From 8defffdaedec466aec3b8dc6870ac5b18f105ed2 Mon Sep 17 00:00:00 2001 From: dpdjvhxm Date: Fri, 19 Apr 2024 10:46:49 +0900 Subject: [PATCH 01/46] Test --- static/admin/config.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/static/admin/config.yml b/static/admin/config.yml index c98f8aae9..8370889ed 100644 --- a/static/admin/config.yml +++ b/static/admin/config.yml @@ -1,6 +1,6 @@ backend: name: github - repo: ( ) + repo: (test) branch: new-web commit_messages: create: "Create {{collection}} “{{slug}}”" From f11b235b5295b1225188218c47cc5cb17de30298 Mon Sep 17 00:00:00 2001 From: dpdjvhxm Date: Fri, 19 Apr 2024 10:47:52 +0900 Subject: [PATCH 02/46] Update gatsby-config.js --- gatsby-config.js | 7 +++++++ static/admin/config.yml | 2 +- 2 files changed, 8 insertions(+), 1 deletion(-) diff --git a/gatsby-config.js b/gatsby-config.js index 5ab15d0ee..5ea311ca5 100644 --- a/gatsby-config.js +++ b/gatsby-config.js @@ -89,6 +89,13 @@ module.exports = { destination: "/", } }, + { + resolve: `gatsby-plugin-canonical-urls`, + options: { + siteUrl: `https://age.apache.org`, + stripQueryString: true, + }, + }, "gatsby-plugin-netlify", // make sure to keep it last in the array ], }; diff --git a/static/admin/config.yml b/static/admin/config.yml index 8370889ed..c98f8aae9 100644 --- a/static/admin/config.yml +++ b/static/admin/config.yml @@ -1,6 +1,6 @@ backend: name: github - repo: (test) + repo: ( ) branch: new-web commit_messages: create: "Create {{collection}} “{{slug}}”" From 96c60e3b8c7171a1ed1d4a162eb4ff2e933b1f55 Mon Sep 17 00:00:00 2001 From: dpdjvhxm Date: Fri, 19 Apr 2024 11:07:15 +0900 Subject: [PATCH 03/46] Changes search result text --- src/components/Layout.js | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/components/Layout.js b/src/components/Layout.js index a8d312a0d..cb2b95c70 100644 --- a/src/components/Layout.js +++ b/src/components/Layout.js @@ -60,7 +60,7 @@ const TemplateWrapper = ({ children }) => { '/download' : 'Download | Apache AGE', 'https://github.com/apache/age' : 'Apache AGE Github | Apache AGE', } - return urlToTitle[pathName] ?? 'Apache AGE | Graph Database Extension for PostgreSQL'; + return urlToTitle[pathName] ?? 'Graph Database Apache AGE'; } else { return 'Apache AGE'; } From 3d1b0ac36c308ba56859b0740df066f350c7559e Mon Sep 17 00:00:00 2001 From: dpdjvhxm Date: Fri, 19 Apr 2024 12:49:28 +0900 Subject: [PATCH 04/46] Fix gatsby-config.js and title --- gatsby-config.js | 7 ------- src/components/Layout.js | 2 +- 2 files changed, 1 insertion(+), 8 deletions(-) diff --git a/gatsby-config.js b/gatsby-config.js index 5ea311ca5..5ab15d0ee 100644 --- a/gatsby-config.js +++ b/gatsby-config.js @@ -89,13 +89,6 @@ module.exports = { destination: "/", } }, - { - resolve: `gatsby-plugin-canonical-urls`, - options: { - siteUrl: `https://age.apache.org`, - stripQueryString: true, - }, - }, "gatsby-plugin-netlify", // make sure to keep it last in the array ], }; diff --git a/src/components/Layout.js b/src/components/Layout.js index cb2b95c70..8acd67616 100644 --- a/src/components/Layout.js +++ b/src/components/Layout.js @@ -60,7 +60,7 @@ const TemplateWrapper = ({ children }) => { '/download' : 'Download | Apache AGE', 'https://github.com/apache/age' : 'Apache AGE Github | Apache AGE', } - return urlToTitle[pathName] ?? 'Graph Database Apache AGE'; + return urlToTitle[pathName] ?? 'Apache AGE Graph Database | Apache AGE'; } else { return 'Apache AGE'; } From 8e465683db6cb46173078d731c54f84fc04ece9a Mon Sep 17 00:00:00 2001 From: dpdjvhxm Date: Fri, 19 Apr 2024 13:41:06 +0900 Subject: [PATCH 05/46] Change meta description --- gatsby-config.js | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gatsby-config.js b/gatsby-config.js index 5ab15d0ee..c6bea0353 100644 --- a/gatsby-config.js +++ b/gatsby-config.js @@ -3,7 +3,7 @@ module.exports = { title: "Apache AGE, Graph database optimized for fast analysis and real-time data processing. It is provided as an extension to PostgreSQL.", - description: "Since May 2022, Apache AGE, a Top-Level Apache Project born from AgensGraph and developed by Bitnine, optimizes graph databases for fast analysis and real-time processing as a PostgreSQL extension under Apache License.", + description: "Apache AGE has been registered as an Apache Top level project since May 2022, and was inspired by AgensGraph developed by Bitnine, optimizing the graph database for fast analysis and real-time processing.", }, pathPrefix: "/", plugins: [ From e288f0ccfa4739b12170f9536a814249cd45a553 Mon Sep 17 00:00:00 2001 From: dpdjvhxm Date: Fri, 19 Apr 2024 13:43:48 +0900 Subject: [PATCH 06/46] Fix sitemap.xml for broken links --- static/sitemap.xml | 6 ------ 1 file changed, 6 deletions(-) diff --git a/static/sitemap.xml b/static/sitemap.xml index 7766bc202..5d693d78b 100644 --- a/static/sitemap.xml +++ b/static/sitemap.xml @@ -38,9 +38,6 @@ https://age.apache.org/overview - - https://age.apache.org/download/apache-age-viewer-1.0.0-rc2-incubating-src.tar.gz.sha512 - https://age.apache.org/age-manual/v0.6.0/functions/scalar_functions.html @@ -56,9 +53,6 @@ https://age.apache.org/age-manual/v0.6.0/clauses/order_by.html - - https://age.apache.org/download/apache-age-viewer-1.0.0-rc2-incubating-src.tar.gz.asc - https://age.apache.org/age-manual/v0.6.0/clauses/skip.html From 2456d608e5094d3fe6cae9af5be8831fbc6e2f26 Mon Sep 17 00:00:00 2001 From: dpdjvhxm Date: Fri, 19 Apr 2024 16:24:33 +0900 Subject: [PATCH 07/46] Update landing page: Key Update and image --- src/templates/index-page.js | 13 +++++++------ static/img/AGE-Architecture.webp | Bin 0 -> 71134 bytes 2 files changed, 7 insertions(+), 6 deletions(-) create mode 100644 static/img/AGE-Architecture.webp diff --git a/src/templates/index-page.js b/src/templates/index-page.js index 6bdfa9b57..347231424 100644 --- a/src/templates/index-page.js +++ b/src/templates/index-page.js @@ -62,6 +62,7 @@ export const IndexPageTemplate = ({ Through Apache AGE, PostgreSQL users will gain access to graph query modeling within the existing relational database.



Users can read and write graph data in nodes and edges. They can also use various algorithms such as variable length and edge traversal when analyzing data. + img
@@ -79,18 +80,18 @@ export const IndexPageTemplate = ({
Give feedback at the GitHub (#1705)


-
Apache AGE on Microsoft Azure PostgreSQL
+
Add graph store implementation for Apache AGE in langchain github
-
Support the integration of Microsoft Azure PostgreSQL with Apache AGE by casting your vote and feedback!
+
Check out the pull request for the implementation of the GraphStore class for the Apache Age graph database in the LangChain repository.


-
PostgresPro adapted Apache AGE
-
Apache_AGE is a Postgres Pro extension that provides graph database functionality. The goal of the project is to create single storage that can handle both relational and graph model data so that users can use standard ANSI SQL along with openCypher.
+
Proposal: Vector handling with extension(pgvector)
+
A proposal for applying PGvector to Apache AGE has been posted on GitHub. Please read and leave a comment or opinion if you are interested.
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z$RKZ=^{if=_;luc|A-t;1k$c^uXyDjB+bIdwCGK#{Qb?`@!lZ)bONEye*R|Z_y49ok)MR*mLKmQXLMWnJS zK=9$1br_tL59HBSSoK35&072k)e#_yH`-x==?#9_Ls3*Ef8 zR)xHBs`L82A1h~-x;(oF(Sa%--q8xCpU;uukZ$ld*|pw-41Owv`dIW27Q^94r z^bKA=Lx?XQJkV}Z4bgr!M&ntn3Oj^jc+EFCdC}b@QK1j5G8jY8Tme-nS?fHY6MYHT$kNpgc1+WAVvsi4mkU*J#OF)y@(q%kPECj2YT zN_=aZm8MHB3SzE zJ%20DV*nF_O@jVQUxA#+Y_s`!2d#z7>sv+D&3uDzAxDvzr6ek-BszaRwxwkM7lEpm zwsRDI{y1a=ELkVWKbDMU1D8)T|MvysO<+w7}R0F2Xj$!H~Hz{xMT z39YsP!}&hPe^`1|VbG`hZgX!i`d3j={)lzfu`Er2&0kl8J2uHOE3er(995aAMm~uI zicwD*^@X|rW&TjAH^hs8q+FJssA3^uV~*Ije{&PBu#cSJf`T>rqQswNr+PrZ$d{_t ztO27=M(5k947>!H<9Kp#l zlyTQxt*&Twju|$8*aHL-!B6A{Sgc=^A1blHt0)PFJ@ot1h8Dx^UDOxgffaO==nkRRY{fm=shUAcG6)#_W1G0^z@c0awF*)>prC7_G!z(^KVMj&g5e6*JfX0Gf5bL;wH) literal 0 HcmV?d00001 From 7af07cad56722b2568a4574d898d5b248f96e899 Mon Sep 17 00:00:00 2001 From: dpdjvhxm <137974842+dpdjvhxm@users.noreply.github.com> Date: Mon, 22 Apr 2024 08:31:59 +0900 Subject: [PATCH 08/46] =?UTF-8?q?Create=20Blog=20=E2=80=9C2024-04-21-how-t?= =?UTF-8?q?o-integrate-langchain-with-apache-age-for-graph-database-applic?= =?UTF-8?q?ations=E2=80=9D?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...che-age-for-graph-database-applications.md | 94 +++++++++++++++++++ 1 file changed, 94 insertions(+) create mode 100644 src/pages/blog/2024-04-21-how-to-integrate-langchain-with-apache-age-for-graph-database-applications.md diff --git a/src/pages/blog/2024-04-21-how-to-integrate-langchain-with-apache-age-for-graph-database-applications.md b/src/pages/blog/2024-04-21-how-to-integrate-langchain-with-apache-age-for-graph-database-applications.md new file mode 100644 index 000000000..52aaa25bc --- /dev/null +++ b/src/pages/blog/2024-04-21-how-to-integrate-langchain-with-apache-age-for-graph-database-applications.md @@ -0,0 +1,94 @@ +--- +templateKey: blog-post +title: How to Integrate LangChain with Apache AGE for Graph Database Applications +date: 2024-04-21T23:25:01.575Z +description: How to Integrate LangChain with Apache AGE for Graph Database Applications +featuredpost: true +featuredimage: /img/ASF_Logo.svg +tags: + - LangChain +--- + + +**Introduction to Graph Databases with Apache AGE and LangChain Integration** + + + + + +Integrating LangChain with Apache AGE can significantly enhance graph database applications by enabling conversational AI capabilities. This integration allows users to query and interact with graph databases using natural language, making complex data more accessible and user-friendly. Below is a comprehensive guide on how to seamlessly integrate these technologies for effective graph database management. + +## 1. Define the Application Scope for Graph Database Interactions + +Before beginning the technical integration, clearly outline the functionalities of your application. Determine the types of graph queries it will handle and how responses should be generated based on the data stored in your Apache AGE graph database. + +## 2. Setup Your Development Environment for Graph Database Management + +* Install Apache AGE: Ensure Apache AGE is properly installed in your PostgreSQL environment. Organize your graph data effectively for optimal performance. +* Setup LangChain: Install LangChain in your Python environment. LangChain facilitates building applications that utilize language models to interact with databases. + +``` +pip install langchain +``` + +## 3. Integrate Apache AGE with LangChain for Enhanced Graph Database Queries + +* Database Connection: Configure LangChain to connect with your PostgreSQL database where Apache AGE is hosted. Use SQLAlchemy or a similar library to manage database sessions efficiently. + +``` +from sqlalchemy import create_engine + +db_string = "postgresql://username:password@localhost:5432/mydatabase" + +engine = create_engine(db_string) +``` + +* Query Interface: Develop functionalities within LangChain to convert natural language inputs into SQL or openCypher queries for Apache AGE, enabling intuitive graph database interactions. + +` +def natural_language_to_age_query(natural_language_text): + +return "MATCH (n) RETURN n" +` + +## 4. Implement Conversational Logic to Enhance User Interaction with Graph Databases + +Create logical workflows in LangChain that handle user inputs, convert them into graph database queries, execute these queries, and return user-friendly results. + +` +from langchain.chains import Chain + +def handle_query(user_input): + +query = natural_language_to_age_query(user_input) + +result = engine.execute(query) + +return format_result(result) + +chain = Chain(\[handle_query]) +` + +# Example usage + +### response = chain.run("Tell me how many users are connected to node A") + +### print(response) + +## 5. Test and Iterate Your Graph Database Application + +Develop robust test cases to ensure the integration handles natural language understanding and graph database queries accurately. Use real user feedback to refine the application. + +## 6. Deploy and Scale Your Graph Database Application + +Consider deploying your application on a cloud platform to ensure scalability and security. Ensure that both the LangChain and Apache AGE components are optimized for cloud environments. + +## 7. Monitor and Update Your Graph Database System + +Continuously monitor your application’s performance and update it based on user feedback and new requirements. This ensures your graph database application remains efficient and relevant. + +## Conclusion + +Integrating LangChain with Apache AGE transforms graph database management by making it more interactive and accessible through natural language processing. This guide provides the steps needed to develop applications that leverage the best of both technologies, enhancing data accessibility and user experience. + + \ No newline at end of file From 8e87f2775e0449a6008320e0fd170f8fff826ea5 Mon Sep 17 00:00:00 2001 From: dpdjvhxm <137974842+dpdjvhxm@users.noreply.github.com> Date: Mon, 22 Apr 2024 08:34:10 +0900 Subject: [PATCH 09/46] =?UTF-8?q?Update=20Blog=20=E2=80=9C2024-04-21-how-t?= =?UTF-8?q?o-integrate-langchain-with-apache-age-for-graph-database-applic?= =?UTF-8?q?ations=E2=80=9D?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...langchain-with-apache-age-for-graph-database-applications.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/pages/blog/2024-04-21-how-to-integrate-langchain-with-apache-age-for-graph-database-applications.md b/src/pages/blog/2024-04-21-how-to-integrate-langchain-with-apache-age-for-graph-database-applications.md index 52aaa25bc..79ab6c181 100644 --- a/src/pages/blog/2024-04-21-how-to-integrate-langchain-with-apache-age-for-graph-database-applications.md +++ b/src/pages/blog/2024-04-21-how-to-integrate-langchain-with-apache-age-for-graph-database-applications.md @@ -4,7 +4,7 @@ title: How to Integrate LangChain with Apache AGE for Graph Database Application date: 2024-04-21T23:25:01.575Z description: How to Integrate LangChain with Apache AGE for Graph Database Applications featuredpost: true -featuredimage: /img/ASF_Logo.svg +featuredimage: /img/ASF_Logo.png tags: - LangChain --- From ad21d656b353425bbb215344d7d50743d91c5202 Mon Sep 17 00:00:00 2001 From: dpdjvhxm <137974842+dpdjvhxm@users.noreply.github.com> Date: Mon, 22 Apr 2024 08:35:01 +0900 Subject: [PATCH 10/46] =?UTF-8?q?Update=20Blog=20=E2=80=9C2024-04-21-how-t?= =?UTF-8?q?o-integrate-langchain-with-apache-age-for-graph-database-applic?= =?UTF-8?q?ations=E2=80=9D?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...langchain-with-apache-age-for-graph-database-applications.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/pages/blog/2024-04-21-how-to-integrate-langchain-with-apache-age-for-graph-database-applications.md b/src/pages/blog/2024-04-21-how-to-integrate-langchain-with-apache-age-for-graph-database-applications.md index 79ab6c181..200606a7c 100644 --- a/src/pages/blog/2024-04-21-how-to-integrate-langchain-with-apache-age-for-graph-database-applications.md +++ b/src/pages/blog/2024-04-21-how-to-integrate-langchain-with-apache-age-for-graph-database-applications.md @@ -6,7 +6,7 @@ description: How to Integrate LangChain with Apache AGE for Graph Database Appli featuredpost: true featuredimage: /img/ASF_Logo.png tags: - - LangChain + - LangChain.ApacheAGE --- From f07c2d83c36627667c558010f4afb7bbe10ecb30 Mon Sep 17 00:00:00 2001 From: dpdjvhxm Date: Mon, 22 Apr 2024 09:14:05 +0900 Subject: [PATCH 11/46] Update blog, cms --- static/admin/config.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/static/admin/config.yml b/static/admin/config.yml index c98f8aae9..3eb2dd0aa 100644 --- a/static/admin/config.yml +++ b/static/admin/config.yml @@ -1,6 +1,6 @@ backend: name: github - repo: ( ) + repo: dpdjvhxm/age-website branch: new-web commit_messages: create: "Create {{collection}} “{{slug}}”" From 8d1565879172316a5329b4836836d9b5dc6843df Mon Sep 17 00:00:00 2001 From: dpdjvhxm Date: Mon, 22 Apr 2024 09:15:08 +0900 Subject: [PATCH 12/46] Update blog --- ...langchain-with-apache-age-for-graph-database-applications.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/pages/blog/2024-04-21-how-to-integrate-langchain-with-apache-age-for-graph-database-applications.md b/src/pages/blog/2024-04-21-how-to-integrate-langchain-with-apache-age-for-graph-database-applications.md index 52aaa25bc..692936aed 100644 --- a/src/pages/blog/2024-04-21-how-to-integrate-langchain-with-apache-age-for-graph-database-applications.md +++ b/src/pages/blog/2024-04-21-how-to-integrate-langchain-with-apache-age-for-graph-database-applications.md @@ -4,7 +4,7 @@ title: How to Integrate LangChain with Apache AGE for Graph Database Application date: 2024-04-21T23:25:01.575Z description: How to Integrate LangChain with Apache AGE for Graph Database Applications featuredpost: true -featuredimage: /img/ASF_Logo.svg +featuredimage: tags: - LangChain --- From 4bdf765221ed8a6b2593ad208a1122d0cab00b62 Mon Sep 17 00:00:00 2001 From: dpdjvhxm Date: Mon, 22 Apr 2024 09:40:29 +0900 Subject: [PATCH 13/46] Fix spelling error --- src/pages/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/pages/index.md b/src/pages/index.md index 3d8edee4f..c2aaf4a02 100644 --- a/src/pages/index.md +++ b/src/pages/index.md @@ -6,7 +6,7 @@ bannerContents: >-

    Apache AGE
    Graph Database for PostgreSQL

    -

    +

    Apache AGE is a PostgreSQL Graph database compatible with PostgreSQL's distributed assets and leverages graph data structures to analyze and use relationships and patterns in data. From 2067d8bd7b7c30c7e9cad0bdda1a34e3f2715b2f Mon Sep 17 00:00:00 2001 From: dpdjvhxm Date: Mon, 22 Apr 2024 10:23:41 +0900 Subject: [PATCH 14/46] Activate "blog" --- src/components/Navbar.js | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/src/components/Navbar.js b/src/components/Navbar.js index 4717bc520..543d2b883 100644 --- a/src/components/Navbar.js +++ b/src/components/Navbar.js @@ -52,11 +52,11 @@ const Navbar = () => { Release Notes - {/* + Blog - */} + @@ -215,11 +215,11 @@ const Navbar = () => { Release Notes - {/* + Blog - */} + From e8b726b9ba951f076cd66fb7828f86afc816a06d Mon Sep 17 00:00:00 2001 From: dpdjvhxm Date: Mon, 22 Apr 2024 13:47:38 +0900 Subject: [PATCH 15/46] Update download page, modified blog --- ...ngchain-with-apache-age-for-graph-database-applications.md | 4 ---- 1 file changed, 4 deletions(-) diff --git a/src/pages/blog/2024-04-21-how-to-integrate-langchain-with-apache-age-for-graph-database-applications.md b/src/pages/blog/2024-04-21-how-to-integrate-langchain-with-apache-age-for-graph-database-applications.md index 09b2d3874..7a253c831 100644 --- a/src/pages/blog/2024-04-21-how-to-integrate-langchain-with-apache-age-for-graph-database-applications.md +++ b/src/pages/blog/2024-04-21-how-to-integrate-langchain-with-apache-age-for-graph-database-applications.md @@ -4,11 +4,7 @@ title: How to Integrate LangChain with Apache AGE for Graph Database Application date: 2024-04-21T23:25:01.575Z description: How to Integrate LangChain with Apache AGE for Graph Database Applications featuredpost: true -<<<<<<< HEAD featuredimage: -======= -featuredimage: /img/ASF_Logo.png ->>>>>>> ad21d656b353425bbb215344d7d50743d91c5202 tags: - LangChain.ApacheAGE --- From 1f44e8114ad0e0562cf19a8ccdbb3f33bbcd951a Mon Sep 17 00:00:00 2001 From: dpdjvhxm Date: Mon, 22 Apr 2024 13:48:26 +0900 Subject: [PATCH 16/46] modified footer, download --- src/components/Footer.js | 3 + src/img/age-02.png | Bin 0 -> 633093 bytes src/pages/download/index.md | 24 ++++++++ src/templates/index-page.js | 119 ++++++++++++++++++------------------ static/img/backgraound.jpg | Bin 0 -> 7440942 bytes static/img/backgraound.png | Bin 0 -> 5803687 bytes 6 files changed, 85 insertions(+), 61 deletions(-) create mode 100644 src/img/age-02.png create mode 100644 static/img/backgraound.jpg create mode 100644 static/img/backgraound.png diff --git a/src/components/Footer.js b/src/components/Footer.js index 00629e132..838f2dde4 100644 --- a/src/components/Footer.js +++ b/src/components/Footer.js @@ -50,6 +50,9 @@ const Footer = class extends React.Component {
  • Release Note
  • +
  • + Blog +
  • - Key Updates
    -
    ASF PROJECT SPOTLIGHT: APACHE AGE
    -
    Read the full blog post now and be a part of shaping the future of graph database technology.
    - -

    - -
    Why do you use Apache AGE?
    -
    Give feedback at the GitHub (#1705)
    -

    - -
    Add graph store implementation for Apache AGE in langchain github
    - -
    Check out the pull request for the implementation of the GraphStore class for the Apache Age graph database in the LangChain repository.
    -
    - Read more -
    - -

    -
    Proposal: Vector handling with extension(pgvector)
    -
    A proposal for applying PGvector to Apache AGE has been posted on GitHub. Please read and leave a comment or opinion if you are interested.
    -
    - Read more - -
    - -

    -
    Apache AGE is now compatible with PostgreSQL 16!
    -
    Check the changelog for the latest version
    -
    - - Read more - -
    - + img

    @@ -133,6 +99,42 @@ export const IndexPageTemplate = ({ */ } +
    +
    +
    +

    Key Updates


    +
    +

    ASF PROJECT SPOTLIGHT: APACHE AGE

    +
    Read the full blog post now and be a part of shaping the future of graph database technology.
    + +

    + +
    Why do you use Apache AGE?
    +
    Give feedback at the GitHub (#1705)
    +

    + +
    Add graph store implementation for Apache AGE in langchain github
    +
    Check out the pull request for the implementation of the GraphStore class for the Apache Age graph database in the LangChain repository.
    + +

    + +
    Proposal: Vector handling with extension(pgvector)
    +
    A proposal for applying PGvector to Apache AGE has been posted on GitHub. Please read and leave a comment or opinion if you are interested.
    + +

    + +
    Apache AGE is now compatible with PostgreSQL 16!
    +
    Check the changelog for the latest version
    + +

    + +
    +
    +
    +
    + +


    +


    @@ -140,31 +142,24 @@ export const IndexPageTemplate = ({

    This week's article by community


    Graph Database in PostgreSQL: Apache AGE

    - -

    - Graph database have gained popularity in recent years due to their ability to handle complex relationships between data. Unlike traditional relational database, which store data in tables, graph database represent data as nodes, edges, and properties. Nodes represent entities, edges represent the relationships between those entities, and properties represent the attributes of both. -

    - -

    - PostgreSQL, a popular relational database, can also function as a graph database through the use of an extension called Apache AGE. With Apache AGE, users can leverage the flexibility and scalability of graph database while still utilizing PostgreSQL's advanced SQL querying capabilities and transaction support. -

    - -

    - To use Apache AGE, users must first install it as an extension and then model their data as nodes and edges. Apache AGE comes with its own set of SQL extensions, similar to Cypher, that allows users to query their graph database. However, users can also still use SQL to query their graph database if desired. -

    - -

    - For those new to graph database, Apache AGE comes with a tutorial to help them get started. Additionally, you may use the #apache-age tag for questions on Stack Overflow, join the project's Discord channel, or open an issue on GitHub, the Apache AGE community is readily available to provide support and answer questions. -

    - -

    - Overall, graph database offer a new way of thinking about how to store and query complex relationships between data. With Apache AGE, users can easily transform their PostgreSQL database into a graph database and take advantage of the benefits that come with this type of database. -

    - - +

    + Graph database have gained popularity in recent years due to their ability to handle complex relationships between data. Unlike traditional relational database, which store data in tables, graph database represent data as nodes, edges, and properties. Nodes represent entities, edges represent the relationships between those entities, and properties represent the attributes of both. +

    +

    + PostgreSQL, a popular relational database, can also function as a graph database through the use of an extension called Apache AGE. With Apache AGE, users can leverage the flexibility and scalability of graph database while still utilizing PostgreSQL's advanced SQL querying capabilities and transaction support. +

    +

    + To use Apache AGE, users must first install it as an extension and then model their data as nodes and edges. Apache AGE comes with its own set of SQL extensions, similar to Cypher, that allows users to query their graph database. However, users can also still use SQL to query their graph database if desired. +

    +

    + For those new to graph database, Apache AGE comes with a tutorial to help them get started. Additionally, you may use the #apache-age tag for questions on Stack Overflow, join the project's Discord channel, or open an issue on GitHub, the Apache AGE community is readily available to provide support and answer questions. +

    +

    + Overall, graph database offer a new way of thinking about how to store and query complex relationships between data. With Apache AGE, users can easily transform their PostgreSQL database into a graph database and take advantage of the benefits that come with this type of database. +

    - +




    @@ -176,14 +171,15 @@ export const IndexPageTemplate = ({ - +








    -
    +
    +

    Comparison of Apache AGE, PostGraphile, and Hasura





    @@ -225,6 +221,7 @@ export const IndexPageTemplate = ({
    +




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dpdjvhxm Date: Mon, 22 Apr 2024 17:23:19 +0900 Subject: [PATCH 17/46] Update landing image and blog scss --- src/components/BlogRoll.js | 32 +++--- src/components/Footer.js | 2 +- src/components/styles/Blog-post.scss | 105 ++++++------------ .../styles/BlogRollTemplate.module.scss | 19 ++++ src/pages/contribution/requirements.md | 6 + src/pages/download/index.md | 5 + src/pages/index.md | 2 +- src/templates/blog-post.js | 1 + src/templates/index-page.js | 8 +- .../img/{backgraound.jpg => background.jpg} | Bin .../img/{backgraound.png => background.png} | Bin static/img/background.webp | Bin 0 -> 4293944 bytes 12 files changed, 87 insertions(+), 93 deletions(-) create mode 100644 src/components/styles/BlogRollTemplate.module.scss rename static/img/{backgraound.jpg => background.jpg} (100%) rename static/img/{backgraound.png => background.png} (100%) create mode 100644 static/img/background.webp diff --git a/src/components/BlogRoll.js b/src/components/BlogRoll.js index 1a55592ea..887544c5c 100644 --- a/src/components/BlogRoll.js +++ b/src/components/BlogRoll.js @@ -2,6 +2,7 @@ import React from 'react' import PropTypes from 'prop-types' import { Link, graphql, StaticQuery } from 'gatsby' import PreviewCompatibleImage from './PreviewCompatibleImage' +import * as styles from './styles/BlogRollTemplate.module.scss'; class BlogRollTemplate extends React.Component { render() { @@ -12,13 +13,24 @@ class BlogRollTemplate extends React.Component {
      {posts && posts.map(({ node: post }) => ( -
      +
      +

      + + {post.frontmatter.title} + +
      + + {post.frontmatter.date} + +

      {post.frontmatter.featuredimage ? (
      ) : null} -

      - - {post.frontmatter.title} - - - - {post.frontmatter.date} - -

      {post.excerpt} diff --git a/src/components/Footer.js b/src/components/Footer.js index 838f2dde4..46f2e40c9 100644 --- a/src/components/Footer.js +++ b/src/components/Footer.js @@ -155,7 +155,7 @@ const Footer = class extends React.Component {

      - Copyright © 2024 The Apache Software Foundation.

      + Copyright © 2024 The Apache Software Foundation. All rights reserved.

      The Apache Software Foundation Apache AGE, AGE, Apache, the Apache feather, and the Apache AGE project logo are either registered trademarks or trademarks of the Apache Software Foundation.

      diff --git a/src/components/styles/Blog-post.scss b/src/components/styles/Blog-post.scss index 6bd4a2ab2..44f52b987 100644 --- a/src/components/styles/Blog-post.scss +++ b/src/components/styles/Blog-post.scss @@ -1,74 +1,37 @@ @import '../../common/variable.scss'; - -// Base styles for the blog post -.blog-post { - font-family: 'Arial', sans-serif; - max-width: 800px; - margin: 20px auto; - padding: 20px; - background-color: #fff; - border: 1px solid #ddd; - border-radius: 8px; -} - -// Title styles -.blog-post h1 { - color: #ff0000; - font-size: 28px; - text-align: center; - margin-bottom: 20px; -} - -// Featured image styles -.blog-post img { - width: 100%; - height: auto; - margin-bottom: 20px; - border-radius: 4px; -} - -// Text content styles -.blog-post p { - color: #666; - font-size: 16px; - line-height: 1.6; - margin-bottom: 20px; -} - -// Tag styles -.blog-post .tags { - list-style: none; - padding: 0; - text-align: center; -} - -.blog-post .tags li { - display: inline; - margin: 0 10px; -} - -.blog-post .tags li:before { - content: '#'; - color: #007bff; -} - -// Visual query image styles -.blog-post .visual-query { - border: 2px solid #007bff; - padding: 10px; - margin-top: 20px; -} - -// Call to Action styles -.blog-post .call-to-action { - background-color: #007bff; - color: #fff; - padding: 10px 20px; - text-align: center; - font-weight: bold; - border-radius: 4px; - display: block; - margin-top: 30px; - text-decoration: none; +.button-back { + // 기본 스타일 설정 + padding: 0.5em 1em; // 패딩 + border: none; // 테두리 없음 + background-color: #4CAF50; // 배경색 + color: white; // 텍스트 색상 + text-align: center; // 텍스트 중앙 정렬 + text-decoration: none; // 텍스트 밑줄 없음 + display: inline-block; // 인라인 블록 표시 + font-size: 16px; // 폰트 크기 + border-radius: 8px; // 모서리 둥글게 + cursor: pointer; // 커서 포인터 모양으로 변경 + + // 마우스 오버 시 스타일 변경 + &:hover { + background-color: #45a049; // 호버 상태의 배경색 + } + + // 포커스 시 스타일 변경 + &:focus { + outline: none; // 포커스 아웃라인 제거 + box-shadow: 0 0 8px rgba(0,0,0,0.6); // 박스 그림자 추가 + } + + // 활성화(클릭) 상태 스타일 + &:active { + background-color: #3e8e41; // 클릭 상태의 배경색 + box-shadow: 0 5px #666; // 그림자 효과 + transform: translateY(4px); // 클릭 시 살짝 아래로 이동 + } +} + +.section { + padding: 5% 10%; // 상하 패딩은 0, 좌우 패딩은 10% } diff --git a/src/components/styles/BlogRollTemplate.module.scss b/src/components/styles/BlogRollTemplate.module.scss new file mode 100644 index 000000000..c9e4f406c --- /dev/null +++ b/src/components/styles/BlogRollTemplate.module.scss @@ -0,0 +1,19 @@ +.articles { + article { + display: flex; + flex-direction: row; + background-color: rgb(255, 255, 255); + border-radius: 16px; + margin-top: 8px; + margin-bottom: 8px; + header { + display: flex; + + max-width: 500px; + min-width: 500px; + } + padding-left: 10%; + padding-right: 10%; + } +} + diff --git a/src/pages/contribution/requirements.md b/src/pages/contribution/requirements.md index 1acbc7c93..79e4255d9 100644 --- a/src/pages/contribution/requirements.md +++ b/src/pages/contribution/requirements.md @@ -8,6 +8,12 @@ title: # Committer Requirements +Contribution to our project requires compliance with certain guidelines and standards. Please review the requirements below before making a commitment.
      +In addition, active participation in project discussions, constructive feedback provision, and driving the project's progress are highly encouraged.
      + +___ +
      + ## 1-Continuous contributions to AGE: Committers should have a decent amount, of continuous engagement and contributions (fixing bugs, writing documentation, answering community questions) to AGE either by contributing on GitHub Repos, documentation and mailing list, etc. diff --git a/src/pages/download/index.md b/src/pages/download/index.md index 76210c8e6..2c13353c5 100644 --- a/src/pages/download/index.md +++ b/src/pages/download/index.md @@ -5,6 +5,11 @@ title: Downloads ---
      +
      +Welcome to our download page!
      +Find the latest version of our software below. +
      + ## Download AGE release for PostgreSQL 16. ## The current stable version and the latest release of the graph extension is 1.5.0. diff --git a/src/pages/index.md b/src/pages/index.md index c2aaf4a02..d8c07f23e 100644 --- a/src/pages/index.md +++ b/src/pages/index.md @@ -1,7 +1,7 @@ --- templateKey: index-page title: Apache AGE | Graph Database for PostgreSQL -bannerImg: /img/landing-banner.webp +bannerImg: /img/background.webp bannerContents: >-

      Apache AGE
      Graph Database for PostgreSQL

      diff --git a/src/templates/blog-post.js b/src/templates/blog-post.js index a3094a79a..9fca3d8f7 100644 --- a/src/templates/blog-post.js +++ b/src/templates/blog-post.js @@ -5,6 +5,7 @@ import { Helmet } from "react-helmet"; import { graphql, Link, navigate } from "gatsby"; // 'navigate'를 임포트합니다. import Layout from "../components/Layout"; import Content, { HTMLContent } from "../components/Content"; +import style from "../components/styles/blog-post.scss" // eslint-disable-next-line export const BlogPostTemplate = ({ diff --git a/src/templates/index-page.js b/src/templates/index-page.js index a463565fd..ca308d21c 100644 --- a/src/templates/index-page.js +++ b/src/templates/index-page.js @@ -109,21 +109,21 @@ export const IndexPageTemplate = ({


      -
      Why do you use Apache AGE?
      +

      Why do you use Apache AGE?

      Give feedback at the GitHub (#1705)


      -
      Add graph store implementation for Apache AGE in langchain github
      +

      Add graph store implementation for Apache AGE in langchain github

      Check out the pull request for the implementation of the GraphStore class for the Apache Age graph database in the LangChain repository.


      -
      Proposal: Vector handling with extension(pgvector)
      +

      Proposal: Vector handling with extension(pgvector)

      A proposal for applying PGvector to Apache AGE has been posted on GitHub. Please read and leave a comment or opinion if you are interested.


      -
      Apache AGE is now compatible with PostgreSQL 16!
      +

      Apache AGE is now compatible with PostgreSQL 16!

      Check the changelog for the latest version


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zl4;L!sasA_egd9YrW%cr86uAsb}L=9@}5!N)yUl2)QC1fYjfDTd(a{EUhk~Gaxj*< za+hLB-h!`F{chw*%xh2Nq%}L0Ihe_bw{Md;|8p((&=iKC3E}sK5R@-+Y#>tsJA_x_ zoCFQQkU<;Bw$d#R-G;Ia*d)6@|9G}QiY>07{o1*s&I7TRex4J*88Rs=*LHXdGnV!2d}-a#_(?1HsH8qey!l3o?8E==L%zqy-$`|#m z#i66bPpzU(%o+bNs!s)%JawPi42k*ifiBRL9lh3xYJg{MbkD16#@WLLSYc0V;H2%S zpjQ{w1l9{13Ifk1;Y5*1V?PA%TIaVK{xcdedu<=lm@0%Jm~nzvJNV4naQ zlFJD8rhR`gHNGLKrvDuVkfU`=|H`7%8D`9Z^SbqjORLmy74CN4wvm#@C2K_JC#O8nu9pFk44^)k_Uz6DMKk-Ao}%dy07un@TScaAM1 Pu2<#y?>k_e0D%7k4j2Uo literal 0 HcmV?d00001 From a9126bb2dfbfd06f8c5f82eefabe4336dc9e6db6 Mon Sep 17 00:00:00 2001 From: dpdjvhxm Date: Mon, 22 Apr 2024 17:29:08 +0900 Subject: [PATCH 18/46] modified: src/components/styles/Blog-post.scss --- src/components/styles/Blog-post.scss | 2 -- 1 file changed, 2 deletions(-) diff --git a/src/components/styles/Blog-post.scss b/src/components/styles/Blog-post.scss index 44f52b987..6a2e4df87 100644 --- a/src/components/styles/Blog-post.scss +++ b/src/components/styles/Blog-post.scss @@ -27,8 +27,6 @@ // 활성화(클릭) 상태 스타일 &:active { background-color: #3e8e41; // 클릭 상태의 배경색 - box-shadow: 0 5px #666; // 그림자 효과 - transform: translateY(4px); // 클릭 시 살짝 아래로 이동 } } From b4470e706b27115f940f553902806b1215efbd6c Mon Sep 17 00:00:00 2001 From: dpdjvhxm Date: Mon, 22 Apr 2024 17:49:48 +0900 Subject: [PATCH 19/46] modified: src/pages/download/index.md --- src/pages/download/index.md | 5 ----- 1 file changed, 5 deletions(-) diff --git a/src/pages/download/index.md b/src/pages/download/index.md index 2c13353c5..9e3b973f0 100644 --- a/src/pages/download/index.md +++ b/src/pages/download/index.md @@ -137,11 +137,6 @@ Archives for all past versions of AGEs are available at the Apache archives ## Language Specific Drivers Starting with Apache AGE is very simple. You can easily select your platform and incorporate the relevant SDK into your code. -

        - - - -

        | Built-in | | --------------------- | From 94acf4a6e110519abed78e7aecbc4a07403f437a Mon Sep 17 00:00:00 2001 From: dpdjvhxm Date: Tue, 23 Apr 2024 08:24:52 +0900 Subject: [PATCH 20/46] modified: src/templates/blog-post.js --- src/templates/blog-post.js | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/templates/blog-post.js b/src/templates/blog-post.js index 9fca3d8f7..b7b653caa 100644 --- a/src/templates/blog-post.js +++ b/src/templates/blog-post.js @@ -5,7 +5,7 @@ import { Helmet } from "react-helmet"; import { graphql, Link, navigate } from "gatsby"; // 'navigate'를 임포트합니다. import Layout from "../components/Layout"; import Content, { HTMLContent } from "../components/Content"; -import style from "../components/styles/blog-post.scss" +import style from "../components/styles/Blog-post.scss" // eslint-disable-next-line export const BlogPostTemplate = ({ From 8be135fc5a7b3b2616e38009896f122091cf6136 Mon Sep 17 00:00:00 2001 From: dpdjvhxm <137974842+dpdjvhxm@users.noreply.github.com> Date: Tue, 23 Apr 2024 10:28:01 +0900 Subject: [PATCH 21/46] =?UTF-8?q?Create=20Blog=20=E2=80=9C2024-04-23-beyon?= =?UTF-8?q?d-chatgpt-how-to-maximize-the-use-of-chatgpt-with-interactive-g?= =?UTF-8?q?raph-models=E2=80=9D?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...f-chatgpt-with-interactive-graph-models.md | 69 +++++++++++++++++++ 1 file changed, 69 insertions(+) create mode 100644 src/pages/blog/2024-04-23-beyond-chatgpt-how-to-maximize-the-use-of-chatgpt-with-interactive-graph-models.md diff --git a/src/pages/blog/2024-04-23-beyond-chatgpt-how-to-maximize-the-use-of-chatgpt-with-interactive-graph-models.md b/src/pages/blog/2024-04-23-beyond-chatgpt-how-to-maximize-the-use-of-chatgpt-with-interactive-graph-models.md new file mode 100644 index 000000000..a1fb7cf6d --- /dev/null +++ b/src/pages/blog/2024-04-23-beyond-chatgpt-how-to-maximize-the-use-of-chatgpt-with-interactive-graph-models.md @@ -0,0 +1,69 @@ +--- +templateKey: blog-post +title: "Beyond ChatGPT: How to Maximize the Use of ChatGPT with Interactive + Graph Models." +date: 2024-04-23T01:18:22.687Z +description: "Beyond ChatGPT: How to Maximize the Use of ChatGPT with + Interactive Graph Models." +featuredpost: true +featuredimage: /img/logo.png +--- + + +n just a matter of a year, ChatGPT has taken the world by storm, ushering in a new era of AI transformation. Its remarkable text-to-text generation capabilities have set new standards for informative and high-quality responses. Knowledge creation AI models like ChatGPT excel at engaging in conversations and retrieving information from vast databases. However, while it produces accurate and diverse answers, the delivery in text format can sometimes be hard to resonate with and fall short of user expectations. What if ChatGPT could also be used in combination with interactive visual representations? In this article, we delve into a method that combines the power of ChatGPT with interactive graph models, elevating the user experience to new heights. + +##### The Role of Graph Data Modeling + +A graph is a data structure that mirrors the natural interconnectedness found in the real world, enabling it to offer not just the desired information but also to suggest and provide additional, related data and information. + +Figure 1. Recommendation system use case, expressing the meta information of a movie through connection relationship in the Graph Model + +Learn more about the mechanics of Graph Databases-> + +##### The intricacies of Input Text & Output Text + +To maximize the potential of ChatGPT, we must extend our goal beyond relying solely on existing text data and embrace the inclusion of "unstructured" information like images in the knowledge generation process. ChatGPT excels at delivering accurate answers, but achieving this precision often demands intricate and nuanced text prompts. Navigation and instruction of ChatGPT could often be challenging without guidance in the input text methods. In this regard, it is imperative to harness GPT's versatility by tapping into a variety of information sources, encompassing images. For instance, by introducing the user's hyper-personalized attributes in a simple user interface, the accuracy and relevance of information can be tailored to individual users. + +To extend the information output from ChatGPT meaningfully, the specific information that the user needs must be identified from the collected pieces of information derived. In reality, however, GPT offers multifaceted textual information, which can be overwhelming for users seeking value within its output. To address this challenge, transforming the information produced by GPT into a 'knowledge graph' becomes a valuable approach. By mapping the connections between data points output by GPT onto a graph, it becomes feasible to visualize and index the desired information, uncover additional insights, and streamline GPT's knowledge management. + + + +Figure 2. ChatGPT's text output in over 70 lines + +##### Creation and management of GPT knowledge using image information and graphs + +Beyond obtaining information through 'text-to-text', let's elucidate on how to easily manage knowledge in the form of a graph by inputting images as a service using an example prompt, 'Generate Food Recipes from the Fridge. + +In today's fast-paced world, many individuals have encountered the predicament of having to prepare a meal when faced with limited ingredients. Furthermore, tailoring a dish to one's specific dietary needs and personal preferences can be challenging. Let's tackle this by leveraging ChatGPT's capabilities along with the knowledge graph framework, empowering users to access top-tier insights with the simplest of inputs. + + + + + +Figure 3. Information extraction process using ChatGPT from an image of the ingredients in the fridge + +The above two models are used for extracting food recipes and information from the images of the ingredients in the fridge. As a first prompt, using object detection algorithms such as R-CNN or YOLO models, the ingredients present in the fridge are extracted. As for the second input value, it pertains to the user's unique preferences. To ensure accurate prompts, predefined options such as select boxes or radio buttons are employed. + +In this workflow, both the ingredient list and the user's preferences are integrated into the ChatGPT prompt using a standardized format. Subsequently, GPT employs this data to craft a text output encompassing potential dishes, ingredients, recipes, and nutritional insights tailored to the user's specific traits, including factors like appetite, age, and time zone. This meticulous prompt engineering, which leverages both images and user characteristics, yields high-quality information to enhance the overall user experience. + +##### Knowledge management using Graphs + +When you input ingredients and user preferences into ChatGPT, the generated output comprises a 'dish,' 'recipe,' available ingredients, and additional ingredients suitable for the user. In Figure 4. below, by entering items like cabbage, tomato, and seaweed in your pantry, along with dinner preferences, dietary requirements, and age, GPT generates results related to 'cooking' and 'nutritional ingredients' as illustrated. This information is easily visualized through graphs and can be extracted into actionable knowledge. + + + +Figure 4. Utilization of information extracted by ChatGPT using a graph (a circle with red border is information that can be obtained through ChatGPT, red dotted arrow indicates additional information that is recommended from information through ChatGPT) + +Given that actual GPT information often spans over 70 lines of text, comprehending the desired details and their relationships can be challenging. Utilizing a graphical representation, you can observe that 'cabbage salad' was suggested as a recommended dish, along with 'salt and pepper' as essential cooking ingredients. Furthermore, dietary insights like 'dietary fiber' and 'digestion' were provided for users in the 'elderly' age group. + +By leveraging these complex information interconnections, users can extract knowledge from GPT-generated data and discern common patterns among essential information elements. + +This graph model can further be advanced into meal planning recommendations using the ingredients or nutrients in the food information. For example, by clicking on the 'cabbage, and 'tomato' nodes of an ingredient and putting them back into the GPT input prompt, it returns a dish that can be made with those ingredients, such as 'tomato-cabbage stir-fry'. If one clicks on the 'dietary fiber' and 'digestion' nodes in the nutrition component, 'older' and 'diet' are the most relevant characteristics through the edge search of the graph, leading to the 'seaweed soup' as a recommended dish. + +This way, knowledge management using graphs enables users to secure visibility and identity and explore relationships between information to identify causality and relevance. A simple graph-type UI/UX also helps improve the productivity of ChatGPT by clicking on the appropriate node providing a data environment in a simple form that allows other food recommendations without having to separate prompt engineering. + +##### Bringing the utilization of ChatGPT to the next level + +ChatGPT holds significant promise for seamless integration with other models and efficient knowledge management. With a broader data spectrum and user-friendly graph-based knowledge management, it unlocks new insights and expands effortlessly across various domains. In particular, Apache AGE makes it easy to search and extract various knowledge by storing data in a graph. Being an extension to PostgreSQL, AGE allows a hybrid approach to databases combining the capabilities of both relational and graph queries, making it straightforward for beginners to view, extract, and manage graph information. Leveraging Apache AGE, the multi-database management system for generative AI knowledge, provides a low-barrier, versatile solution for making the most of this new era in knowledge management. + + \ No newline at end of file From f555fae5ed2b01772890f1db52acfc3810d077ba Mon Sep 17 00:00:00 2001 From: dpdjvhxm <137974842+dpdjvhxm@users.noreply.github.com> Date: Tue, 23 Apr 2024 10:33:24 +0900 Subject: [PATCH 22/46] =?UTF-8?q?Create=20Blog=20=E2=80=9C2024-04-23-comba?= =?UTF-8?q?ting-cyber-attack-with-apache-ages-link-analysis=E2=80=9D?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...r-attack-with-apache-ages-link-analysis.md | 72 +++++++++++++++++++ 1 file changed, 72 insertions(+) create mode 100644 src/pages/blog/2024-04-23-combating-cyber-attack-with-apache-ages-link-analysis.md diff --git a/src/pages/blog/2024-04-23-combating-cyber-attack-with-apache-ages-link-analysis.md b/src/pages/blog/2024-04-23-combating-cyber-attack-with-apache-ages-link-analysis.md new file mode 100644 index 000000000..b268fada7 --- /dev/null +++ b/src/pages/blog/2024-04-23-combating-cyber-attack-with-apache-ages-link-analysis.md @@ -0,0 +1,72 @@ +--- +templateKey: blog-post +title: Combating cyber attack with Apache AGE's link analysis. +date: 2024-04-23T01:28:20.437Z +description: |- + Combating cyber attack with Apache AGE's link + analysis. +featuredpost: true +featuredimage: /img/logo.png +--- + + +##### Cybersecurity Background + +In today's world, the internet has transformed how we connect, work, and access information, bringing unparalleled convenience and connectivity. This digital revolution, however, has its challenges, especially in cybersecurity. As we depend more on technology, the need to protect sensitive data and counteract cyber threats becomes increasingly critical. + +Cybersecurity is about safeguarding our computer systems, networks, and data from theft, damage, or unauthorized access. A key part of this field is network intrusion detection. It's like a digital watchdog, constantly monitoring network traffic for any signs of suspicious activities, such as hacking attempts or unauthorized entries. The aim is to catch and address these threats early, preventing any harm to our networks or data. + +In essence, cybersecurity and network intrusion detection are vital in our tech-driven world, ensuring our digital safety and resilience against ever-evolving cyber threats. + +##### Data Overview + +In the realm of cybersecurity, graph analysis plays a crucial role. To illustrate this, we use the CIDDS-001 dataset, specifically designed for network intrusion detection. This dataset serves as a benchmark for evaluating how different intrusion detection systems perform in identifying network attacks. It simulates a small business environment, complete with clients and servers, like email and web servers. The behaviors on these clients are mimicked using Python scripts, and the dataset includes unidirectional NetFlow data, capturing the flow of network traffic. + +![img](../../img/xai-1.png) + +##### Figure1. Graph Modeling + +Graph analysis in cybersecurity involves transforming relational data into a graph format, consisting of nodes (points) and edges (connections). There are two main types of graphs: + +1. **Homogeneous Graphs:** These have nodes and edges of a single type, similar to what you'd find in social networks with user nodes connected by 'follow' edges. +2. **Heterogeneous Graphs:** These contain multiple types of nodes and edges, like in e-commerce networks where interactions between users and items are tracked through different types of connections such as 'buy,' 'cart,' and 'view.' + +Using the [CIDDS-001 dataset](https://www.hs-coburg.de/forschung/forschungsprojekte-oeffentlich/informationstechnologie/cidds-coburg-intrusion-detection-data-sets.html) , we create a graph for network flow analysis. This includes nodes representing start and end points in the network, and edges of various types like 'benign,' 'dos,' 'portscan,' 'pingscan,' and 'bruteforce.' A key feature of this graph is the detailed information attached to each node and edge, such as ports, packet sizes, and timestamps. This level of detail is particularly valuable in cybersecurity, where quick and precise responses are crucial. + +##### Cypher Query with Analysis Scenario + +In this section, we explore how the PostgreSQL extension [Apache AGE's](https://age.apache.org/) graph analysis capabilities, particularly through Cypher queries, can be leveraged in cybersecurity. We'll look at three different scenarios, each demonstrating the power and flexibility of Cypher queries in analyzing network data. + +![img](../../img/xai-2.png) + +##### Figure2. Graph Query with Condition + +Figure 2 showcases a Cypher query that focuses on identifying network packets larger than a certain size. This is a prime example of the 'condition' function in Apache AGE, which is crucial for pinpointing specific scenarios. Here, the query filters out packets larger than 10 units, helping analysts spot potential anomalies or threats in network traffic. This function isn't limited to just packet size; it can be tailored to various criteria, making it a versatile tool for cybersecurity analysis. + +![img](../../img/xai-3.png) + +##### Figure3. Multi-path Search with Union Query + +In Figure 3, we see a union query in [Apache AGE](https://age.apache.org/), demonstrating its ability to handle complex, multi-path searches. This is particularly useful in scenarios where you need to analyze data across different types of connections, like 'eg_portscan' and 'eg_pingscan.' [Apache AGE](https://age.apache.org/) also enhances this analysis with visual aids, like representing the weight of packets through the thickness of edges in the graph, making it easier to interpret the data visually. + +![img](../../img/xai-4.png) + +##### Figure4. Bi-directional Path Search + +The final example, illustrated in Figure 4, is a bi-directional path search. This Cypher query uncovers one-to-many relationships between nodes, providing a detailed view of network interactions and potential security incidents. It's particularly adept at revealing complex patterns and relationships that might be missed with traditional analysis methods. By tracing paths involving different types of nodes and connections, this query helps in identifying multi-step attack patterns and potential sources or targets of cyberattacks. + +In summary, these scenarios highlight the strength of [Apache AGE](https://age.apache.org/) in cybersecurity. Through its advanced graph analysis capabilities, [Apache AGE](https://age.apache.org/) enables cybersecurity professionals to uncover deep insights into network activities, detect threats more effectively, and enhance the overall security of network infrastructures. + +##### Conclusion + +Throughout this discussion, we've delved into the essentials of cybersecurity, examined the CIDDS-001 dataset for cyber intrusion analysis, and explored the dynamic use of Cypher queries in data analysis. The journey underscores the immense value of graph analysis in cybersecurity, offering profound insights and robust tools to combat digital threats. + +[Apache AGE](https://age.apache.org/) stands out in this landscape, particularly for its user-friendly approach to graph analysis. The use of Cypher queries, known for their power and intuitiveness, simplifies complex data analysis. This language enables cybersecurity experts to craft detailed queries, uncovering complex attack patterns and suspicious network activities. Such flexibility is crucial in enhancing threat detection and response capabilities. + +Graph visualization emerges as a key player in making intricate cybersecurity data more understandable and actionable. By graphically representing network nodes, edges, and their attributes, it becomes easier for analysts to decode complex structures, spot anomalies, and respond swiftly to potential threats. This visual approach aids in proactive security measures, allowing teams to trace unusual activities, map out attack routes, and pinpoint potential sources of cyber threats, thereby improving overall security awareness and decision-making. + +Another significant aspect of [Apache AGE's](https://age.apache.org/)graph analysis is the labeled property graph model. This approach enriches nodes and edges with detailed metadata, capturing essential information about cyber incidents. Such rich data representation not only aids in precise threat categorization but also supports thorough post-incident analysis. This helps organizations learn from past experiences, enhancing their defensive strategies against future cyber threats. + +In conclusion, [Apache AGE](https://age.apache.org/) presents a powerful suite of tools for cybersecurity professionals, significantly bolstering their ability to detect, investigate, and neutralize cyber threats. The combination of easy-to-use Cypher queries and effective graph visualization positions [Apache AGE](https://age.apache.org/) as an indispensable asset in contemporary cybersecurity operations. By adopting graph analysis, organizations can effectively stay ahead of cyber adversaries, protect sensitive data, and maintain the integrity of their digital ecosystems. + + \ No newline at end of file From 87a401911d93ebcec4ead95eb1c8e9d04393b9cc Mon Sep 17 00:00:00 2001 From: dpdjvhxm <137974842+dpdjvhxm@users.noreply.github.com> Date: Tue, 23 Apr 2024 11:12:56 +0900 Subject: [PATCH 23/46] =?UTF-8?q?Create=20Blog=20=E2=80=9C2024-04-23-from-?= =?UTF-8?q?data-to-connections-leveraging-hyperconnectivity-in-e-commerce-?= =?UTF-8?q?data-part-1=E2=80=9D?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...rconnectivity-in-e-commerce-data-part-1.md | 63 +++++++++++++++++++ 1 file changed, 63 insertions(+) create mode 100644 src/pages/blog/2024-04-23-from-data-to-connections-leveraging-hyperconnectivity-in-e-commerce-data-part-1.md diff --git a/src/pages/blog/2024-04-23-from-data-to-connections-leveraging-hyperconnectivity-in-e-commerce-data-part-1.md b/src/pages/blog/2024-04-23-from-data-to-connections-leveraging-hyperconnectivity-in-e-commerce-data-part-1.md new file mode 100644 index 000000000..2135d9b31 --- /dev/null +++ b/src/pages/blog/2024-04-23-from-data-to-connections-leveraging-hyperconnectivity-in-e-commerce-data-part-1.md @@ -0,0 +1,63 @@ +--- +templateKey: blog-post +title: "From Data to Connections: Leveraging Hyperconnectivity in E-commerce + Data (Part 1.)" +date: 2024-04-23T02:11:47.653Z +description: "From Data to Connections: Leveraging Hyperconnectivity in + E-commerce Data (Part 1.)" +featuredpost: true +featuredimage: /img/logo.png +--- + + + + +Hyper-connectivity implies that everything is connected — people, data, and things — depending on the development of the internet, communication technologies, and more. When individuals generate data from various sources, such as technology or environmental sensors, the resultant connection networks are referred to as the hyper-connected society. With this heightened level of connectivity, there is significant potential to drive innovation and transformation in all aspects of society. In other words, hyper-connectivity is a term that describes the logical and interrelated connection of data throughout all services and devices. + +E-commerce is an example of where the data transform into connections among people (customers & sellers), data, and things (products.) It implements the commercial process of buying and selling over the Internet, moreover, oversees the production and consumption process. In this article, we'll build the concept of Hyper-connectivity by taking E-commerce sample data and let you see the data in action! + +##### Data Modeling with Relational Database + +Below is a table around a hypothetical customer's purchases at shopping mall A modeled in Relational Database, including products, stores, payment information, and the embedding relationships between stores. + +![img](../../img/b-028.webp) + +Figure 1. RDB Modeling with Payment Sample Data Table + +To achieve hyper-connectivity among datasets, it is crucial to understand the relationships between them. This is where the 'JOIN' method comes into play for analyzing tables. An entity-relationship diagram (ERD) visualizes real-world data and facilitates the connections between tables of varying natures. The relational database not only enables to comprehend the flow of data between tables, but also uncovers the relationships among tables. + +##### Data Modeling with Graph Database + +A graph database is a NoSQL database that stores and manages relationships in the form of visualization using 'nodes' and 'edges'. These databases enable hyperconnectivity by searching the relationships between data. + +![img](../../img/b-029.webp) + +##### Querying Graph Relationship + +A graph database provides a more efficient way to query data connected in a hyperconnected system. For example, Apache AGE provides a hybrid of table and graph data operations, allowing you to write intuitive queries such as Cypher (a graph query) along with SQL. Let's see how to extract and retrieve it using both Cypher and SQL. + +##### Querying the Single Relationships + +First, we can ask simple questions to find out what products customers buy and what services they use. + +![img](../../img/b-030.webp) + +Figure 3. Relationship Querying for Graph Relationships in Apache AGE + +The above query is a graph query (Cypher) that traverses the graph to extract the relationship in the “match (a: customer)-\[r]-(b: product)” clause and what product the customer buys. The graph query extracts the graph relationships and utilizes the hybrid query, “select\~” and “from\~” clauses available in Apache AGE to make the graph output easier to manipulate for users unfamiliar with graph databases. + +##### Querying the Complex Relationships + +You can also use Apache AGE to query for complex queries. In addition to the customer who bought the example product above, you can query complex relationships such as which product they bought, what product category, which bank they used, and more. + +![img](../../img/b-031.webp) + +Figure 4. Sophisticated Relationship Querying for Graph Relationships in Apache AGE + +In the MATCH clause, you can search the path you want to extract. In the {"(a: customer)-\[r1]-(b: bank {name: 'AGE bank'})"} clause, you can query what bank they used; in the (a)-\[r3]-(d: card) clause, what card they used, and in the (a)-\[r4]-(e: product)-\[r5]-(f:category_3) clause, what products they bought and what product category they belong to. These allow us to explore complex relationships and extract which customers have the same card, bank, and more. + +As seen above, graph database queries exhibit hyperconnectivity implementation much more efficiently. Notably, Apache AGE stands out as a database with its hybrid query capability, enabling many including non-experts in graph queries and derive results using SQL. Being a PostgreSQL extension, Apache AGE offers the flexibility to leverage extensions tailored to specific situations and domains, enhancing its practicality and adaptability. + +Are you interested in learning more about Apache AGE? [Learn More Now](http://agedb.io/From-Data-to-Connections-Leveraging-Hyperconnectivity-in-E-commerce-Data.jsp#). + + \ No newline at end of file From 3dfeef38035bf085cdaa8d4806261b14b164ff6b Mon Sep 17 00:00:00 2001 From: dpdjvhxm <137974842+dpdjvhxm@users.noreply.github.com> Date: Tue, 23 Apr 2024 11:14:26 +0900 Subject: [PATCH 24/46] =?UTF-8?q?Create=20Blog=20=E2=80=9C2024-04-23-learn?= =?UTF-8?q?-machine-learning-with-graph-in-hyperconnected-data-part-2?= =?UTF-8?q?=E2=80=9D?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...ith-graph-in-hyperconnected-data-part-2.md | 71 +++++++++++++++++++ 1 file changed, 71 insertions(+) create mode 100644 src/pages/blog/2024-04-23-learn-machine-learning-with-graph-in-hyperconnected-data-part-2.md diff --git a/src/pages/blog/2024-04-23-learn-machine-learning-with-graph-in-hyperconnected-data-part-2.md b/src/pages/blog/2024-04-23-learn-machine-learning-with-graph-in-hyperconnected-data-part-2.md new file mode 100644 index 000000000..2bca3b997 --- /dev/null +++ b/src/pages/blog/2024-04-23-learn-machine-learning-with-graph-in-hyperconnected-data-part-2.md @@ -0,0 +1,71 @@ +--- +templateKey: blog-post +title: Learn Machine Learning with Graph in Hyperconnected Data (Part 2.) +date: 2024-04-23T02:13:19.760Z +description: Learn Machine Learning with Graph in Hyperconnected Data (Part 2.) +featuredpost: true +featuredimage: /img/logo.png +--- + + +Following our previous article on Hyperconnected data, this article will delve deeper into the applications of graph-based machine learning specifically in the realm of e-commerce data. We will explore the practical implementation of unsupervised learning-based 'Graph Clustering' and supervised learning-based 'Graph node/Link Prediction' techniques, highlighting their significance in storing and harnessing results within a graph database. By understanding these powerful approaches, we can uncover valuable insights and optimize decision-making processes in the dynamic world of e-commerce. + +[Read part 1 Now - From Data to Connections: Leveraging Hyperconnectivity in E-commerce Data.]() + +Graph Projection and Clustering Analysis + +![img](../../img/b-032.webp) + +Figure 1. Graph DB Modeling with Payment Sample Data Table + +Once we understand the intricate product-purchase relationships within our e-commerce data, the next step is to cluster customers based on their shared properties. To accomplish this, we employ the 'Graph Projection' technique, which constructs a unified network by projecting a graph along specific relationships. By utilizing this approach, we can effectively visualize and analyze distinct customer clusters, uncovering valuable insights that can drive personalized marketing strategies and enhance overall customer experience. + +![img](../../img/b-033.webp) + +Figure 2. Graph Database Modeling with Payment Sample Data Table + +The relationship between Customer A and Customer B, connected through the purchase relationships of the same product, creates a view that facilitates a seamless connection between preceding relationships. This view acts as an edge, linking the edges of the graph projection. + +##### Querying the Single Relationships + +First, we can ask simple questions to find out what products customers buy and what services they use. + +Essentially, we can construct a network comprising customers who have purchased the same product. From this network, we can employ a graph clustering algorithm to group customers based on structural similarities within the graph. The graph clustering algorithm analyzes the graph's structural features independently, allowing for the identification of distinctive characteristics within each cluster. Below is an example of a single network configuration and its characteristics when using the clustering algorithm, visualized as a graph. + +![img](../../img/b-034.webp) + +Figure 3. Creating Homogeneous Network and Graph Cluster in Apache AGE + +##### Querying Features of Clusters + +Focusing on products emerges a single network, revealing distinctive characteristics specific to each cluster. This network offers valuable insights and provides personalized recommendations to assist customers in finding what they truly need. Moreover, the configuration of this network unveils new meta-information beyond traditional categorization. Uncovering patterns of products purchased together suggests previously unseen characteristics of products, allowing for a deeper understanding of customer preferences and facilitating more targeted recommendations. + +![img](../../img/b-035.webp) + +Figure 4. Exploration of Cluster Graph Merging with Meta Info in Apache AGE + +##### Node/Link Prediction + +The results above demonstrate the meaningfulness of item purchases within the identified clusters. For instance, in Cluster 2, the purchase of cleaning supplies is appropriately grouped with the keyword 'Moving and Cleaning', enabling targeted product recommendations to customers in this cluster. Similarly, in Cluster 4, the purchases of furniture supplies are accurately associated with the keyword 'Interior,' allowing for precise suggestions to enhance customers interior design needs. These cluster-specific associations highlight the power of leveraging customer behavior data to provide relevant and contextually appropriate recommendations, ultimately improving the overall customer experience. + +Beyond the established product categories, grouping, giving meta-information, and managing products relevant to the current era are also of the AGE's capability. These can be achieved utilizing Node/Link Prediction techniques, which enable the prediction and establishment of connections for items that have not yet been categorized or linked. By leveraging this approach, we can offer tailored recommendations that suggest customers with customer's genuine needs. + +Node prediction operates at the individual node level within the graph, allowing for accurate predictions regarding the characteristics of a given node. This prediction is analyzed based on the relationships with irregular neighbouring nodes. + +Link Prediction is a technique to predict the likelihood of a connection between two individual nodes without existing connections. By successfully establishing these previously unexplored relationships, the utility of information within the network significantly enhances. + +In addition to traditional recommendation systems that rely on similarity-based approaches, Link prediction, when combined with Node prediction, facilitates recommendations based on similarities and discovers valuable information about the characteristics of newly defined nodes. This expanded data discovery opens up new avenues for exploration and analysis, ultimately boosting confidence in the generated results. + +![img](../../img/b-036.webp) + +Figure 5. Example of Node Prediction (Source: CS224W Chapter 6 Message passing and Node Classification) + +![img](../../img/b-037.webp) + +Figure 6. Example of Link Prediction in Apache AGE (Source: Missing Link Prediction using Common Neighbour and Centrality based Parameterized Algorithm) + +This article explored the concept of graph hyper-connection, the connection of table data from a relational database to a graph structure. By harnessing the power of hyperconnectivity between relational and graph databases, data can be managed flexibly, leading to the creation of new value through not only simple queries but also advanced analytics. + +To efficiently model and manage different types of data, an enterprise DBMS like AGEDB is an ideal solution. It enables the identification and management of relationships between data in tables, facilitating the extension of knowledge associations and enabling comprehensive analysis through graph-based approaches. + + \ No newline at end of file From 3f9fcfeb08b09e1bb39b43f34666a2e6055683aa Mon Sep 17 00:00:00 2001 From: dpdjvhxm <137974842+dpdjvhxm@users.noreply.github.com> Date: Tue, 23 Apr 2024 11:16:30 +0900 Subject: [PATCH 25/46] =?UTF-8?q?Create=20Blog=20=E2=80=9C2024-04-23-graph?= =?UTF-8?q?-database-in-postgresql-apache-age=E2=80=9D?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...graph-database-in-postgresql-apache-age.md | 21 +++++++++++++++++++ 1 file changed, 21 insertions(+) create mode 100644 src/pages/blog/2024-04-23-graph-database-in-postgresql-apache-age.md diff --git a/src/pages/blog/2024-04-23-graph-database-in-postgresql-apache-age.md b/src/pages/blog/2024-04-23-graph-database-in-postgresql-apache-age.md new file mode 100644 index 000000000..ccf840437 --- /dev/null +++ b/src/pages/blog/2024-04-23-graph-database-in-postgresql-apache-age.md @@ -0,0 +1,21 @@ +--- +templateKey: blog-post +title: "Graph Database in PostgreSQL: Apache AGE" +date: 2024-04-23T02:15:59.564Z +description: "Graph Database in PostgreSQL: Apache AGE" +featuredpost: true +featuredimage: /img/logo.png +--- + + +Graph database have gained popularity in recent years due to their ability to handle complex relationships between data. Unlike traditional relational database, which store data in tables, graph database represent data as nodes, edges, and properties. Nodes represent entities, edges represent the relationships between those entities, and properties represent the attributes of both. + +PostgreSQL, a popular relational database, can also function as a graph database through the use of an extension called Apache AGE. With Apache AGE, users can leverage the flexibility and scalability of graph database while still utilizing PostgreSQL's advanced SQL querying capabilities and transaction support. + +To use Apache AGE, users must first install it as an extension and then model their data as nodes and edges. Apache AGE comes with its own set of SQL extensions, similar to Cypher, that allows users to query their graph database. However, users can also still use SQL to query their graph database if desired. + +For those new to graph database, Apache AGE comes with a tutorial to help them get started. Additionally, you may use the #apache-age tag for questions on Stack Overflow, join the project's Discord channel, or open an issue on GitHub, the Apache AGE community is readily available to provide support and answer questions. + +Overall, graph database offer a new way of thinking about how to store and query complex relationships between data. With Apache AGE, users can easily transform their PostgreSQL database into a graph database and take advantage of the benefits that come with this type of database. + + \ No newline at end of file From 2df007f88d0db394b209e95c811c84b97fb52f48 Mon Sep 17 00:00:00 2001 From: dpdjvhxm <137974842+dpdjvhxm@users.noreply.github.com> Date: Tue, 23 Apr 2024 11:17:39 +0900 Subject: [PATCH 26/46] =?UTF-8?q?Create=20Blog=20=E2=80=9C2024-04-23-compa?= =?UTF-8?q?rison-of-apache-age-postgraphile-and-hasura=E2=80=9D?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...n-of-apache-age-postgraphile-and-hasura.md | 48 +++++++++++++++++++ 1 file changed, 48 insertions(+) create mode 100644 src/pages/blog/2024-04-23-comparison-of-apache-age-postgraphile-and-hasura.md diff --git a/src/pages/blog/2024-04-23-comparison-of-apache-age-postgraphile-and-hasura.md b/src/pages/blog/2024-04-23-comparison-of-apache-age-postgraphile-and-hasura.md new file mode 100644 index 000000000..a5f42181b --- /dev/null +++ b/src/pages/blog/2024-04-23-comparison-of-apache-age-postgraphile-and-hasura.md @@ -0,0 +1,48 @@ +--- +templateKey: blog-post +title: Comparison of Apache AGE, PostGraphile, and Hasura +date: 2024-04-23T02:17:02.242Z +description: Comparison of Apache AGE, PostGraphile, and Hasura +featuredpost: true +featuredimage: /img/logo.png +--- + + +### **◾ Apache AGE: Graph Database for PostgreSQL** + +Apache AGE extends PostgreSQL, transforming it into a graph database. This enables handling complex data relationships and graph structures, supplemented by the power of Cypher queries for graph-specific operations. + + + +- - - + + + +### **◾ PostGraphile: GraphQL API from PostgreSQL Schema** + +PostGraphile is a tool for building a GraphQL API on top of an existing PostgreSQL schema.\ +It's designed for efficiency in web development, allowing real-time data access and manipulation through GraphQL interface. + + + +- - - + + + +### **◾ Hasura: Real-Time GraphQL Engine for PostgreSQL** + +Hasura is a GraphQL server engine that dynamically generates real-time GraphQL APIs from a PostgreSQL database.\ +It emphasizes performance and developer productivity, with an easy setup process and tools for managing access and queries. + + + +- - - + + + +### **◾ Conclusion** + +Apache AGE is ideal for sophisticated graph data tasks within PostgreSQL.\ +PostGraphile and Hasura excel in quick GraphQL API deployment for example for web and mobile, complementing AGE's rich graph features. + + \ No newline at end of file From d894a1935daa4e15d126f4d88041c6a12e599426 Mon Sep 17 00:00:00 2001 From: dpdjvhxm Date: Tue, 23 Apr 2024 11:18:37 +0900 Subject: [PATCH 27/46] modified: static/admin/config.yml --- static/admin/config.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/static/admin/config.yml b/static/admin/config.yml index 3eb2dd0aa..49b49d4ef 100644 --- a/static/admin/config.yml +++ b/static/admin/config.yml @@ -199,6 +199,6 @@ collections: - { label: "Publish Date", name: "date", widget: "datetime" } - { label: "Description", name: "description", widget: "text" } - { label: "Featured Post", name: "featuredpost", widget: "boolean" } - - { label: "Featured Image", name: "featuredimage", widget: image } + - { label: "Featured Image", name: "featuredimage", widget: image, required: "false" } - { label: "Body", name: "body", widget: "markdown" } - { label: "Tags", name: "tags", widget: "list" } From 38abf6e1f40a52f5ef2f1353278b43b576375cf4 Mon Sep 17 00:00:00 2001 From: dpdjvhxm Date: Tue, 23 Apr 2024 13:11:56 +0900 Subject: [PATCH 28/46] modified: src/components/styles/Blog-post.scss modified: src/components/styles/BlogRollTemplate.module.scss modified: src/templates/blog-post.js --- src/components/styles/Blog-post.scss | 2 +- .../styles/BlogRollTemplate.module.scss | 35 +++++++++++++++++++ src/templates/blog-post.js | 3 ++ 3 files changed, 39 insertions(+), 1 deletion(-) diff --git a/src/components/styles/Blog-post.scss b/src/components/styles/Blog-post.scss index 6a2e4df87..e735ca1b0 100644 --- a/src/components/styles/Blog-post.scss +++ b/src/components/styles/Blog-post.scss @@ -31,5 +31,5 @@ } .section { - padding: 5% 10%; // 상하 패딩은 0, 좌우 패딩은 10% + padding: 1% 10%; // 상하 패딩은 0, 좌우 패딩은 10% } diff --git a/src/components/styles/BlogRollTemplate.module.scss b/src/components/styles/BlogRollTemplate.module.scss index c9e4f406c..b48448888 100644 --- a/src/components/styles/BlogRollTemplate.module.scss +++ b/src/components/styles/BlogRollTemplate.module.scss @@ -17,3 +17,38 @@ } } +.articles { + margin-bottom: 8px; + padding-bottom: 8px; + border-bottom: 1px solid #ccc; // 구분선 색상과 두께 조정 + + &:last-child { + border-bottom: none; // 마지막 요소에는 구분선 없음 + } + } + + +.post-meta { + position: absolute; // 이미지를 절대 위치로 설정합니다. + left: 50%; // 부모 컨테이너의 가운데로 설정합니다. + top: 50%; // 부모 컨테이너의 가운데로 설정합니다. + transform: translate(-50%, -50%); // 정확히 중앙에 오도록 조정합니다. + z-index: 10; // 다른 요소들 위에 오도록 z-index 값을 설정합니다. + + // 필요한 경우 width와 height도 고정할 수 있습니다. + width: 100px; // 고정된 너비를 원할 경우 + height: 100px; // 고정된 높이를 원할 경우 +} + +.featured-thumbnail { + position: absolute; // 이미지를 절대 위치로 설정합니다. + left: 50%; // 부모 컨테이너의 가운데로 설정합니다. + top: 50%; // 부모 컨테이너의 가운데로 설정합니다. + transform: translate(-50%, -50%); // 정확히 중앙에 오도록 조정합니다. + z-index: 10; // 다른 요소들 위에 오도록 z-index 값을 설정합니다. + + // 필요한 경우 width와 height도 고정할 수 있습니다. + width: 100px; // 고정된 너비를 원할 경우 + height: 100px; // 고정된 높이를 원할 경우 +} + \ No newline at end of file diff --git a/src/templates/blog-post.js b/src/templates/blog-post.js index b7b653caa..49f723755 100644 --- a/src/templates/blog-post.js +++ b/src/templates/blog-post.js @@ -7,6 +7,9 @@ import Layout from "../components/Layout"; import Content, { HTMLContent } from "../components/Content"; import style from "../components/styles/Blog-post.scss" +// 블로그 글 게시하는 곳에 대한 내용 + + // eslint-disable-next-line export const BlogPostTemplate = ({ content, From 09461bd7cb6a9eb681138b99b0bbcbc1e87e2fea Mon Sep 17 00:00:00 2001 From: dpdjvhxm <137974842+dpdjvhxm@users.noreply.github.com> Date: Tue, 23 Apr 2024 13:27:20 +0900 Subject: [PATCH 29/46] =?UTF-8?q?Update=20Blog=20=E2=80=9Cblog=5F240401?= =?UTF-8?q?=E2=80=9D?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/pages/blog/blog_240401.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/src/pages/blog/blog_240401.md b/src/pages/blog/blog_240401.md index 75bf970cc..21b2e894d 100644 --- a/src/pages/blog/blog_240401.md +++ b/src/pages/blog/blog_240401.md @@ -1,11 +1,11 @@ --- templateKey: blog-post title: Easy and Smooth Start to Graph Databases -date: 2024-04-01T17:00:00Z -description: +date: 2024-04-01T17:00:00.000Z +description: Easy and Smooth Start to Graph Databases featuredpost: true -featuredimage: '/img/blogimg01.png' -tags: +featuredimage: /img/blogimg01.png +tags: - Apache AGE - Graph Database --- From 48a83e0dab71e494aa7a4ab53134c9a1511664cd Mon Sep 17 00:00:00 2001 From: dpdjvhxm Date: Tue, 23 Apr 2024 13:28:50 +0900 Subject: [PATCH 30/46] Match file name format --- ...asy-and-smooth-start-to-graph-databases.md | 73 +++++++++++++++++++ 1 file changed, 73 insertions(+) create mode 100644 src/pages/blog/2024-04-01-easy-and-smooth-start-to-graph-databases.md diff --git a/src/pages/blog/2024-04-01-easy-and-smooth-start-to-graph-databases.md b/src/pages/blog/2024-04-01-easy-and-smooth-start-to-graph-databases.md new file mode 100644 index 000000000..75bf970cc --- /dev/null +++ b/src/pages/blog/2024-04-01-easy-and-smooth-start-to-graph-databases.md @@ -0,0 +1,73 @@ +--- +templateKey: blog-post +title: Easy and Smooth Start to Graph Databases +date: 2024-04-01T17:00:00Z +description: +featuredpost: true +featuredimage: '/img/blogimg01.png' +tags: + - Apache AGE + - Graph Database +--- +Blog Image + +

        Easy and Smooth Start to Graph Databases

        + +Graph databases are like digital maps that show how different pieces of information are connected. Apache AGE makes these maps using the popular relational database called PostgreSQL, making it smarter. Graphizer is a tool developed by Bitnine for Apache AGE to make creating and understanding these maps easier for everyone. + + +**The Need for Simplicity in Graph Databases** + +Starting with graph databases can be tough, like learning a new language. It's hard to see how information connects and to ask the right questions. Tools like Graphizer are important because they make this learning journey smoother, letting more people use these smart databases without getting lost. + + + +One of the standout features of Graphizer is its ability to create graph models and data without needing to learn or use openCypher. For many who are used to traditional relational databases, the jump to graph databases can be scary, primarily due to the need to understand new querying languages like openCypher. Graphizer simplifies this transition. It offers intuitive tools that allow users to build and explore graph data with visual interfaces, bypassing the complexity of coding queries. This means you can focus on what your data tells you, not on memorizing syntax and commands, making the move to graph databases smoother and more approachable for relational database users. + +Take for example the following openCypher query: + +``` +MATCH (m:Movie)<-[:Act_in]-(p:Person)-[:Produced]->(m), +RETURN p, m +``` + +Which looks for a pattern within the graph database where a person has acted in one movie and also produced it. With Graphizer you can just enter a “Visual Query” like in the following image, having the same result as the openCypher query we used. Simply like that, you are in the world of graph databases. + + + +**Graph Data Viewing Feature** + +Graphizer lets you see the connections between points of information (nodes) and the lines that connect them (edges) easily. Imagine looking at a family tree; Graphizer helps you see who is related to whom at a glance, without having to dig through lots of details. + + + +**Data Loading Feature** + +Graphizer is great at taking lists of information, like those in table data files (CSV and RDB files), and turning them into a graph. This is like turning a flat picture into a 3D model, showing how everything is connected in a way that’s easy to understand. + + + + +**Exploring Feature** + +With Graphizer, you can dive into your data like a detective, using tools to find out how things are related. It's like having a magnifying glass to see the clues in your data, helping you make sense of it through pictures, patterns and filters. + + + +**Integrating Graphizer with Apache AGE** + +Graphizer and Apache AGE are packaged together and work along seamlessly. It's like having a guide while exploring a new city, making it easier to navigate and discover connections within your data. This teamwork makes starting with graph databases as easy as starting a car. + +**Practical Applications** + +Graphizer and Apache AGE can be used in cool ways, like figuring out who knows whom on ocial media, spotting fraudsters, or recommending what book you should read next. They turn complex info into easy-to-understand maps, making solving problems or finding opportunities easier. + +Graphizer, along with Apache AGE, is like a pair of glasses that brings the world of graph databases into clear focus. Whether you're new to this world or have been exploring it for a while, these tools make the journey easier and more enjoyable. + +**Call to Action** + +Give Graphizer and Apache AGE a try! They're here to help you navigate the complex world of graph databases with ease. Whether you're looking for guides, tutorials, or a community to join, there's plenty of support to get you started on your adventure. Visit Apache AGE’s GitHub page, you can contribute or send your question through the issues: + +Apache AGE’s website: https://age.apache.org/ + +Apache AGE’s GitHub repository: https://github.com/apache/age \ No newline at end of file From fad1bbc4269a266a9512d427c9d2410f8e93ac39 Mon Sep 17 00:00:00 2001 From: dpdjvhxm Date: Tue, 23 Apr 2024 13:30:27 +0900 Subject: [PATCH 31/46] deleted: src/pages/blog/blog_240401.md --- src/pages/blog/blog_240401.md | 73 ----------------------------------- 1 file changed, 73 deletions(-) delete mode 100644 src/pages/blog/blog_240401.md diff --git a/src/pages/blog/blog_240401.md b/src/pages/blog/blog_240401.md deleted file mode 100644 index 21b2e894d..000000000 --- a/src/pages/blog/blog_240401.md +++ /dev/null @@ -1,73 +0,0 @@ ---- -templateKey: blog-post -title: Easy and Smooth Start to Graph Databases -date: 2024-04-01T17:00:00.000Z -description: Easy and Smooth Start to Graph Databases -featuredpost: true -featuredimage: /img/blogimg01.png -tags: - - Apache AGE - - Graph Database ---- -Blog Image - -

        Easy and Smooth Start to Graph Databases

        - -Graph databases are like digital maps that show how different pieces of information are connected. Apache AGE makes these maps using the popular relational database called PostgreSQL, making it smarter. Graphizer is a tool developed by Bitnine for Apache AGE to make creating and understanding these maps easier for everyone. - - -**The Need for Simplicity in Graph Databases** - -Starting with graph databases can be tough, like learning a new language. It's hard to see how information connects and to ask the right questions. Tools like Graphizer are important because they make this learning journey smoother, letting more people use these smart databases without getting lost. - - - -One of the standout features of Graphizer is its ability to create graph models and data without needing to learn or use openCypher. For many who are used to traditional relational databases, the jump to graph databases can be scary, primarily due to the need to understand new querying languages like openCypher. Graphizer simplifies this transition. It offers intuitive tools that allow users to build and explore graph data with visual interfaces, bypassing the complexity of coding queries. This means you can focus on what your data tells you, not on memorizing syntax and commands, making the move to graph databases smoother and more approachable for relational database users. - -Take for example the following openCypher query: - -``` -MATCH (m:Movie)<-[:Act_in]-(p:Person)-[:Produced]->(m), -RETURN p, m -``` - -Which looks for a pattern within the graph database where a person has acted in one movie and also produced it. With Graphizer you can just enter a “Visual Query” like in the following image, having the same result as the openCypher query we used. Simply like that, you are in the world of graph databases. - - - -**Graph Data Viewing Feature** - -Graphizer lets you see the connections between points of information (nodes) and the lines that connect them (edges) easily. Imagine looking at a family tree; Graphizer helps you see who is related to whom at a glance, without having to dig through lots of details. - - - -**Data Loading Feature** - -Graphizer is great at taking lists of information, like those in table data files (CSV and RDB files), and turning them into a graph. This is like turning a flat picture into a 3D model, showing how everything is connected in a way that’s easy to understand. - - - - -**Exploring Feature** - -With Graphizer, you can dive into your data like a detective, using tools to find out how things are related. It's like having a magnifying glass to see the clues in your data, helping you make sense of it through pictures, patterns and filters. - - - -**Integrating Graphizer with Apache AGE** - -Graphizer and Apache AGE are packaged together and work along seamlessly. It's like having a guide while exploring a new city, making it easier to navigate and discover connections within your data. This teamwork makes starting with graph databases as easy as starting a car. - -**Practical Applications** - -Graphizer and Apache AGE can be used in cool ways, like figuring out who knows whom on ocial media, spotting fraudsters, or recommending what book you should read next. They turn complex info into easy-to-understand maps, making solving problems or finding opportunities easier. - -Graphizer, along with Apache AGE, is like a pair of glasses that brings the world of graph databases into clear focus. Whether you're new to this world or have been exploring it for a while, these tools make the journey easier and more enjoyable. - -**Call to Action** - -Give Graphizer and Apache AGE a try! They're here to help you navigate the complex world of graph databases with ease. Whether you're looking for guides, tutorials, or a community to join, there's plenty of support to get you started on your adventure. Visit Apache AGE’s GitHub page, you can contribute or send your question through the issues: - -Apache AGE’s website: https://age.apache.org/ - -Apache AGE’s GitHub repository: https://github.com/apache/age \ No newline at end of file From 7bde7facaf237ea2ad02b4a8d013d5ce7e2ff322 Mon Sep 17 00:00:00 2001 From: dpdjvhxm Date: Tue, 23 Apr 2024 13:37:57 +0900 Subject: [PATCH 32/46] Change button color --- src/components/styles/Blog-post.scss | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/components/styles/Blog-post.scss b/src/components/styles/Blog-post.scss index e735ca1b0..674ff79ee 100644 --- a/src/components/styles/Blog-post.scss +++ b/src/components/styles/Blog-post.scss @@ -4,7 +4,7 @@ // 기본 스타일 설정 padding: 0.5em 1em; // 패딩 border: none; // 테두리 없음 - background-color: #4CAF50; // 배경색 + background-color: #B8145A; // 배경색 color: white; // 텍스트 색상 text-align: center; // 텍스트 중앙 정렬 text-decoration: none; // 텍스트 밑줄 없음 @@ -15,7 +15,7 @@ // 마우스 오버 시 스타일 변경 &:hover { - background-color: #45a049; // 호버 상태의 배경색 + background-color: #B8145A; // 호버 상태의 배경색 } // 포커스 시 스타일 변경 @@ -26,7 +26,7 @@ // 활성화(클릭) 상태 스타일 &:active { - background-color: #3e8e41; // 클릭 상태의 배경색 + background-color: #B8145A; // 클릭 상태의 배경색 } } From e182e63e5e959b948eeabfefdfb4ef63f9260bb2 Mon Sep 17 00:00:00 2001 From: dpdjvhxm Date: Tue, 23 Apr 2024 18:41:26 +0900 Subject: [PATCH 33/46] modified: static/sitemap.xml --- static/sitemap.xml | 720 +++++++++++++++++++++++++++++---------------- 1 file changed, 465 insertions(+), 255 deletions(-) diff --git a/static/sitemap.xml b/static/sitemap.xml index 5d693d78b..8003a377a 100644 --- a/static/sitemap.xml +++ b/static/sitemap.xml @@ -1,257 +1,467 @@ - - - - https://age.apache.org/age-manual/v0.6.0/functions/string_functions.html - - - https://age.apache.org/age-manual/master/intro/agload.html - - - https://age.apache.org/age-manual/v0.6.0/intro/cypher.html - - - https://age.apache.org/age-manual/master/clauses/merge.html - - - 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Subject: [PATCH 34/46] modified blog --- .../2024-04-01-easy-and-smooth-start-to-graph-databases.md | 2 +- ...imize-the-use-of-chatgpt-with-interactive-graph-models.md | 2 +- ...-combating-cyber-attack-with-apache-ages-link-analysis.md | 2 +- ...04-23-comparison-of-apache-age-postgraphile-and-hasura.md | 2 +- ...leveraging-hyperconnectivity-in-e-commerce-data-part-1.md | 5 ++--- ...hine-learning-with-graph-in-hyperconnected-data-part-2.md | 2 +- 6 files changed, 7 insertions(+), 8 deletions(-) diff --git a/src/pages/blog/2024-04-01-easy-and-smooth-start-to-graph-databases.md b/src/pages/blog/2024-04-01-easy-and-smooth-start-to-graph-databases.md index 75bf970cc..857c22a65 100644 --- a/src/pages/blog/2024-04-01-easy-and-smooth-start-to-graph-databases.md +++ b/src/pages/blog/2024-04-01-easy-and-smooth-start-to-graph-databases.md @@ -4,7 +4,7 @@ title: Easy and Smooth Start to Graph Databases date: 2024-04-01T17:00:00Z description: featuredpost: true -featuredimage: '/img/blogimg01.png' +featuredimage: tags: - Apache AGE - Graph Database diff --git a/src/pages/blog/2024-04-23-beyond-chatgpt-how-to-maximize-the-use-of-chatgpt-with-interactive-graph-models.md b/src/pages/blog/2024-04-23-beyond-chatgpt-how-to-maximize-the-use-of-chatgpt-with-interactive-graph-models.md index a1fb7cf6d..43bb391d4 100644 --- a/src/pages/blog/2024-04-23-beyond-chatgpt-how-to-maximize-the-use-of-chatgpt-with-interactive-graph-models.md +++ b/src/pages/blog/2024-04-23-beyond-chatgpt-how-to-maximize-the-use-of-chatgpt-with-interactive-graph-models.md @@ -6,7 +6,7 @@ date: 2024-04-23T01:18:22.687Z description: "Beyond ChatGPT: How to Maximize the Use of ChatGPT with Interactive Graph Models." featuredpost: true -featuredimage: /img/logo.png +featuredimage: --- diff --git a/src/pages/blog/2024-04-23-combating-cyber-attack-with-apache-ages-link-analysis.md b/src/pages/blog/2024-04-23-combating-cyber-attack-with-apache-ages-link-analysis.md index b268fada7..73dfd5ecd 100644 --- a/src/pages/blog/2024-04-23-combating-cyber-attack-with-apache-ages-link-analysis.md +++ b/src/pages/blog/2024-04-23-combating-cyber-attack-with-apache-ages-link-analysis.md @@ -6,7 +6,7 @@ description: |- Combating cyber attack with Apache AGE's link analysis. featuredpost: true -featuredimage: /img/logo.png +featuredimage: --- diff --git a/src/pages/blog/2024-04-23-comparison-of-apache-age-postgraphile-and-hasura.md b/src/pages/blog/2024-04-23-comparison-of-apache-age-postgraphile-and-hasura.md index a5f42181b..71c3ac132 100644 --- a/src/pages/blog/2024-04-23-comparison-of-apache-age-postgraphile-and-hasura.md +++ b/src/pages/blog/2024-04-23-comparison-of-apache-age-postgraphile-and-hasura.md @@ -4,7 +4,7 @@ title: Comparison of Apache AGE, PostGraphile, and Hasura date: 2024-04-23T02:17:02.242Z description: Comparison of Apache AGE, PostGraphile, and Hasura featuredpost: true -featuredimage: /img/logo.png +featuredimage: --- diff --git a/src/pages/blog/2024-04-23-from-data-to-connections-leveraging-hyperconnectivity-in-e-commerce-data-part-1.md b/src/pages/blog/2024-04-23-from-data-to-connections-leveraging-hyperconnectivity-in-e-commerce-data-part-1.md index 2135d9b31..27d62eb5a 100644 --- a/src/pages/blog/2024-04-23-from-data-to-connections-leveraging-hyperconnectivity-in-e-commerce-data-part-1.md +++ b/src/pages/blog/2024-04-23-from-data-to-connections-leveraging-hyperconnectivity-in-e-commerce-data-part-1.md @@ -3,10 +3,9 @@ templateKey: blog-post title: "From Data to Connections: Leveraging Hyperconnectivity in E-commerce Data (Part 1.)" date: 2024-04-23T02:11:47.653Z -description: "From Data to Connections: Leveraging Hyperconnectivity in - E-commerce Data (Part 1.)" +description: "From Data to Connections: Leveraging Hyperconnectivity in E-commerce Data (Part 1.)" featuredpost: true -featuredimage: /img/logo.png +featuredimage: --- diff --git a/src/pages/blog/2024-04-23-learn-machine-learning-with-graph-in-hyperconnected-data-part-2.md b/src/pages/blog/2024-04-23-learn-machine-learning-with-graph-in-hyperconnected-data-part-2.md index 2bca3b997..ea29c32f5 100644 --- a/src/pages/blog/2024-04-23-learn-machine-learning-with-graph-in-hyperconnected-data-part-2.md +++ b/src/pages/blog/2024-04-23-learn-machine-learning-with-graph-in-hyperconnected-data-part-2.md @@ -4,7 +4,7 @@ title: Learn Machine Learning with Graph in Hyperconnected Data (Part 2.) date: 2024-04-23T02:13:19.760Z description: Learn Machine Learning with Graph in Hyperconnected Data (Part 2.) featuredpost: true -featuredimage: /img/logo.png +featuredimage: --- From 09ebb8cb316d176762f3b1b1a8eb63d86217db01 Mon Sep 17 00:00:00 2001 From: dpdjvhxm Date: Thu, 25 Apr 2024 17:20:57 +0900 Subject: [PATCH 35/46] modified: src/pages/404.js modified: src/pages/index.md --- src/pages/404.js | 2 +- src/pages/index.md | 5 ++--- 2 files changed, 3 insertions(+), 4 deletions(-) diff --git a/src/pages/404.js b/src/pages/404.js index 18229f40d..33168053b 100644 --- a/src/pages/404.js +++ b/src/pages/404.js @@ -4,7 +4,7 @@ import Layout from "../components/Layout"; const NotFoundPage = () => (
        -

        NOT FOUND

        +

        404 : NOT FOUND 테스트

        You just hit a route that doesn't exist... the sadness.

        diff --git a/src/pages/index.md b/src/pages/index.md index d8c07f23e..0ac4217ee 100644 --- a/src/pages/index.md +++ b/src/pages/index.md @@ -1,10 +1,9 @@ --- templateKey: index-page -title: Apache AGE | Graph Database for PostgreSQL +title: Apache AGE | Apache AGE Graph Database for PostgreSQL bannerImg: /img/background.webp bannerContents: >- -

        Apache AGE
        - Graph Database for PostgreSQL

        +

        Apache AGE
        Graph Database for PostgreSQL

        From db28f4085a55e84d6a81509380274d7912f65e1a Mon Sep 17 00:00:00 2001 From: dpdjvhxm <137974842+dpdjvhxm@users.noreply.github.com> Date: Thu, 25 Apr 2024 17:32:19 +0900 Subject: [PATCH 36/46] =?UTF-8?q?Create=20Blog=20=E2=80=9C2024-04-25-disco?= =?UTF-8?q?vering-the-inner-workings-of-postgresqls-data-allocation?= =?UTF-8?q?=E2=80=9D?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...workings-of-postgresqls-data-allocation.md | 63 +++++++++++++++++++ 1 file changed, 63 insertions(+) create mode 100644 src/pages/blog/2024-04-25-discovering-the-inner-workings-of-postgresqls-data-allocation.md diff --git a/src/pages/blog/2024-04-25-discovering-the-inner-workings-of-postgresqls-data-allocation.md b/src/pages/blog/2024-04-25-discovering-the-inner-workings-of-postgresqls-data-allocation.md new file mode 100644 index 000000000..81dd705f2 --- /dev/null +++ b/src/pages/blog/2024-04-25-discovering-the-inner-workings-of-postgresqls-data-allocation.md @@ -0,0 +1,63 @@ +--- +templateKey: blog-post +title: Discovering the Inner Workings of PostgreSQL's Data Allocation +date: 2024-04-25T08:29:55.344Z +description: PostgreSQL is a true gem among open-source relational database + management systems. Its impressive robustness, flexibility, and scalability + make it a favourite choice for many developers and businesses alike. One of + the most crucial components of any database system is how it manages and + allocates data. In this article, we'll take a deep dive into PostgreSQL's data + allocation process to reveal its inner workings and discover how it ensures + optimal performance and reliability. +featuredpost: true +featuredimage: /img/logo.png +--- + + +PostgreSQL is a true gem among open-source relational database management systems. Its impressive robustness, flexibility, and scalability make it a favourite choice for many developers and businesses alike. One of the most crucial components of any database system is how it manages and allocates data. In this article, we'll take a deep dive into PostgreSQL's data allocation process to reveal its inner workings and discover how it ensures optimal performance and reliability. + +1.  The Art of Data Storage: + +PostgreSQL stores data in tables, with each table containing data rows. Each row is made up of one or more columns representing various attributes of the data. When you create a PostgreSQL table, you must define its schema, specifying data types and constraints for each column. It's like giving your data a personality and a set of rules to follow. + +2. The Science of Storage Management: + +PostgreSQL's storage management strategy involves breaking data into smaller chunks, or pages. Each page is like a container with a fixed capacity, and PostgreSQL organizes data by placing it into these containers. Just like arranging items in multiple containers makes it easier to manage them, PostgreSQL's page-based storage allows for efficient data organization. + +3. Tablespaces: A World of Possibilities + + PostgreSQL organizes data into tablespaces, each representing a physical location on the disk where data is stored. A PostgreSQL database can have multiple tablespaces, allowing you to distribute data across different physical disks or file systems. This feature enhances performance and enables better control over storage resources. An example is having a house with multiple rooms, each with its unique purpose. + +4. Heap Files: The Foundation of PostgreSQL's Data Storage + + PostgreSQL stores data in heap files when you create a table. A heap file is an uncomplicated append-only file containing a series of data pages. When you insert new rows into a table, PostgreSQL appends the data to the heap file's pages, ensuring that new data is always written sequentially. This approach reduces fragmentation and optimizes disk I/O performance for write operations. It's reminiscent of having a notebook where you write your thoughts in chronological order. + +5. Page Structure: The Building Blocks of PostgreSQL's Data Allocation + +PostgreSQL employs a page structure within each heap file to organize data efficiently. Each page consists of a header, containing metadata about the page, followed by the actual data rows. The header includes information such as the page number, the number of items stored on the page, and a pointer to the next page. Imagine having a folder with labels for each paper inside. + +6. The Magic of Data Allocation: + +PostgreSQL automatically manages data allocation when you insert data into a table. When a new row is added, PostgreSQL determines the appropriate page to allocate the data. If the existing page has enough space to accommodate the new row, it will place it there. Otherwise, it will create a new page and link it to the existing pages to form a chain. It's like having a magician that knows all the tricks to make your data disappear and reappear in the right place. + + 7. Free Space Map (FSM): The Map to Your Data's Destination + +To keep track of available space within each page, PostgreSQL maintains a Free Space Map (FSM). The FSM records which bytes within each page are used and which ones are still available for data allocation. When inserting or updating data, PostgreSQL checks the FSM to find the page with sufficient free space. It's like having a treasure map that leads you to the right spot. + +8. Indexes and Data Allocation: The Key to Your Data's Acceleration + +PostgreSQL's indexing mechanism plays a vital role in data allocation efficiency. When you create an index on a table, PostgreSQL creates a separate B-tree data structure that allows for faster data retrieval based on the indexed columns. The presence of indexes influences how PostgreSQL allocates data to maintain optimal access and search performance. It’s the same as having a GPS that guides you to the quickest route. + +9. Apache AGE: Expanding PostgreSQL with Graph Database Functionality + +While PostgreSQL excels in managing relational data, its capabilities extend into the realm of graph databases, thanks to its integration with Apache AGE (A Graph Extension). Apache AGE is an extension that transforms PostgreSQL into a graph database, allowing it to handle complex, connected data structures alongside traditional relational data. This integration offers the best of both worlds – the robust, tried-and-tested features of PostgreSQL for relational data, and the flexibility of graph database capabilities for handling interconnected data. Think of it as adding an advanced toolset to an already well-equipped toolbox, making PostgreSQL not just a database, but a versatile data processing engine.  + +With Apache AGE, PostgreSQL becomes an even more powerful option for developers needing to manage diverse data models, from social networks to recommendation systems, without sacrificing the reliability and performance PostgreSQL is known for. This seamless blending of technologies demonstrates PostgreSQL's adaptability and its commitment to evolving with the changing landscape of data management. + +\*\** + +In conclusion, PostgreSQL's data allocation process is a fascinating and efficient system that ensures optimal performance and data organization. By dividing data into manageable pages, employing tablespaces for better storage management, and utilizing indexing mechanisms, PostgreSQL offers a reliable and scalable solution for handling large volumes of data. Understanding how PostgreSQL manages data allocation is crucial for database administrators and developers to optimize their applications and make the most out of this powerful database management system. Let's build a better world of data together! + + + + \ No newline at end of file From b0093a8d66f7a8cf5d316c28fcde06de52928f40 Mon Sep 17 00:00:00 2001 From: dpdjvhxm <137974842+dpdjvhxm@users.noreply.github.com> Date: Thu, 25 Apr 2024 17:34:17 +0900 Subject: [PATCH 37/46] =?UTF-8?q?Create=20Blog=20=E2=80=9C2024-04-25-seaml?= =?UTF-8?q?ess-data-migration-migrating-apache-age-data-between-different-?= =?UTF-8?q?versions-of-postgresql=E2=80=9D?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...etween-different-versions-of-postgresql.md | 164 ++++++++++++++++++ 1 file changed, 164 insertions(+) create mode 100644 src/pages/blog/2024-04-25-seamless-data-migration-migrating-apache-age-data-between-different-versions-of-postgresql.md diff --git a/src/pages/blog/2024-04-25-seamless-data-migration-migrating-apache-age-data-between-different-versions-of-postgresql.md b/src/pages/blog/2024-04-25-seamless-data-migration-migrating-apache-age-data-between-different-versions-of-postgresql.md new file mode 100644 index 000000000..b01f9bac4 --- /dev/null +++ b/src/pages/blog/2024-04-25-seamless-data-migration-migrating-apache-age-data-between-different-versions-of-postgresql.md @@ -0,0 +1,164 @@ +--- +templateKey: blog-post +title: "Seamless Data Migration: Migrating Apache AGE Data Between Different + Versions of PostgreSQL" +date: 2024-04-25T08:33:00.464Z +description: > + Apache AGE serves as an open-source graph database, akin to Neo4j. Although + they share similar functionalities, they also exhibit significant differences. + AGE stands out as a PostgreSQL extension which leverages the capabilities of a + relational database (RDBMS), enabling users to seamlessly integrate both + relational data (tables) and graph data, which is a key strength setting it + apart from Neo4j. +featuredpost: true +featuredimage: /img/logo.png +--- + + +Apache AGE serves as an open-source graph database, akin to Neo4j. Although they share similar functionalities, they also exhibit significant differences. AGE stands out as a PostgreSQL extension which leverages the capabilities of a relational database (RDBMS), enabling users to seamlessly integrate both relational data (tables) and graph data, which is a key strength setting it apart from Neo4j. + + + +AGE is available in various versions, each compatible with specific PostgreSQL versions. One common challenge users encounter is data migration when transitioning from one AGE version to another. Fortunately, PostgreSQL provides a utility called "pg_dump", which seamlessly functions with AGE for data migration. + + + +Let's exemplify the data migration process by creating a graph with some nodes: + + + +SELECT create_graph('test'); + + + +NOTICE:  graph "test" has been created + + create_graph  + +\-------------- + +  + +(1 row) + + + +SELECT * FROM cypher('test', $$ + +    CREATE (:Person {name: "John Doe"}) + +    CREATE (:Person {name: "Jane Smith"}) + +$$) AS (res agtype); + + + + res  + +\----- + +(0 rows) + + + +SELECT * FROM cypher('test', $$ + +    MATH (n) + +    RETURN n + +$$) AS (res agtype); + + + +                                    res                                      + +\---------------------------------------------------------------------------- + + {"id": 1, "label": "Person", "properties": {"name": "John Doe"}}::vertex + + {"id": 2, "label": "Person", "properties": {"name": "Jane Smith"}}::vertex + +(2 rows) + + + +Now, we have a graph called "test" containing two vertices (keeping it simple). + + + +Using pg_dump for Backup: + + + +To create a backup, open a new terminal in your current postgreSQL directory and execute the following command: + + + +bin/pg_dump -d postgres > age_backup.sql + + + +Here, "postgres" represents the name of the database (which might differ in your case), and "age_backup" is the chosen name for this demonstration. The generated "age_backup.sql" file will contain all the necessary queries to restore this backup whenever needed. + + + +Now, if you open “age_backup.sql”, which will be in your PostgreSQL directory, its initial content will resemble this: + + + +\-- PostgreSQL database dump + +\-- Dumped from database version 13.9 + +\-- Dumped by pg_dump version 13.9 + + + +Using psql for Migration: + + + +Navigate to the directory containing the desired version of PostgreSQL in your terminal—the one to which you intend to migrate the data. Ensure that you have also installed the desired version of AGE as well. + + + +Execute the following command: + + + +bin/psql -d postgres-f path/to/age_dump.sql + + + +Now, it's time to check whether the data migration has been successfully executed. To do this, enter the following command in the psql terminal: + + + +SELECT * FROM cypher('test', $$ + +    MATH (n) + +    RETURN n + +$$) AS (res agtype); + + + +                                    res                                      + +\---------------------------------------------------------------------------- + + {"id": 1, "label": "Person", "properties": {"name": "John Doe"}}::vertex + + {"id": 2, "label": "Person", "properties": {"name": "Jane Smith"}}::vertex + +(2 rows) + + + +As you can see, the data has been successfully migrated to the new version of AGE. + + + + \ No newline at end of file From a2cc5fab1ccfd4600bec4257b07d41626e856a07 Mon Sep 17 00:00:00 2001 From: dpdjvhxm <137974842+dpdjvhxm@users.noreply.github.com> Date: Thu, 25 Apr 2024 17:38:13 +0900 Subject: [PATCH 38/46] =?UTF-8?q?Create=20Blog=20=E2=80=9C2024-04-25-how-t?= =?UTF-8?q?o-deploy-apache-age-docker-image-on-aws=E2=80=9D?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...o-deploy-apache-age-docker-image-on-aws.md | 89 +++++++++++++++++++ 1 file changed, 89 insertions(+) create mode 100644 src/pages/blog/2024-04-25-how-to-deploy-apache-age-docker-image-on-aws.md diff --git a/src/pages/blog/2024-04-25-how-to-deploy-apache-age-docker-image-on-aws.md b/src/pages/blog/2024-04-25-how-to-deploy-apache-age-docker-image-on-aws.md new file mode 100644 index 000000000..e80c355cc --- /dev/null +++ b/src/pages/blog/2024-04-25-how-to-deploy-apache-age-docker-image-on-aws.md @@ -0,0 +1,89 @@ +--- +templateKey: blog-post +title: How to Deploy Apache AGE Docker Image on AWS +date: 2024-04-25T08:37:04.997Z +description: > + Apache AGE® is a PostgreSQL extension that provides graph database + functionality. Apache AGE provides graph data processing and analytics + capability to all relational databases. + + This article will explore how to set up Amazon Web Services (AWS), deploy the Apache AGE Docker image, and query the PostgreSQL database running in the Docker container on Amazon Web Services (AWS). +featuredpost: true +featuredimage: /img/logo.png +--- + + +Apache AGE® is a PostgreSQL extension that provides graph database functionality. Apache AGE provides graph data processing and analytics capability to all relational databases.  + +This article will explore how to set up Amazon Web Services (AWS), deploy the Apache AGE Docker image, and query the PostgreSQL database running in the Docker container on Amazon Web Services (AWS). + +Prerequisites + +1. AWS Account: If you don’t already have one, sign up for an AWS account at [Amazon Web Services](https://aws.amazon.com/). +2. Docker: Install Docker on your local machine. You can download & install Docker from [here](https://www.docker.com/get-started). + + + +Step 1: Set Up AWS Account + +1. Log in to AWS: Access the AWS Management Console using your AWS account credentials. +2. Create an IAM User: +3. 1. Navigate to the IAM (Identity and Access Management) dashboard. + 2. Click on “Users” & then “Add user.” + 3. Provide a username and select “Programmatic access” to generate an access key and secret key for Docker authentication. + +Step 2: Deploy Apache AGE Docker Image + +1. Open a terminal on your local machine. +2. Pull the Apache AGE Docker image from Docker Hub: ![](https://lh7-us.googleusercontent.com/6nFRHrsfi6mHtkh15ID0WR3ac8tdiZ7WLCbJYQQojyYbswAm5Q8bbFLhLNL85ZnYGaqEbEcI_fkt64yygvZ-2DzfG9pui4thQM9gPF24r7ZC_WzZ0vzTmdO-t5o4wc294Xey3A7IV98HAnr7Bg1Jxw) +3. Run the Docker container locally to verify its functionality: ![](https://lh7-us.googleusercontent.com/0TgaSXMeJe3Ypped8Q0ibqZe_x-9X8Df7HVPcr7ABIhU8XiS8ul9tFQxifHeb3bHm9eEW3LzoirQMwNDW-Nuz2X4kADDyIn2j9lLBeX1drMIZuBjqq6BrKSpg4sv5-kqIc-5tjC_mY51605MhzQ64Q) +4. Tag the Docker image with the Amazon Elastic Container Registry (ECR) repository URL: ![](https://lh7-us.googleusercontent.com/axtCSLN2EpvE3BKjHI18uPM0uIPW7BOQy9MpP5d91XPOjbiIkOVq_GtGq_QXnmWfuzD4odGwvjb4WMacZFET_cGmJV3fPfgrxHR643XT5Zr9tC3V51XE5i42MdwJMLw95lPpgcmPQZUgp84YR-6_sA) +5. Authenticate Docker to the Amazon ECR:![](https://lh7-us.googleusercontent.com/4ofNVcRhkH1dZlXIA6gHs5wvsMx5wwtgjUT1TgrLtzjvPaFNPhBCoCv0NkPXFxOeU-0ECSEKChiDw5lLz-Uw3QfhB3ENN7tzfNqyCp7n4xVF6XRxfelSF_pTf9M813LUpi94xlJGXjfSSH1nMpjrvw) +6. Push the Docker image to your Amazon ECR repository: ![](https://lh7-us.googleusercontent.com/AvJD8NMaZH2r8RlWhkNBZps-0FVryeEuvdm7Orr8zcwwDCoOWDhVMJfp-tUlbkZh4XJAvm4lMHegwv1fWVnxxR0aMjOSvXvLgwysH3XOVCZ0TVP9TVTu6eGtKxkxt6YfN8QhG_n8nCMnV9ZamjKESg) + + + +Step 3: Launch the Amazon EC2 Instance + +1. In the AWS Management Console, go to the EC2 Dashboard. +2. Click “Launch Instance” and choose an Amazon Machine Image (AMI) of your choice. +3. Configure the instance settings: +4. * Choose an instance type. + * Configure instance details and storage. + * Add tags if necessary. + * Configure security group rules to allow incoming traffic on port 5432 (PostgreSQL). +5. Review the configurations and launch the instance. + + + +Step 4: Deploy Docker Image on Amazon EC2 + +1. SSH into your Amazon EC2 instance using the key pair associated with the instance:![](https://lh7-us.googleusercontent.com/uMdyOrdtw_eNyJe2B2-u56su4FxhHSfic4RfrUQnKhGvEj_xbPzwqO5nm8Pj8BppZhaJa--r1-HMuKoVUZSfNrKM_qf2WzGj_Fg3KWgWsVZnqRRrj1BMl0g7IoKwp_I_XXpyIBP0F5OyRA30XFfPkg) +2. Install Docker on the EC2 instance: + +![](https://lh7-us.googleusercontent.com/NYK-wsc0WeuaTnwQ7t_4oWkEIYl-iEyUOBFN1FilZHSOcW8UNGNuoWBH3qZgvaFoGlPANR3WVXRg_O14Ot7DAPOweFFP8exvtMF1ycB-oJNn271T7157Ucqg8QbA-7_HfUsxzpn3XjjUGxoSaoGlLg) + +![](https://lh7-us.googleusercontent.com/7CctADC_EVZSDC1XThYj_qo631j1Fp_LKuX5bSvFzu52i0fDiMl0ol7P1tybkrES6hHC9HPsffQaMI_5m7u218M1wVaewBFReYmzfiRaH-ftWM0vCRQ0Qv88LsOQmScwHvBumhvNvhKP8oXNZHWvDg) + +![](https://lh7-us.googleusercontent.com/tsL9-TfHumrTK-lYj8xLz7F0Jrgio_Z7AYzl6HG4jK_U2qUyAA7dvxJ1dW7lLdK9az1jw0YdiT2wRIuKJbd_nsWxDRntAvMnPQ8vfVpBcWh8be6mxmRdhJAQ9FAWSUYuCZvFZkvKC8KfWpS09J0ycA) + +![](https://lh7-us.googleusercontent.com/orfuWcUbbs_AnDRRrXoP_SphDdPRZ8oUvxrYKSGh1oIqLiRYy4wmr1IC8LnOQEm51geFSVvTEu8OaM8EzIaB_brmGui3u-O5LYeC6YK6WQ8E8pKghzsJx40Zrjhi0fM_E9DGTV8DoYxvlqxhpFWFZw) + +3. Pull and run the Apache AGE Docker image on the instance: + +![](https://lh7-us.googleusercontent.com/p8OdSdB632x4DGcTekVMo2b1RlEFCX9VKdDhxMfUOaSjfwBpGRRjhZVlz2DH3866Xx22LT9jb_w5A75eUe9JcMnrinVpVequRrjRZWsrITS24i_SDpMUm5VMpkb3BOPRBUFJrdkBAyUwuKJ7z0ahnA) + +Step 5: Access & Query PostgreSQL Database + +1. Install the PostgreSQL client on the EC2 instance:![](https://lh7-us.googleusercontent.com/yPKtED71f06maB_4FPymzRcljCkgjC3-yyCKnckA1vEulBIU5mprhKyQUAPwVPQ-__ZFWcN6lWxtTTdPDO5HBl_LTpcYCB6KZG32_bTcV3EVTJGzsMe_pyb8EVGLbbhNjSCw01_az0z8Qt6W6RklOg) +2. Connect to the Postgres database running in the Docker container:![](https://lh7-us.googleusercontent.com/yv_uYVG0g_Oe8WE3Oc7xpZSbd3UhhOk5WRVRxMG5a7uwbyHOBS2ZJ5cB45z7slHIJfO6rseFccjInwTYH9jZLWPeiVqs4Qm4OLJQ5TMjj4Y_qAlVrEWvy1WkUqRE_yt83NyZRBVBsPFkRosMcKLz7g) + +You are now connected to the PostgreSQL database. Execute SQL queries and interact with the database as needed. + +Conclusion + +By following this comprehensive guide, you have successfully set up an AWS account, deployed the Apache AGE Docker image on an Amazon EC2 instance, and accessed the PostgreSQL database within the Docker container. This setup provides a secure and scalable environment for your data encryption and storage needs. + + + + \ No newline at end of file From 23ed8ae9026e6bf0ba53a94c9724c87ff422de6f Mon Sep 17 00:00:00 2001 From: dpdjvhxm Date: Thu, 25 Apr 2024 17:40:12 +0900 Subject: [PATCH 39/46] modified: static/admin/config.yml --- static/admin/config.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/static/admin/config.yml b/static/admin/config.yml index 49b49d4ef..5770fbed8 100644 --- a/static/admin/config.yml +++ b/static/admin/config.yml @@ -199,6 +199,6 @@ collections: - { label: "Publish Date", name: "date", widget: "datetime" } - { label: "Description", name: "description", widget: "text" } - { label: "Featured Post", name: "featuredpost", widget: "boolean" } - - { label: "Featured Image", name: "featuredimage", widget: image, required: "false" } + - { label: "Featured Image", name: "featuredimage", widget: "image" } - { label: "Body", name: "body", widget: "markdown" } - { label: "Tags", name: "tags", widget: "list" } From f0f5c677e3b70e847581bcaacef5dab323e99ea0 Mon Sep 17 00:00:00 2001 From: dpdjvhxm Date: Thu, 25 Apr 2024 17:40:33 +0900 Subject: [PATCH 40/46] modified: src/pages/blog/ modified: src/pages/blog/2024-04-25-how-to-deploy-apache-age-docker-image-on-aws.md modified: src/pages/blog/2024-04-25-seamless-data-migration-migrating-apache-age-data-between-different-versions-of-postgresql.md --- .../2024-04-25-how-to-deploy-apache-age-docker-image-on-aws.md | 2 +- ...-apache-age-data-between-different-versions-of-postgresql.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/src/pages/blog/2024-04-25-how-to-deploy-apache-age-docker-image-on-aws.md b/src/pages/blog/2024-04-25-how-to-deploy-apache-age-docker-image-on-aws.md index e80c355cc..e49bb2376 100644 --- a/src/pages/blog/2024-04-25-how-to-deploy-apache-age-docker-image-on-aws.md +++ b/src/pages/blog/2024-04-25-how-to-deploy-apache-age-docker-image-on-aws.md @@ -9,7 +9,7 @@ description: > This article will explore how to set up Amazon Web Services (AWS), deploy the Apache AGE Docker image, and query the PostgreSQL database running in the Docker container on Amazon Web Services (AWS). featuredpost: true -featuredimage: /img/logo.png +featuredimage: --- diff --git a/src/pages/blog/2024-04-25-seamless-data-migration-migrating-apache-age-data-between-different-versions-of-postgresql.md b/src/pages/blog/2024-04-25-seamless-data-migration-migrating-apache-age-data-between-different-versions-of-postgresql.md index b01f9bac4..46c8adfaa 100644 --- a/src/pages/blog/2024-04-25-seamless-data-migration-migrating-apache-age-data-between-different-versions-of-postgresql.md +++ b/src/pages/blog/2024-04-25-seamless-data-migration-migrating-apache-age-data-between-different-versions-of-postgresql.md @@ -11,7 +11,7 @@ description: > relational data (tables) and graph data, which is a key strength setting it apart from Neo4j. featuredpost: true -featuredimage: /img/logo.png +featuredimage: --- From 034fc3a3c68799a8ad8fca32c29dc5ff8e37bffc Mon Sep 17 00:00:00 2001 From: dpdjvhxm <137974842+dpdjvhxm@users.noreply.github.com> Date: Wed, 1 May 2024 17:27:15 +0900 Subject: [PATCH 41/46] =?UTF-8?q?Create=20Blog=20=E2=80=9C2024-05-01-what-?= =?UTF-8?q?is-a-graph-database=E2=80=9D?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../2024-05-01-what-is-a-graph-database.md | 75 +++++++++++++++++++ 1 file changed, 75 insertions(+) create mode 100644 src/pages/blog/2024-05-01-what-is-a-graph-database.md diff --git a/src/pages/blog/2024-05-01-what-is-a-graph-database.md b/src/pages/blog/2024-05-01-what-is-a-graph-database.md new file mode 100644 index 000000000..0d5147d60 --- /dev/null +++ b/src/pages/blog/2024-05-01-what-is-a-graph-database.md @@ -0,0 +1,75 @@ +--- +templateKey: blog-post +title: What is a Graph Database? +date: 2024-05-01T08:26:14.983Z +description: | + Understanding the World of Graph Databases +featuredpost: true +featuredimage: /img/logo.png +tags: + - Apache AGE +--- + + +## Understanding the World of Graph Databases + + + +Graph databases are like a bustling party, filled with conversations, connections, and, of course, a tangled web of who knows whom and how. A type of database complex and rich in connections, designed to treat relationships between data points as equally important as the data points themselves. Unlike traditional relational databases that store data in tabular rows and columns, graph databases use nodes (entities), edges (relationships), and properties to represent and store data. + + + +![](https://lh7-us.googleusercontent.com/LmjoGsu27-rd5tF3lVH_Kir0w-sqkzNAzbHU2jEvgGHmgmE95VnnrPfvOfkoZlebwqobqqMDlyIHQdT1N3DQRkJyk17PN42IoiqSffu9TLd4suC5IqBs8k5AzKksCYOh8N9_KAhKpb8BLYKTXmD4Sb4) + + + +The strength of a graph database lies in its ability to efficiently manage, query, and visualize densely connected data. Think about finding the shortest path in a network, such as the quickest route for data to travel from point A to point B, or identifying all the immediate neighbors of a node. This is where graph databases shine, offering high performance for data-intensive operations that involve complex relationships and dynamic schema. + + + +## What is Apache AGE? + + + +Apache AGE is an open-source extension that brings graph database functionality to [PostgreSQL](https://www.postgresql.org/), one of the world’s most advanced open-source databases. This means you can manage both relational and graph data models under a single, unified system. Apache AGE adds a graph layer to PostgreSQL, enabling it to handle complex, connected data structures more natively and efficiently. + + + +With Apache AGE, users don't have to choose between relational and graph databases; they can leverage the strengths of both. It supports the [openCypher](https://opencypher.org/) query language, which is widely used for querying graph databases, thus allowing developers to use familiar syntax and operations to manage their data. + + + +## Why Graph Databases? + + + +Graph databases are ideal for handling interconnected data, which is increasingly prevalent in today’s networked world. Here’s why they’re becoming a go-to solution: + + + +* Efficiency in Relationship-Intensive Queries: Graph databases are optimized for queries that involve deep relationships, such as social networks, recommendation engines, and fraud detection systems. +* Flexibility: They offer schema-less and schema-flexible options, allowing for on-the-fly modifications and easy adaptation to complex data structures. +* Intuitive Data Modeling: Graph databases model data in a way that’s visually and logically intuitive, making it easier for developers to map real-world scenarios to the database. + + + +## Where and How is Apache AGE Used? + + + +Apache AGE finds its utility in various domains, reflecting the versatility and power of graph databases. Some of the key use cases include: + + + +* Social Networking: By managing vast networks of users and their interactions, Apache AGE can drive complex social graphs to deliver personalized content, suggest connections, and analyze trends. +* Fraud Detection: In financial services, Apache AGE can help map transaction patterns to detect and prevent fraud more effectively by spotting anomalies in densely connected data. Learn more about it here: +* Recommendation Engines: Retail and entertainment sectors use Apache AGE to analyze customer preferences and social interactions to recommend products or content. You can learn more about it in this blog article: +* Network and IT Operations: Apache AGE can be used to monitor networks and manage IT infrastructure by providing insights into the relationships and dependencies among various IT components. + + + +By integrating Apache AGE, organizations can harness the power of graph databases within the robust, proven framework of PostgreSQL, making it a powerful tool for those needing to manage complex, connected datasets with agility and speed. Whether you’re plotting the next big social network, untangling complex data puzzles, or paving the way for next-gen recommendation engines, Apache AGE equips you with the right tools to connect the dots, literally and figuratively! So, next time you find yourself lost in a maze of data, consider a graph database as your compass. Happy data navigating! + + + + \ No newline at end of file From ad5bba484862c6f2138984ed5534173357cc9fbe Mon Sep 17 00:00:00 2001 From: dpdjvhxm Date: Wed, 1 May 2024 17:27:37 +0900 Subject: [PATCH 42/46] modified: src/templates/blog-post.js --- src/templates/blog-post.js | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/templates/blog-post.js b/src/templates/blog-post.js index 49f723755..6fdcab408 100644 --- a/src/templates/blog-post.js +++ b/src/templates/blog-post.js @@ -7,7 +7,7 @@ import Layout from "../components/Layout"; import Content, { HTMLContent } from "../components/Content"; import style from "../components/styles/Blog-post.scss" -// 블로그 글 게시하는 곳에 대한 내용 +// 블로그 글 내부 설정 대한 내용 // eslint-disable-next-line From 5b0901b0654dceae8f036ca33258422d868e1060 Mon Sep 17 00:00:00 2001 From: dpdjvhxm Date: Wed, 1 May 2024 17:34:38 +0900 Subject: [PATCH 43/46] modified: src/pages/blog/ --- gatsby-node.js | 1 - .../blog/2024-04-23-graph-database-in-postgresql-apache-age.md | 1 + ...vering-the-inner-workings-of-postgresqls-data-allocation.md | 2 +- src/pages/blog/2024-05-01-what-is-a-graph-database.md | 3 ++- 4 files changed, 4 insertions(+), 3 deletions(-) diff --git a/gatsby-node.js b/gatsby-node.js index 25e54add5..5f164eaf7 100644 --- a/gatsby-node.js +++ b/gatsby-node.js @@ -85,4 +85,3 @@ exports.onCreateNode = ({ node, actions, getNode }) => { }) } } - diff --git a/src/pages/blog/2024-04-23-graph-database-in-postgresql-apache-age.md b/src/pages/blog/2024-04-23-graph-database-in-postgresql-apache-age.md index ccf840437..e37a5eeff 100644 --- a/src/pages/blog/2024-04-23-graph-database-in-postgresql-apache-age.md +++ b/src/pages/blog/2024-04-23-graph-database-in-postgresql-apache-age.md @@ -5,6 +5,7 @@ date: 2024-04-23T02:15:59.564Z description: "Graph Database in PostgreSQL: Apache AGE" featuredpost: true featuredimage: /img/logo.png + --- diff --git a/src/pages/blog/2024-04-25-discovering-the-inner-workings-of-postgresqls-data-allocation.md b/src/pages/blog/2024-04-25-discovering-the-inner-workings-of-postgresqls-data-allocation.md index 81dd705f2..c0b553c16 100644 --- a/src/pages/blog/2024-04-25-discovering-the-inner-workings-of-postgresqls-data-allocation.md +++ b/src/pages/blog/2024-04-25-discovering-the-inner-workings-of-postgresqls-data-allocation.md @@ -10,7 +10,7 @@ description: PostgreSQL is a true gem among open-source relational database allocation process to reveal its inner workings and discover how it ensures optimal performance and reliability. featuredpost: true -featuredimage: /img/logo.png +featuredimage: --- diff --git a/src/pages/blog/2024-05-01-what-is-a-graph-database.md b/src/pages/blog/2024-05-01-what-is-a-graph-database.md index 0d5147d60..725447ed5 100644 --- a/src/pages/blog/2024-05-01-what-is-a-graph-database.md +++ b/src/pages/blog/2024-05-01-what-is-a-graph-database.md @@ -5,9 +5,10 @@ date: 2024-05-01T08:26:14.983Z description: | Understanding the World of Graph Databases featuredpost: true -featuredimage: /img/logo.png +featuredimage: tags: - Apache AGE + - Graph Database --- From cea916ab2c05f9720a307d0fbe5964cdf293e84f Mon Sep 17 00:00:00 2001 From: dpdjvhxm Date: Wed, 1 May 2024 18:02:01 +0900 Subject: [PATCH 44/46] modified: src/pages/blog/ --- .../2024-04-23-graph-database-in-postgresql-apache-age.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/src/pages/blog/2024-04-23-graph-database-in-postgresql-apache-age.md b/src/pages/blog/2024-04-23-graph-database-in-postgresql-apache-age.md index e37a5eeff..dcf57d220 100644 --- a/src/pages/blog/2024-04-23-graph-database-in-postgresql-apache-age.md +++ b/src/pages/blog/2024-04-23-graph-database-in-postgresql-apache-age.md @@ -5,6 +5,10 @@ date: 2024-04-23T02:15:59.564Z description: "Graph Database in PostgreSQL: Apache AGE" featuredpost: true featuredimage: /img/logo.png +tags: + - Apache AGE + - Graph Database + - PostgreSQL --- From f2aae703be164a6e89e70c22069b77bea341b82e Mon Sep 17 00:00:00 2001 From: dpdjvhxm Date: Wed, 8 May 2024 09:31:48 +0900 Subject: [PATCH 45/46] =?UTF-8?q?Remove=20sign=C2=AE?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...-04-25-how-to-deploy-apache-age-docker-image-on-aws.md | 4 ++-- src/pages/overview/index.md | 8 ++++---- src/templates/index-page.js | 6 +++--- static/rdf/age-doap.rdf | 4 ++-- 4 files changed, 11 insertions(+), 11 deletions(-) diff --git a/src/pages/blog/2024-04-25-how-to-deploy-apache-age-docker-image-on-aws.md b/src/pages/blog/2024-04-25-how-to-deploy-apache-age-docker-image-on-aws.md index e49bb2376..6bbac4a17 100644 --- a/src/pages/blog/2024-04-25-how-to-deploy-apache-age-docker-image-on-aws.md +++ b/src/pages/blog/2024-04-25-how-to-deploy-apache-age-docker-image-on-aws.md @@ -3,7 +3,7 @@ templateKey: blog-post title: How to Deploy Apache AGE Docker Image on AWS date: 2024-04-25T08:37:04.997Z description: > - Apache AGE® is a PostgreSQL extension that provides graph database + Apache AGE is a PostgreSQL extension that provides graph database functionality. Apache AGE provides graph data processing and analytics capability to all relational databases. @@ -13,7 +13,7 @@ featuredimage: --- -Apache AGE® is a PostgreSQL extension that provides graph database functionality. Apache AGE provides graph data processing and analytics capability to all relational databases.  +Apache AGE is a PostgreSQL extension that provides graph database functionality. Apache AGE provides graph data processing and analytics capability to all relational databases.  This article will explore how to set up Amazon Web Services (AWS), deploy the Apache AGE Docker image, and query the PostgreSQL database running in the Docker container on Amazon Web Services (AWS). diff --git a/src/pages/overview/index.md b/src/pages/overview/index.md index ce0096627..6fd07420a 100644 --- a/src/pages/overview/index.md +++ b/src/pages/overview/index.md @@ -10,7 +10,7 @@ bannerContents: >- # Introduction of AGE # (A Graph Extension) -title: What is Apache AGE®? +title: What is Apache AGE? subcon: >-

        @@ -20,13 +20,13 @@ subcon: >- # Key Features --- -**Apache AGE®** is a PostgreSQL extension that provides graph database functionality. The goal of Apache AGE is to enable Postgres users to gain access to graph query modeling in Postgres’ existing relational model. +**Apache AGE** is a PostgreSQL extension that provides graph database functionality. The goal of Apache AGE is to enable Postgres users to gain access to graph query modeling in Postgres’ existing relational model. -**Apache AGE®** is a Top-Level Project of the Apache Software Foundation since May 2022. It is licensed under the Apache License 2.0. +**Apache AGE** is a Top-Level Project of the Apache Software Foundation since May 2022. It is licensed under the Apache License 2.0. Below is a brief overview of the AGE architecture, similar to the PostgreSQL architecture and backend. Every component runs on the PostgreSQL transaction cache layer and storage layer. -**Apache AGE®** will be compatible with all relational databases in the future development, starting with MariaDB and MySQL. +**Apache AGE** will be compatible with all relational databases in the future development, starting with MariaDB and MySQL.


        diff --git a/src/templates/index-page.js b/src/templates/index-page.js index 8e0268276..5388dfce9 100644 --- a/src/templates/index-page.js +++ b/src/templates/index-page.js @@ -54,10 +54,10 @@ export const IndexPageTemplate = ({
        - Apache AGE®, Graph database

        + Apache AGE, Graph database

        - Apache AGE® is a PostgreSQL that provides graph database functionality.



        - The goal of Apache AGE® is to provide graph data processing and analytics capability to all relational databases. + Apache AGE is a PostgreSQL that provides graph database functionality.



        + The goal of Apache AGE is to provide graph data processing and analytics capability to all relational databases.



        Through Apache AGE, PostgreSQL users will gain access to graph query modeling within the existing relational database.



        diff --git a/static/rdf/age-doap.rdf b/static/rdf/age-doap.rdf index b7262aba8..bd48ca950 100644 --- a/static/rdf/age-doap.rdf +++ b/static/rdf/age-doap.rdf @@ -27,9 +27,9 @@ Apache AGE - Apache AGE® is a PostgreSQL extension that provides graph database functionality. + Apache AGE is a PostgreSQL extension that provides graph database functionality. - The goal of Apache AGE® is to provide graph data processing and analytics capability to all relational databases. + The goal of Apache AGE is to provide graph data processing and analytics capability to all relational databases. Through Apache AGE, PostgreSQL users will gain access to graph query modeling within the existing relational database. From 967dcfcb060b4fba415005aa9b700ec9945d5fca Mon Sep 17 00:00:00 2001 From: dpdjvhxm Date: Thu, 9 May 2024 08:28:43 +0900 Subject: [PATCH 46/46] =?UTF-8?q?modified=20=E2=84=A2?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...-04-25-how-to-deploy-apache-age-docker-image-on-aws.md | 4 ++-- src/pages/index.md | 4 ++-- src/pages/overview/index.md | 8 ++++---- src/templates/index-page.js | 6 +++--- static/rdf/age-doap.rdf | 4 ++-- 5 files changed, 13 insertions(+), 13 deletions(-) diff --git a/src/pages/blog/2024-04-25-how-to-deploy-apache-age-docker-image-on-aws.md b/src/pages/blog/2024-04-25-how-to-deploy-apache-age-docker-image-on-aws.md index 6bbac4a17..1555d0de9 100644 --- a/src/pages/blog/2024-04-25-how-to-deploy-apache-age-docker-image-on-aws.md +++ b/src/pages/blog/2024-04-25-how-to-deploy-apache-age-docker-image-on-aws.md @@ -3,7 +3,7 @@ templateKey: blog-post title: How to Deploy Apache AGE Docker Image on AWS date: 2024-04-25T08:37:04.997Z description: > - Apache AGE is a PostgreSQL extension that provides graph database + Apache AGE™ is a PostgreSQL extension that provides graph database functionality. Apache AGE provides graph data processing and analytics capability to all relational databases. @@ -13,7 +13,7 @@ featuredimage: --- -Apache AGE is a PostgreSQL extension that provides graph database functionality. Apache AGE provides graph data processing and analytics capability to all relational databases.  +Apache AGE™ is a PostgreSQL extension that provides graph database functionality. Apache AGE provides graph data processing and analytics capability to all relational databases.  This article will explore how to set up Amazon Web Services (AWS), deploy the Apache AGE Docker image, and query the PostgreSQL database running in the Docker container on Amazon Web Services (AWS). diff --git a/src/pages/index.md b/src/pages/index.md index 0ac4217ee..9348c261f 100644 --- a/src/pages/index.md +++ b/src/pages/index.md @@ -3,11 +3,11 @@ templateKey: index-page title: Apache AGE | Apache AGE Graph Database for PostgreSQL bannerImg: /img/background.webp bannerContents: >- -

        Apache AGE
        Graph Database for PostgreSQL

        +

        Apache AGE™
        Graph Database for PostgreSQL

        - Apache AGE is a PostgreSQL Graph database compatible with PostgreSQL's distributed assets and leverages graph data structures to analyze and use relationships and patterns in data. + Apache AGE™ is a PostgreSQL Graph database compatible with PostgreSQL's distributed assets and leverages graph data structures to analyze and use relationships and patterns in data.

        diff --git a/src/pages/overview/index.md b/src/pages/overview/index.md index 6fd07420a..f8a5e586a 100644 --- a/src/pages/overview/index.md +++ b/src/pages/overview/index.md @@ -10,7 +10,7 @@ bannerContents: >- # Introduction of AGE # (A Graph Extension) -title: What is Apache AGE? +title: What is Apache AGE™? subcon: >-

        @@ -20,13 +20,13 @@ subcon: >- # Key Features --- -**Apache AGE** is a PostgreSQL extension that provides graph database functionality. The goal of Apache AGE is to enable Postgres users to gain access to graph query modeling in Postgres’ existing relational model. +**Apache AGE™** is a PostgreSQL extension that provides graph database functionality. The goal of Apache AGE is to enable Postgres users to gain access to graph query modeling in Postgres’ existing relational model. -**Apache AGE** is a Top-Level Project of the Apache Software Foundation since May 2022. It is licensed under the Apache License 2.0. +**Apache AGE™** is a Top-Level Project of the Apache Software Foundation since May 2022. It is licensed under the Apache License 2.0. Below is a brief overview of the AGE architecture, similar to the PostgreSQL architecture and backend. Every component runs on the PostgreSQL transaction cache layer and storage layer. -**Apache AGE** will be compatible with all relational databases in the future development, starting with MariaDB and MySQL. +**Apache AGE™** will be compatible with all relational databases in the future development, starting with MariaDB and MySQL.


        diff --git a/src/templates/index-page.js b/src/templates/index-page.js index 5388dfce9..4e73f6d4a 100644 --- a/src/templates/index-page.js +++ b/src/templates/index-page.js @@ -54,10 +54,10 @@ export const IndexPageTemplate = ({
        - Apache AGE, Graph database

        + Apache AGE™, Graph database

        - Apache AGE is a PostgreSQL that provides graph database functionality.



        - The goal of Apache AGE is to provide graph data processing and analytics capability to all relational databases. + Apache AGE™ is a PostgreSQL that provides graph database functionality.



        + The goal of Apache AGE™ is to provide graph data processing and analytics capability to all relational databases.



        Through Apache AGE, PostgreSQL users will gain access to graph query modeling within the existing relational database.



        diff --git a/static/rdf/age-doap.rdf b/static/rdf/age-doap.rdf index bd48ca950..204b2ac88 100644 --- a/static/rdf/age-doap.rdf +++ b/static/rdf/age-doap.rdf @@ -27,9 +27,9 @@ Apache AGE - Apache AGE is a PostgreSQL extension that provides graph database functionality. + Apache AGE™ is a PostgreSQL extension that provides graph database functionality. - The goal of Apache AGE is to provide graph data processing and analytics capability to all relational databases. + The goal of Apache AGE™ is to provide graph data processing and analytics capability to all relational databases. Through Apache AGE, PostgreSQL users will gain access to graph query modeling within the existing relational database.

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