4,765 research outputs found
VerdictDB: Universalizing Approximate Query Processing
Despite 25 years of research in academia, approximate query processing (AQP)
has had little industrial adoption. One of the major causes of this slow
adoption is the reluctance of traditional vendors to make radical changes to
their legacy codebases, and the preoccupation of newer vendors (e.g.,
SQL-on-Hadoop products) with implementing standard features. Additionally, the
few AQP engines that are available are each tied to a specific platform and
require users to completely abandon their existing databases---an unrealistic
expectation given the infancy of the AQP technology. Therefore, we argue that a
universal solution is needed: a database-agnostic approximation engine that
will widen the reach of this emerging technology across various platforms.
Our proposal, called VerdictDB, uses a middleware architecture that requires
no changes to the backend database, and thus, can work with all off-the-shelf
engines. Operating at the driver-level, VerdictDB intercepts analytical queries
issued to the database and rewrites them into another query that, if executed
by any standard relational engine, will yield sufficient information for
computing an approximate answer. VerdictDB uses the returned result set to
compute an approximate answer and error estimates, which are then passed on to
the user or application. However, lack of access to the query execution layer
introduces significant challenges in terms of generality, correctness, and
efficiency. This paper shows how VerdictDB overcomes these challenges and
delivers up to 171 speedup (18.45 on average) for a variety of
existing engines, such as Impala, Spark SQL, and Amazon Redshift, while
incurring less than 2.6% relative error. VerdictDB is open-sourced under Apache
License.Comment: Extended technical report of the paper that appeared in Proceedings
of the 2018 International Conference on Management of Data, pp. 1461-1476.
ACM, 201
Database Learning: Toward a Database that Becomes Smarter Every Time
In today's databases, previous query answers rarely benefit answering future
queries. For the first time, to the best of our knowledge, we change this
paradigm in an approximate query processing (AQP) context. We make the
following observation: the answer to each query reveals some degree of
knowledge about the answer to another query because their answers stem from the
same underlying distribution that has produced the entire dataset. Exploiting
and refining this knowledge should allow us to answer queries more
analytically, rather than by reading enormous amounts of raw data. Also,
processing more queries should continuously enhance our knowledge of the
underlying distribution, and hence lead to increasingly faster response times
for future queries.
We call this novel idea---learning from past query answers---Database
Learning. We exploit the principle of maximum entropy to produce answers, which
are in expectation guaranteed to be more accurate than existing sample-based
approximations. Empowered by this idea, we build a query engine on top of Spark
SQL, called Verdict. We conduct extensive experiments on real-world query
traces from a large customer of a major database vendor. Our results
demonstrate that Verdict supports 73.7% of these queries, speeding them up by
up to 23.0x for the same accuracy level compared to existing AQP systems.Comment: This manuscript is an extended report of the work published in ACM
SIGMOD conference 201
A Big Data Analyzer for Large Trace Logs
Current generation of Internet-based services are typically hosted on large
data centers that take the form of warehouse-size structures housing tens of
thousands of servers. Continued availability of a modern data center is the
result of a complex orchestration among many internal and external actors
including computing hardware, multiple layers of intricate software, networking
and storage devices, electrical power and cooling plants. During the course of
their operation, many of these components produce large amounts of data in the
form of event and error logs that are essential not only for identifying and
resolving problems but also for improving data center efficiency and
management. Most of these activities would benefit significantly from data
analytics techniques to exploit hidden statistical patterns and correlations
that may be present in the data. The sheer volume of data to be analyzed makes
uncovering these correlations and patterns a challenging task. This paper
presents BiDAl, a prototype Java tool for log-data analysis that incorporates
several Big Data technologies in order to simplify the task of extracting
information from data traces produced by large clusters and server farms. BiDAl
provides the user with several analysis languages (SQL, R and Hadoop MapReduce)
and storage backends (HDFS and SQLite) that can be freely mixed and matched so
that a custom tool for a specific task can be easily constructed. BiDAl has a
modular architecture so that it can be extended with other backends and
analysis languages in the future. In this paper we present the design of BiDAl
and describe our experience using it to analyze publicly-available traces from
Google data clusters, with the goal of building a realistic model of a complex
data center.Comment: 26 pages, 10 figure
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