Azure Cosmos DB: Create, query, and traverse a graph in the Gremlin console

Azure Cosmos DB is Microsoft’s globally distributed multi-model database service. You can quickly create and query document, key/value, and graph databases, all of which benefit from the global distribution and horizontal scale capabilities at the core of Azure Cosmos DB.

This quick start demonstrates how to create an Azure Cosmos DB account, database, and graph (container) using the Azure portal and then use the Gremlin Console from Apache TinkerPop to work with Graph API (preview) data. In this tutorial, you create and query vertices and edges, updating a vertex property, query vertices, traverse the graph, and drop a vertex.

The Gremlin console is Groovy/Java based and runs on Linux, Mac, and Windows. You can download it from the Apache TinkerPop site.

Prerequisites

You need to have an Azure subscription to create an Azure Cosmos DB account for this quickstart.

If you don't have an Azure subscription, create a free account before you begin.

You also need to install the Gremlin Console. Use version 3.2.5 or above.

Create a database account

1. In a new window, sign in to the Azure portal.

2. In the left pane, select New > Databases > Azure Cosmos DB > Create.

   

3. Under New account, specify the configuration that you want for this Azure Cosmos DB account.

   With Azure Cosmos DB, you can choose one of four programming models: Gremlin (graph), MongoDB, SQL (DocumentDB), and Table (key-value). Each model currently requires a separate account.

   In this Quick Start article, we program against the Graph API, so choose Gremlin (graph) as you fill out the form. If you have document data from a catalog app, key/value (table) data, or data that's migrated from a MongoDB app, realize that Azure Cosmos DB can provide a highly available, globally distributed database service platform for all your mission-critical applications.

   Fill in the fields on the New account blade by using the information in the following screenshot as a guide. Your values might be different from the values in the screenshot.

   

   Setting	Suggested value	Description
   ID	Unique value	A unique name that identifies this Azure Cosmos DB account. Because documents.azure.com is appended to the ID that you provide to create your URI, use a unique but identifiable ID. The ID must contain only lowercase letters, numbers, and the hyphen (-) character. It must contain from 3 to 50 characters.
   API	Gremlin (graph)	We program against the Graph API later in this article.
   Subscription	Your subscription	The Azure subscription that you want to use for this Azure Cosmos DB account.
   Resource group	The same value as ID	The new resource group name for your account. For simplicity, you can use the same name as your ID.
   Location	The region closest to your users	The geographic location in which to host your Azure Cosmos DB account. Choose the location closest to your users to give them the fastest access to the data.

4. Select Create to create the account.

5. On the toolbar, select the Notifications icon to monitor the deployment process.

   

6. When the Notifications window indicates the deployment succeeded, close the window. Open the new account from the All resources tile on the Dashboard.

   

Add a graph

You can now use the Data Explorer tool in the Azure portal to create a graph database.

1. In the Azure portal, in the menu on the left, select Data Explorer (Preview).

2. Under Data Explorer (Preview), select New Graph. Then fill in the page by using the following information:

   

   Setting	Suggested value	Description
   Database id	sample-database	The ID for your new database. Database names must be between 1 and 255 characters and can't contain / \ # ? or a trailing space.
   Graph id	sample-graph	The ID for your new graph. Graph names have the same character requirements as database IDs.
   Storage capacity	10 GB	Leave the default value. This is the storage capacity of the database.
   Throughput	400 RUs	Leave the default value. You can scale up the throughput later if you want to reduce latency.
   Partition key	/userid	A partition key that distributes data evenly to each partition. Selecting the correct partition key is important in creating a performant graph. For more information, see Designing for partitioning.

3. After the form is filled out, select OK.

Connect to your app service

1. Before starting the Gremlin Console, create or modify the remote-secure.yaml configuration file in the apache-tinkerpop-gremlin-console-3.2.5/conf directory.

2. Fill in your host, port, username, password, connectionPool, and serializer configurations:

   Setting	Suggested value	Description
   hosts	[***.graphs.azure.com]	See screenshot below. This is the Gremlin URI value on the Overview page of the Azure portal, in square brackets, with the trailing :443/ removed. This value can also be retrieved from the Keys tab, using the URI value by removing https://, changing documents to graphs, and removing the trailing :443/.
   port	443	Set to 443.
   username	Your username	The resource of the form /dbs/<db>/colls/<coll> where <db> is your database name and <coll> is your collection name.
   password	Your primary key	See second screenshot below. This is your primary key, which you can retrieve from the Keys page of the Azure portal, in the Primary Key box. Use the copy button on the left side of the box to copy the value.
   connectionPool	{enableSsl: true}	Your connection pool setting for SSL.
   serializer	{ className: org.apache.tinkerpop.gremlin. driver.ser.GraphSONMessageSerializerV1d0, config: { serializeResultToString: true }}	Set to this value and delete any \n line breaks when pasting in the value.

   For the hosts value, copy the Gremlin URI value from the Overview page:

   For the password value, copy the Primary key from the Keys page:

3. In your terminal, run bin/gremlin.bat or bin/gremlin.sh to start the Gremlin Console.

4. In your terminal, run :remote connect tinkerpop.server conf/remote-secure.yaml to connect to your app service.

   Tip

   If you receive the error No appenders could be found for logger ensure that you updated the serializer value in the remote-secure.yaml file as described in step 2.

Great! Now that we finished the setup, let's start running some console commands.

Let's try a simple count() command. Type the following into the console at the prompt:

:> g.V().count()

Create vertices and edges

Let's begin by adding five person vertices for Thomas, Mary Kay, Robin, Ben, and Jack.

Input (Thomas):

:> g.addV('person').property('firstName', 'Thomas').property('lastName', 'Andersen').property('age', 44).property('userid', 1)

Output:

==>[id:796cdccc-2acd-4e58-a324-91d6f6f5ed6d,label:person,type:vertex,properties:[firstName:[[id:f02a749f-b67c-4016-850e-910242d68953,value:Thomas]],lastName:[[id:f5fa3126-8818-4fda-88b0-9bb55145ce5c,value:Andersen]],age:[[id:f6390f9c-e563-433e-acbf-25627628016e,value:44]],userid:[[id:796cdccc-2acd-4e58-a324-91d6f6f5ed6d|userid,value:1]]]]

Input (Mary Kay):

:> g.addV('person').property('firstName', 'Mary Kay').property('lastName', 'Andersen').property('age', 39).property('userid', 2)

Output:

==>[id:0ac9be25-a476-4a30-8da8-e79f0119ea5e,label:person,type:vertex,properties:[firstName:[[id:ea0604f8-14ee-4513-a48a-1734a1f28dc0,value:Mary Kay]],lastName:[[id:86d3bba5-fd60-4856-9396-c195ef7d7f4b,value:Andersen]],age:[[id:bc81b78d-30c4-4e03-8f40-50f72eb5f6da,value:39]],userid:[[id:0ac9be25-a476-4a30-8da8-e79f0119ea5e|userid,value:2]]]]

Input (Robin):

:> g.addV('person').property('firstName', 'Robin').property('lastName', 'Wakefield').property('userid', 3)

Output:

==>[id:8dc14d6a-8683-4a54-8d74-7eef1fb43a3e,label:person,type:vertex,properties:[firstName:[[id:ec65f078-7a43-4cbe-bc06-e50f2640dc4e,value:Robin]],lastName:[[id:a3937d07-0e88-45d3-a442-26fcdfb042ce,value:Wakefield]],userid:[[id:8dc14d6a-8683-4a54-8d74-7eef1fb43a3e|userid,value:3]]]]

Input (Ben):

:> g.addV('person').property('firstName', 'Ben').property('lastName', 'Miller').property('userid', 4)

Output:

==>[id:ee86b670-4d24-4966-9a39-30529284b66f,label:person,type:vertex,properties:[firstName:[[id:a632469b-30fc-4157-840c-b80260871e9a,value:Ben]],lastName:[[id:4a08d307-0719-47c6-84ae-1b0b06630928,value:Miller]],userid:[[id:ee86b670-4d24-4966-9a39-30529284b66f|userid,value:4]]]]

Input (Jack):

:> g.addV('person').property('firstName', 'Jack').property('lastName', 'Connor').property('userid', 5)

Output:

==>[id:4c835f2a-ea5b-43bb-9b6b-215488ad8469,label:person,type:vertex,properties:[firstName:[[id:4250824e-4b72-417f-af98-8034aa15559f,value:Jack]],lastName:[[id:44c1d5e1-a831-480a-bf94-5167d133549e,value:Connor]],userid:[[id:4c835f2a-ea5b-43bb-9b6b-215488ad8469|userid,value:5]]]]

Next, let's add edges for relationships between our people.

Input (Thomas -> Mary Kay):

:> g.V().hasLabel('person').has('firstName', 'Thomas').addE('knows').to(g.V().hasLabel('person').has('firstName', 'Mary Kay'))

Output:

==>[id:c12bf9fb-96a1-4cb7-a3f8-431e196e702f,label:knows,type:edge,inVLabel:person,outVLabel:person,inV:0d1fa428-780c-49a5-bd3a-a68d96391d5c,outV:1ce821c6-aa3d-4170-a0b7-d14d2a4d18c3]

Input (Thomas -> Robin):

:> g.V().hasLabel('person').has('firstName', 'Thomas').addE('knows').to(g.V().hasLabel('person').has('firstName', 'Robin'))

Output:

==>[id:58319bdd-1d3e-4f17-a106-0ddf18719d15,label:knows,type:edge,inVLabel:person,outVLabel:person,inV:3e324073-ccfc-4ae1-8675-d450858ca116,outV:1ce821c6-aa3d-4170-a0b7-d14d2a4d18c3]

Input (Robin -> Ben):

:> g.V().hasLabel('person').has('firstName', 'Robin').addE('knows').to(g.V().hasLabel('person').has('firstName', 'Ben'))

Output:

==>[id:889c4d3c-549e-4d35-bc21-a3d1bfa11e00,label:knows,type:edge,inVLabel:person,outVLabel:person,inV:40fd641d-546e-412a-abcc-58fe53891aab,outV:3e324073-ccfc-4ae1-8675-d450858ca116]

Update a vertex

Let's update the Thomas vertex with a new age of 45.

Input:

:> g.V().hasLabel('person').has('firstName', 'Thomas').property('age', 45)

Output:

==>[id:ae36f938-210e-445a-92df-519f2b64c8ec,label:person,type:vertex,properties:[firstName:[[id:872090b6-6a77-456a-9a55-a59141d4ebc2,value:Thomas]],lastName:[[id:7ee7a39a-a414-4127-89b4-870bc4ef99f3,value:Andersen]],age:[[id:a2a75d5a-ae70-4095-806d-a35abcbfe71d,value:45]]]]

Query your graph

Now, let's run a variety of queries against your graph.

First, let's try a query with a filter to return only people who are older than 40 years old.

Input (filter query):

:> g.V().hasLabel('person').has('age', gt(40))

Output:

==>[id:ae36f938-210e-445a-92df-519f2b64c8ec,label:person,type:vertex,properties:[firstName:[[id:872090b6-6a77-456a-9a55-a59141d4ebc2,value:Thomas]],lastName:[[id:7ee7a39a-a414-4127-89b4-870bc4ef99f3,value:Andersen]],age:[[id:a2a75d5a-ae70-4095-806d-a35abcbfe71d,value:45]]]]

Next, let's project the first name for the people who are older than 40 years old.

Input (filter + projection query):

:> g.V().hasLabel('person').has('age', gt(40)).values('firstName')

Output:

==>Thomas

Traverse your graph

Let's traverse the graph to return all of Thomas's friends.

Input (friends of Thomas):

:> g.V().hasLabel('person').has('firstName', 'Thomas').outE('knows').inV().hasLabel('person')

Output:

==>[id:f04bc00b-cb56-46c4-a3bb-a5870c42f7ff,label:person,type:vertex,properties:[firstName:[[id:14feedec-b070-444e-b544-62be15c7167c,value:Mary Kay]],lastName:[[id:107ab421-7208-45d4-b969-bbc54481992a,value:Andersen]],age:[[id:4b08d6e4-58f5-45df-8e69-6b790b692e0a,value:39]]]]
==>[id:91605c63-4988-4b60-9a30-5144719ae326,label:person,type:vertex,properties:[firstName:[[id:f760e0e6-652a-481a-92b0-1767d9bf372e,value:Robin]],lastName:[[id:352a4caa-bad6-47e3-a7dc-90ff342cf870,value:Wakefield]]]]

Next, let's get the next layer of vertices. Traverse the graph to return all the friends of Thomas's friends.

Input (friends of friends of Thomas):

:> g.V().hasLabel('person').has('firstName', 'Thomas').outE('knows').inV().hasLabel('person').outE('knows').inV().hasLabel('person')

Output:

==>[id:a801a0cb-ee85-44ee-a502-271685ef212e,label:person,type:vertex,properties:[firstName:[[id:b9489902-d29a-4673-8c09-c2b3fe7f8b94,value:Ben]],lastName:[[id:e084f933-9a4b-4dbc-8273-f0171265cf1d,value:Miller]]]]

Drop a vertex

Let's now delete a vertex from the graph database.

Input (drop Jack vertex):

:> g.V().hasLabel('person').has('firstName', 'Jack').drop()

Clear your graph

Finally, let's clear the database of all vertices and edges.

Input:

:> g.E().drop()
:> g.V().drop()

Congratulations! You've completed this Azure Cosmos DB: Graph API tutorial!

Review SLAs in the Azure portal

Now that your app is up and running, you'll want to ensure business continuity and watch user access to ensure high availability. You can use the Azure portal to review the availability, latency, throughput, and consistency of your collection.

Each graph that's associated with the Azure Cosmos DB Service Level Agreements (SLAs) provides a line that shows the quota required to meet the SLA and your actual usage. This information gives you a clear view into your database performance. Additional metrics, such as storage usage and number of requests per minute, are also included in the portal.

• In the Azure portal, in the pane on the left, under Monitoring, select Metrics.

  

Clean up resources

If you're not going to continue to use this app, delete all resources created by this quickstart in the Azure portal with the following steps:

1. From the left-hand menu in the Azure portal, click Resource groups and then click the name of the resource you created.
2. On your resource group page, click Delete, type the name of the resource to delete in the text box, and then click Delete.

Next steps

In this quickstart, you've learned how to create an Azure Cosmos DB account, create a graph using the Data Explorer, create vertices and edges, and traverse your graph using the Gremlin console. You can now build more complex queries and implement powerful graph traversal logic using Gremlin.