scholarly journals Query processing in main memory database management systems

1986 ◽  
Vol 15 (2) ◽  
pp. 239-250 ◽  
Author(s):  
Tobin J. Lehman ◽  
Michael J. Carey
Keyword(s):  
Query Processing ◽  
Database Management ◽  
Database Management Systems ◽  
Main Memory ◽  
Management Systems ◽  
Main Memory Database

scholarly journals Experimenting with recursive queries in database and logic programming systems

2008 ◽  
Vol 8 (2) ◽  
pp. 129-165 ◽  
Author(s):  
G. TERRACINA ◽  
N. LEONE ◽  
V. LIO ◽  
C. PANETTA
Keyword(s):  
Database Management ◽  
State Of The Art ◽  
Database Systems ◽  
Database Management Systems ◽  
Main Memory ◽  
Management Systems ◽  
Distributed Data ◽  
Complex Reasoning ◽  
Programming Systems ◽  
New System

AbstractThis article considers the problem of reasoning on massive amounts of (possibly distributed) data. Presently, existing proposals show some limitations: (i) the quantity of data that can be handled contemporarily is limited, because reasoning is generally carried out in main-memory; (ii) the interaction with external (and independent) Database Management Systems is not trivial and, in several cases, not allowed at all; and (iii) the efficiency of present implementations is still not sufficient for their utilization in complex reasoning tasks involving massive amounts of data. This article provides a contribution in this setting; it presents a new system, called DLVDB, which aims to solve these problems. Moreover, it reports the results of a thorough experimental analysis we have carried out for comparing our system with several state-of-the-art systems (both logic and databases) on some classical deductive problems; the other tested systems are LDL++, XSB, Smodels, and three top-level commercial Database Management Systems. DLVDB significantly outperforms even the commercial database systems on recursive queries.

scholarly journals Columnar storage and list-based processing for graph database management systems

2021 ◽  
Vol 14 (11) ◽  
pp. 2491-2504
Author(s):  
Pranjal Gupta ◽  
Amine Mhedhbi ◽  
Semih Salihoglu
Keyword(s):  
Query Processing ◽  
Database Management ◽  
Data Access ◽  
Database Management Systems ◽  
Management Systems ◽  
Graph Database ◽  
Block Based ◽  
Data Access Patterns ◽  
Access Patterns ◽  
Processing Techniques

We revisit column-oriented storage and query processing techniques in the context of contemporary graph database management systems (GDBMSs). Similar to column-oriented RDBMSs, GDBMSs support read-heavy analytical workloads that however have fundamentally different data access patterns than traditional analytical workloads. We first derive a set of desiderata for optimizing storage and query processors of GDBMS based on their access patterns. We then present the design of columnar storage, compression, and query processing techniques based on these desiderata. In addition to showing direct integration of existing techniques from columnar RDBMSs, we also propose novel ones that are optimized for GDBMSs. These include a novel list-based query processor, which avoids expensive data copies of traditional block-based processors under many-to-many joins, a new data structure we call single-indexed edge property pages and an accompanying edge ID scheme, and a new application of Jacobson's bit vector index for compressing NULL values and empty lists. We integrated our techniques into the GraphflowDB in-memory GDBMS. Through extensive experiments, we demonstrate the scalability and query performance benefits of our techniques.

scholarly journals Exploiting Database Management Systems and Treewidth for Counting

2021 ◽  
pp. 1-30
Author(s):  
JOHANNES K. FICHTE ◽  
MARKUS HECHER ◽  
PATRICK THIER ◽  
STEFAN WOLTRAN
Keyword(s):  
Database Management ◽  
State Of The Art ◽  
Database Management Systems ◽  
Main Memory ◽  
Management Systems ◽  
Boolean Formula ◽  
Counting Problems ◽  
Counting Problem ◽  
Bounded Treewidth ◽  
Natural Approach

Abstract Bounded treewidth is one of the most cited combinatorial invariants in the literature. It was also applied for solving several counting problems efficiently. A canonical counting problem is #Sat, which asks to count the satisfying assignments of a Boolean formula. Recent work shows that benchmarking instances for #Sat often have reasonably small treewidth. This paper deals with counting problems for instances of small treewidth. We introduce a general framework to solve counting questions based on state-of-the-art database management systems (DBMSs). Our framework takes explicitly advantage of small treewidth by solving instances using dynamic programming (DP) on tree decompositions (TD). Therefore, we implement the concept of DP into a DBMS (PostgreSQL), since DP algorithms are already often given in terms of table manipulations in theory. This allows for elegant specifications of DP algorithms and the use of SQL to manipulate records and tables, which gives us a natural approach to bring DP algorithms into practice. To the best of our knowledge, we present the first approach to employ a DBMS for algorithms on TDs. A key advantage of our approach is that DBMSs naturally allow for dealing with huge tables with a limited amount of main memory (RAM).

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