GraphAr is an open source, standard data file format for graph data storage and retrieval. It defines a standardized file format for graph data, and provides a set of interfaces for generating, accessing, and transforming these formatted files.
This post is a quick guide that explains how to work with GraphAr, using the C++ SDK it provides.
In this post, we will introduce GraphAr, which is an open source, standard data file format for graph data storage and retrieval. It defines a standardized file format for graph data, independent of the computation/storage system, and provides a set of interfaces for generating, accessing, and transforming these formatted files.
In this post, we will introduce FLASH, which is a distributed programming model for programming a broad spectrum of graph algorithms, including clustering, centrality, traversal, matching, mining, etc. It makes diverse complex graph algorithms easy to write at the distributed runtime. The algorithms expressed in FLASH take only a few lines of code, and provide a satisfactory performance.
Graph algorithms serve as essential building blocks for a wide range of applications, such as social network analytics, routing, constructing protein network and De Bruijn graphs, and mining valuable information in RDF (Resource Description Framework) graphs. Generally, graph analytics involve propagating labels across edges or iteratively accumulating values from adjacent vertices. Existing engines in both academia and industry, like PowerGraph, Pregel, and GraphX, have paved the way. However, in the era of big data, the computational and storage complexity of sophisticated algorithms coupled with rapidly growing datasets have exhausted the limits of a single device.
Graph neural networks(GNNs) learn graph vertex representations by aggregating multi-hop neighbor information. Industrial applications often adopt mini-batch training to scale out GNNs on large-scale graphs, where neighbor sampling is used during both model training and inference. Since the structure and attributes of real-world graphs often change dynamically, it is imperative that the inferred vertex representation can accurately reflect these updates.
We provide a template repository for graph analysis applications, where users can customize graph analysis algorithms by replacing several C++ functions with their own logic, and run them on GraphScope.
Recently, LDBC (Linked Data Benchmark Council) announced the latest results of the LDBC Social Network Benchmark Interactive workload. GraphScope ranked first with a throughput of over 33,000 QPS, which is over two times higher than the second-place (previous record holder).
Schema construction and graph data loading are usually the complicated steps in graph computing processes. Currently, GraphScope has released a graphscope-notebook plugin which through an interactive way help users complete the graph loading in the Jupyterlab environment. This article will provide a detailed introduction to the use of this plugin, and users can try it in the Playground environment.
We are thrilled to introduce a range of enhancements to GraphScope, with the GraphScope 0.23.0 release. This release encompasses significant features and improvements in Graph Interactive Engine (GIE), GraphScope Flex, and Deployment.
GraphScope now supports serving as the backend engine for G6VP, an open-sourced graph visualization and analysis platform. With G6VP and GraphScope, users can import graph data and analyze graph data easily. This article mainly introduces how to deploy G6VP and GraphScope and perform data analysis.
In this blog, we introduce GAIA-IR, an interactive graph query engine for GraphScope.
GAIA-IR not only showcases exceptional efficiency in handling Gremlin queries within a distributed framework but also present a unified intermediate representation layer that offers remarkable adaptability for incorporating additional query languages.
This feature makes the engine scalable, efficient, and user-friendly, rendering it an invaluable tool for individuals engaged in graph databases.
In this blog, we present GRAPE-JDK,
an efficient cross-language graph analysis development kit of GraphScope. GRAPE-JDK enables users to write customized graph algorithms in Java and run them efficiently on GraphScope by addressing various challenges in cross-language graph computation.
In this way, Java algorithms developed based on GRAPE-JDK can achieve performance similar to C++ algorithms.
This article demonstrate how to analyze graph with GraphScope in the style of NetworkX.
NetworkX is a tool for graph theory and complex network modeling developed in Python
and it has a simple and easy-to-use graph analysis interface. GraphScope provides a set of NetworkX-compatible graph analysis interfaces that not only support the use of simple and easy-to-use interfaces like NetworkX but also provide high-performance graph analysis algorithms to support the processing of ultra-large-scale graph data.
This article describes how to deploy and use GraphScope clusters using the Helm tool.
Helm is a software package management tool in the K8s ecosystem, similar to Ubuntu’s apt or Python’s pip, designed for managing K8s application resources. Using Helm, you can easily package, distribute, install, upgrade, and rollback kubernetes applications. GraphScope also supports deployment by Helm.
This article will provide a detailed introduction on how to deploy GraphScope on a Kubernetes cluster. In real industrial scenarios, the scale of graph data that needs to be processed is huge and has far exceeded the processing capacity of a single machine. Therefore, in addition to the single-machine deployment method, GraphScope also supports running on a Kubernetes cluster with the distributed memory data management capability provided by vineyard. It will cover the following topics: 1) How to deploy GraphScope based on a Kubernetes cluster; 2) The details of the work behind it; 3) How to use your own built GraphScope development image in a distributed environment.