Pinot data explorer

Explore the data on our Pinot cluster

Now that the QuickStartCluster is setup, we can start exploring the data and the APIs. Head over to http://localhost:9000 in your browser.

You are now connected to the Pinot controller. Let's take a look at the following two features.

Query Console

Query Console let's us run queries on the data in the Pinot cluster

We can see our baseballStats table listed on the left (you will see meetupRSVP or airlineStats if you used the streaming or the hybrid quick start). Clicking on the table name should display all the names and data types of the columns of the table, and also execute a sample query select * from baseballStats limit 10 . You can query this table by typing your query in the text box and clicking the Run Query button.

Pinot Data Explorer

Here's some other queries you can try out:

select playerName, max(hits) from baseballStats group by playerName order by max(hits) desc

select sum(hits), sum(homeRuns), sum(numberOfGames) from baseballStats where yearID > 2010

select * from baseballStats order by league

Pinot supports a subset of standard SQL. See Pinot Query Language for more information.

Rest API

The Pinot Admin UI contains all the APIs that you will need to operate and manage your cluster. It provides a set of APIs for Pinot cluster management including health check, instances management, schema and table management, data segments management.

Let's check out the tables in this cluster by going to Table -> List all tables in cluster and click on Try it out!. We can see the baseballStats table listed here. We can also see the exact curl call made to the controller API.

List all tables in cluster

You can look at the configuration of this table by going to Tables -> Get/Enable/Disable/Drop a table, type in baseballStats in the table name, and click Try it out!

Let's check out the schemas in the cluster by going to Schema -> List all schemas in the cluster and click Try it out!. We can see a schema called baseballStats in this list.

List all schemas in the cluster

Take a look at the schema by going to Schema -> Get a schema, type baseballStats in the schema name, and click Try it out!.

{
  "schemaName": "baseballStats",
  "dimensionFieldSpecs": [
    {
      "name": "playerID",
      "dataType": "STRING"
    },
    {
      "name": "yearID",
      "dataType": "INT"
    },
    {
      "name": "teamID",
      "dataType": "STRING"
    },
    {
      "name": "league",
      "dataType": "STRING"
    },
    {
      "name": "playerName",
      "dataType": "STRING"
    }
  ],
  "metricFieldSpecs": [
    {
      "name": "playerStint",
      "dataType": "INT"
    },
    {
      "name": "numberOfGames",
      "dataType": "INT"
    },
    {
      "name": "numberOfGamesAsBatter",
      "dataType": "INT"
    },
    {
      "name": "AtBatting",
      "dataType": "INT"
    },
    {
      "name": "runs",
      "dataType": "INT"
    },
    {
      "name": "hits",
      "dataType": "INT"
    },
    {
      "name": "doules",
      "dataType": "INT"
    },
    {
      "name": "tripples",
      "dataType": "INT"
    },
    {
      "name": "homeRuns",
      "dataType": "INT"
    },
    {
      "name": "runsBattedIn",
      "dataType": "INT"
    },
    {
      "name": "stolenBases",
      "dataType": "INT"
    },
    {
      "name": "caughtStealing",
      "dataType": "INT"
    },
    {
      "name": "baseOnBalls",
      "dataType": "INT"
    },
    {
      "name": "strikeouts",
      "dataType": "INT"
    },
    {
      "name": "intentionalWalks",
      "dataType": "INT"
    },
    {
      "name": "hitsByPitch",
      "dataType": "INT"
    },
    {
      "name": "sacrificeHits",
      "dataType": "INT"
    },
    {
      "name": "sacrificeFlies",
      "dataType": "INT"
    },
    {
      "name": "groundedIntoDoublePlays",
      "dataType": "INT"
    },
    {
      "name": "G_old",
      "dataType": "INT"
    }
  ]
}

Finally, let's checkout the data segments in the cluster by going to Segment -> List all segments, type in baseballStats in the table name, and click Try it out!. There's 1 segment for this table, called baseballStats_OFFLINE_0.

You might have figured out by now, in order to get data into the Pinot cluster, we need a table, a schema and segments. Let's head over to Batch upload sample data, to find out more about these components and learn how to create them for your own data.