nitowa
/
Distributed-Union-Find
Beobachten 1
Favorisieren 0
Fork 0
Code Issues 0 Pull-Requests 0 Releases 0 Wiki Aktivität
Du kannst nicht mehr als 25 Themen auswählen Themen müssen mit entweder einem Buchstaben oder einer Ziffer beginnen. Sie können Bindestriche („-“) enthalten und bis zu 35 Zeichen lang sein.
17 Commits
1 Branch
Branch: master
Branches Tags
${ item.name }
Erstelle Branch ${ searchTerm }
von „master“
${ noResults }
nitowa 681b09e5e0 more merging algorithms, add bench script vor 1 Jahr
config/db more merging algorithms, add bench script vor 1 Jahr
spark-packages working graph implementation and improved shell scripts vor 1 Jahr
src/spark more merging algorithms, add bench script vor 1 Jahr
.gitignore more merging algorithms, add bench script vor 1 Jahr
README.md add clarification to README vor 1 Jahr
bench.py more merging algorithms, add bench script vor 1 Jahr
clean.py progress on mapping data, finding clusters, probably inefficient vor 1 Jahr
settings.json checkpoint dir to settings, rename main_back to main_with_collect vor 1 Jahr
setup.py more merging algorithms, add bench script vor 1 Jahr
small_test_data.csv progress on mapping data, finding clusters, probably inefficient vor 1 Jahr
start_services.sh working graph implementation and improved shell scripts vor 1 Jahr
submit.sh working graph implementation and improved shell scripts vor 1 Jahr
submit_graph.sh working graph implementation and improved shell scripts vor 1 Jahr
submit_partition.sh union find with partition clustering vor 1 Jahr

README.md

Project Description

TODO

Installation

Prerequisites:

For the graph implementation specifically you need to install graphframes manually from a third party since the official release is incompatible with spark 3.x (pull request pending). A prebuilt copy is supplied in the spark-packages directory.

Setting up

  • Modify settings.json to reflect your setup. If you are running everything locally you can use start_services.sh to turn everything on in one swoop. It might take a few minutes for Cassandra to become available.
  • Load the development database by running python3 setup.py from the project root. Per default this will move small_test_data.csv into the transactions table.

Deploying:

  • Start the spark workload by either running submit.sh (slow) or submit_graph.sh (faster)
  • If you need to clean out the Database you can run python3 clean.py. Be wary that this wipes all table definitions and data.