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InfluxDB

InfluxDB is schema-less.

InfluxDB is a purpose-built time series database designed to handle high volumes of timestamped data. It excels at collecting, storing, processing, and visualizing metrics and events from applications, IoT devices, and infrastructure monitoring systems.

• Easy to get started - just write data and InfluxDB handles the structure
• Flexible for evolving data requirements
• No database migrations needed when adding new fields or tags

Key Features

Time Series Optimized: Built from the ground up for time-stamped data, providing efficient storage and fast queries.

High Write Performance: Can handle millions of data points per second, making it ideal for real-time monitoring applications.

Flux Query Language: A powerful functional data scripting language for analytics, monitoring, and alerting.

Bucket Storage: Organizes data into buckets with customizable retention policies.

Downsampling & Retention: Automatically manages data lifecycle with features to aggregate older data and purge expired points.

Built-in UI: Comes with a comprehensive web interface for data visualization, dashboarding, and administration.

Data Model

Measurements: Similar to tables in relational databases (e.g., temperature, cpu_usage)

Tags: Indexed metadata for fast filtering (e.g., location=kitchen)

Fields: The actual data values being stored (e.g., value=22.5)

Timestamps: When each data point was recorded

Common Use Cases

IoT Sensor Data: Tracking environmental conditions, equipment performance

DevOps Monitoring: System metrics, application performance, infrastructure health

Real-time Analytics: Business metrics, user activity, performance trends

Financial Market Data: Stock prices, trading volumes, economic indicators

Epoch Time (https://epochconverter.com)

Installation

Cloud

Influxdata Cloud

Via Docker

mkdir influxdb
cd influxdb

mkdir data
mkdir config

Docker Hub

podman run -d -p 8086:8086 \
  --name myinflux \
  -v "$PWD/data:/var/lib/influxdb2" \
  -v "$PWD/config:/etc/influxdb2" \
  -e DOCKER_INFLUXDB_INIT_MODE=setup \
  -e DOCKER_INFLUXDB_INIT_USERNAME=admin \
  -e DOCKER_INFLUXDB_INIT_PASSWORD=P@ssw0rd1 \
  -e DOCKER_INFLUXDB_INIT_ORG=rowan \
  -e DOCKER_INFLUXDB_INIT_BUCKET=mybucket \
  -e DOCKER_INFLUXDB_INIT_RETENTION=1h \
  influxdb:latest

At this time latest is Version 2.x Minimum Retention period is 1 hr

24h = 1 day
168h = 7 days
720h = 30 days
8760h = 1 year

Format of Sample Data

In InfluxDB, a “measurement” is a fundamental concept that represents the data structure that stores time series data. You can think of a measurement as similar to a table in a traditional relational database.

Note:

• Use singular form for measurement names (e.g., “temperature” not “temperatures”)
• Be consistent with tag and field names
• Consider using a naming convention (e.g., snake_case or camelCase)

Example 1

temperature,location=kitchen value=22.5

• temperature -> measurement
• location=kitchen -> tags
• value=22.5 -> field
• if TimeStamp is missing then it assumes current TimeStamp

Example 2

temperature,location=kitchen,sensor=thermometer value=22.5 1614556800000000000

Example 3

Multiple Tags and Multiple Fields

temperature,location=kitchen,sensor=thermometer temp_c=22.5,humidity_pct=45.2

• location=kitchen,sensor=thermometer -> Tags
• temp_c=22.5,humidity_pct=45.2 -> Field

Example 4

temperature,location=kitchen,sensor=thermometer reading=22.5,battery_level=98,type="smart",active=true

Load Data

Login via UI

http://localhost:8086

Username: admin Password: P@ssw0rd1

• Navigate to “Data” in the left sidebar, then select “Buckets”
• You should see your “mybucket” listed. Click on it.
• Look for “Add Data” button and select “Line Protocol”

Line Protocol

Line protocol, is InfluxDB’s text-based format for writing time series data into the database. It’s designed to be both human-readable and efficient for machine parsing.

temperature,location=kitchen value=22.5
temperature,location=living_room value=21.8
temperature,location=bedroom value=20.3

temperature,location=kitchen value=23.1
temperature,location=living_room value=22.0
temperature,location=bedroom value=20.7

temperature,location=kitchen value=22.8
temperature,location=living_room value=21.5
temperature,location=bedroom value=20.1

temperature,location=kitchen value=23.5
temperature,location=living_room value=21.9
temperature,location=bedroom value=19.8

temperature,location=kitchen value=24.2
temperature,location=living_room value=22.3
temperature,location=bedroom value=20.5

temperature,location=kitchen value=23.7
temperature,location=living_room value=22.8
temperature,location=bedroom value=21.0

temperature,location=kitchen value=22.9
temperature,location=living_room value=22.5
temperature,location=bedroom value=20.8

• Click “Write Data” to insert these values

• To verify your data was written, go to “Explore” in the left sidebar

• Create a simple query using the query builder:

• Select your “mybucket” bucket

• Filter by measurement: “temperature”

• Select fields: “value”

• Group by: “location”

• Click “Submit”

You should see a graph with your temperature readings for different locations.

Upload bulk data

Create a TEXT file with all the values and choose Upload File

Contents of the text file can be as-is.

humidity,location=kitchen value=45.2
humidity,location=living_room value=42.8
humidity,location=bedroom value=48.3

humidity,location=kitchen value=46.1
humidity,location=living_room value=43.5
humidity,location=bedroom value=49.1

humidity,location=kitchen value=45.8
humidity,location=living_room value=42.3
humidity,location=bedroom value=48.7

humidity,location=kitchen value=46.5
humidity,location=living_room value=44.2
humidity,location=bedroom value=49.8

humidity,location=kitchen value=47.2
humidity,location=living_room value=45.1
humidity,location=bedroom value=50.2

humidity,location=kitchen value=46.8
humidity,location=living_room value=44.8
humidity,location=bedroom value=49.6

humidity,location=kitchen value=45.9
humidity,location=living_room value=43.7
humidity,location=bedroom value=48.5

co2_ppm,location=kitchen value=612
co2_ppm,location=living_room value=578
co2_ppm,location=bedroom value=495

co2_ppm,location=kitchen value=635
co2_ppm,location=living_room value=582
co2_ppm,location=bedroom value=510

co2_ppm,location=kitchen value=621
co2_ppm,location=living_room value=565
co2_ppm,location=bedroom value=488

co2_ppm,location=kitchen value=642
co2_ppm,location=living_room value=595
co2_ppm,location=bedroom value=502

co2_ppm,location=kitchen value=658
co2_ppm,location=living_room value=612
co2_ppm,location=bedroom value=521

co2_ppm,location=kitchen value=631
co2_ppm,location=living_room value=586
co2_ppm,location=bedroom value=508

co2_ppm,location=kitchen value=618
co2_ppm,location=living_room value=572
co2_ppm,location=bedroom value=491

Create a CSV file

create iot.csv inside the data folder.

#datatype measurement,tag,tag,tag,double,long,string,boolean
measurement,location,sensor,type,reading,battery_level,status,active
temperature,kitchen,thermometer,smart,22.5,98,normal,true
temperature,living_room,thermometer,smart,21.8,97,normal,true
temperature,bedroom,thermometer,smart,20.3,96,normal,true
temperature,kitchen,thermometer,smart,23.1,95,normal,true
temperature,living_room,thermometer,smart,22.0,94,normal,true
temperature,bedroom,thermometer,smart,20.7,93,normal,true
humidity,kitchen,hygrometer,smart,45.2,97,normal,true
humidity,living_room,hygrometer,smart,42.8,96,normal,true
humidity,bedroom,hygrometer,smart,48.3,95,normal,true
co2_ppm,kitchen,air_quality,smart,612,99,normal,true
co2_ppm,living_room,air_quality,smart,578,98,normal,true
co2_ppm,bedroom,air_quality,smart,495,97,normal,true

Login to Client CLI

podman exec -it myinflux bash 

ls /var/lib/influxdb2

you will see the iot.csv (magic ;))

More Info

https://docs.influxdata.com/influxdb/cloud/write-data/developer-tools/csv/

USE CLI

influx write -b mybucket -f iot.csv --format csv

ISO 8601 time format.

T: Starting for Time
Z: UTC
-05:00: EST

influx delete --bucket mybucket --start 2025-02-26T00:00:00Z --stop 2025-03-06T23:59:59Z

REST API

Uses HTTP-based communication to write data to InfluxDB.

Login to Portal and Generate a API Key.

GitBash / Mac / Linux

create another bucket mybucket2

export TOKEN="<your token>"
export BUCKET="mybucket2"
export ORG="rowan"

Note: Bucket and Org properties were defined in Container.

Measurement: CPU

This uses system DateTime when inserting data.

curl -i -XPOST "http://localhost:8086/api/v2/write?org=$ORG&bucket=$BUCKET&precision=s" \
  --header "Authorization: Token $TOKEN" \
  --data-raw "cpu,host=server01,region=us-west cpu_usage=45.2,cpu_temp=62.1,cpu_idle=54.8"

precision=s to specify seconds precision.

Date Time along with data

curl -i -XPOST "http://localhost:8086/api/v2/write?org=$ORG&bucket=$BUCKET" \
  --header "Authorization: Token $TOKEN" \
  --data-raw "cpu,host=server01,region=us-west cpu_usage=44.2,cpu_temp=60,cpu_idle=52 $(date +%s)000000000"

Measurement: Memory

curl -i -XPOST "http://localhost:8086/api/v2/write?org=$ORG&bucket=$BUCKET&precision=s" \
  --header "Authorization: Token $TOKEN" \
  --data-raw "memory,host=server01,region=us-west used_percent=72.1,available_mb=3945.7,total_mb=16384"

Combining Measurements

curl -i -XPOST "http://localhost:8086/api/v2/write?org=$ORG&bucket=$BUCKET&precision=s" \
  --header "Authorization: Token $TOKEN" \
  --data-raw "cpu,host=server02,region=us-central cpu_usage=22.2,cpu_idle=75.8
memory,host=server02,region=us-central used_percent=27.1,available_mb=14945.7,total_mb=24384"

curl -i -XPOST "http://localhost:8086/api/v2/write?org=$ORG&bucket=$BUCKET&precision=s" \
  --header "Authorization: Token $TOKEN" \
  --data-raw "cpu,host=server02,region=us-central cpu_usage=23.2,cpu_idle=76.8
memory,host=server02,region=us-central used_percent=28.1,available_mb=15945.7,total_mb=24384"

InfluxDB CLI

Install CLI

https://docs.influxdata.com/influxdb/cloud/tools/influx-cli/

FLUX Query Language

FLUX Query Language

In InfluxDB 3.0 they are introducing SQL back.

influx query 'from(bucket: "mybucket") 
  |> range(start: -50m)
  |> filter(fn: (r) => r._measurement == "temperature") '

Python Examples

git clone https://github.com/gchandra10/python_influxdb_examples.git

Telegraf

Telegraf, a server-based agent, collects and sends metrics and events from databases, systems, and IoT sensors. Written in Go, Telegraf compiles into a single binary with no external dependencies–requiring very minimal memory.

Install Telegraf Cli

Telegraf Plugins

Telegraf Plugins Github

export MQTT_HOST_NAME=""
export MQTT_PORT=
export MQTT_USER_NAME=""
export MQTT_PASSWORD=""
export INFLUX_TOKEN=""
export INFLUX_DB_ORG=""
export INFLUX_DB_BUCKET=""

telegraf.conf

# Global agent configuration
[agent]
  interval = "5s"
  round_interval = true
  metric_batch_size = 1000
  metric_buffer_limit = 10000
  collection_jitter = "0s"
  flush_interval = "10s"
  flush_jitter = "0s"
  precision = ""
  debug = false
  quiet = false
  hostname = ""
  omit_hostname = false

# MQTT Consumer Input Plugin
[[inputs.mqtt_consumer]]
  servers = ["ssl://${MQTT_HOST_NAME}:${MQTT_PORT}"]
  username = "${MQTT_USER_NAME}"
  password = "${MQTT_PASSWORD}"

  # Set custom measurement name
  name_override = "my_python_sensor_temp"
  
  # Topics to subscribe to
  topics = [
    "sensors/temp",
  ]
  
  # Connection timeout
  connection_timeout = "30s"
  
  # TLS/SSL configuration
  insecure_skip_verify = true
  
  # QoS level
  qos = 1
  
  # Client ID
  client_id = "telegraf_mqtt_consumer"
  
  # Data format
  data_format = "value"
  data_type = "float"

# InfluxDB v2 Output Plugin
[[outputs.influxdb_v2]]
  # URL for your local InfluxDB
  urls = ["http://localhost:8086"]
  
  # InfluxDB token
  token = "${INFLUX_TOKEN}"
  
  # Organization name
  organization = "${INFLUX_DB_ORG}"
  
  # Destination bucket
  bucket = "${INFLUX_DB_BUCKET}"

  # Add tags - match the location from your MQTT script
  [outputs.influxdb_v2.tags]
    location = "room1"

Run Telegraph

telegraf --config telegraf.conf --debug

Storing output in InfluxDB and S3

export MQTT_HOST_NAME=""
export MQTT_PORT=
export MQTT_USER_NAME=""
export MQTT_PASSWORD=""
export INFLUX_TOKEN=""
export INFLUX_DB_ORG=""
export INFLUX_DB_BUCKET=""
export S3_BUCKET=""
export AWS_REGION=""
export AWS_ACCESS_KEY_ID=""
export AWS_SECRET_ACCESS_KEY=""

telegraf.conf

# Global agent configuration
[agent]
  interval = "5s"
  round_interval = true
  metric_batch_size = 1000
  metric_buffer_limit = 10000
  collection_jitter = "0s"
  flush_interval = "10s"
  flush_jitter = "0s"
  precision = ""
  debug = false
  quiet = false
  hostname = ""
  omit_hostname = false

# MQTT Consumer Input Plugin
[[inputs.mqtt_consumer]]
  servers = ["ssl://${MQTT_HOST_NAME}:${MQTT_PORT}"]
  username = "${MQTT_USER_NAME}"
  password = "${MQTT_PASSWORD}"

  # Set custom measurement name
  name_override = "my_python_sensor_temp"
  
  # Topics to subscribe to
  topics = [
    "sensors/temp",
  ]
  
  # Connection timeout
  connection_timeout = "30s"
  
  # TLS/SSL configuration
  insecure_skip_verify = true
  
  # QoS level
  qos = 1
  
  # Client ID
  client_id = "telegraf_mqtt_consumer"
  
  # Data format
  data_format = "value"
  data_type = "float"

# InfluxDB v2 Output Plugin
[[outputs.influxdb_v2]]
  # URL for your local InfluxDB
  urls = ["http://localhost:8086"]
  
  # InfluxDB token
  token = "${INFLUX_TOKEN}"
  
  # Organization name
  organization = "${INFLUX_DB_ORG}"
  
  # Destination bucket
  bucket = "${INFLUX_DB_BUCKET}"

  # Add tags - match the location from your MQTT script
  [outputs.influxdb_v2.tags]
    location = "room1"

# S3 Output Plugin with CSV format
[[outputs.remotefile]]
  remote = 's3,provider=AWS,access_key_id=${AWS_ACCESS_KEY_ID},secret_access_key=${AWS_SECRET_ACCESS_KEY},region=${AWS_REGION}:${S3_BUCKET}'
 
  # File naming
  files = ['{{.Name}}-{{.Time.Format "2025-03-26"}}']

InfluxDB University

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