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An open source, standard data file format for graph data storage and retrieval
Graph processing serves as the essential building block for a diverse variety of real-world applications such as social network analytics, data mining, network routing, and scientific computing.
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.
It can be used for importing/exporting and persistent storage of graph data, thereby reducing the burden on systems when working together. Additionally, it can serve as a direct data source for graph processing applications.
To achieve this, GraphAr project provides:
By using GraphAr, you can:
The GraphAr format is designed for storing property graphs. It uses metadata to record all the necessary information of a graph, and maintains the actual data in a chunked way.
A property graph consists of vertices and edges, with each vertex contains a unique identifier and:
Each edge contains a unique identifier and:
The following is an example property graph containing two types of vertices ("person" and "comment") and three types of edges.
Each type of vertices (with the same label) constructs a logical vertex table, with each vertex assigned with a global index inside this type (called internal vertex id) starting from 0, corresponding to the row number of the vertex in the logical vertex table. An example layout for a logical table of vertices under the label "person" is provided for reference.
Given an internal vertex id and the vertex label, a vertex is uniquely identifiable and its respective properties can be accessed from this table. The internal vertex id is further used to identify the source and destination vertices when maintaining the topology of the graph.
The logical vertex table will be partitioned into multiple continuous vertex chunks for enhancing the reading/writing efficiency. To maintain the ability of random access, the size of vertex chunks for the same label is fixed. To support to access required properties avoiding reading all properties from the files, and to add properties for vertices without modifying the existing files, the columns of the logical table will be divided into several column groups.
Take the person vertex table as an example, if the chunk size is set
to be 500, the logical table will be separated into sub-logical-tables
of 500 rows with the exception of the last one, which may have less than
500 rows. The columns for maintaining properties will also be divided
into distinct groups (e.g., 2 for our example). As a result, a total of
4 physical vertex tables are created for storing the example logical
table, which can be seen from the following figure.
Note: For efficiently utilize the filter push-down of the payload file format like Parquet, the internal vertex id is stored in the payload file as a column. And since the internal vertex id is continuous, the payload file format can use the delta encoding for the internal vertex id column, which would not bring too much overhead for the storage.
For maintaining a type of edges (that with the same triplet of the source label, edge label, and destination label), a logical edge table is established. And in order to support quickly creating a graph from the graph storage file, the logical edge table could maintain the topology information in a way similar to CSR/CSC (learn more about CSR/CSC), that is, the edges are ordered by the internal vertex id of either source or destination. In this way, an offset table is required to store the start offset for each vertex's edges, and the edges with the same source/destination will be stored continuously in the logical table.
Take the logical table for person knows person edges as an example,
the logical edge table looks like:
As same with the vertex table, the logical edge table is also partitioned into some sub-logical-tables, with each sub-logical-table contains edges that the source (or destination) vertices are in the same vertex chunk. According to the partition strategy and the order of the edges, edges can be stored in GraphAr following one of the four types:
After that, a sub-logical-table is further divided into edge chunks of a predefined, fixed number of rows (referred to as edge chunk size). Finally, an edge chunk is separated into physical tables in the following way:
Additionally, there would be an offset table for ordered_by_source or ordered_by_dest edges. The offset table is used to record the starting point of the edges for each vertex. The partition of the offset table should be in alignment with the partition of the corresponding vertex table. The first row of each offset chunk is always 0, indicating the starting point for the corresponding sub-logical-table for edges.
Take the person knows person edges to illustrate. Suppose the vertex
chunk size is set to 500 and the edge chunk size is 1024, and the edges
are ordered_by_source, then the edges could be saved in the
following physical tables:
GraphAr offers a collection of libraries for the purpose of reading, writing and transforming files. Currently, the following libraries are available, and plans are in place to expand support to additional programming language.
See GraphAr C++ Library for details about the building of the C++ library.
The GraphAr Java library is created with bindings to the C++ library (currently at version v0.10.0), utilizing Alibaba-FastFFI for implementation. See GraphAr Java Library for details about the building of the Java library.
See GraphAr Spark Library for details about the Spark library.
The GraphAr PySpark library is developed as bindings to the GraphAr Spark library. See GraphAr PySpark Library for details about the PySpark library.
GraphAr is distributed under Apache License 2.0. Please note that third-party libraries may not have the same license as GraphAr.
@article{li2023enhancing,
author = {Xue Li and Weibin Zeng and Zhibin Wang and Diwen Zhu and Jingbo Xu and Wenyuan Yu and Jingren Zhou},
title = {Enhancing Data Lakes with GraphAr: Efficient Graph Data Management with a Specialized Storage Scheme},
year = {2023},
url = {https://doi.org/10.48550/arXiv.2312.09577},
doi = {10.48550/ARXIV.2312.09577},
eprinttype = {arXiv},
eprint = {2312.09577},
biburl = {https://dblp.org/rec/journals/corr/abs-2312-09577.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
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