# HugeGraph Architecture Overview

LLMS index: [llms.txt](/llms.txt)

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### 1 Overview

As a full-stack graph system covering **Graph Database, Graph Computing, and Graph AI**, HugeGraph is centered around a high-performance graph engine (HugeGraph Server) and supports both OLTP and OLAP graph computation types. For the OLTP layer, it implements the [Apache TinkerPop3](https://tinkerpop.apache.org) framework and supports the [Gremlin](https://tinkerpop.apache.org/gremlin.html) and [Cypher](https://en.wikipedia.org/wiki/Cypher) query languages. It comes with a complete application toolchain and provides a pluggable backend storage driver framework.

Below is the overall architecture diagram of HugeGraph:

<div style="text-align: center;">
  <img src="/docs/images/design/architectural-revised.png" alt="image">
</div>

HugeGraph consists of three layers of functionality: the application layer, the graph engine layer, and the storage layer.

- Application Layer:
  - [Hubble](/docs/quickstart/toolchain/hugegraph-hubble): A one-stop visual analysis platform that covers the entire process from data modeling to rapid data import, online and offline analysis, and unified graph management, realizing wizard-style operations for the entire graph application process.
  - [Loader](/docs/quickstart/toolchain/hugegraph-loader): A data import component that can transform data from multiple data sources into graph vertices and edges and batch import them into the graph database.
  - [Tools](/docs/quickstart/toolchain/hugegraph-tools): Command-line tools for deploying, managing, and backing up/restoring data in HugeGraph.
  - [Computer](/docs/quickstart/computing/hugegraph-computer): A distributed graph processing system (OLAP), which is an implementation of [Pregel](https://kowshik.github.io/JPregel/pregel_paper.pdf) and can run on Kubernetes.
  - [Client](/docs/quickstart/client/hugegraph-client): Client SDKs encapsulate the core operations for connecting to HugeGraph Server, managing schemas, reading and writing graph data, and running queries. HugeGraph currently provides [Java](/docs/quickstart/client/hugegraph-client/), [Python](/docs/quickstart/client/hugegraph-client-python/), and [Go](/docs/quickstart/client/hugegraph-client-go/) clients, while a Rust client is under development.
- [Graph Engine Layer](/docs/quickstart/hugegraph/hugegraph-server):
  - REST Server: Provides a RESTful API for querying graph/schema information, supports the [Gremlin](https://tinkerpop.apache.org/gremlin.html) and [Cypher](https://en.wikipedia.org/wiki/Cypher) query languages, and offers APIs for service monitoring and operations.
  - Graph Engine: Supports both OLTP and OLAP graph computation types, with OLTP implementing the [Apache TinkerPop3](https://tinkerpop.apache.org) framework.
  - Backend Interface: Implements the storage of graph data to the backend.
- Storage Layer:
  - Storage Backend: Version 1.7.0 supports RocksDB, HStore, HBase, and Memory. Custom backends can be added through plugins.