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new 0fde5cb7 update README.md (#208)
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commit 0fde5cb7a51565145a2c76638d1b082ab5ce49b2
Author: John Whelan <[email protected]>
AuthorDate: Tue May 2 23:18:32 2023 -0500
update README.md (#208)
I made some slight grammatical fixes and removed a redundant paragraph.
---
content/en/docs/introduction/README.md | 20 +++++++++-----------
1 file changed, 9 insertions(+), 11 deletions(-)
diff --git a/content/en/docs/introduction/README.md
b/content/en/docs/introduction/README.md
index fd0c1e93..ca6eeef5 100644
--- a/content/en/docs/introduction/README.md
+++ b/content/en/docs/introduction/README.md
@@ -8,12 +8,10 @@ weight: 1
HugeGraph is an easy-to-use, efficient, general-purpose open source graph
database system(Graph Database, [GitHub project
address](https://github.com/hugegraph/hugegraph)),
implemented the [Apache TinkerPop3](https://tinkerpop.apache.org) framework
and is fully compatible with the
[Gremlin](https://tinkerpop.apache.org/gremlin.html) query language,
-With complete toolchain components, it helps users to easily build
applications and products based on graph databases. HugeGraph supports fast
import of more than 10 billion vertices and edges, and provides
millisecond-level relational query capability (OLTP).
+With complete toolchain components, it helps users easily build applications
and products based on graph databases. HugeGraph supports fast import of more
than 10 billion vertices and edges, and provides millisecond-level relational
query capability (OLTP).
It supports large-scale distributed graph computing (OLAP).
-Typical application scenarios of HugeGraph include deep relationship
exploration, association analysis, path search, feature extraction, data
clustering, community detection, knowledge graph, etc., and are applicable to
business fields such as network security, telecommunication fraud, financial
risk control, advertising recommendation, social network and intelligence
Robots etc.
-
-Typical application scenarios of HugeGraph include deep relationship
exploration, association analysis, path search, feature extraction, data
clustering, community detection, knowledge graph, etc., and are applicable to
business fields such as network security, telecommunication fraud, financial
risk control, advertising recommendation, social network and intelligence
Robots etc.
+Typical application scenarios of HugeGraph include deep relationship
exploration, association analysis, path search, feature extraction, data
clustering, community detection, knowledge graph, etc., and are applicable to
business fields such as network security, telecommunication fraud, financial
risk control, advertising recommendation, social network, and intelligence
Robots, etc.
### Features
@@ -24,14 +22,14 @@ This system has the following features:
- Ease of use: HugeGraph supports Gremlin graph query language and RESTful
API, provides common interfaces for graph retrieval, and has peripheral tools
with complete functions to easily implement various graph-based query and
analysis operations.
- Efficiency: HugeGraph has been deeply optimized in graph storage and graph
computing, and provides a variety of batch import tools, which can easily
complete the rapid import of tens of billions of data, and achieve
millisecond-level response for graph retrieval through optimized queries.
Supports simultaneous online real-time operations of thousands of users.
-- Universal: HugeGraph supports the Apache Gremlin standard graph query
language and the Property Graph standard graph modeling method, and supports
graph-based OLTP and OLAP schemes. Integrate Apache Hadoop and Apache Spark big
data platform.
-- Scalable: supports distributed storage, multiple copies of data and
horizontal expansion, built-in multiple back-end storage engines, and can
easily expand the back-end storage engine through plug-ins.
-- Open: HugeGraph code is open source (Apache 2 License), customers can modify
and customize independently, and selectively give back to the open source
community.
+- Universal: HugeGraph supports the Apache Gremlin standard graph query
language and the Property Graph standard graph modeling method, and supports
graph-based OLTP and OLAP schemes. Integrate Apache Hadoop and Apache Spark big
data platforms.
+- Scalable: supports distributed storage, multiple copies of data, and
horizontal expansion, built-in multiple back-end storage engines, and can
easily expand the back-end storage engine through plug-ins.
+- Open: HugeGraph code is open source (Apache 2 License), customers can modify
and customize independently, and selectively give back to the open-source
community.
The functions of this system include but are not limited to:
-- Supports batch import of data from multiple data sources (including local
files, HDFS files, MySQL databases and other data sources), and supports import
of multiple file formats (including TXT, CSV, JSON and other formats)
-- With a visual operation interface, it can be used for operation, analysis
and display diagrams, reducing the threshold for users to use
+- Supports batch import of data from multiple data sources (including local
files, HDFS files, MySQL databases, and other data sources), and supports
import of multiple file formats (including TXT, CSV, JSON, and other formats)
+- With a visual operation interface, it can be used for operation, analysis,
and display diagrams, reducing the threshold for users to use
- Optimized graph interface: shortest path (Shortest Path), K-step connected
subgraph (K-neighbor), K-step to reach the adjacent point (K-out), personalized
recommendation algorithm PersonalRank, etc.
- Implemented based on Apache TinkerPop3 framework, supports Gremlin graph
query language
- Support attribute graph, attributes can be added to vertices and edges, and
support rich attribute types
@@ -46,11 +44,11 @@ The functions of this system include but are not limited to:
- [HugeGraph-Server](/docs/quickstart/hugegraph-server): HugeGraph-Server is
the core part of the HugeGraph project, including submodules such as Core,
Backend, and API;
- Core: Graph engine implementation, connecting the Backend module downward
and supporting the API module upward;
- - Backend: Realize the storage of graph data to the backend. The supported
backends include: Memory, Cassandra, ScyllaDB, RocksDB, HBase, MySQL and
PostgreSQL. Users can choose one according to the actual situation;
+ - Backend: Realize the storage of graph data to the backend. The supported
backends include: Memory, Cassandra, ScyllaDB, RocksDB, HBase, MySQL, and
PostgreSQL. Users can choose one according to the actual situation;
- API: Built-in REST Server, provides RESTful API to users, and is fully
compatible with Gremlin query.
- [HugeGraph-Client](/docs/quickstart/hugegraph-client): HugeGraph-Client
provides a RESTful API client for connecting to HugeGraph-Server. Currently,
only Java version is implemented. Users of other languages can implement it by
themselves;
- [HugeGraph-Loader](/docs/quickstart/hugegraph-loader): HugeGraph-Loader is a
data import tool based on HugeGraph-Client, which converts ordinary text data
into graph vertices and edges and inserts them into graph database;
-- [HugeGraph-Computer](/docs/quickstart/hugegraph-computer):
HugeGraph-Computer is a distributed graph processing system for HugeGraph
(OLAP). It is an implementation of
[Pregel](https://kowshik.github.io/JPregel/pregel_paper.pdf). It runs on
Kubernetes framework;
+- [HugeGraph-Computer](/docs/quickstart/hugegraph-computer):
HugeGraph-Computer is a distributed graph processing system for HugeGraph
(OLAP). It is an implementation of
[Pregel](https://kowshik.github.io/JPregel/pregel_paper.pdf). It runs on the
Kubernetes framework;
- [HugeGraph-Hubble](/docs/quickstart/hugegraph-hubble): HugeGraph-Hubble is
HugeGraph's web visualization management platform, a one-stop visual analysis
platform. The platform covers the whole process from data modeling, to rapid
data import, to online and offline analysis of data, and unified management of
graphs;
- [HugeGraph-Tools](/docs/quickstart/hugegraph-tools): HugeGraph-Tools is
HugeGraph's deployment and management tools, including functions such as
managing graphs, backup/restore, Gremlin execution, etc.
