Merge v3.1.0 into master

.github/workflows/deploy.yml

@@ -6,37 +6,27 @@ on:
 
 jobs:
   deploy:
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v2
         with:
-          fetch-depth: 0 # fetch all commits/branches
+          fetch-depth: 0 # fetch all commits/branches for gitversion
 
       - name: Setup Python
         uses: actions/setup-python@v1
         with:
-          python-version: '3.7'
+          python-version: '3.8'
           architecture: 'x64'
 
-      - name: apt install
-        run: |
-          sudo apt update -y
-          sudo apt install -y $(cat pkglist.txt)
-
-      - name: Install dependencies
-        run: |
-          pip3 install --upgrade pip
-          pip3 install -r ./requirements.txt
+      - name : prepare
+        run: sh ./prepare.sh 
 
       - name: Git Config
         run: git config user.name whitewum && git config user.email min.wu@vesoft.com
-
-      - name: Mike Deploy
-        run: |
-          mike list
 
       - name: Mike Deploy master
         run: |
+          # mike delete master -p
           mike deploy master -p --rebase
           mike list
 

docs-2.0/1.introduction/0-0-graph.md

@@ -2,7 +2,7 @@
 
 People from tech giants (such as Amazon and Facebook) to small research teams are devoting significant resources to exploring the potential of graph databases to solve data relationships problems. What exactly is a graph database? What can it do? Where does it fit in the database landscape? To answer these questions, we first need to understand graphs.
 
-Graphs are one of the main areas of research in computer science. Graphs can efficiently solve many of the problems that exist today. This topic will start with graphs to explain the advantages of graph databases and their great potential in modern application development, and then describe the differences between distributed graph databases and several other types of databases.
+Graphs are one of the main areas of research in computer science. Graphs can efficiently solve many of the problems that exist today. This topic will start with graphs and explain the advantages of graph databases and their great potential in modern application development, and then describe the differences between distributed graph databases and several other types of databases.
 
 ## What are graphs?
 
@@ -30,7 +30,7 @@ Simply put, graph theory is the study of graphs. Graph theory began in the early
 
 [^171]: Souce of the picture: https://medium.freecodecamp.org/i-dont-understand-graph-theory-1c96572a1401.
 
-To solve this problem, the great mathematician Euler by abstracting the four regions of the city into points and the seven bridges connecting the city into edges connecting the points, proved that the problem was unsolvable. The simplified abstract diagram is as follows [^063].
+To solve this problem, the great mathematician Euler proved that the problem was unsolvable by abstracting the four regions of the city into points and the seven bridges connecting the city into edges connecting the points. The simplified abstract diagram is as follows [^063].
 
 ![image](https://user-images.githubusercontent.com/42762957/91538126-e578b900-e949-11ea-980c-5704254e8063.png)
 
@@ -48,15 +48,13 @@ From a mathematical point of view, graph theory studies the relationships betwee
 
 In real life, there are many examples of property graphs.
 
-For example, Qichacha or BOSS Zhipin use graphs to model business equity relationships. A vertex is usually a natural person or a business, and the edge is the equity relationship between a person and a business. The properties on vertices can be the name, age, ID number, etc. of the natural person. The properties on edges can be the investment amount, investment time, position such as director and supervisor.
-
-![image](https://docs-cdn.nebula-graph.com.cn/books/images/enterprise-relations.png)
+For example, Qichacha or BOSS Zhipin use graphs to model business equity relationships. A vertex usually represents a natural person or a business, and the edge represents the equity relationship between a person and a business. The properties on vertices can be the name, age, ID number, etc. of the natural person. The properties on edges can be the investment amount, investment time, position such as director and supervisor.
 
 A vertex can be a listed company and an edge can be a correlation between listed companies. The vertex property can be a stock code, abbreviation, market capitalization, sector, etc. The edge property can be the time-series correlation coefficient of the stock price [^T01].
 
 [^T01]: https://nebula-graph.com.cn/posts/stock-interrelation-analysis-jgrapht-nebula-graph/
 
-The graph relationship can also be similar to the character relationship in a TV series like Game of Thrones [^s-01]. Vertices are the characters. Edges are the interactions between the characters. Vertex properties are the character's names, ages, camps, etc., and edge properties are the number of interactions between two characters. 
+The graph relationship can also be similar to the character relationship in a TV series like Game of Thrones [^s-01]. Vertices stand for the characters. Edges represent the interactions between the characters. Vertex properties are the character's names, ages, camps, etc., and edge properties are the number of interactions between two characters. 
 
 ![image](https://docs-cdn.nebula-graph.com.cn/books/images/game-of-thrones-01.png)
 

docs-2.0/1.introduction/0-1-graph-database.md

@@ -4,7 +4,7 @@ Now that we have discussed what a graph is, let's move on to further understandi
 
 Different graph databases may differ slightly in terms of terminology, but in the end, they all talk about vertices, edges, and properties. As for more advanced features such as labels, indexes, constraints, TTL, long tasks, stored procedures, and UDFs, these advanced features will vary significantly from one graph database to another.
 
-Graph databases use graphs to store data, and graphs are one of the closest to highly flexible, high-performance data structures. A graph database is a storage engine specifically designed to store and retrieve large information, which efficiently stores data as vertices and edges and allows high-performance retrieval and querying of these vertex-edge structures. We can also add properties to these vertices and edges.
+Graph databases use graphs to store data, and the graph structure is one of the structures that are closest to high flexibility and high performance. A graph database is a storage engine specifically designed to store and retrieve large information, which efficiently stores data as vertices and edges and allows high-performance retrieval and querying of these vertex-edge structures. We can also add properties to these vertices and edges.
 
 ## Third-party services market predictions
 
@@ -38,11 +38,11 @@ Until recently, graph databases and related graph technologies were ranked in th
 
 [^Gartner2]: https://www.gartner.com/smarterwithgartner/gartner-top-10-data-and-analytics-trends-for-2021/
 
-It can be noted that Gartner's predictions match the DB-Engines ranking well. There is usually a period of rapid bubble development, then a plateau period, followed by a new bubble period due to the emergence of new technologies, and then a plateau period. And so on in a spiral.
+It can be noted that Gartner's predictions match the DB-Engines ranking well. There is usually a period of rapid bubble development, then a plateau period, followed by a new bubble period due to the emergence of new technologies, and then a plateau period again. 
 
 ### Market size of graph databases
 
-According to statistics and forecasts from Verifiedmarketresearc[^ver], fnfresearch[^fnf], MarketsandMarkets[^mam], and Gartner[^gar], the global graph database market size to grow from about USD 0.8 billion in 2019 to USD 3-4 billion by 2026, at a Compound Annual Growth Rate (CAGR) of about 25%, which corresponds to about 5%-10% market share of the global database market. 
+According to statistics and forecasts from Verifiedmarketresearc[^ver], fnfresearch[^fnf], MarketsandMarkets[^mam], and Gartner[^gar], the global graph database market size is about to grow from about USD 0.8 billion in 2019 to USD 3-4 billion by 2026, at a Compound Annual Growth Rate (CAGR) of about 25%, which corresponds to about 5%-10% market share of the global database market. 
 
 ![Image](https://www.verifiedmarketresearch.com/wp-content/uploads/2020/10/Graph-Database-Market-Size.jpg)
 
@@ -104,6 +104,7 @@ Although this network model greatly improved productivity, its performance has b
 In the first public release of Neo4j ( Neo4j 1.4，2011), the data model was consisted of vertices and typed edges. Vertices and edges have properties. The early versions of Neo4j did not have indexes. Applications had to construct their search structure from the root vertex. Because this was very unwieldy for the applications, Neo4j 2.0 (2013.12) introduced a new concept label on vertices. Based on labels, Neo4j can index some predefined vertex properties.
 
 "Vertex", "Relationship", "Property", "Relationships can only have one label.", "Vertices can have zero or multiple labels.". All these concepts form the data model definitions for Neo4j property graphs. With the later addition of indexing, Cypher became the main way of interacting with Neo4j. This is because the application developer only needs to focus on the data itself, not on the search structure that the developer built himself as mentioned above.
+
 #### The creation of Gremlin 
 
 Gremlin is a graph query language based on Apache TinkerPop, which is close in style to a sequence of function (procedure) calls. Initially, Neo4j was queried through the Java API. applications could embed the query engine as a library into the application and then use the API to query the graph.