Integrating with Yandex Data Processing

You can use the Apache Spark™ clusters deployed in Yandex Data Processing, in your Yandex DataSphere projects. To set up integration with Yandex Data Processing in DataSphere:

1. Prepare your infrastructure.
2. Create a bucket.
3. Create a Yandex Data Processing cluster.
4. Set up the DataSphere project.
5. Run your computations.

If you no longer need the resources you created, delete them.

Getting startedGetting started

Before getting started, register in Yandex Cloud, set up a community, and link your billing account to it.

1. On the DataSphere home page, click Try for free and select an account to log in with: Yandex ID or your working account in the identity federation (SSO).
2. Select the Yandex Cloud Organization organization you are going to use in Yandex Cloud.
3. Create a community.
4. Link your billing account to the DataSphere community you are going to work in. Make sure that you have a billing account linked and its status is ACTIVE or TRIAL_ACTIVE. If you do not have a billing account yet, create one in the DataSphere interface.

Required paid resourcesRequired paid resources

The Yandex Data Processing cluster support cost covers the computing resources of the cluster and the storage size (see Yandex Data Processing pricing).

Prepare the infrastructurePrepare the infrastructure

Log in to the Yandex Cloud management console and select the organization you use to access DataSphere. On the Yandex Cloud Billing page, make sure you have a billing account linked.

If you have an active billing account, you can create or select a folder to deploy your infrastructure in, on the cloud page.

Create a folder and networkCreate a folder and network

Create a folder where your Yandex Data Processing cluster will run.

Learn more about clouds and folders.

Create an egress NAT gatewayCreate an egress NAT gateway

Create a service account for the clusterCreate a service account for the cluster

Create an SSH key pairCreate an SSH key pair

To ensure a safe connection to the Yandex Data Processing cluster hosts, you will need SSH keys. You may skip this step if you have already generated your SSH keys.

How to generate an SSH key pair

Linux/macOS

Windows 10/11

Windows 7/8

1. Open the terminal.

2. Use the ssh-keygen command to create a new key:

   ssh-keygen -t ed25519 -C "<optional_comment>"
   

   You can specify an empty string in the -C parameter to avoid adding a comment, or you may not specify the -C parameter at all: in this case, a default comment will be added.

   After running this command, you will be prompted to specify the name and path to the key files, as well as enter the password for the private key. If you only specify the name, the key pair will be created in the current directory. The public key will be saved in a file with the .pub extension, while the private key, in a file without extension.

   By default, the command prompts you to save the key under the id_ed25519 name in the following directory: /home/<username>/.ssh. If there is already an SSH key named id_ed25519 in this directory, you may accidentally overwrite it and lose access to the resources it is used in. Therefore, you may want to use unique names for all SSH keys.

If you do not have OpenSSH installed yet, follow this guide to install it.

1. Run cmd.exe or powershell.exe (make sure to update PowerShell before doing so).

2. Use the ssh-keygen command to create a new key:

   ssh-keygen -t ed25519 -C "<optional_comment>"
   

   You can specify an empty string in the -C parameter to avoid adding a comment, or you may not specify the -C parameter at all: in this case, a default comment will be added.

   After running this command, you will be prompted to specify the name and path to the key files, as well as enter the password for the private key. If you only specify the name, the key pair will be created in the current directory. The public key will be saved in a file with the .pub extension, while the private key, in a file without extension.

   By default, the command prompts you to save the key under the id_ed25519 name in the following directory: C:\Users\<username>/.ssh. If there is already an SSH key named id_ed25519 in this directory, you may accidentally overwrite it and lose access to the resources it is used in. Therefore, you may want to use unique names for all SSH keys.

Create keys using the PuTTY app:

1. Download and install PuTTY.

2. Make sure the directory where you installed PuTTY is included in PATH:

   1. Right-click My computer. Click Properties.
   2. In the window that opens, select Additional system parameters, then Environment variables (located in the lower part of the window).
   3. Under System variables, find PATH and click Edit.
   4. In the Variable value field, append the path to the directory where you installed PuTTY.

3. Launch the PuTTYgen app.

4. Select EdDSA as the pair type to generate. Click Generate and move the cursor in the field above it until key creation is complete.

   

5. In Key passphrase, enter a strong password. Enter it again in the field below.

6. Click Save private key and save the private key. Do not share its key phrase with anyone.

7. Click Save public key and save the public key in the following file: <key_name>.pub.

Warning

Save the private key in a secure location, as you will not be able to connect to the VM without it.

Configure DataSphereConfigure DataSphere

To work with Yandex Data Processing clusters in DataSphere, create and set up a project.

Create a projectCreate a project

1. Open the DataSphere home page.
2. In the left-hand panel, select Communities.
3. Select the community to create a project in.
4. On the community page, click Create project.
5. In the window that opens, enter a name and description (optional) for the project.
6. Click Create.

Edit the project settingsEdit the project settings

1. Go to the Settings tab.

2. Under Advanced settings, click Edit.

3. Specify the parameters:

   • Default folder: data-folder.

   • Service account: sa-for-data-proc.

   • Subnet: Subnet of the ru-central1-a availability zone in data-folder.

     Note

     If you specified a subnet in the project settings, the time to allocate computing resources may be increased.

   • Security groups, if used in your organization.

4. Click Save.

Create a bucketCreate a bucket

Create a Yandex Data Processing clusterCreate a Yandex Data Processing cluster

Before creating a cluster, make sure that your cloud has enough total SSD space (200 GB is allocated for a new cloud by default).

You can view your current resources under Quotas in the management console.

Yandex Data Processing will run the cluster create operation. After the cluster status changes to Running, you can connect to any active subcluster using the specified SSH key.

The Yandex Data Processing cluster you created will be added to your DataSphere project under Project resources ⟶ Yandex Data Processing ⟶ Available clusters.

Run your computations on the clusterRun your computations on the cluster

1. Open the DataSphere project:

   1. Select the relevant project in your community or on the DataSphere homepage in the Recent projects tab.

   2. Click Open project in JupyterLab and wait for the loading to complete.
   3. Open the notebook tab.

2. In the cell, insert the code to compute. For example:

   #!spark --cluster <cluster_name>
   import random
   
   def inside(p):
       x, y = random.random(), random.random()
       return x*x + y*y < 1
   
   NUM_SAMPLES = 1_000_000
   
   count = sc.parallelize(range(0, NUM_SAMPLES)) \
       .filter(inside).count()
   print("Pi is roughly %f" % (4.0 * count / NUM_SAMPLES))
   

   Where #!spark --cluster <cluster_name> is a required system command to run computations on a cluster.

   Wait for the computation to start. While it is in progress, you will see logs under the cell.

3. Write data to S3 by specifying the bucket name:

   #!spark
   data = [[1, "tiger"], [2, "lion"], [3, "snow leopard"]]
   df = spark.createDataFrame(df, schema="id LONG, name STRING")
   df.repartition(1).write.option("header", True).csv("s3://<bucket_name>/")
   

4. Run the cells. To do this, select Run ⟶ Run Selected Cells or press Shift + Enter.

The file will appear in the bucket. To view bucket contents in the JupyterLab interface, create and activate an S3 connector in your project.

To learn more about running computations on Yandex Data Processing clusters in DataSphere, see this concept.

Delete the resources you createdDelete the resources you created

Some resources are not free of charge. To avoid paying for them, delete the resources you no longer need:

• Objects from the bucket
• Bucket
• Cluster
• Project
• Subnet
• Route table
• NAT gateway
• Network
• Service account

See alsoSee also

Temporary Yandex Data Processing clusters deployed in DataSphere