and 5 times in total execution time.
Abstract. Although MapReduce has been praised for its high scalability and fault tolerance, it has been criticized in some points, in particular, its poor performance in the case of data skew. There are important cases where a high percentage of processing in the reduce side is done by a few nodes, or even one node, while the others remain idle. There have been some attempts to address the problem of data skew, but only for specific cases. In particular, there is no proposed solution for the cases where most of the intermediate values correspond to a single key, or when the number of keys is less than the number of reduce workers. In this paper, we propose FP-Hadoop, a system that makes the reduce side of MapReduce more parallel, and efficiently deals with the problem of data skew in the reduce side. In FP-Hadoop, there is a new phase, called intermediate reduce (IR), in which blocks of intermediate values, constructed dynamically, are processed by intermediate reduce workers in parallel, by using a scheduling strategy. By using the IR phase, even if all intermediate values belong to only one key, the main part of the reducing work can be done in parallel by using the computing resources of all available workers. We implemented a prototype of FP-Hadoop, and conducted extensive experiments over synthetic and real datasets. We achieved excellent performance gains compared to native Hadoop, e.g. more than 10 times in reduce time and 5 times in total execution time.
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