Access path selection in a relational database management system

Selinger, Patricia G.; Astrahan, M. M.; Chamberlin, Donald D.; Lorie, R. A.; Price, T. G.

doi:10.1145/582096.582099

Cited by 343 publications

(494 citation statements)

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“…The potential to generate such different ordered tuple streams leads to different, and sometimes conflicting sandwiching opportunities higher up in the plan. Due to space restrictions, the question how to choose the best orderings is beyond the scope of this paper, but we can note here that our solution seamlessly fits into the well known concept of interesting order optimization [13], and on which we will report in a subsequent paper. …”

Section: Query Optimizationmentioning

confidence: 98%

Query Processing of Pre-partitioned Data Using Sandwich Operators

Baumann

Boncz

Sattler

2013

Lecture Notes in Business Information Processing

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Abstract. In this paper we present the "Sandwich Operators", an elegant approach to exploit pre-sorting or pre-grouping from clustered storage schemes in operators such as Aggregation/Grouping, HashJoin, and Sort of a database management system. Thereby, each of these operator types is "sandwiched" by two new operators, namely PartitionSplit and PartitionRestart. PartitionSplit splits the input relation into its smaller independent groups on which the sandwiched operator is executed. After a group is processed, PartitionRestart is used to trigger the execution on the following group. Executing one of these operator types with the help of the Sandwich Operators introduces minimal overhead and does not penalize performance of the sandwiched operator, as its implementation remains unchanged. On the contrary, we show that sandwiched execution of an operator results in lower memory consumption and faster execution time. PartitionSplit and PartitionRestart replace special implementations of partitioned versions of these operator. Sandwich Operators also turn blocking operators into streaming operators, resulting in faster response times for the first query results.

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Section: Query Optimizationmentioning

confidence: 98%

Query Processing of Pre-partitioned Data Using Sandwich Operators

Baumann

Boncz

Sattler

2013

Lecture Notes in Business Information Processing

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“…The order is determined by "access path selection", a standard optimization from the database literature [14], to estimate the cheapest way to join the predicates together. The overall process involves a number of steps.…”

Section: Implementation and Optimizationmentioning

confidence: 99%

PQL: A Purely-Declarative Java Extension for Parallel Programming

Reichenbach

Smaragdakis

Immerman

2012

ECOOP 2012 – Object-Oriented Programming

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Abstract. The popularization of parallelism is arguably the most fundamental computing challenge for years to come. We present an approach where parallel programming takes place in a restricted (sub-Turing-complete), logic-based declarative language, embedded in Java. Our logic-based language, PQL, can express the parallel elements of a computing task, while regular Java code captures sequential elements. This approach offers a key property: the purely declarative nature of our language allows for aggressive optimization, in much the same way that relational queries are optimized by a database engine. At the same time, declarative queries can operate on plain Java data, extending patterns such as map-reduce to arbitrary levels of nesting and composition complexity. We have implemented PQL as extension to a Java compiler and showcase its expressiveness as well as its scalability compared to competitive techniques for similar tasks (Java + relational queries, in-memory Hadoop, etc.).

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“…The execution cost is largely dependent upon the number of rows (the row cardinality) that will be processed by each operator in the QEP, so the optimizer first estimates this incrementally as each predicate is applied by multiplying the base table's row cardinality by a filter factor or selectivity for each predicate in the Query [5,6,7,8].…”

Section: Related Workmentioning

confidence: 99%

A Model for Building Dynamic Indexes & Storage and Re-use of Optimal Query Plans Generated thru Progressive Optimization (POP)

Vobugari¹,

Somayajulu²,

Subraya³

2012

IJMLC

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Abstract-Query Optimization is at the core for contribution towards performance improvements in application systems. A lot of ideas have been proposed towards Query Optimization and there is lot of On-going research happening in this area. Virtually every commercial query optimizer chooses the best plan for a query using a cost model which is based on cardinality estimation. If cardinality estimation is inaccurate, then this may result in optimizer to choose a sub-optimal plan. But once the optimizer chooses an optimal plan for execution based on the approach of POP, the need for generating an optimal plan for subsequent execution of the same query at a later point in time can be minimized/reduced/exempted by storing the execution plan. This paper proposes a Model for building Dynamic Indexes & Storage and Re-Use of Optimal Query plans generated thru Progressive Optimization (POP) for performance gains. This approach is an extension to the work implemented in "Robust Query Processing through Progressive Optimization". This paper proposes a model to build Learning system within the database to analyze the stream of incoming queries and project viable indexes as against the initial indexes created by the Administrator and also store and re-use of Optimal Query Plans generated thru Progressive Query Optimization (POP).Index Terms-QoS, PoP.

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Access path selection in a relational database management system

Cited by 343 publications

References 0 publications

Query Processing of Pre-partitioned Data Using Sandwich Operators

Query Processing of Pre-partitioned Data Using Sandwich Operators

PQL: A Purely-Declarative Java Extension for Parallel Programming

A Model for Building Dynamic Indexes & Storage and Re-use of Optimal Query Plans Generated thru Progressive Optimization (POP)

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