1,015 research outputs found
LogBase: A Scalable Log-structured Database System in the Cloud
Numerous applications such as financial transactions (e.g., stock trading)
are write-heavy in nature. The shift from reads to writes in web applications
has also been accelerating in recent years. Write-ahead-logging is a common
approach for providing recovery capability while improving performance in most
storage systems. However, the separation of log and application data incurs
write overheads observed in write-heavy environments and hence adversely
affects the write throughput and recovery time in the system. In this paper, we
introduce LogBase - a scalable log-structured database system that adopts
log-only storage for removing the write bottleneck and supporting fast system
recovery. LogBase is designed to be dynamically deployed on commodity clusters
to take advantage of elastic scaling property of cloud environments. LogBase
provides in-memory multiversion indexes for supporting efficient access to data
maintained in the log. LogBase also supports transactions that bundle read and
write operations spanning across multiple records. We implemented the proposed
system and compared it with HBase and a disk-based log-structured
record-oriented system modeled after RAMCloud. The experimental results show
that LogBase is able to provide sustained write throughput, efficient data
access out of the cache, and effective system recovery.Comment: VLDB201
DualTable: A Hybrid Storage Model for Update Optimization in Hive
Hive is the most mature and prevalent data warehouse tool providing SQL-like
interface in the Hadoop ecosystem. It is successfully used in many Internet
companies and shows its value for big data processing in traditional
industries. However, enterprise big data processing systems as in Smart Grid
applications usually require complicated business logics and involve many data
manipulation operations like updates and deletes. Hive cannot offer sufficient
support for these while preserving high query performance. Hive using the
Hadoop Distributed File System (HDFS) for storage cannot implement data
manipulation efficiently and Hive on HBase suffers from poor query performance
even though it can support faster data manipulation.There is a project based on
Hive issue Hive-5317 to support update operations, but it has not been finished
in Hive's latest version. Since this ACID compliant extension adopts same data
storage format on HDFS, the update performance problem is not solved.
In this paper, we propose a hybrid storage model called DualTable, which
combines the efficient streaming reads of HDFS and the random write capability
of HBase. Hive on DualTable provides better data manipulation support and
preserves query performance at the same time. Experiments on a TPC-H data set
and on a real smart grid data set show that Hive on DualTable is up to 10 times
faster than Hive when executing update and delete operations.Comment: accepted by industry session of ICDE201
BigDataBench: a Big Data Benchmark Suite from Internet Services
As architecture, systems, and data management communities pay greater
attention to innovative big data systems and architectures, the pressure of
benchmarking and evaluating these systems rises. Considering the broad use of
big data systems, big data benchmarks must include diversity of data and
workloads. Most of the state-of-the-art big data benchmarking efforts target
evaluating specific types of applications or system software stacks, and hence
they are not qualified for serving the purposes mentioned above. This paper
presents our joint research efforts on this issue with several industrial
partners. Our big data benchmark suite BigDataBench not only covers broad
application scenarios, but also includes diverse and representative data sets.
BigDataBench is publicly available from http://prof.ict.ac.cn/BigDataBench .
Also, we comprehensively characterize 19 big data workloads included in
BigDataBench with varying data inputs. On a typical state-of-practice
processor, Intel Xeon E5645, we have the following observations: First, in
comparison with the traditional benchmarks: including PARSEC, HPCC, and
SPECCPU, big data applications have very low operation intensity; Second, the
volume of data input has non-negligible impact on micro-architecture
characteristics, which may impose challenges for simulation-based big data
architecture research; Last but not least, corroborating the observations in
CloudSuite and DCBench (which use smaller data inputs), we find that the
numbers of L1 instruction cache misses per 1000 instructions of the big data
applications are higher than in the traditional benchmarks; also, we find that
L3 caches are effective for the big data applications, corroborating the
observation in DCBench.Comment: 12 pages, 6 figures, The 20th IEEE International Symposium On High
Performance Computer Architecture (HPCA-2014), February 15-19, 2014, Orlando,
Florida, US
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