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new 29a59dc1 update README.md (#208)
29a59dc1 is described below
commit 29a59dc1bbc6d9182fa95343bdac3c874a347a87
Author: imbajin <[email protected]>
AuthorDate: Wed May 3 04:19:18 2023 +0000
update README.md (#208)
I made some slight grammatical fixes and removed a redundant paragraph.
0fde5cb7a51565145a2c76638d1b082ab5ce49b2
---
docs/_print/index.html | 6 +++---
docs/index.xml | 19 +++++++++----------
docs/introduction/readme/index.html | 8 ++++----
en/sitemap.xml | 2 +-
sitemap.xml | 2 +-
5 files changed, 18 insertions(+), 19 deletions(-)
diff --git a/docs/_print/index.html b/docs/_print/index.html
index b83b8a8a..45502397 100644
--- a/docs/_print/index.html
+++ b/docs/_print/index.html
@@ -3,9 +3,9 @@
<link rel=stylesheet href=/css/prism.css><script
type=application/javascript>var
doNotTrack=!1;doNotTrack||(window.ga=window.ga||function(){(ga.q=ga.q||[]).push(arguments)},ga.l=+new
Date,ga("create","UA-00000000-0","auto"),ga("send","pageview"))</script><script
async src=https://www.google-analytics.com/analytics.js></script></head><body
class=td-section><header><nav class="js-navbar-scroll navbar navbar-expand
navbar-dark flex-column flex-md-row td-navbar"><a class=navbar-brand href=/>
[...]
<a href=# onclick="return print(),!1">Click here to print</a>.</p><p><a
href=/docs/>Return to the regular view of this page</a>.</p></div><h1
class=title>Documentation</h1><ul><li>1: <a
href=#pg-7dd24b6126e5b249f96de862bfc51025>Introduction with
HugeGraph</a></li><li>2: <a href=#pg-64d47cfe1880443239e7c31f8fd1ac1a>Download
HugeGraph</a></li><li>3: <a href=#pg-08b1b69f6319108b0455d24614fdd660>Quick
Start</a></li><ul><li>3.1: <a
href=#pg-8ec2ee5fd8ff8e48255d55eab65d92b6>HugeGraph-Server Qu [...]
implemented the <a href=https://tinkerpop.apache.org>Apache TinkerPop3</a>
framework and is fully compatible with the <a
href=https://tinkerpop.apache.org/gremlin.html>Gremlin</a> 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).
-It supports large-scale distributed graph computing (OLAP).</p><p>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.</p><p>Typical application scenarios of Huge [...]
-HugeGraph supports multi-user parallel operations. Users can enter Gremlin
query statements and get graph query results in time. They can also call
HugeGraph API in user programs for graph analysis or query.</p><p>This system
has the following features:</p><ul><li>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 an [...]
+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).</p><p>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.</p><h3 id=features>Features</h3><p>HugeGr [...]
+HugeGraph supports multi-user parallel operations. Users can enter Gremlin
query statements and get graph query results in time. They can also call
HugeGraph API in user programs for graph analysis or query.</p><p>This system
has the following features:</p><ul><li>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 an [...]
</span></span><span style=display:flex><span><span
style=color:#8f5902;font-style:italic># please check the latest version (e.g.
here is 1.0.0)</span>
</span></span><span style=display:flex><span>wget
https://dist.apache.org/repos/dist/dev/incubator/hugegraph/1.0.0/apache-hugegraph-toolchain-incubating-1.0.0.tar.gz
</span></span><span style=display:flex><span>tar zxf *hugegraph-*.tar.gz
diff --git a/docs/index.xml b/docs/index.xml
index 9b4c6462..400daaa8 100644
--- a/docs/index.xml
+++ b/docs/index.xml
@@ -1632,10 +1632,9 @@ restserver.url=http://0.0.0.0:8080
<h3 id="summary">Summary</h3>
<p>HugeGraph is an easy-to-use, efficient, general-purpose open source
graph database system(Graph Database, <a
href="https://github.com/hugegraph/hugegraph";>GitHub project address</a>),
implemented the <a href="https://tinkerpop.apache.org";>Apache
TinkerPop3</a> framework and is fully compatible with the <a
href="https://tinkerpop.apache.org/gremlin.html";>Gremlin</a> 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).</p>
-<p>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.</p>
-<p>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.</p>
+<p>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.</p>
<h3 id="features">Features</h3>
<p>HugeGraph supports graph operations in online and offline environments,
supports batch import of data, supports efficient complex relationship
analysis, and can be seamlessly integrated with big data platforms.
HugeGraph supports multi-user parallel operations. Users can enter Gremlin
query statements and get graph query results in time. They can also call
HugeGraph API in user programs for graph analysis or query.</p>
@@ -1643,14 +1642,14 @@ HugeGraph supports multi-user parallel operations.
Users can enter Gremlin query
<ul>
<li>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.</li>
<li>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.</li>
-<li>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.</li>
-<li>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.</li>
-<li>Open: HugeGraph code is open source (Apache 2 License), customers can
modify and customize independently, and selectively give back to the open
source community.</li>
+<li>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.</li>
+<li>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.</li>
+<li>Open: HugeGraph code is open source (Apache 2 License), customers can
modify and customize independently, and selectively give back to the
open-source community.</li>
</ul>
<p>The functions of this system include but are not limited to:</p>
<ul>
-<li>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)</li>
-<li>With a visual operation interface, it can be used for operation,
analysis and display diagrams, reducing the threshold for users to use</li>
+<li>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)</li>
+<li>With a visual operation interface, it can be used for operation,
analysis, and display diagrams, reducing the threshold for users to use</li>
<li>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.</li>
<li>Implemented based on Apache TinkerPop3 framework, supports Gremlin
graph query language</li>
<li>Support attribute graph, attributes can be added to vertices and edges,
and support rich attribute types</li>
@@ -1666,13 +1665,13 @@ HugeGraph supports multi-user parallel operations.
Users can enter Gremlin query
<li><a href="/docs/quickstart/hugegraph-server">HugeGraph-Server</a>:
HugeGraph-Server is the core part of the HugeGraph project, including
submodules such as Core, Backend, and API;
<ul>
<li>Core: Graph engine implementation, connecting the Backend module
downward and supporting the API module upward;</li>
-<li>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;</li>
+<li>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;</li>
<li>API: Built-in REST Server, provides RESTful API to users, and is fully
compatible with Gremlin query.</li>
</ul>
</li>
<li><a href="/docs/quickstart/hugegraph-client">HugeGraph-Client</a>:
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;</li>
<li><a href="/docs/quickstart/hugegraph-loader">HugeGraph-Loader</a>:
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;</li>
-<li><a
href="/docs/quickstart/hugegraph-computer">HugeGraph-Computer</a>:
HugeGraph-Computer is a distributed graph processing system for HugeGraph
(OLAP). It is an implementation of <a
href="https://kowshik.github.io/JPregel/pregel_paper.pdf";>Pregel</a>. It
runs on Kubernetes framework;</li>
+<li><a
href="/docs/quickstart/hugegraph-computer">HugeGraph-Computer</a>:
HugeGraph-Computer is a distributed graph processing system for HugeGraph
(OLAP). It is an implementation of <a
href="https://kowshik.github.io/JPregel/pregel_paper.pdf";>Pregel</a>. It
runs on the Kubernetes framework;</li>
<li><a href="/docs/quickstart/hugegraph-hubble">HugeGraph-Hubble</a>:
HugeGraph-Hubble is HugeGraph&rsquo;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;</li>
<li><a href="/docs/quickstart/hugegraph-tools">HugeGraph-Tools</a>:
HugeGraph-Tools is HugeGraph&rsquo;s deployment and management tools,
including functions such as managing graphs, backup/restore, Gremlin execution,
etc.</li>
</ul>
diff --git a/docs/introduction/readme/index.html
b/docs/introduction/readme/index.html
index 65d19112..0e68fe65 100644
--- a/docs/introduction/readme/index.html
+++ b/docs/introduction/readme/index.html
@@ -1,6 +1,6 @@
<!doctype html><html lang=en class=no-js><head><meta charset=utf-8><meta
name=viewport
content="width=device-width,initial-scale=1,shrink-to-fit=no"><meta
name=generator content="Hugo 0.102.3"><meta name=robots content="index,
follow"><link rel="shortcut icon" href=/favicons/favicon.ico><link
rel=apple-touch-icon href=/favicons/apple-touch-icon-180x180.png
sizes=180x180><link rel=icon type=image/png href=/favicons/favicon-16x16.png
sizes=16x16><link rel=icon type=image/png href=/favicons [...]
HugeGraph is an easy-to-use, efficient, general-purpose open source graph
database system(Graph Database, GitHub project address),
-implemented …"><meta property="og:title" content="Introduction with
HugeGraph"><meta property="og:description" content="Summary HugeGraph is an
easy-to-use, efficient, general-purpose open source graph database system(Graph
Database, GitHub project address), implemented the Apache TinkerPop3 framework
and is fully compatible with the Gremlin query language, With complete
toolchain components, it helps users to easily build applications and products
based on graph databases. HugeGraph sup [...]
+implemented …"><meta property="og:title" content="Introduction with
HugeGraph"><meta property="og:description" content="Summary HugeGraph is an
easy-to-use, efficient, general-purpose open source graph database system(Graph
Database, GitHub project address), implemented the Apache TinkerPop3 framework
and is fully compatible with the Gremlin query language, With complete
toolchain components, it helps users easily build applications and products
based on graph databases. HugeGraph suppor [...]
<link rel=stylesheet href=/css/prism.css><script
type=application/javascript>var
doNotTrack=!1;doNotTrack||(window.ga=window.ga||function(){(ga.q=ga.q||[]).push(arguments)},ga.l=+new
Date,ga("create","UA-00000000-0","auto"),ga("send","pageview"))</script><script
async src=https://www.google-analytics.com/analytics.js></script></head><body
class=td-page><header><nav class="js-navbar-scroll navbar navbar-expand
navbar-dark flex-column flex-md-row td-navbar"><a class=navbar-brand href=/><sp
[...]
<a
href=https://github.com/apache/incubator-hugegraph-doc/edit/master/content/en/docs/introduction/README.md
class=td-page-meta--edit target=_blank rel=noopener><i class="fa fa-edit
fa-fw"></i> Edit this page</a>
<a
href="https://github.com/apache/incubator-hugegraph-doc/new/master/content/en/docs/introduction/README.md?filename=change-me.md&value=---%0Atitle%3A+%22Long+Page+Title%22%0AlinkTitle%3A+%22Short+Nav+Title%22%0Aweight%3A+100%0Adescription%3A+%3E-%0A+++++Page+description+for+heading+and+indexes.%0A---%0A%0A%23%23+Heading%0A%0AEdit+this+template+to+create+your+new+page.%0A%0A%2A+Give+it+a+good+name%2C+ending+in+%60.md%60+-+e.g.+%60getting-started.md%60%0A%2A+Edit+the+%22front+matter%22+s
[...]
@@ -8,9 +8,9 @@ implemented …"><meta property="og:title" content="Introduction
with HugeGraph"
<a href=https://github.com/apache/incubator-hugegraph/issues/new
class=td-page-meta--project-issue target=_blank rel=noopener><i class="fas
fa-tasks fa-fw"></i> Create project issue</a>
<a id=print href=/docs/_print/><i class="fa fa-print fa-fw"></i> Print entire
section</a></div><div class=td-toc><nav id=TableOfContents><ul><li><ul><li><a
href=#summary>Summary</a></li><li><a href=#features>Features</a></li><li><a
href=#modules>Modules</a></li><li><a href=#contact-us>Contact
Us</a></li></ul></li></ul></nav></div></aside><main class="col-12 col-md-9
col-xl-8 pl-md-5" role=main><nav aria-label=breadcrumb class=td-breadcrumbs><ol
class=breadcrumb><li class=breadcrumb-item> [...]
implemented the <a href=https://tinkerpop.apache.org>Apache TinkerPop3</a>
framework and is fully compatible with the <a
href=https://tinkerpop.apache.org/gremlin.html>Gremlin</a> 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).
-It supports large-scale distributed graph computing (OLAP).</p><p>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.</p><p>Typical application scenarios of Huge [...]
-HugeGraph supports multi-user parallel operations. Users can enter Gremlin
query statements and get graph query results in time. They can also call
HugeGraph API in user programs for graph analysis or query.</p><p>This system
has the following features:</p><ul><li>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 an [...]
+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).</p><p>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.</p><h3 id=features>Features</h3><p>HugeGr [...]
+HugeGraph supports multi-user parallel operations. Users can enter Gremlin
query statements and get graph query results in time. They can also call
HugeGraph API in user programs for graph analysis or query.</p><p>This system
has the following features:</p><ul><li>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 an [...]
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diff --git a/en/sitemap.xml b/en/sitemap.xml
index 67fdf4b9..8fc9004d 100644
--- a/en/sitemap.xml
+++ b/en/sitemap.xml
@@ -1 +1 @@
-<?xml version="1.0" encoding="utf-8" standalone="yes"?><urlset
xmlns="http://www.sitemaps.org/schemas/sitemap/0.9";
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[...]
\ No newline at end of file
diff --git a/sitemap.xml b/sitemap.xml
index 0f3c7555..9b0c85d5 100644
--- a/sitemap.xml
+++ b/sitemap.xml
@@ -1 +1 @@
-<?xml version="1.0" encoding="utf-8" standalone="yes"?><sitemapindex
xmlns="http://www.sitemaps.org/schemas/sitemap/0.9";><sitemap><loc>/en/sitemap.xml</loc><lastmod>2023-02-23T14:50:09+08:00</lastmod></sitemap><sitemap><loc>/cn/sitemap.xml</loc><lastmod>2023-02-23T14:50:09+08:00</lastmod></sitemap></sitemapindex>
\ No newline at end of file
+<?xml version="1.0" encoding="utf-8" standalone="yes"?><sitemapindex
xmlns="http://www.sitemaps.org/schemas/sitemap/0.9";><sitemap><loc>/en/sitemap.xml</loc><lastmod>2023-05-02T23:18:32-05:00</lastmod></sitemap><sitemap><loc>/cn/sitemap.xml</loc><lastmod>2023-02-23T14:50:09+08:00</lastmod></sitemap></sitemapindex>
\ No newline at end of file
