GraphScope Blog

We are delighted to present the release of GraphScope 0.14.0. This release is composed of many updates on backend engines and system stability. The new Graph Interactive Engine (GIE), GAIA-IR, has supported more types of operators and graph schemas for diverse graph queries. Meanwhile, the persistent storage of GraphScope, Groot, is further enhanced with a series of new functions. In addition, we continuously work on improving the performance of GraphScope, and developer usability.

We are delighted to announce the release of GraphScope v0.13.0. This release is focused on providing a JupyterLab extension for GraphScope to make your graph computation workflows better. In addition, we continuously work on improving performance of GraphScope, and developer usability.

The GraphScope v0.12.0 release is a major update on many aspects of the project including backend engines, APIs, and system stability. It introduces an intermediate representation (IR) layer into the graph interactive engine (GIE) named GAIA, to decouple query languages from query execution engines. Meanwhile, this release supports Giraph APIs to allow Giraph apps directly running on the Graph Analytics Engine (GAE) of GraphScope.

We are glad to announce a number of new features and improvements to GraphScope, alongside the GraphScope v0.11.0 release. This major release introduces mutable graphs into GraphScope, and adds GPU supports for graph analytics engine (GAE). It also focuses on user-friendly improvements, code quality, and a series of bug fixes.

We are glad to announce the availability of GraphScope v0.10. This release supports users to run GraphScope on MacOS powered by Apple’s new M1 chip. In addition, it allows to serialize/deserialize graph data to/from the disk under the standalone mode.

We are glad to announce the availability of GraphScope v0.9. In this release, we revisit the Dev-infra to improve productivity. Now, you can enjoy GraphScope with standalone mode in both our PlayGround and Google Colab. We also continuously make GraphScope more user-friendly and update the documents and tutorials based on the latest version. Further, we have preliminary supported Java in Graph Analytics Engine (GAE), and users can succinctly develop graph analytics applications with Java (see this document for more details).

We are glad to announce the availability of GraphScope v0.8. This release is a major update on many aspects of the project including deployment, system speed and APIs. For quickly getting started, this release supports to use GraphScope on standalone mode without Kubernetes. To improve the efficiency of operators and applications in NetworkX module, an immutable graph is applied by default, while it is converted to a dynamic graph only if modification operators for graphs are triggered. In addition, a notebook is integrated into the helm charts.

We are glad to announce the availability of GraphScope v0.7. This release includes major updates for the persistent graph store in GraphScope, providing APIs for real-time graph updates (inserts and deletes of individual vertices and edges). It also focuses on user-friendly improvements, security issues, code quality, and a series of bug fixes.

We are glad to announce the release of GraphScope 0.6. This major release integrates a new graph interactive engine GAIA, which supports efficient parallel execution and bounded-memory execution for Gremlin queries. More technical details of GAIA can refer to our published tech blog. Note that currently the integration of GAIA with GraphScope is experimental, and is not recommended for production use yet! In addition, this release improves the experience of local deployment on MacOS, Ubuntu and CentOS, and adds more graph analytics algorithms.

Last time, we presented an overview of the GAIA engine for scaling Gremlin for large distributed graphs. In contrast to other, existing batch-oriented big graph processing systems, such as Google Pregel, Apache Giraph, GraphLab PowerGraph, and Apache Spark GraphX, GAIA focuses on low-latency graph traversal at scale. Achieving this goal requires a different distributed infrastructure. Today, we continue to explain why with highlighting two unique and key features of GAIA.

We are glad to announce the GraphScope 0.5 release. As the first step towards the ease of deployment in production, this major release includes two new features, namely a persistent graph store to enable a “service mode” for real-time graph computing, and lazy evaluation of GraphScope programs–an execution strategy which delays the execution of a GraphScope program until later when needed for efficiency. In addition, we improve the compatibility with NetworkX. ​ We highlight the following improvements included in this release: ​

1. GraphScope-Store: A persistent store for mutable graphs. Currently, it has supported the following features/functions:

In this post, we will present a high-level road-map of the GraphScope project with highlighting new exciting features coming in the v0.5 release.

Today, we’re announcing the availability of GraphScope v0.4.0. This release focuses on the compatibility improvement with NetworkX, with the aim of allowing users to develop graph applications on large-scale graphs in a distributed environment just like doing this on a single machine. In addition, this release improves the experience of standalone deployment.

GAIA extends GraphScope with Gremlin, the industry’s de facto standard property graph query language defined and maintained by the Apache TinkerPop project, which is widely adopted by popular graph database vendors such as Neo4j, OrientDB, JanusGraph, Microsoft Cosmos DB, and Amazon Neptune. GAIA is the first open-source implementation of Gremlin in a distributed or big-data environment in the industry.

GraphScope v0.3.0 is released as scheduled. This release includes new features and major updates for frontend APIs for graph manipulation, integration with other systems as well as code optimization for some operators. Another direction we are working on is to ease the deployment of GraphScope with/without Kubernetes.