Multi-threaded execution of Cypher queries
(2017) In LU-CS-EX 2017-19 EDA920 20161Department of Computer Science
- Abstract
- In this report we investigate parallel execution of queries in graph databases. We analyse different methods of parallelization, how to introduce query parallelization to a graph database, which query operations that are suitable for parallelization and if we can improve the execution time of a single query. We do this by designing and implementing a parallel runtime for the Cypher query language in the graph database Neo4j, but many of the design ideas and operators investigated are applicable to any graph database. We focus on increasing performance for a select few operators, while still being fully integrated with Neo4j. We take much inspiration from a design called morsel-driven parallelism. This means that we strive to split the... (More)
- In this report we investigate parallel execution of queries in graph databases. We analyse different methods of parallelization, how to introduce query parallelization to a graph database, which query operations that are suitable for parallelization and if we can improve the execution time of a single query. We do this by designing and implementing a parallel runtime for the Cypher query language in the graph database Neo4j, but many of the design ideas and operators investigated are applicable to any graph database. We focus on increasing performance for a select few operators, while still being fully integrated with Neo4j. We take much inspiration from a design called morsel-driven parallelism. This means that we strive to split the workload into many small pieces, “morsels”, and then hand these morsels to the threads executing the query. This is in contrast to a more classical parallelization approach, where you split the workload into a few big parts of equal size. We conclude that the operators best suited for parallelization are the operators that can be split into several smaller parts, where each part can be computed independently. We successfully introduce parallel execution of Cypher queries to Neo4j and by doing so we increase the performance of a single query by up to 15 times under certain conditions (Less)
- Popular Abstract (Swedish)
- Grafdatabaser blir allt vanligare, samtidigt som antalet processorer i moderna datorer ökar mer och mer. Vi tittar i detta arbete på hur parallelliserad sökning kan leda till prestandavinster i den populära grafdatabashanteraren Neo4j.
För att ta reda på om det går att parallellisera en enskild sökning i en grafdatabas och hur stor påverkan detta då har på svarstider, skapade vi vår egen modifierade version av Neo4j. Vi började med att ta reda på vilka delar av mjukvaran som bäst lämpade sig för parallellisering, med hänsyn till hur ofta de förekom i sökningar samt hur pass stora krav de ställde på processorn. Efter att ha valt ut ett antal av dessa så gick vi vidare med att ta fram metoder för att dela upp dem i mindre uppgifter som... (More) - Grafdatabaser blir allt vanligare, samtidigt som antalet processorer i moderna datorer ökar mer och mer. Vi tittar i detta arbete på hur parallelliserad sökning kan leda till prestandavinster i den populära grafdatabashanteraren Neo4j.
För att ta reda på om det går att parallellisera en enskild sökning i en grafdatabas och hur stor påverkan detta då har på svarstider, skapade vi vår egen modifierade version av Neo4j. Vi började med att ta reda på vilka delar av mjukvaran som bäst lämpade sig för parallellisering, med hänsyn till hur ofta de förekom i sökningar samt hur pass stora krav de ställde på processorn. Efter att ha valt ut ett antal av dessa så gick vi vidare med att ta fram metoder för att dela upp dem i mindre uppgifter som kunde köras i olika delar av processorn samtidigt, för att slutligen införa dessa ändringar i Neo4j.
Resultatet är en version av Neo4j som under rätt förhållanden ger upp till 15 gånger snabbare svar på enskilda sökningar. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/8926892
- author
- Åkerlund, Felix LU and Mellbin, Ragnar LU
- supervisor
- organization
- course
- EDA920 20161
- year
- 2017
- type
- H3 - Professional qualifications (4 Years - )
- subject
- keywords
- Cypher, Neo4j, query, graph database, parallel execution, parallel query, parallel database, openCypher
- publication/series
- LU-CS-EX 2017-19
- report number
- LU-CS-EX 2017-19
- ISSN
- 1650-2884
- language
- English
- id
- 8926892
- date added to LUP
- 2017-10-18 13:31:24
- date last changed
- 2017-10-18 13:31:24
@misc{8926892,
abstract = {{In this report we investigate parallel execution of queries in graph databases. We analyse different methods of parallelization, how to introduce query parallelization to a graph database, which query operations that are suitable for parallelization and if we can improve the execution time of a single query. We do this by designing and implementing a parallel runtime for the Cypher query language in the graph database Neo4j, but many of the design ideas and operators investigated are applicable to any graph database. We focus on increasing performance for a select few operators, while still being fully integrated with Neo4j. We take much inspiration from a design called morsel-driven parallelism. This means that we strive to split the workload into many small pieces, “morsels”, and then hand these morsels to the threads executing the query. This is in contrast to a more classical parallelization approach, where you split the workload into a few big parts of equal size. We conclude that the operators best suited for parallelization are the operators that can be split into several smaller parts, where each part can be computed independently. We successfully introduce parallel execution of Cypher queries to Neo4j and by doing so we increase the performance of a single query by up to 15 times under certain conditions}},
author = {{Åkerlund, Felix and Mellbin, Ragnar}},
issn = {{1650-2884}},
language = {{eng}},
note = {{Student Paper}},
series = {{LU-CS-EX 2017-19}},
title = {{Multi-threaded execution of Cypher queries}},
year = {{2017}},
}