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.
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 >= '2022-01-01T08:00:00Z'
AND time <= '2022-01-01T20:00:00Z'
GROUP BY
room
Example SQL queries
Select all data in a measurement
Select all data in a measurement within time bounds
SELECT
*
FROM
home
WHERE
time >= '2022-01-01T08:00:00Z'
AND time <= '2022-01-01T20:00:00Z'
Select a specific field within relative time bounds
SELECT temp FROM home WHERE time >= now() - INTERVAL '1 day'
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
SELECT
*
FROM
home
WHERE
time >= '2022-01-01T08:00:00Z'
AND time <= '2022-01-01T20:00:00Z'
AND room = 'Living Room'
Downsample data by applying interval-based aggregates
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
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 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
2022-01-01T08:00:00Z and 2022-01-01T20:00:00Z.
SELECT
*
FROM
home
WHERE
time >= '2022-01-01T08:00:00Z'
AND time <= '2022-01-01T20:00:00Z'
Some examples in this getting started tutorial assume your InfluxDB
credentials (URL and token) are provided by
environment variables.
Use the influxctl query
command
to query the home sensor sample data in your
InfluxDB cluster.
Provide the following:
- Database name to query using the
--database
flag
- Database token using the
--token
flag (use the INFLUX_TOKEN
environment variable created in
Get started–Set up InfluxDB Clustered)
- SQL query
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'"
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.
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.
-
Create a directory for your project and change into it:
mkdir influx3-query-example && cd $_
-
To create and activate a Python virtual environment, run the following command:
python -m venv envs/virtual-env && . envs/virtual-env/bin/activate
-
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.
-
Create the config.json
configuration.
influx3 config \
--name="config-clustered" \
--database="get-started" \
--host="cluster-host.com" \
--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)
-
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.
Use the influxdb_client_3
client library module to integrate InfluxDB Clustered 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.
-
Open a terminal in the influxdb_py_client
module directory you created in the
Write data section:
-
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
-
Install the following dependencies:
* Already installed in the Write data section
influxdb3-python
*: 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
-
In your terminal or editor, create a new file for your code–for example: query.py
.
-
In query.py
, enter the following sample code:
from influxdb_client_3 import InfluxDBClient3
client = InfluxDBClient3(
host=f"cluster-host.com",
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())
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:
-
In your terminal, install the Python certifi
package.
-
In your Python code, import certifi
and call the certifi.where()
method to retrieve the certificate path.
-
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"cluster-host.com",
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.
The sample code does the following:
-
Imports the InfluxDBClient3
constructor from the influxdb_client_3
module.
-
Calls the InfluxDBClient3()
constructor method with credentials to instantiate an InfluxDB client
with the following credentials:
host
: InfluxDB 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 InfluxDB Clustered database to query
-
Defines the SQL query to execute and assigns it to a query
variable.
-
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.
-
Calls the to_pandas()
method
to convert the Arrow table to a pandas.DataFrame
.
-
Calls the pandas.DataFrame.to_markdown()
method
to convert the DataFrame to a markdown table.
-
Calls the print()
method to print the markdown table to stdout.
-
Enter the following command to run the program and query your InfluxDB cluster:
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 |
-
In the influxdb_go_client
directory you created in the
Write data section,
create a new file named query.go
.
-
In query.go
, enter the following sample code:
package main
import (
"context"
"fmt"
"io"
"os"
"time"
"text/tabwriter"
"github.com/apache/arrow/go/v13/arrow"
"github.com/InfluxCommunity/influxdb3-go/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 >= '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 arrow and time packages to format unix timestamp
// as a time with timezone layout (RFC3339).
time := (row["time"].(arrow.Timestamp)).
ToTime(arrow.TimeUnit(arrow.Nanosecond)).
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:
-
Imports the following packages:
context
fmt
io
os
text/tabwriter
github.com/apache/arrow/go/v13/arrow
github.com/InfluxCommunity/influxdb3-go/influxdb3
-
Defines a Query()
function that does the following:
-
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.
-
Defines a deferred function to close the client after execution.
-
Defines a string variable for the SQL query.
-
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.
-
Iterates over rows, formats the timestamp as an RFC3339 timestamp, and prints the data in table format to stdout.
-
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()
}
-
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.
This tutorial assumes you installed Node.js and npm, and created an influxdb_js_client
npm project as described in the Write data section.
-
In your terminal or editor, change to the influxdb_js_client
directory you created in the
Write data section.
-
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
-
Create a file named query.mjs
. The .mjs
extension tells the Node.js interpreter that you’re using ES6 module syntax.
-
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://cluster-host.com";
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:
-
Imports the following:
InfluxDBClient
class
tableFromArrays
function
-
Calls new InfluxDBClient()
and passes a ClientOptions
object to instantiate a client configured
with InfluxDB credentials.
host
: your InfluxDB 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.
-
Defines a string variable (sql
) for the SQL query.
-
Defines an object (data
) with column names for keys and array values for storing row data.
-
Calls the InfluxDBClient.query()
method with the following arguments:
sql
: the query to execute
database
: the name of the InfluxDB Clustered database to query
query()
returns a stream of row vectors.
-
Iterates over rows and adds the column data to the arrays in data
.
-
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.
-
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();
-
In your terminal, execute index.mjs
to write to and query InfluxDB Clustered:
-
In the influxdb_csharp_client
directory you created in the
Write data section,
create a new file named Query.cs
.
-
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://cluster-host.com", 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:
-
Imports the following classes:
System
System.Threading.Tasks
;
InfluxDB3.Client
;
InfluxDB3.Client.Query
;
-
Defines a Query
class with a QuerySQL()
method that does the following:
-
Calls the new InfluxDBClient()
constructor to instantiate a client configured
with InfluxDB credentials.
host
: your InfluxDB cluster URL.
database
: the name of the InfluxDB Clustered 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.
-
Defines a string variable for the SQL query.
-
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.
-
Iterates over rows and prints the data in table format to stdout.
-
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();
}
}
-
To build and execute the program and query your InfluxDB cluster,
enter the following command in your terminal:
This tutorial assumes using Maven version 3.9, Java version >= 15, and an influxdb_java_client
Maven project created in the Write data section.
-
In your terminal or editor, change to the influxdb_java_client
directory you created in the
Write data section.
-
Inside of the src/main/java/com/influxdbv3
directory, create a new file named Query.java
.
-
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://cluster-host.com";
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:
-
Assigns the com.influxdbv3
package name (the Maven groupId).
-
Imports the following classes:
com.influxdb.v3.client.InfluxDBClient
java.util.stream.Stream
-
Defines a Query
class with a querySQL()
method that does the following:
-
Calls InfluxDBClient.getInstance()
to instantiate a client configured
with InfluxDB credentials.
host
: your InfluxDB cluster URL
database
: the name of the InfluxDB Clustered 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.
-
Defines a string variable (sql
) for the SQL query.
-
Defines a Markdown table format layout for headings and data rows.
-
Calls the InfluxDBClient.query()
method to send the query request with the SQL string.
query()
returns a stream of rows.
-
Iterates over rows and prints the data in the specified layout to stdout.
-
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()
.
-
In your terminal or editor, use Maven to install dependencies and compile the project code–for example:
-
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"
-
To run the app to write to and query InfluxDB Clustered, execute App.main()
–for example, using Maven:
mvn exec:java -Dexec.mainClass="com.influxdbv3.App"
Query results
View query results
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 |
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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