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

Azure Cosmos DB from the Apache Gremlin console

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. (To use Gremlin Console on Windows, you need to install Java Runtime.)

Create a database account

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

  2. Click Create a resource > Databases > Azure Cosmos DB.

    Azure portal "Databases" pane

  3. In the Create Azure Cosmos DB Account page, enter the settings for the new Azure Cosmos DB account.

    Setting Value Description
    Subscription Your subscription Select the Azure subscription that you want to use for this Azure Cosmos DB account.
    Resource Group Create new

    Then enter the same unique name as provided in ID
    Select Create new. Then enter a new resource-group name for your account. For simplicity, use the same name as your ID.
    Account Name Enter a unique name Enter a unique name to identify your Azure Cosmos DB account. Because documents.azure.com is appended to the ID that you provide to create your URI, use a unique ID.

    The ID can use only lowercase letters, numbers, and the hyphen (-) character. It must be between 3 and 31 characters in length.
    API Gremlin (graph) The API determines the type of account to create. Azure Cosmos DB provides five APIs: Core(SQL) for document databases, Gremlin for graph databases, MongoDB for document databases, Azure Table, and Cassandra. Currently, you must create a separate account for each API.

    Select Gremlin (graph) because in this quickstart you are creating a table that works with the Gremlin API.

    Learn more about the Graph API.
    Location Select the region closest to your users Select a geographic location to host your Azure Cosmos DB account. Use the location that's closest to your users to give them the fastest access to the data.

    Select Review+Create. You can skip the Network and Tags section.

    The new account section for Azure Cosmos DB

  4. The account creation takes a few minutes. Wait for the portal to display the Congratulations! Your Azure Cosmos DB account was created page.

    The Azure portal Notifications pane

Add a graph

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

  1. Select Data Explorer > New Graph.

    The Add Graph area is displayed on the far right, you may need to scroll right to see it.

    The Azure portal Data Explorer, Add Graph page

  2. In the Add graph page, enter the settings for the new graph.

    Setting Suggested value Description
    Database ID sample-database Enter sample-database as the name for the new database. Database names must be between 1 and 255 characters, and cannot contain / \ # ? or a trailing space.
    Throughput 400 RUs Change the throughput to 400 request units per second (RU/s). If you want to reduce latency, you can scale up the throughput later.
    Graph ID sample-graph Enter sample-graph as the name for your new collection. Graph names have the same character requirements as database IDs.
    Partition Key /pk All Cosmos DB accounts need a partition key to horizontally scale. Learn how to select an appropriate partition key in the Graph Data Partitioning article.
  3. Once 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 as defined in the following table:

    Setting Suggested value Description
    hosts [account-name.gremlin.cosmos.azure.com] See the following screenshot. This is the Gremlin URI value on the Overview page of the Azure portal, in square brackets, with the trailing :443/ removed. Note: Be sure to use the Gremlin value, and not the URI that ends with [account-name.documents.azure.com] which would likely result in a "Host did not respond in a timely fashion" exception when attempting to execute Gremlin queries later.
    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.GraphSONMessageSerializerV2d0,
    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: View and copy the Gremlin URI value on the Overview page in the Azure portal

    For the password value, copy the Primary key from the Keys page: View and copy your primary key in the Azure portal, Keys page

Your remote-secure.yaml file should look like this:

hosts: [your_database_server.gremlin.cosmos.azure.com] 
port: 443
username: /dbs/your_database_account/colls/your_collection
password: your_primary_key
connectionPool: {
  enableSsl: true
}
serializer: { className: org.apache.tinkerpop.gremlin.driver.ser.GraphSONMessageSerializerV2d0, config: { serializeResultToString: true }}

make sure to wrap the value of hosts parameter within brackets [].

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

  2. 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. If your configuration is correct, then this warning can be safely ignored as it should not impact the use of the console.

  3. Next run :remote console to redirect all console commands to the remote server.

    Note

    If you don't run the :remote console command but would like to redirect all console commands to the remote server, you should prefix the command with :>, for example you should run the command as :> g.V().count(). This prefix is a part of the command and it is important when using the Gremlin console with Azure Cosmos DB. Omitting this prefix instructs the console to execute the command locally, often against an in-memory graph. Using this prefix :> tells the console to execute a remote command, in this case against Azure Cosmos DB (either the localhost emulator, or an Azure instance).

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).property('pk', 'pk')

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).property('pk', 'pk')

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).property('pk', 'pk')

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).property('pk', 'pk')

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).property('pk', 'pk')

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: Gremlin API tutorial!

Review SLAs in the Azure portal

The Azure portal monitors your Cosmos DB account throughput, storage, availability, latency, and consistency. Charts for metrics associated with an Azure Cosmos DB Service Level Agreement (SLA) show the SLA value compared to actual performance. This suite of metrics makes monitoring your SLAs transparent.

To review metrics and SLAs:

  1. Select Metrics in your Cosmos DB account's navigation menu.

  2. Select a tab such as Latency, and select a timeframe on the right. Compare the Actual and SLA lines on the charts.

    Azure Cosmos DB metrics suite

  3. Review the metrics on the other tabs.

Clean up resources

When you're done with your web app and Azure Cosmos DB account, you can delete the Azure resources you created so you don't incur more charges. To delete the resources:

  1. In the Azure portal, select Resource groups on the far left. If the left menu is collapsed, select Expand button to expand it.

  2. Select the resource group you created for this quickstart.

    Select the resource group to delete

  3. In the new window, select Delete resource group.

    Delete the resource group

  4. In the next window, enter the name of the resource group to delete, and then select 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.