MapReduce, Spark, Java, and Scala for Data Algorithms Book
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Updated
Apr 21, 2023 - Java
MapReduce, Spark, Java, and Scala for Data Algorithms Book
Partition management extension for PostgreSQL
A scikit-learn based module for multi-label et. al. classification
Bao, a Lightweight Static Partitioning Hypervisor
Header-Only C++ Library for Graph Representation and Algorithms
Docker compose config for mongodb cluster
A starting point to build a web API to work with Azure Cosmos DB using .NET 5 and Azure Cosmos DB .NET SDK V3, based on Clean Architecture and repository design pattern. Partition key is also implemented through the repository pattern.
KaHIP -- Karlsruhe HIGH Quality Partitioning.
PySpark Algorithms Book: https://www.amazon.com/dp/B07X4B2218/ref=sr_1_2
KaHyPar (Karlsruhe Hypergraph Partitioning) is a multilevel hypergraph partitioning framework providing direct k-way and recursive bisection based partitioning algorithms that compute solutions of very high quality.
Partitioning tool for PostgreSQL
Official PyTorch implementation of Superpoint Transformer introduced in [ICCV'23] "Efficient 3D Semantic Segmentation with Superpoint Transformer" and SuperCluster introduced in [3DV'24 Oral] "Scalable 3D Panoptic Segmentation As Superpoint Graph Clustering"
Analyze Data with Pandas-based Networks. Documentation:
Partitioned ETS tables for Erlang and Elixir
A Django extension that supports PostgreSQL 11 time ranges and list partitioning.
ActiveRecord PostgreSQL Partitioning
Mt-KaHyPar (Multi-Threaded Karlsruhe Hypergraph Partitioner) is a shared-memory multilevel graph and hypergraph partitioner equipped with parallel implementations of techniques used in the best sequential partitioning algorithms. Mt-KaHyPar can partition extremely large hypergraphs very fast and with high quality.
This script is a script written in Perl to partition the Zabbix database tables in time based chunks. We can use this script to replace the Zabbix housekeeper process which tends to get too slow once you hit a certain database size.
The Partitioning Around Medoids (PAM) implementation of the K-Medoids algorithm in Python [Unmaintained]
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