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Get started querying data

InfluxDB Clustered supports multiple query languages:

  • SQL: Traditional SQL powered by the Apache Arrow DataFusion query engine. The supported SQL syntax is similar to PostgreSQL.
  • InfluxQL: An SQL-like query language designed to query time series data stored in InfluxDB.

This tutorial walks you through the fundamentals of querying data in InfluxDB and focuses on using SQL to query your time series data. The InfluxDB SQL implementation is built using Arrow Flight SQL, a protocol for interacting with SQL databases using the Arrow in-memory format and the Flight RPC framework. It leverages the performance of Apache Arrow with the simplicity of SQL.

The examples in this section of the tutorial query the get-started database for data written in the Get started writing data section.

Tools to execute queries

InfluxDB Clustered supports many different tools for querying data, including:

* Covered in this tutorial

/api/v2/query not supported

The /api/v2/query API endpoint and associated tooling, such as the influx CLI and InfluxDB v2 client libraries, aren’t supported in InfluxDB Clustered.

SQL query basics

The InfluxDB Clustered SQL implementation is powered by the Apache Arrow DataFusion query engine which provides an SQL syntax similar to PostgreSQL.

This is a brief introduction to writing SQL queries for InfluxDB. For more in-depth details, see Query data with SQL.

InfluxDB SQL queries most commonly include the following clauses:

* Required
  • * SELECT: Identify specific fields and tags to query from a measurement or use the wildcard alias (*) to select all fields and tags from a measurement.
  • * FROM: Identify the measurement to query. If coming from an SQL background, an InfluxDB measurement is the equivalent of a relational table.
  • WHERE: Only return data that meets defined conditions such as falling within a time range, containing specific tag values, etc.
  • GROUP BY: Group data into SQL partitions and apply an aggregate or selector function to each group.
-- Return the average temperature and humidity within time bounds from each room
SELECT
  avg(temp),
  avg(hum),
  room
FROM
  home
WHERE
  time >= '2025-06-11T08:00:00Z'
  AND time <= '2025-06-11T20:00:00Z'
GROUP BY
  room
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Example SQL queries

Select all data in a measurement
SELECT * FROM home
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Select all data in a measurement within time bounds
SELECT
  *
FROM
  home
WHERE
  time >= '2025-06-11T08:00:00Z'
  AND time <= '2025-06-11T20:00:00Z'
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Select a specific field within relative time bounds
SELECT temp FROM home WHERE time >= now() - INTERVAL '1 day'
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Select specific fields and tags from a measurement
SELECT temp, room FROM home
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Select data based on tag value
SELECT * FROM home WHERE room = 'Kitchen'
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Select data based on tag value within time bounds
SELECT
  *
FROM
  home
WHERE
  time >= '2025-06-11T08:00:00Z'
  AND time <= '2025-06-11T20:00:00Z'
  AND room = 'Living Room'
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Downsample data by applying interval-based aggregates
SELECT
  DATE_BIN(INTERVAL '1 hour', time, '2025-06-11T00:00:00Z'::TIMESTAMP) as _time,
  room,
  selector_max(temp, time)['value'] AS 'max temp'
FROM
  home
GROUP BY
  _time,
  'max temp',
  room
ORDER BY room, _time
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Execute an SQL query

Get started with one of the following tools for querying data stored in an InfluxDB Clustered database:

  • influxctl CLI: Query data from your command-line using the influxctl CLI.
  • influx3 CLI: Query data from your terminal command-line using the Python-based influx3 CLI.
  • InfluxDB 3 client libraries: Use language-specific (Python, Go, etc.) clients to execute queries in your terminal or custom code.
  • Grafana: Use the FlightSQL Data Source plugin, to query, connect, and visualize data.

For this example, use the following query to select all the data written to the get-started database between 2025-06-11T08:00:00Z and 2025-06-11T20:00:00Z.

SELECT
  *
FROM
  home
WHERE
  time >= '2025-06-11T08:00:00Z'
  AND time <= '2025-06-11T20:00:00Z'
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Some examples in this getting started tutorial assume your InfluxDB credentials (URL and token) are provided by environment variables.

  1. In the influxdb_go_client directory you created in the Write data section, create a new file named query.go.

  2. In query.go, enter the following sample code:

    package main
    
    import (
      "context"
      "fmt"
      "io"
      "os"
      "time"
      "text/tabwriter"
    
      "github.com/InfluxCommunity/influxdb3-go/v2/influxdb3"
    )
    
    func Query() error {
    
      // INFLUX_TOKEN is an environment variable you created
      // for your database read token.
      token := os.Getenv("INFLUX_TOKEN")
    
      // Instantiate the client.
      client, err := influxdb3.New(influxdb3.ClientConfig{
        Host:     "https://cluster-host.com",
        Token:    token,
        Database: "get-started",
      })
    
      // Close the client when the function returns.
      defer func(client *influxdb3.Client) {
        err := client.Close()
        if err != nil {
          panic(err)
        }
      }(client)
    
      // Define the query.
      query := `SELECT *
        FROM home
        WHERE time >= '2025-06-11T08:00:00Z'
        AND time <= '2025-06-11T20:00:00Z'`
    
      // Execute the query.
      iterator, err := client.Query(context.Background(), query)
    
      if err != nil {
        panic(err)
      }
    
      w := tabwriter.NewWriter(io.Discard, 4, 4, 1, ' ', 0)
      w.Init(os.Stdout, 0, 8, 0, '\t', 0)
      fmt.Fprintln(w, "time\troom\ttemp\thum\tco")
    
      // Iterate over rows and prints column values in table format.
      for iterator.Next() {
        row := iterator.Value()
        // Use Go time package to format unix timestamp
        // as a time with timezone layout (RFC3339).
        time := (row["time"].(time.Time)).
          Format(time.RFC3339)
        fmt.Fprintf(w, "%s\t%s\t%d\t%.1f\t%.1f\n",
          time, row["room"], row["co"], row["hum"], row["temp"])
      }
    
      w.Flush()
      return nil
    }
    
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    The sample code does the following:

    1. Imports the following packages:

      • context
      • fmt
      • io
      • os
      • text/tabwriter
      • github.com/InfluxCommunity/influxdb3-go/v2/influxdb3
    2. Defines a Query() function that does the following:

      1. Instantiates influx.Client with the following parameters for InfluxDB credentials:

        • Host: your InfluxDB cluster URL
        • Database: the name of your InfluxDB Clustered database
        • Token: a database token with read permission on the specified database. Store this in a secret store or environment variable to avoid exposing the raw token string.
      2. Defines a deferred function to close the client after execution.

      3. Defines a string variable for the SQL query.

      4. Calls the influxdb3.Client.Query(sql string) method and passes the SQL string to query InfluxDB. Query(sql string) method returns an iterator for data in the response stream.

      5. Iterates over rows, formats the timestamp as an RFC3339 timestamp, and prints the data in table format to stdout.

  3. In your editor, open the main.go file you created in the Write data section and insert code to call the Query() function–for example:

    package main
    
    func main() {	
      WriteLineProtocol()
      Query()
    }
    
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  4. In your terminal, enter the following command to install the necessary packages, build the module, and run the program:

    go mod tidy && go run influxdb_go_client
    
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    The program executes the main() function that writes the data and prints the query results to the console.

Query results

View query results

Congratulations! You’ve learned the basics of querying data in InfluxDB with SQL. For a deep dive into all the ways you can query InfluxDB Clustered, see the Query data in InfluxDB section of documentation.


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