Pinot data explorer

Once you have set up the Cluster, you can start exploring the data and the APIs. Pinot 0.5.0 comes bundled with a completely new UI. Just head over to http://localhost:9000 in your browser to open the controller UI.

Let's take a look at the following two features on the UI

Query Console

Let us run some queries on the data in the Pinot cluster. Head over to Query Console to see the querying interface.

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). Click on the table name to display all the names along with the data types of the columns of the table. You can also execute a sample query select * from baseballStats limit 10 by typing it in the text box and clicking the Run Query button.

You can also try out the following queries:

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 thebaseballStats table listed here. We can also see the exactcurl call made to the controller API.

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.

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 can head over to Batch Ingestion or Stream ingestion to learn how to upload your own data and schema in Pinot.