Glossary#

This page lists all the glossary used in the GraphScope project.

FLASH#

FLASH is a distributed programming model for programming a broad spectrum of graph algorithms, including clustering, centrality, traversal, matching, mining, etc. FLASH follows the vertex-centric philosophy, but it moves a step further for stronger expressiveness by providing flexible control flow, the operations on arbitrary vertex sets and beyond-neighborhood communication. FLASH 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.

FLASH is planned to be integrated to GraphScope, as a module in the analytical engine(GAE).

Learn more: Paper on ICDE2023, Doc

GRAPE#

the code name of analytical engine in GraphScope. It was first introduced in the SIGMOD2017 paper, represents GRAph Parallel processing Engine. The analytical engine inherits the design proposed in this paper.

The core part of GRAPE is opensourced at https://github.com/alibaba/libgrape-lite, which is a dependency and serves as the analytical engine in GraphScope.

GAE#

GAE is short for GraphScope Analytical Engine, a.k.a., GRAPE.

GART#

GART is short for Graph Analysis and Relational Transactions. It is a loosely-coupled framework that can be deployed to bridge an existing relational OLTP system (e.g., MySQL) with the graph-specific system GraphScope. In a nutshell, GART exploits the high availability mechanism of modern OLTP systems, and leverages write-ahead logs (WALs) of OLTP systems to extract graphs on which to perform graph-related workloads over execution engines of GraphScope. Instead of offline data migration, GART reuses WALs to replay graph data online with multi-version support.

GART is planned to be integrated to GraphScope, in the storage layer.

GRIN#

GRIN is a proposed standard Graph Retrieval INterface in GraphScope. The goal of GRIN is to provide a common way for the graph computing engines to retrieve graph data stored in different storage engines in GraphScope, and to simplify the integration of these engines with each other.

GRIN is defined in C, which makes it portable to systems written in different programming languages, such as C++, Rust and Java. It provides a set of common operations and data structure handlers that can be used to access graph data, regardless of the underlying storage engine.

Repo: https://github.com/graphscope/grin

GraphAr#

GraphAr (short for Graph Archive) is a project that aims to make it easier for diverse applications and systems (in-memory and out-of-core storages, databases, graph computing systems, and interactive graph query frameworks) to build and access graph data conveniently and efficiently.

Repo: https://github.com/alibaba/GraphAr

Groot#

Groot is the code name of the persistent storage in GraphScope. It was named after the tree Groot in the movie “Guardians of the Galaxy”. The name “g-root” also indicates the fundamental role of the storage component in the graph system.

GUM#

GUM is a highly efficient multi-GPU graph analytics engine that employs a work-stealing mechanism to tackle both dynamic load imbalances and long-tail issues. It does so by utilizing the high-speed NVLinks found in modern multi-GPU servers. Additionally, GUM is acutely aware of the asymmetric topology of GPU connections and generates an optimal task-stealing plan from a holistic perspective during each iteration. With this innovative approach, GUM can offer a significant order-of-magnitude improvement in performance.

GUM serves as an GPU-speedup module for GRAPE when applicable.

MaxGraph (deprecated)#

MaxGraph is the deprecated interactive engine in GraphScope. It was replaced by a new designed engine GAIA with multiple query language (e.g., Gremlin/Cypher…) support.

Vineyard, or v6d#

Vineyard is the code name of the immutable memory management. Its name suggests it was a underlying component of GRAPE.

Vineyard (v6d) is an innovative in-memory immutable data manager that offers out-of-the-box high-level abstractions and zero-copy in-memory sharing for distributed data in various big data tasks, such as graph analytics (e.g., GraphScope), numerical computing (e.g., Mars), and machine learning. It is a CNCF sandbox project.

Repo: https://github.com/v6d-io/v6d

GIE#

GraphScope Interactive Engine, or GIE for short, utilizes Gaia (or Pegasus) to enable parallel execution in distribution. It offers storage options consisting of the immutable, in-memory v6d and the mutable, persistent groot. GIE can process queries using Tinkerpop’s Gremlin language, with support for Neo4j’s openCypher to come soon. The capabilities of GIE are amplified by a sophisticated Intermediate Representation (IR) Layer that dissociates the query languages from the computing engines and stores.

GAIA#

Gaia is a distributed data-parallel compute engine based on the cyclic dataflow computation model, which has been employed by GIE to support large-scale interactive graph queries. Gaia serves as the computation engine, lying at the core of the GraphScope system. Users can construct the computation via a directed acyclic graph (DAG), and easily run the job on their laptop or even a distributed environment across a cluster of computers. Note that cycle can be introduced via the loop control flow, while the whole loop contexts will be wrapped in a scope within which the cycle is completely hidden from the users. Scope is a unique concept of Gaia to handle complex control flow such as loop and conditional. In addition, it is also the key to many application-level primitives, e.g. correlated subtask, as well as advanced scheduling techniques, e.g. early-stop mechanism.

Pegasus#

A legacy name of Gaia, for which we use Gaia and Pegasus interchangeably in the project.

Ingress#

Ingress is an automated system for incremental graph processing. It is able to incrementalize batch vertex-centric algorithms into their incremental counterparts as a whole, without the need of redesigned logic or data structures from users. Underlying Ingress is an automated incrementalization framework equipped with four different memoization policies, to support all kinds of vertex-centric computations with optimized memory utilization.

Ingress is planned to be integrated into GraphScope, as an incrementalization module in the analytical engine(GAE).

PIE-model#

The subgraph centric programming model proposed in GRAPE, which is short for Partial evaluation, Incremental evaluation and assemblE, the three core functions to fulfill in GRAPE.