1,099 research outputs found
Apache Calcite: A Foundational Framework for Optimized Query Processing Over Heterogeneous Data Sources
Apache Calcite is a foundational software framework that provides query
processing, optimization, and query language support to many popular
open-source data processing systems such as Apache Hive, Apache Storm, Apache
Flink, Druid, and MapD. Calcite's architecture consists of a modular and
extensible query optimizer with hundreds of built-in optimization rules, a
query processor capable of processing a variety of query languages, an adapter
architecture designed for extensibility, and support for heterogeneous data
models and stores (relational, semi-structured, streaming, and geospatial).
This flexible, embeddable, and extensible architecture is what makes Calcite an
attractive choice for adoption in big-data frameworks. It is an active project
that continues to introduce support for the new types of data sources, query
languages, and approaches to query processing and optimization.Comment: SIGMOD'1
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
Enhanced Stream Processing in a DBMS Kernel
Continuous query processing has emerged as a promising query processing paradigm with numerous applications. A recent development is the need to handle both streaming queries and typical one-time queries in the same application. For example, data warehousing can greatly benefit from the integration of stream semantics, i.e., online analysis of incoming data and combination with existing data. This is especially useful to provide low latency in data-intensive analysis in big data warehouses that are augmented with new data on a daily basis.
However, state-of-the-art database technology cannot handle streams efficiently due to their "continuous" nature. At the same time, state-of-the-art stream technology is purely focused on stream applications. The research efforts are mostly geared towards the creation of specialized stream management systems built with a different philosophy than a DBMS. The drawback of this approach is the limited opportunities to exploit successful past data processing technology, e.g., query optimization techniques.
For this new problem we need to combine the best of both worlds. Here we take a completely different route by designing a stream engine on top of an existing relational database kernel. This includes reuse of both its storage/execution engine and its optimizer infrastructure. The major challenge then becomes the efficient support for specialized stream features. This paper focuses on incremental window-based processing, arguably the most crucial stream-specific requirement. In order to maintain and reuse the generic storage and execution model of the DBMS, we elevate the problem at the query plan level. Proper op
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