scholarly journals Dependence-Cognizant Locking Improvement for the Main Memory Database Systems

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Ouya Pei ◽  
Zhanhuai Li ◽  
Hongtao Du ◽  
Wenjie Liu ◽  
Jintao Gao
Keyword(s):  
Distributed Databases ◽  
Database Systems ◽  
Main Memory ◽  
Space Complexity ◽  
Time And Space ◽  
Main Memory Database ◽  
Time And Space Complexity

The traditional lock manager (LM) seriously limits the transaction throughput of the main memory database systems (MMDB). In this paper, we introduce dependence-cognizant locking (DCLP), an efficient improvement to the traditional LM, which dramatically reduces the locking space while offering efficiency. With DCLP, one transaction and its direct successors are collocated in its context. Whenever a transaction is committed, it wakes up its direct successors immediately avoiding the expensive operations, such as lock detection and latch contention. We also propose virtual transaction which has better time and space complexity by compressing continuous read-only transactions/operations. We implement DCLP in Calvin and carry out experiments in both multicore and shared-nothing distributed databases. Experiments demonstrate that, in contrast with existing algorithms, DCLP can achieve better performance in many workloads, especially high-contention workloads.

scholarly journals Beyond equi-joins

2021 ◽  
Vol 14 (11) ◽  
pp. 2599-2612
Author(s):  
Nikolaos Tziavelis ◽  
Wolfgang Gatterbauer ◽  
Mirek Riedewald
Keyword(s):  
Experimental Study ◽  
State Of The Art ◽  
Database Systems ◽  
Ranking Function ◽  
Space Complexity ◽  
Time And Space ◽  
Running Time ◽  
Join Queries ◽  
Time And Space Complexity ◽  
Memory Efficient

We study theta-joins in general and join predicates with conjunctions and disjunctions of inequalities in particular, focusing on ranked enumeration where the answers are returned incrementally in an order dictated by a given ranking function. Our approach achieves strong time and space complexity properties: with n denoting the number of tuples in the database, we guarantee for acyclic full join queries with inequality conditions that for every value of k , the k top-ranked answers are returned in O ( n polylog n + k log k ) time. This is within a polylogarithmic factor of O ( n + k log k ), i.e., the best known complexity for equi-joins, and even of O ( n + k ), i.e., the time it takes to look at the input and return k answers in any order. Our guarantees extend to join queries with selections and many types of projections (namely those called "free-connex" queries and those that use bag semantics). Remarkably, they hold even when the number of join results is n ℓ for a join of ℓ relations. The key ingredient is a novel O ( n polylog n )-size factorized representation of the query output , which is constructed on-the-fly for a given query and database. In addition to providing the first nontrivial theoretical guarantees beyond equi-joins, we show in an experimental study that our ranked-enumeration approach is also memory-efficient and fast in practice, beating the running time of state-of-the-art database systems by orders of magnitude.

scholarly journals Searching using B/B+ Tree in Database Management System

2021 ◽  
Vol 9 (VII) ◽  
pp. 3702-3706
Author(s):  
Anshita Garg
Keyword(s):  
Data Transfer ◽  
Main Memory ◽  
Database Management System ◽  
Memory Access ◽  
Space Complexity ◽  
Longest Common Subsequence ◽  
Time And Space ◽  
Basic Point ◽  
Disk Access ◽  
Time And Space Complexity

This is a research-based project and the basic point motivating this project is learning and implementing algorithms that reduce time and space complexity. In the first part of the project, we reduce the time taken to search a given record by using a B/B+ tree rather than indexing and traditional sequential access. It is concluded that disk-access times are much slower than main memory access times. Typical seek times and rotational delays are of the order of 5 to 6 milliseconds and typical data transfer rates are of the range of 5 to 10 million bytes per second and therefore, main memory access times are likely to be at least 4 or 5 orders of magnitude faster than disk access on any given system. Therefore, the objective is to minimize the number of disk accesses, and thus, this project is concerned with techniques for achieving that objective i.e. techniques for arranging the data on a disk so that any required piece of data, say some specific record, can be located in a few I/O’s as possible. In the second part of the project, Dynamic Programming problems were solved with Recursion, Recursion With Storage, Iteration with Storage, Iteration with Smaller Storage. The problems which have been solved in these 4 variations are Fibonacci, Count Maze Path, Count Board Path, and Longest Common Subsequence. All 4 variations are an improvement over one another and thus time and space complexity are reduced significantly as we go from Recursion to Iteration with Smaller Storage.

Main Memory Database Systems

2017 ◽  
Vol 8 (1-2) ◽  
pp. 1-130 ◽  
Author(s):  
Frans Faerber ◽  
Alfons Kemper ◽  
Per-Åke Larson ◽  
Justin Levandoski ◽  
Tjomas Neumann ◽  
...  
Keyword(s):  
Database Systems ◽  
Main Memory ◽  
Main Memory Database

Improved time and space complexity for Kianfar's inequality rotation algorithm

2009 ◽  
Vol 3 (1) ◽  
pp. 90
Author(s):  
D. El Baz ◽  
M. Elkihel ◽  
L. Gely ◽  
G. Plateau
Keyword(s):  
Space Complexity ◽  
Time And Space ◽  
Time And Space Complexity
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