Pinot Data Explorer

Pinot Data Explorer is a user-friendly interface in Apache Pinot for interactive data exploration, querying, and visualization.

Once you have set up a cluster, you can start exploring the data and the APIs using the Pinot Data Explorer.

Navigate to http://localhost:9000 in your browser to open the Data Explorer UI.

Cluster Manager

The first screen that you'll see when you open the Pinot Data Explorer is the Cluster Manager. The Cluster Manager provides a UI to operate and manage your cluster, giving you an overview of tenants, instances, tables, and their current status.

Pinot Cluster Manager

If you want to view the contents of a server, click on its instance name. You'll then see the following:

Pinot Server

Table management

To view a table, click on its name from the tables list. From the table detail screen, you can edit or delete the table, edit or adjust its schema, and perform several other operations.

baseballStats Table

For example, if we want to add yearID to the list of inverted indexes, click on Edit Table, add the extra column, and click Save:

Pause and resume consumption

For real-time tables, the table detail screen includes a Pause/Resume Consumption button (#15657). This lets you pause ingestion on a real-time table directly from the UI without issuing REST API calls, and resume it when ready. This is useful during maintenance windows or when you need to temporarily halt data ingestion.

Consuming segments info

A Consuming Segments Info button (#15623) is available on real-time tables, providing a quick view of all currently consuming segments. This shows details such as the partition, current offset, and consumption state, making it easier to monitor real-time ingestion health.

Reset segment

The UI now supports a Reset Segment operation (#16078), allowing you to reset a segment directly from the table detail screen. This is helpful when a segment is stuck in an error state and needs to be re-processed.

Segment state filter

A segment state filter (#16085) has been added to the table detail screen. You can filter segments by their state (e.g., ONLINE, CONSUMING, ERROR) to quickly locate segments that need attention, which is especially valuable for tables with a large number of segments.

Table rebalance

The table detail screen also provides access to table rebalance operations. Several UI fixes and improvements (#15511) have been made to improve the reliability and usability of the rebalance workflow, including better parameter validation and progress display.

Logical table management

Starting with Pinot 1.4 and later, the Data Explorer includes a logical table management UI (#17878). Logical tables are collections of physical tables (REALTIME and OFFLINE) that can be queried as a single unified table.

The logical tables listing is accessible from the main Tables page, alongside physical tables and schemas. From there you can:

• Browse all logical tables in the cluster with search support.

• View details of a logical table, including its configuration, the list of physical tables it maps to, and metadata.

• Edit a logical table's configuration.

• Delete a logical table with a confirmation dialog.

For more information about logical tables, see the Logical Table Support section in the 1.4.0 release notes.

Query Console

Navigate to Query Console to see the querying interface. The Query Console lets you run SQL queries against your Pinot cluster and view the results interactively.

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.

Cmd + Enter can also be used to run the query when focused on the console.

Here are some sample queries you can try:

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. For more information, see Pinot Query Language.

Time-series query execution

The Query Console also supports time-series query execution (#16305), introduced as part of the Time Series Engine beta. This feature provides a dedicated interface for running and visualizing time-series queries using languages such as PromQL. It connects to a Prometheus-compatible /query_range endpoint (#16286) exposed by the Pinot controller, letting you explore time-series data and inspect query execution plans directly from the UI.

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, click Try it out, and then click Execute. We can see thebaseballStats 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, click Try it out, type baseballStats in the table name, and then click Execute.

Let's check out the schemas in the cluster by going to Schema -> List all schemas in the cluster, click Try it out, and then click Execute. 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, click Try it out, type baseballStats in the schema name, and then click Execute.

baseballStats Schema

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

To learn how to upload your own data and schema, see Batch Ingestion or Stream ingestion.