# Dolha - an Efficient and Exact Data Structure for Streaming Graphs

## TL;DR

The paper introduces Dolha, a novel data structure optimized for high-speed, memory-efficient processing of streaming graphs, supporting real-time updates and neighborhood queries for large-scale dynamic graph data.

## Contribution

Dolha is a new data structure that enables constant-time edge insertion, near-linear space efficiency, and linear-time neighborhood queries for streaming graphs, with a persistent version for sliding window and time-based queries.

## Key findings

- Dolha can contain billions of edges in memory.
- Supports edge processing in nanoseconds.
- Enables efficient neighborhood queries and sliding window updates.

## Abstract

A streaming graph is a graph formed by a sequence of incoming edges with time stamps. Unlike static graphs, the streaming graph is highly dynamic and time related. In the real world, the high volume and velocity streaming graphs such as internet traffic data, social network communication data and financial transfer data are bringing challenges to the classic graph data structures. We present a new data structure: double orthogonal list in hash table (Dolha) which is a high speed and high memory efficiency graph structure applicable to streaming graph. Dolha has constant time cost for single edge and near linear space cost that we can contain billions of edges information in memory size and process an incoming edge in nanoseconds. Dolha also has linear time cost for neighborhood queries, which allow it to support most algorithms in graphs without extra cost. We also present a persistent structure based on Dolha that has the ability to handle the sliding window update and time related queries.