13,773 research outputs found
Pregelix: Big(ger) Graph Analytics on A Dataflow Engine
There is a growing need for distributed graph processing systems that are
capable of gracefully scaling to very large graph datasets. Unfortunately, this
challenge has not been easily met due to the intense memory pressure imposed by
process-centric, message passing designs that many graph processing systems
follow. Pregelix is a new open source distributed graph processing system that
is based on an iterative dataflow design that is better tuned to handle both
in-memory and out-of-core workloads. As such, Pregelix offers improved
performance characteristics and scaling properties over current open source
systems (e.g., we have seen up to 15x speedup compared to Apache Giraph and up
to 35x speedup compared to distributed GraphLab), and makes more effective use
of available machine resources to support Big(ger) Graph Analytics
Massively Parallel Sort-Merge Joins in Main Memory Multi-Core Database Systems
Two emerging hardware trends will dominate the database system technology in
the near future: increasing main memory capacities of several TB per server and
massively parallel multi-core processing. Many algorithmic and control
techniques in current database technology were devised for disk-based systems
where I/O dominated the performance. In this work we take a new look at the
well-known sort-merge join which, so far, has not been in the focus of research
in scalable massively parallel multi-core data processing as it was deemed
inferior to hash joins. We devise a suite of new massively parallel sort-merge
(MPSM) join algorithms that are based on partial partition-based sorting.
Contrary to classical sort-merge joins, our MPSM algorithms do not rely on a
hard to parallelize final merge step to create one complete sort order. Rather
they work on the independently created runs in parallel. This way our MPSM
algorithms are NUMA-affine as all the sorting is carried out on local memory
partitions. An extensive experimental evaluation on a modern 32-core machine
with one TB of main memory proves the competitive performance of MPSM on large
main memory databases with billions of objects. It scales (almost) linearly in
the number of employed cores and clearly outperforms competing hash join
proposals - in particular it outperforms the "cutting-edge" Vectorwise parallel
query engine by a factor of four.Comment: VLDB201
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
Partout: A Distributed Engine for Efficient RDF Processing
The increasing interest in Semantic Web technologies has led not only to a
rapid growth of semantic data on the Web but also to an increasing number of
backend applications with already more than a trillion triples in some cases.
Confronted with such huge amounts of data and the future growth, existing
state-of-the-art systems for storing RDF and processing SPARQL queries are no
longer sufficient. In this paper, we introduce Partout, a distributed engine
for efficient RDF processing in a cluster of machines. We propose an effective
approach for fragmenting RDF data sets based on a query log, allocating the
fragments to nodes in a cluster, and finding the optimal configuration. Partout
can efficiently handle updates and its query optimizer produces efficient query
execution plans for ad-hoc SPARQL queries. Our experiments show the superiority
of our approach to state-of-the-art approaches for partitioning and distributed
SPARQL query processing
The End of Slow Networks: It's Time for a Redesign
Next generation high-performance RDMA-capable networks will require a
fundamental rethinking of the design and architecture of modern distributed
DBMSs. These systems are commonly designed and optimized under the assumption
that the network is the bottleneck: the network is slow and "thin", and thus
needs to be avoided as much as possible. Yet this assumption no longer holds
true. With InfiniBand FDR 4x, the bandwidth available to transfer data across
network is in the same ballpark as the bandwidth of one memory channel, and it
increases even further with the most recent EDR standard. Moreover, with the
increasing advances of RDMA, the latency improves similarly fast. In this
paper, we first argue that the "old" distributed database design is not capable
of taking full advantage of the network. Second, we propose architectural
redesigns for OLTP, OLAP and advanced analytical frameworks to take better
advantage of the improved bandwidth, latency and RDMA capabilities. Finally,
for each of the workload categories, we show that remarkable performance
improvements can be achieved
- …