2,238 research outputs found
Materialisierte views in verteilten key-value stores
Distributed key-value stores have become the solution of choice for warehousing large volumes of data. However, their architecture is not suitable for real-time analytics. To achieve the required velocity, materialized views can be used to provide summarized data for fast access. The main challenge then, is the incremental, consistent maintenance of views at large scale. Thus, we introduce our View Maintenance System (VMS) to maintain SQL queries in a data-intensive real-time scenario.Verteilte key-value stores sind ein Typ moderner Datenbanken um große Mengen an Daten zu verarbeiten. Trotzdem erlaubt ihre Architektur keine analytischen Abfragen in Echtzeit. Materialisierte Views können diesen Nachteil ausgleichen, indem sie schnellen Zuriff auf Ergebnisse ermöglichen. Die Herausforderung ist dann, das inkrementelle und konsistente Aktualisieren der Views. Daher präsentieren wir unser View Maintenance System (VMS), das datenintensive SQL Abfragen in Echtzeit berechnet
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A unifying approach for queries and updates in deductive databases
This dissertation presents a unifying approach to process (recursive) queries and updates in a deductive database. To improve query performance, a combined top-down and bottom-up evaluation method is used to compile rules into iterative programs that contain relational algebra operators. This method is based on the lemma resolution that retains previous results to guarantee termination.Due to locality in database processing, it is desirable to materialize frequently used queries against views of the database. Unfortunately, if updates are allowed, maintaining materialized view tables becomes a major problem. We propose to materialize views incrementally, as queries are being answered. Hence views in our approach are only partially materialized. For such views, we design algorithms to perform updates only when the underlying view tables are actually affected.We compare our approach to two conventional methods for dealing with views: total materialization and query-modification. The first method materializes the entire view when it is defined while the second recomputes the view on the fly without maintaining any physical view tables. We demonstrate that our approach is a compromise between these two methods and performs better than either one in many situations.It is also desirable to be able to update views just like updating base tables. However, view updates are inherently ambiguous and the semantics of update propagation on recursively defined views were not well understood in the past. Using dynamic logic programming and lemma resolution, we are able to define the semantics of recursive view updates. These are expressed in the form of update translators specified by the database administrator when the view is defined. To guarantee completeness, we identify a subset of safe update translators. We prove that this subset of translators always terminate and are complete
How to Price Shared Optimizations in the Cloud
Data-management-as-a-service systems are increasingly being used in
collaborative settings, where multiple users access common datasets. Cloud
providers have the choice to implement various optimizations, such as indexing
or materialized views, to accelerate queries over these datasets. Each
optimization carries a cost and may benefit multiple users. This creates a
major challenge: how to select which optimizations to perform and how to share
their cost among users. The problem is especially challenging when users are
selfish and will only report their true values for different optimizations if
doing so maximizes their utility. In this paper, we present a new approach for
selecting and pricing shared optimizations by using Mechanism Design. We first
show how to apply the Shapley Value Mechanism to the simple case of selecting
and pricing additive optimizations, assuming an offline game where all users
access the service for the same time-period. Second, we extend the approach to
online scenarios where users come and go. Finally, we consider the case of
substitutive optimizations. We show analytically that our mechanisms induce
truth- fulness and recover the optimization costs. We also show experimentally
that our mechanisms yield higher utility than the state-of-the-art approach
based on regret accumulation.Comment: VLDB201
Trade-offs in Static and Dynamic Evaluation of Hierarchical Queries
We investigate trade-offs in static and dynamic evaluation of hierarchical
queries with arbitrary free variables. In the static setting, the trade-off is
between the time to partially compute the query result and the delay needed to
enumerate its tuples. In the dynamic setting, we additionally consider the time
needed to update the query result in the presence of single-tuple inserts and
deletes to the input database.
Our approach observes the degree of values in the database and uses different
computation and maintenance strategies for high-degree and low-degree values.
For the latter it partially computes the result, while for the former it
computes enough information to allow for on-the-fly enumeration.
The main result of this work defines the preprocessing time, the update time,
and the enumeration delay as functions of the light/heavy threshold and of the
factorization width of the hierarchical query. By conveniently choosing this
threshold, our approach can recover a number of prior results when restricted
to hierarchical queries.
For a restricted class of hierarchical queries, our approach can achieve
worst-case optimal update time and enumeration delay conditioned on the Online
Matrix-Vector Multiplication Conjecture.Comment: Technical Report; 52 pages. The updated version contains: new
diagrams and plots summarizing known results and putting the results of the
paper into context; introduction of delta_i-hieararchical queries, for any
non-negative integer i; optimality results for delta_0- and
delta_1-hieararchical querie
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