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title seotitle list_title description menu weight metadata related
Get started querying data Query data | Get started with InfluxDB Clustered Query data Get started querying data in InfluxDB Clustered by learning about SQL and InfluxQL, and using tools like the influx3 CLI and InfluxDB client libraries.
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Query data Get started get-started-query-data
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{{% product-name %}} 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.

{{% note %}} The examples in this section of the tutorial query the get-started database for data written in the Get started writing data section. {{% /note %}}

Tools to execute queries

{{% product-name %}} supports many different tools for querying data, including:

{{< req type="key" text="Covered in this tutorial" color="magenta" >}}

{{% warn %}}

/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 {{% product-name %}}.

{{% /warn %}}

SQL query basics

The {{% product-name %}} SQL implementation is powered by the Apache Arrow DataFusion query engine which provides an SQL syntax similar to PostgreSQL.

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

InfluxDB SQL queries most commonly include the following clauses:

{{< req type="key" >}}

  • {{< req "*">}} 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.
  • {{< req "*">}} 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.

{{% influxdb/custom-timestamps %}}

-- Return the average temperature and humidity within time bounds from each room
SELECT
  avg(temp),
  avg(hum),
  room
FROM
  home
WHERE
  time >= '2022-01-01T08:00:00Z'
  AND time <= '2022-01-01T20:00:00Z'
GROUP BY
  room

{{% /influxdb/custom-timestamps %}}

Example SQL queries

Select all data in a measurement
SELECT * FROM home
Select all data in a measurement within time bounds

{{% influxdb/custom-timestamps %}}

SELECT
  *
FROM
  home
WHERE
  time >= '2022-01-01T08:00:00Z'
  AND time <= '2022-01-01T20:00:00Z'

{{% /influxdb/custom-timestamps %}}

Select a specific field within relative time bounds
SELECT temp FROM home WHERE time >= now() - INTERVAL '1 day'
Select specific fields and tags from a measurement
SELECT temp, room FROM home
Select data based on tag value
SELECT * FROM home WHERE room = 'Kitchen'
Select data based on tag value within time bounds

{{% influxdb/custom-timestamps %}}

SELECT
  *
FROM
  home
WHERE
  time >= '2022-01-01T08:00:00Z'
  AND time <= '2022-01-01T20:00:00Z'
  AND room = 'Living Room'

{{% /influxdb/custom-timestamps %}}

Downsample data by applying interval-based aggregates

{{% influxdb/custom-timestamps %}}

SELECT
  DATE_BIN(INTERVAL '1 hour', time, '2022-01-01T00:00:00Z'::TIMESTAMP) as _time,
  room,
  selector_max(temp, time)['value'] AS 'max temp'
FROM
  home
GROUP BY
  _time,
  'max temp',
  room
ORDER BY room, _time

{{% /influxdb/custom-timestamps %}}

Execute an SQL query

Get started with one of the following tools for querying data stored in an {{% product-name %}} 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 v3 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 {{% influxdb/custom-timestamps-span %}} 2022-01-01T08:00:00Z and 2022-01-01T20:00:00Z. {{% /influxdb/custom-timestamps-span %}}

{{% influxdb/custom-timestamps %}}

SELECT
  *
FROM
  home
WHERE
  time >= '2022-01-01T08:00:00Z'
  AND time <= '2022-01-01T20:00:00Z'

{{% /influxdb/custom-timestamps %}}

{{% note %}} Some examples in this getting started tutorial assume your InfluxDB credentials (URL and token) are provided by environment variables. {{% /note %}}

{{< tabs-wrapper >}} {{% tabs %}} influxctl CLI influx3 CLI Python Go Node.js C# Java {{% /tabs %}} {{% tab-content %}}

Use the influxctl query command to query the home sensor sample data in your {{< product-name omit=" Clustered" >}} cluster. Provide the following:

{{% influxdb/custom-timestamps %}} {{% code-placeholders "get-started" %}}

influxctl query \
  --database get-started \
  --token $INFLUX_TOKEN \
  "SELECT
    *
    FROM
      home
    WHERE
      time >= '2022-01-01T08:00:00Z'
      AND time <= '2022-01-01T20:00:00Z'"

{{% /code-placeholders %}} {{% /influxdb/custom-timestamps %}}

{{% note %}}

Query using stored credentials

Optionally, you can configure database and token query credentials in your influxctl connection profile.

The --database and --token command line flags override credentials in your configuration file. {{% /note %}}

{{% /tab-content %}} {{% tab-content %}}

{{% influxdb/custom-timestamps %}}

Query InfluxDB v3 using SQL and the influx3 CLI.

The following steps include setting up a Python virtual environment already covered in Get started writing data. If your project's virtual environment is already running, skip to step 3.

  1. Create a directory for your project and change into it:

    mkdir influx3-query-example && cd $_

  2. To create and activate a Python virtual environment, run the following command:

    python -m venv envs/virtual-env && . envs/virtual-env/bin/activate

  3. Install the CLI package (already installed in the Write data section).

    pip install influxdb3-python-cli

    Installing influxdb3-python-cli also installs the pyarrow library for working with Arrow data returned from queries.

  4. Create the config.json configuration.

    influx3 config \
  --name="config-clustered" \
  --database="get-started" \
  --host="{{< influxdb/host >}}" \
  --token="DATABASE_TOKEN" \
  --org="ORG_ID"

    Replace the following:

    • DATABASE_TOKEN: a database token with read access to the get-started database
    • ORG_ID: any non-empty string (InfluxDB ignores this parameter, but the client requires it)

  5. Enter the influx3 sql command and your SQL query statement.

    influx3 sql "SELECT *
            FROM home
            WHERE time >= '2022-01-01T08:00:00Z'
            AND time <= '2022-01-01T20:00:00Z'"

influx3 displays query results in your terminal.

{{% /influxdb/custom-timestamps %}}

{{% /tab-content %}} {{% tab-content %}}

{{% influxdb/custom-timestamps %}} Use the influxdb_client_3 client library module to integrate {{< product-name >}} with your Python code. The client library supports writing data to InfluxDB and querying data using SQL or InfluxQL.

The following steps include setting up a Python virtual environment already covered in Get started writing data. If your project's virtual environment is already running, skip to step 3.

  1. Open a terminal in the influxdb_py_client module directory you created in the Write data section:

    1. To create and activate your Python virtual environment, enter the following command in your terminal:

      python -m venv envs/virtual-env && . ./envs/virtual-env/bin/activate

    2. Install the following dependencies:

      {{< req type="key" text="Already installed in the Write data section" color="magenta" >}}

      • influxdb3-python{{< req text="* " color="magenta" >}}: Provides the InfluxDB influxdb_client_3 Python client library module and also installs the pyarrow package for working with Arrow data returned from queries.
      • pandas: Provides pandas functions, modules, and data structures for analyzing and manipulating data.
      • tabulate: Provides the tabulate function for formatting tabular data. pandas requires this module for formatting data as Markdown.

      In your terminal, enter the following command:

      pip install influxdb3-python pandas tabulate

    3. In your terminal or editor, create a new file for your code--for example: query.py.

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

    from influxdb_client_3 import InfluxDBClient3

client = InfluxDBClient3(
    host=f"{{< influxdb/host >}}",
    token=f"DATABASE_TOKEN",
    database=f"get-started",
)

sql = '''
  SELECT
    *
  FROM
    home
  WHERE
    time >= '2022-01-01T08:00:00Z'
    AND time <= '2022-01-01T20:00:00Z'
'''

table = client.query(query=sql)
assert table['room'], "Expect table to have room column."
print(table.to_pandas().to_markdown())

{{< expand-wrapper >}} {{% expand "Important: If using Windows, specify the Windows certificate path" %}}

When instantiating the client, Python looks for SSL/TLS certificate authority (CA) certificates for verifying the server's authenticity. If using a non-POSIX-compliant operating system (such as Windows), you need to specify a certificate bundle path that Python can access on your system.

The following example shows how to use the Python certifi package and client library options to provide a bundle of trusted certificates to the Python Flight client:

  1. In your terminal, install the Python certifi package.

    pip install certifi

  2. In your Python code, import certifi and call the certifi.where() method to retrieve the certificate path.

  3. When instantiating the client, pass the flight_client_options.tls_root_certs=<ROOT_CERT_PATH> option with the certificate path--for example:

    from influxdb_client_3 import InfluxDBClient3, flight_client_options
import os
import certifi

fh = open(certifi.where(), "r")
cert = fh.read()
fh.close()

client = InfluxDBClient3(
    host=f"{{< influxdb/host >}}",
    token=f"DATABASE_TOKEN",
    database=f"get-started",
    flight_client_options=flight_client_options(
        tls_root_certs=cert))

For more information, see influxdb_client_3 query exceptions.

{{% /expand %}} {{< /expand-wrapper >}}

The sample code does the following:

  1. Imports the InfluxDBClient3 constructor from the influxdb_client_3 module.

  2. Calls the InfluxDBClient3() constructor method with credentials to instantiate an InfluxDB client with the following credentials:

    • host: {{% product-name omit=" Clustered" %}} cluster URL (without https:// protocol or trailing slash)
    • token: a database token with read access to the specified database. Store this in a secret store or environment variable to avoid exposing the raw token string.
    • database: the name of the {{% product-name %}} database to query

  3. Defines the SQL query to execute and assigns it to a query variable.

  4. Calls the client.query() method with the SQL query. query() sends a Flight request to InfluxDB, queries the database, retrieves result data from the endpoint, and then returns a pyarrow.Table assigned to the table variable.

  5. Calls the to_pandas() method to convert the Arrow table to a pandas.DataFrame.

  6. Calls the pandas.DataFrame.to_markdown() method to convert the DataFrame to a markdown table.

  7. Calls the print() method to print the markdown table to stdout.

  8. Enter the following command to run the program and query your {{% product-name omit=" Clustered" %}} cluster:

    python query.py

{{< expand-wrapper >}} {{% expand "View returned markdown table" %}}

co hum room temp time
0 0 35.9 Kitchen 21 2022-01-01 08:00:00
1 0 36.2 Kitchen 23 2022-01-01 09:00:00
2 0 36.1 Kitchen 22.7 2022-01-01 10:00:00
3 0 36 Kitchen 22.4 2022-01-01 11:00:00
4 0 36 Kitchen 22.5 2022-01-01 12:00:00
5 1 36.5 Kitchen 22.8 2022-01-01 13:00:00
6 1 36.3 Kitchen 22.8 2022-01-01 14:00:00
7 3 36.2 Kitchen 22.7 2022-01-01 15:00:00
8 7 36 Kitchen 22.4 2022-01-01 16:00:00
9 9 36 Kitchen 22.7 2022-01-01 17:00:00
10 18 36.9 Kitchen 23.3 2022-01-01 18:00:00
11 22 36.6 Kitchen 23.1 2022-01-01 19:00:00
12 26 36.5 Kitchen 22.7 2022-01-01 20:00:00
13 0 35.9 Living Room 21.1 2022-01-01 08:00:00
14 0 35.9 Living Room 21.4 2022-01-01 09:00:00
15 0 36 Living Room 21.8 2022-01-01 10:00:00
16 0 36 Living Room 22.2 2022-01-01 11:00:00
17 0 35.9 Living Room 22.2 2022-01-01 12:00:00
18 0 36 Living Room 22.4 2022-01-01 13:00:00
19 0 36.1 Living Room 22.3 2022-01-01 14:00:00
20 1 36.1 Living Room 22.3 2022-01-01 15:00:00
21 4 36 Living Room 22.4 2022-01-01 16:00:00
22 5 35.9 Living Room 22.6 2022-01-01 17:00:00
23 9 36.2 Living Room 22.8 2022-01-01 18:00:00
24 14 36.3 Living Room 22.5 2022-01-01 19:00:00
25 17 36.4 Living Room 22.2 2022-01-01 20:00:00

{{% /expand %}} {{< /expand-wrapper >}} {{% /influxdb/custom-timestamps %}}

{{% /tab-content %}} {{% tab-content %}}

{{% influxdb/custom-timestamps %}}

  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/influxdb3/v1"
)

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://{{< influxdb/host >}}",
    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 >= '2022-01-01T08:00:00Z'
    AND time <= '2022-01-01T20:00:00Z'`

  // 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
}

    The sample code does the following:

    1. Imports the following packages:

      • context
      • fmt
      • io
      • os
      • text/tabwriter
      • github.com/InfluxCommunity/influxdb3-go/influxdb3/v1

    2. Defines a Query() function that does the following:

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

        • Host: your {{% product-name omit=" Clustered" %}} cluster URL
        • Database: the name of your {{% product-name %}} 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()
}

  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

    The program executes the main() function that writes the data and prints the query results to the console.

{{% /influxdb/custom-timestamps %}}

{{% /tab-content %}} {{% tab-content %}} {{% influxdb/custom-timestamps %}}

This tutorial assumes you installed Node.js and npm, and created an influxdb_js_client npm project as described in the Write data section.

  1. In your terminal or editor, change to the influxdb_js_client directory you created in the Write data section.

  2. If you haven't already, install the @influxdata/influxdb3-client JavaScript client library as a dependency to your project:

    npm install --save @influxdata/influxdb3-client

  3. Create a file named query.mjs. The .mjs extension tells the Node.js interpreter that you're using ES6 module syntax.

  4. Inside of query.mjs, enter the following sample code:

    // query.mjs
import {InfluxDBClient} from '@influxdata/influxdb3-client'
import {tableFromArrays} from 'apache-arrow';

/**
* Set InfluxDB credentials.
*/
const host = "https://{{< influxdb/host >}}";
const database = 'get-started';
/**
* INFLUX_TOKEN is an environment variable you assigned to your
* database READ token value.
*/
const token = process.env.INFLUX_TOKEN;

/**
* Query InfluxDB with SQL using the JavaScript client library.
*/
export async function querySQL() {
  /**
  * Instantiate an InfluxDBClient
  */
  const client = new InfluxDBClient({host, token})
  const sql = `
  SELECT *
  FROM home
  WHERE time >= '2022-01-01T08:00:00Z'
    AND time <= '2022-01-01T20:00:00Z'
  `

  const data = {time: [], room: [], co: [], hum: [], temp: []};
  const result = client.query(query, database);

  for await (const row of result) {
    data.time.push(new Date(row._time))
    data.room.push(row.room)
    data.co.push(row.co);
    data.hum.push(row.hum);
    data.temp.push(row.temp);
  }

  console.table([...tableFromArrays(data)])

  client.close()
}

    The sample code does the following:

    1. Imports the following:

      • InfluxDBClient class
      • tableFromArrays function

    2. Calls new InfluxDBClient() and passes a ClientOptions object to instantiate a client configured with InfluxDB credentials.

      • host: your {{% product-name omit=" Clustered" %}} cluster URL
      • token: a database token with read permission on the database you want to query. Store this in a secret store or environment variable to avoid exposing the raw token string.

    3. Defines a string variable (sql) for the SQL query.

    4. Defines an object (data) with column names for keys and array values for storing row data.

    5. Calls the InfluxDBClient.query() method with the following arguments:

      • sql: the query to execute
      • database: the name of the {{% product-name %}} database to query

      query() returns a stream of row vectors.

    6. Iterates over rows and adds the column data to the arrays in data.

    7. Passes data to the Arrow tableFromArrays() function to format the arrays as a table, and then passes the result to the console.table() method to output a highlighted table in the terminal.

  5. Inside of index.mjs (created in the Write data section), enter the following sample code to import the modules and call the functions:

    // index.mjs
import { writeLineProtocol } from "./write.mjs";
import { querySQL } from "./query.mjs";

/**
* Execute the client functions.
*/
async function main() {
  /** Write line protocol data to InfluxDB. */
  await writeLineProtocol();
  /** Query data from InfluxDB using SQL. */
  await querySQL();
}

main();

  6. In your terminal, execute index.mjs to write to and query {{% product-name %}}:

    node index.mjs

{{% /influxdb/custom-timestamps %}} {{% /tab-content %}} {{% tab-content %}}

{{% influxdb/custom-timestamps %}}

  1. In the influxdb_csharp_client directory you created in the Write data section, create a new file named Query.cs.

  2. In Query.cs, enter the following sample code:

    // Query.cs

using System;
using System.Threading.Tasks;
using InfluxDB3.Client;
using InfluxDB3.Client.Query;

namespace InfluxDBv3;

public class Query
{
  /**
    * Queries an InfluxDB database using the C# .NET client
    * library.
    **/
  public static async Task QuerySQL()
  {
    /** INFLUX_TOKEN is an environment variable you assigned to your
      * database READ token value.
      **/
    string? token = System.Environment
        .GetEnvironmentVariable("INFLUX_TOKEN");

    /**
      * Instantiate the InfluxDB client with credentials.
      **/
    using var client = new InfluxDBClient(
        "https://{{< influxdb/host >}}", token: token, database: database);

    const string sql = @"
      SELECT time, room, temp, hum, co
      FROM home
      WHERE time >= '2022-01-01T08:00:00Z'
      AND time <= '2022-01-01T20:00:00Z'
    ";

    Console.WriteLine("{0,-30}{1,-15}{2,-15}{3,-15}{4,-15}",
        "time", "room", "co", "hum", "temp");

    await foreach (var row in client.Query(query: sql))
    {
      {
        /** 
          * Iterate over rows and print column values in table format.
          * Format the timestamp as sortable UTC format.
          */
        Console.WriteLine("{0,-30:u}{1,-15}{4,-15}{3,-15}{2,-15}",
            row[0], row[1], row[2], row[3], row[4]);
      }
    }
    Console.WriteLine();
  }
}

    The sample code does the following:

    1. Imports the following classes:

      • System
      • System.Threading.Tasks;
      • InfluxDB3.Client;
      • InfluxDB3.Client.Query;

    2. Defines a Query class with a QuerySQL() method that does the following:

      1. Calls the new InfluxDBClient() constructor to instantiate a client configured with InfluxDB credentials.

        • host: your {{% product-name omit=" Clustered" %}} cluster URL.
        • database: the name of the {{% product-name %}} database to query
        • 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 string variable for the SQL query.

      3. Calls the InfluxDBClient.Query() method to send the query request with the SQL string. Query() returns batches of rows from the response stream as a two-dimensional array--an array of rows in which each row is an array of values.

      4. Iterates over rows and prints the data in table format to stdout.

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

    // Program.cs

using System;
using System.Threading.Tasks;

namespace InfluxDBv3;

public class Program
{
  public static async Task Main()
  {
    await Write.WriteLineProtocol();
    await Query.QuerySQL();
  }
}

  4. To build and execute the program and query your {{% product-name omit=" Clustered" %}} cluster, enter the following command in your terminal:

    dotnet run

{{% /influxdb/custom-timestamps %}}

{{% /tab-content %}} {{% tab-content %}}

{{% influxdb/custom-timestamps %}}

This tutorial assumes using Maven version 3.9, Java version >= 15, and an influxdb_java_client Maven project created in the Write data section.

  1. In your terminal or editor, change to the influxdb_java_client directory you created in the Write data section.

  2. Inside of the src/main/java/com/influxdbv3 directory, create a new file named Query.java.

  3. In Query.java, enter the following sample code:

    // Query.java
package com.influxdbv3;

import com.influxdb.v3.client.InfluxDBClient;
import java.util.stream.Stream;

/**
  * Queries an InfluxDB database using the Java client
  * library.
  **/
public final class Query {

    private Query() {
        //not called
    }

    /**
    * @throws Exception
    */
    public static void querySQL() throws Exception {
        /**
        * Query using SQL.
        */

        /** Set InfluxDB credentials. **/
        final String host = "https://{{< influxdb/host >}}";
        final String database = "get-started";

        /** INFLUX_TOKEN is an environment variable you assigned to your
          * database READ token value.
          **/
        final char[] token = (System.getenv("INFLUX_TOKEN")).
        toCharArray();

        try (InfluxDBClient client = InfluxDBClient.getInstance(host,
        token, database)) {
            String sql =
                """
                SELECT time, room, temp, hum, co
                FROM home
                WHERE time >= '2022-01-01T08:00:00Z'
                AND time <= '2022-01-01T20:00:00Z'""";

            String layoutHead = "| %-16s | %-12s | %-6s | %-6s | %-6s |%n";
            System.out.printf(
            "--------------------------------------------------------%n");
            System.out.printf(layoutHead,
            "time", "room", "co", "hum", "temp");
            System.out.printf(
            "--------------------------------------------------------%n");
            String layout = "| %-16s | %-12s | %-6s | %.1f | %.1f |%n";

            try (Stream<Object[]> stream = client.query(sql)) {
                stream.forEach(row -> 
                  System.out.printf(layout,
                  row[0], row[1], row[4], row[3], row[2])
                );
            }
        }
    }
}

    The sample code does the following:

    1. Assigns the com.influxdbv3 package name (the Maven groupId).

    2. Imports the following classes:

      • com.influxdb.v3.client.InfluxDBClient
      • java.util.stream.Stream

    3. Defines a Query class with a querySQL() method that does the following:

      1. Calls InfluxDBClient.getInstance() to instantiate a client configured with InfluxDB credentials.

        • host: your {{% product-name omit=" Clustered" %}} cluster URL
        • database: the name of the {{% product-name %}} database to write to
        • 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 string variable (sql) for the SQL query.

      3. Defines a Markdown table format layout for headings and data rows.

      4. Calls the InfluxDBClient.query() method to send the query request with the SQL string. query() returns a stream of rows.

      5. Iterates over rows and prints the data in the specified layout to stdout.

  4. In your editor, open the src/main/java/com/influxdbv3/App.java file and replace its contents with the following sample code:

    // App.java

package com.influxdbv3;

/**
* Execute the client functions.
*
*/
public class App {

    /**
    * @param args
    * @throws Exception
    */
    public static void main(final String[] args) throws Exception {
        // Write data to InfluxDB v3.
        Write.writeLineProtocol();
        // Run the SQL query.
        Query.querySQL();
    }
}
    • The App, Write, and Query classes belong to the com.influxdbv3 package (your project groupId).
    • App defines a main() function that calls Write.writeLineProtocol() and Query.querySQL().

  5. In your terminal or editor, use Maven to install dependencies and compile the project code--for example:

    mvn compile

  6. Set the --add-opens=java.base/java.nio=ALL-UNNAMED Java option for your environment. The Apache Arrow Flight library requires this setting for access to the java.nio API package.

    For example, enter the following command in your terminal:

    Linux/MacOS

    export MAVEN_OPTS="--add-opens=java.base/java.nio=ALL-UNNAMED"

    Windows PowerShell

    $env:MAVEN_OPTS="--add-opens=java.base/java.nio=ALL-UNNAMED"

  7. To run the app to write to and query {{% product-name %}}, execute App.main()--for example, using Maven:

    mvn exec:java -Dexec.mainClass="com.influxdbv3.App"

{{% /influxdb/custom-timestamps %}}

{{% /tab-content %}} {{< /tabs-wrapper >}}

Query results

{{< expand-wrapper >}} {{% expand "View query results" %}}

{{% influxdb/custom-timestamps %}}

time room co hum temp
2022-01-01T08:00:00Z Kitchen 0 35.9 21
2022-01-01T09:00:00Z Kitchen 0 36.2 23
2022-01-01T10:00:00Z Kitchen 0 36.1 22.7
2022-01-01T11:00:00Z Kitchen 0 36 22.4
2022-01-01T12:00:00Z Kitchen 0 36 22.5
2022-01-01T13:00:00Z Kitchen 1 36.5 22.8
2022-01-01T14:00:00Z Kitchen 1 36.3 22.8
2022-01-01T15:00:00Z Kitchen 3 36.2 22.7
2022-01-01T16:00:00Z Kitchen 7 36 22.4
2022-01-01T17:00:00Z Kitchen 9 36 22.7
2022-01-01T18:00:00Z Kitchen 18 36.9 23.3
2022-01-01T19:00:00Z Kitchen 22 36.6 23.1
2022-01-01T20:00:00Z Kitchen 26 36.5 22.7
2022-01-01T08:00:00Z Living Room 0 35.9 21.1
2022-01-01T09:00:00Z Living Room 0 35.9 21.4
2022-01-01T10:00:00Z Living Room 0 36 21.8
2022-01-01T11:00:00Z Living Room 0 36 22.2
2022-01-01T12:00:00Z Living Room 0 35.9 22.2
2022-01-01T13:00:00Z Living Room 0 36 22.4
2022-01-01T14:00:00Z Living Room 0 36.1 22.3
2022-01-01T15:00:00Z Living Room 1 36.1 22.3
2022-01-01T16:00:00Z Living Room 4 36 22.4
2022-01-01T17:00:00Z Living Room 5 35.9 22.6
2022-01-01T18:00:00Z Living Room 9 36.2 22.8
2022-01-01T19:00:00Z Living Room 14 36.3 22.5
2022-01-01T20:00:00Z Living Room 17 36.4 22.2
{{% /influxdb/custom-timestamps %}}

{{% /expand %}} {{< /expand-wrapper >}}

Congratulations! You've learned the basics of querying data in InfluxDB with SQL. For a deep dive into all the ways you can query {{% product-name %}}, see the Query data in InfluxDB section of documentation.

{{< page-nav prev="/influxdb/clustered/get-started/write/" keepTab=true >}}