3,282 research outputs found
Stocator: A High Performance Object Store Connector for Spark
We present Stocator, a high performance object store connector for Apache
Spark, that takes advantage of object store semantics. Previous connectors have
assumed file system semantics, in particular, achieving fault tolerance and
allowing speculative execution by creating temporary files to avoid
interference between worker threads executing the same task and then renaming
these files. Rename is not a native object store operation; not only is it not
atomic, but it is implemented using a costly copy operation and a delete.
Instead our connector leverages the inherent atomicity of object creation, and
by avoiding the rename paradigm it greatly decreases the number of operations
on the object store as well as enabling a much simpler approach to dealing with
the eventually consistent semantics typical of object stores. We have
implemented Stocator and shared it in open source. Performance testing shows
that it is as much as 18 times faster for write intensive workloads and
performs as much as 30 times fewer operations on the object store than the
legacy Hadoop connectors, reducing costs both for the client and the object
storage service provider
Storage Solutions for Big Data Systems: A Qualitative Study and Comparison
Big data systems development is full of challenges in view of the variety of
application areas and domains that this technology promises to serve.
Typically, fundamental design decisions involved in big data systems design
include choosing appropriate storage and computing infrastructures. In this age
of heterogeneous systems that integrate different technologies for optimized
solution to a specific real world problem, big data system are not an exception
to any such rule. As far as the storage aspect of any big data system is
concerned, the primary facet in this regard is a storage infrastructure and
NoSQL seems to be the right technology that fulfills its requirements. However,
every big data application has variable data characteristics and thus, the
corresponding data fits into a different data model. This paper presents
feature and use case analysis and comparison of the four main data models
namely document oriented, key value, graph and wide column. Moreover, a feature
analysis of 80 NoSQL solutions has been provided, elaborating on the criteria
and points that a developer must consider while making a possible choice.
Typically, big data storage needs to communicate with the execution engine and
other processing and visualization technologies to create a comprehensive
solution. This brings forth second facet of big data storage, big data file
formats, into picture. The second half of the research paper compares the
advantages, shortcomings and possible use cases of available big data file
formats for Hadoop, which is the foundation for most big data computing
technologies. Decentralized storage and blockchain are seen as the next
generation of big data storage and its challenges and future prospects have
also been discussed
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
ARM Wrestling with Big Data: A Study of Commodity ARM64 Server for Big Data Workloads
ARM processors have dominated the mobile device market in the last decade due
to their favorable computing to energy ratio. In this age of Cloud data centers
and Big Data analytics, the focus is increasingly on power efficient
processing, rather than just high throughput computing. ARM's first commodity
server-grade processor is the recent AMD A1100-series processor, based on a
64-bit ARM Cortex A57 architecture. In this paper, we study the performance and
energy efficiency of a server based on this ARM64 CPU, relative to a comparable
server running an AMD Opteron 3300-series x64 CPU, for Big Data workloads.
Specifically, we study these for Intel's HiBench suite of web, query and
machine learning benchmarks on Apache Hadoop v2.7 in a pseudo-distributed
setup, for data sizes up to files, web pages and tuples. Our
results show that the ARM64 server's runtime performance is comparable to the
x64 server for integer-based workloads like Sort and Hive queries, and only
lags behind for floating-point intensive benchmarks like PageRank, when they do
not exploit data parallelism adequately. We also see that the ARM64 server
takes the energy, and has an Energy Delay Product (EDP) that
is lower than the x64 server. These results hold promise for ARM64
data centers hosting Big Data workloads to reduce their operational costs,
while opening up opportunities for further analysis.Comment: Accepted for publication in the Proceedings of the 24th IEEE
International Conference on High Performance Computing, Data, and Analytics
(HiPC), 201
Experimental Performance Evaluation of Cloud-Based Analytics-as-a-Service
An increasing number of Analytics-as-a-Service solutions has recently seen
the light, in the landscape of cloud-based services. These services allow
flexible composition of compute and storage components, that create powerful
data ingestion and processing pipelines. This work is a first attempt at an
experimental evaluation of analytic application performance executed using a
wide range of storage service configurations. We present an intuitive notion of
data locality, that we use as a proxy to rank different service compositions in
terms of expected performance. Through an empirical analysis, we dissect the
performance achieved by analytic workloads and unveil problems due to the
impedance mismatch that arise in some configurations. Our work paves the way to
a better understanding of modern cloud-based analytic services and their
performance, both for its end-users and their providers.Comment: Longer version of the paper in Submission at IEEE CLOUD'1
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