Welcome to the Oxla Cloud Quickstart Guide! This guide is designed to help you quickly get up and running with Oxla Cloud by guiding you through the process of setting up your workloads using a sample dataset as your starting point.

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Sign Up or Log In

New to Oxla Cloud? Create an account, signing up is quick and straightforward! If you already have an account, just log in using your existing credentials to access your dashboard.

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Configure Data Storage using Sample Datasets

As a newcomer, after logging in, please navigate either to the Overview > Data storages or Data section via the left sidebar menu of your dashboard. Click on the Create Data Storage button positioned in the center of your dashboard. In the subsequent pop-up window, provide a name for your data storage and select one of the pre-loaded datasets from the list and click Save changes.

For this example, you will select the Social Media dataset.

This may take a moment, so please hold on while your setup is being finalized.

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Initiate a Cluster

Next, create a cluster for querying the social media sample dataset stored in your data storage, navigate to the Clusters section in the left sidebar menu and click the Start a new Cluster button.

Alternatively, you can create a cluster directly from the Data > Data Storages table by clicking on use to create cluster button found in the Related Cluster field.

Before starting the cluster, please provide a name for the cluster, create access credentials and in this case select the DataStorage101 data storage option from the dropdown list. Once you have completed these steps, you can proceed to create and start the cluster.

Warning: This is the only time your access credentials will be displayed. We don’t store them, so please ensure you save them securely. You can also download the credentials as a CSV file by clicking the Download CSV button once the setup is done.

This process may take a moment, so please hold on while the cluster becomes operational.

By default, the cluster starts running once you create it. The clusters table indicates the state of this cluster as Running. To stop the cluster, navigate through the horizontal ellipsis and click Stop

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Querying Data

Once your cluster is up and running, you can execute the query below by selecting the Run Query button found beneath the horizontal ellipsis in your cluster details.

Alternatively, you can connect Oxla to either the Looker Studio business intelligence tool or the TablePlus database management tool by following their respective instructions.

After clicking the Run Query button, you will be redirected to the Oxla console, where you can execute the following query. This query example identifies the ten most active users on GitHub by counting their triggered events within the social media dataset.

SELECT u.user_id, u.login, COUNT(e.event_id) AS event_count
FROM github_users u
JOIN github_events e ON u.user_id = e.user_id
GROUP BY u.user_id, u.login
ORDER BY event_count DESC
LIMIT 10;

The result joins the github_users table with the github_events table to show the ten most active users.

 user_id  |        login         | event_count 
----------+----------------------+-------------
  8517910 | LombiqBot            |       16605
 14175800 | OpenLocalizationTest |       11963
 10810283 | direwolf-github      |        4774
  3226435 | digimatic            |        2596
    57483 | tmtmtmtm             |        2338
    53164 | avsm                 |        1747
  2354108 | coveralls            |        1648
 13653959 | k8s-merge-robot      |        1458
 12490588 | suzutan0s2           |        1431
   406518 | davecramer           |        1417
(10 rows)

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Manage Your Cluster

As long as your cluster is in the Running state, you can terminate its operations at any time. Once you stop the cluster, it will cease to consume any credits.

When you’re finished with your tests, you can also delete the cluster. Deleting the cluster only releases the compute resources; it does not delete the data itself.

Deleting a cluster is only possible for clusters in a STOPPED or FAILED state.

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What’s next?

Check out additional sample datasets and run the example queries provided for each.