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Installing ThingsBoard using Docker (Linux or Mac OS)

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ThingsBoard Cloud

We recommend to use ThingsBoard Cloud - fully managed, scalable and fault-tolerant platform for your IoT applications
ThingsBoard Cloud is for everyone who would like to use ThingsBoard but don’t want to host their own instance of the platform.

This guide will help you to install and start ThingsBoard using Docker on Linux or Mac OS.

Prerequisites

Install Docker:

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Don’t forget to add your linux user to the docker group. See Manage Docker as a non-root user.

Running

In this instruction thingsboard/tb-postgres image will be used. It contains a single instance of ThingsBoard with PostgreSQL database.

Running this image requires a server with at least 4GB of RAM (8GB is recommended) and minimum load (few messages per second).

Choose ThingsBoard queue service

ThingsBoard is able to use various messaging systems/brokers for storing the messages and communication between ThingsBoard services. How to choose the right queue implementation?

  • In Memory queue implementation is built-in and default. It is useful for development(PoC) environments and is not suitable for production deployments or any sort of cluster deployments.

  • Kafka is recommended for production deployments. This queue is used on the most of ThingsBoard production environments now. It is useful for both on-prem and private cloud deployments. It is also useful if you like to stay independent from your cloud provider. However, some providers also have managed services for Kafka. See AWS MSK for example.

  • RabbitMQ is recommended if you don’t have much load and you already have experience with this messaging system.

  • AWS SQS is a fully managed message queuing service from AWS. Useful if you plan to deploy ThingsBoard on AWS.

  • Google Pub/Sub is a fully managed message queuing service from Google. Useful if you plan to deploy ThingsBoard on Google Cloud.

  • Azure Service Bus is a fully managed message queuing service from Azure. Useful if you plan to deploy ThingsBoard on Azure.

  • Confluent Cloud is a fully managed streaming platform based on Kafka. Useful for a cloud agnostic deployments.

See corresponding architecture page and rule engine page for more details.

In Memory (built-in and default)
Kafka (recommended for on-prem, production installations)
AWS SQS (managed service from AWS)
Google Pub/Sub (managed service from Google)
Azure Service Bus (managed service from Azure)
RabbitMQ (for small on-prem installations)
Confluent Cloud (Event Streaming Platform based on Kafka)

ThingsBoard includes In Memory Queue service and use it by default without extra settings.

Create docker compose file for ThingsBoard queue service:

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nano docker-compose.yml

Add the following lines to the yml file:

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version: '3.0'
services:
  mytb:
    restart: always
    image: "thingsboard/tb-postgres"
    ports:
      - "8080:9090"
      - "1883:1883"
      - "7070:7070"
      - "5683-5688:5683-5688/udp"
    environment:
      TB_QUEUE_TYPE: in-memory
    volumes:
      - ~/.mytb-data:/data
      - ~/.mytb-logs:/var/log/thingsboard

Apache Kafka is an open-source stream-processing software platform.

Create docker compose file for ThingsBoard queue service:

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nano docker-compose.yml

Add the following lines to the yml file.

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version: '3.2'
services:
  kafka:
    restart: always
    image: bitnami/kafka:3.8.1
    ports:
      - 9092:9092 #to localhost:9092 from host machine
      - 9093 #for Kraft
      - 9094 #to kafka:9094 from within Docker network
    environment:
      ALLOW_PLAINTEXT_LISTENER: "yes"
      KAFKA_CFG_LISTENERS: "OUTSIDE://:9092,CONTROLLER://:9093,INSIDE://:9094"
      KAFKA_CFG_ADVERTISED_LISTENERS: "OUTSIDE://localhost:9092,INSIDE://kafka:9094"
      KAFKA_CFG_LISTENER_SECURITY_PROTOCOL_MAP: "INSIDE:PLAINTEXT,OUTSIDE:PLAINTEXT,CONTROLLER:PLAINTEXT"
      KAFKA_CFG_INTER_BROKER_LISTENER_NAME: "INSIDE"
      KAFKA_CFG_AUTO_CREATE_TOPICS_ENABLE: "false"
      KAFKA_OFFSETS_TOPIC_REPLICATION_FACTOR: "1"
      KAFKA_TRANSACTION_STATE_LOG_MIN_ISR: "1"
      KAFKA_TRANSACTION_STATE_LOG_REPLICATION_FACTOR: "1"
      KAFKA_CFG_PROCESS_ROLES: "controller,broker" #KRaft
      KAFKA_CFG_NODE_ID: "0" #KRaft
      KAFKA_CFG_CONTROLLER_LISTENER_NAMES: "CONTROLLER" #KRaft
      KAFKA_CFG_CONTROLLER_QUORUM_VOTERS: "0@kafka:9093" #KRaft
    volumes:
      - kafka-data:/bitnami
  mytb:
    restart: always
    image: "thingsboard/tb-postgres"
    depends_on:
      - kafka
    ports:
      - "8080:9090"
      - "1883:1883"
      - "7070:7070"
      - "5683-5688:5683-5688/udp"
    environment:
      TB_QUEUE_TYPE: kafka
      TB_KAFKA_SERVERS: kafka:9094
    volumes:
      - ~/.mytb-data:/data
      - ~/.mytb-logs:/var/log/thingsboard
volumes:
  kafka-data:
    driver: local

AWS SQS Configuration

To access AWS SQS service, you first need to create an AWS account.

To work with AWS SQS service you will need to create your next credentials using this instruction:

  • Access key ID
  • Secret access key

Create docker compose file for ThingsBoard queue service:

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nano docker-compose.yml

Add the following lines to the yml file. Don’t forget to replace “YOUR_KEY”, “YOUR_SECRET” with your real AWS SQS IAM user credentials and “YOUR_REGION” with your real AWS SQS account region:

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version: '3.0'
services:
  mytb:
    restart: always
    image: "thingsboard/tb-postgres"
    ports:
      - "8080:9090"
      - "1883:1883"
      - "7070:7070"
      - "5683-5688:5683-5688/udp"
    environment:
      TB_QUEUE_TYPE: aws-sqs
      TB_QUEUE_AWS_SQS_ACCESS_KEY_ID: YOUR_KEY
      TB_QUEUE_AWS_SQS_SECRET_ACCESS_KEY: YOUR_SECRET
      TB_QUEUE_AWS_SQS_REGION: YOUR_REGION

      # These params affect the number of requests per second from each partitions per each queue.
      # Number of requests to particular Message Queue is calculated based on the formula:
      # ((Number of Rule Engine and Core Queues) * (Number of partitions per Queue) + (Number of transport queues)
      #  + (Number of microservices) + (Number of JS executors)) * 1000 / POLL_INTERVAL_MS
      # For example, number of requests based on default parameters is:
      # Rule Engine queues:
      # Main 10 partitions + HighPriority 10 partitions + SequentialByOriginator 10 partitions = 30
      # Core queue 10 partitions
      # Transport request Queue + response Queue = 2
      # Rule Engine Transport notifications Queue + Core Transport notifications Queue = 2
      # Total = 44
      # Number of requests per second = 44 * 1000 / 25 = 1760 requests
      # 
      # Based on the use case, you can compromise latency and decrease number of partitions/requests to the queue, if the message load is low.
      # By UI set the parameters - interval (1000) and partitions (1) for Rule Engine queues.
      # Sample parameters to fit into 10 requests per second on a "monolith" deployment: 
      TB_QUEUE_CORE_POLL_INTERVAL_MS: 1000
      TB_QUEUE_CORE_PARTITIONS: 2
      TB_QUEUE_RULE_ENGINE_POLL_INTERVAL_MS: 1000
      TB_QUEUE_TRANSPORT_REQUEST_POLL_INTERVAL_MS: 1000
      TB_QUEUE_TRANSPORT_RESPONSE_POLL_INTERVAL_MS: 1000
      TB_QUEUE_TRANSPORT_NOTIFICATIONS_POLL_INTERVAL_MS: 1000
      TB_QUEUE_VC_INTERVAL_MS: 1000
      TB_QUEUE_VC_PARTITIONS: 1
    volumes:
      - ~/.mytb-data:/data
      - ~/.mytb-logs:/var/log/thingsboard

You can update default Rule Engine queues configuration using UI. More about ThingsBoard Rule Engine queues see in documentation.

Google Pub/Sub Configuration

To access Pub/Sub service, you first need to create an Google cloud account.

To work with Pub/Sub service you will need to create a project using this instruction.

Create service account credentials with the role “Editor” or “Admin” using this instruction, and save json file with your service account credentials step 9 here.

Create docker compose file for ThingsBoard queue service:

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nano docker-compose.yml

Add the following lines to the yml file. Don’t forget to replace “YOUR_PROJECT_ID”, “YOUR_SERVICE_ACCOUNT” with your real Pub/Sub project id, and service account (it is whole data from json file):

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version: '3.0'
services:
  mytb:
    restart: always
    image: "thingsboard/tb-postgres"
    ports:
      - "8080:9090"
      - "1883:1883"
      - "7070:7070"
      - "5683-5688:5683-5688/udp"
    environment:
      TB_QUEUE_TYPE: pubsub
      TB_QUEUE_PUBSUB_PROJECT_ID: YOUR_PROJECT_ID
      TB_QUEUE_PUBSUB_SERVICE_ACCOUNT: YOUR_SERVICE_ACCOUNT

      # These params affect the number of requests per second from each partitions per each queue.
      # Number of requests to particular Message Queue is calculated based on the formula:
      # ((Number of Rule Engine and Core Queues) * (Number of partitions per Queue) + (Number of transport queues)
      #  + (Number of microservices) + (Number of JS executors)) * 1000 / POLL_INTERVAL_MS
      # For example, number of requests based on default parameters is:
      # Rule Engine queues:
      # Main 10 partitions + HighPriority 10 partitions + SequentialByOriginator 10 partitions = 30
      # Core queue 10 partitions
      # Transport request Queue + response Queue = 2
      # Rule Engine Transport notifications Queue + Core Transport notifications Queue = 2
      # Total = 44
      # Number of requests per second = 44 * 1000 / 25 = 1760 requests
      # 
      # Based on the use case, you can compromise latency and decrease number of partitions/requests to the queue, if the message load is low.
      # By UI set the parameters - interval (1000) and partitions (1) for Rule Engine queues.
      # Sample parameters to fit into 10 requests per second on a "monolith" deployment: 
      TB_QUEUE_CORE_POLL_INTERVAL_MS: 1000
      TB_QUEUE_CORE_PARTITIONS: 2
      TB_QUEUE_RULE_ENGINE_POLL_INTERVAL_MS: 1000
      TB_QUEUE_TRANSPORT_REQUEST_POLL_INTERVAL_MS: 1000
      TB_QUEUE_TRANSPORT_RESPONSE_POLL_INTERVAL_MS: 1000
      TB_QUEUE_TRANSPORT_NOTIFICATIONS_POLL_INTERVAL_MS: 1000
      TB_QUEUE_VC_INTERVAL_MS: 1000
      TB_QUEUE_VC_PARTITIONS: 1
    volumes:
      - ~/.mytb-data:/data
      - ~/.mytb-logs:/var/log/thingsboard

You can update default Rule Engine queues configuration using UI. More about ThingsBoard Rule Engine queues see in documentation.

Azure Service Bus Configuration

To access Azure Service Bus, you first need to create an Azure account.

To work with Service Bus service you will need to create a Service Bus Namespace using this instruction.

Create Shared Access Signature using this instruction.

Create docker compose file for ThingsBoard queue service:

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nano docker-compose.yml

Add the following lines to the yml file. Don’t forget to replace “YOUR_NAMESPACE_NAME” with your real Service Bus namespace name, and “YOUR_SAS_KEY_NAME”, “YOUR_SAS_KEY” with your real Service Bus credentials. Note: “YOUR_SAS_KEY_NAME” it is “SAS Policy”, “YOUR_SAS_KEY” it is “SAS Policy Primary Key”:

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version: '3.0'
services:
  mytb:
    restart: always
    image: "thingsboard/tb-postgres"
    ports:
      - "8080:9090"
      - "1883:1883"
      - "7070:7070"
      - "5683-5688:5683-5688/udp"
    environment:
      TB_QUEUE_TYPE: service-bus
      TB_QUEUE_SERVICE_BUS_NAMESPACE_NAME: YOUR_NAMESPACE_NAME
      TB_QUEUE_SERVICE_BUS_SAS_KEY_NAME: YOUR_SAS_KEY_NAME
      TB_QUEUE_SERVICE_BUS_SAS_KEY: YOUR_SAS_KEY

      # These params affect the number of requests per second from each partitions per each queue.
      # Number of requests to particular Message Queue is calculated based on the formula:
      # ((Number of Rule Engine and Core Queues) * (Number of partitions per Queue) + (Number of transport queues)
      #  + (Number of microservices) + (Number of JS executors)) * 1000 / POLL_INTERVAL_MS
      # For example, number of requests based on default parameters is:
      # Rule Engine queues:
      # Main 10 partitions + HighPriority 10 partitions + SequentialByOriginator 10 partitions = 30
      # Core queue 10 partitions
      # Transport request Queue + response Queue = 2
      # Rule Engine Transport notifications Queue + Core Transport notifications Queue = 2
      # Total = 44
      # Number of requests per second = 44 * 1000 / 25 = 1760 requests
      # 
      # Based on the use case, you can compromise latency and decrease number of partitions/requests to the queue, if the message load is low.
      # By UI set the parameters - interval (1000) and partitions (1) for Rule Engine queues.
      # Sample parameters to fit into 10 requests per second on a "monolith" deployment: 
      TB_QUEUE_CORE_POLL_INTERVAL_MS: 1000
      TB_QUEUE_CORE_PARTITIONS: 2
      TB_QUEUE_RULE_ENGINE_POLL_INTERVAL_MS: 1000
      TB_QUEUE_TRANSPORT_REQUEST_POLL_INTERVAL_MS: 1000
      TB_QUEUE_TRANSPORT_RESPONSE_POLL_INTERVAL_MS: 1000
      TB_QUEUE_TRANSPORT_NOTIFICATIONS_POLL_INTERVAL_MS: 1000
      TB_QUEUE_VC_INTERVAL_MS: 1000
      TB_QUEUE_VC_PARTITIONS: 1
    volumes:
      - ~/.mytb-data:/data
      - ~/.mytb-logs:/var/log/thingsboard

You can update default Rule Engine queues configuration using UI. More about ThingsBoard Rule Engine queues see in documentation.

For installing RabbitMQ use this instruction.

Create docker compose file for ThingsBoard queue service:

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nano docker-compose.yml

Add the following lines to the yml file. Don’t forget to replace “YOUR_USERNAME” and “YOUR_PASSWORD” with your real user credentials, “localhost” and “5672” with your real RabbitMQ host and port:

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version: '3.0'
services:
  mytb:
    restart: always
    image: "thingsboard/tb-postgres"
    ports:
      - "8080:9090"
      - "1883:1883"
      - "7070:7070"
      - "5683-5688:5683-5688/udp"
    environment:
      TB_QUEUE_TYPE: rabbitmq
      TB_QUEUE_RABBIT_MQ_USERNAME: YOUR_USERNAME
      TB_QUEUE_RABBIT_MQ_PASSWORD: YOUR_PASSWORD
      TB_QUEUE_RABBIT_MQ_HOST: localhost
      TB_QUEUE_RABBIT_MQ_PORT: 5672
    volumes:
      - ~/.mytb-data:/data
      - ~/.mytb-logs:/var/log/thingsboard

Confluent Cloud Configuration

To access Confluent Cloud you should first create an account, then create a Kafka cluster and get your API Key.

Create docker compose file for ThingsBoard queue service:

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nano docker-compose.yml

Add the following line to the yml file. Don’t forget to replace “CLUSTER_API_KEY”, “CLUSTER_API_SECRET” and “localhost:9092” with your real Confluent Cloud bootstrap servers:

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version: '3.0'
services:
  mytb:
    restart: always
    image: "thingsboard/tb-postgres"
    ports:
      - "8080:9090"
      - "1883:1883"
      - "7070:7070"
      - "5683-5688:5683-5688/udp"
    environment:
      TB_QUEUE_TYPE: kafka
      TB_KAFKA_SERVERS: localhost:9092
      TB_QUEUE_KAFKA_REPLICATION_FACTOR: 3
      TB_QUEUE_KAFKA_USE_CONFLUENT_CLOUD: true
      TB_QUEUE_KAFKA_CONFLUENT_SASL_JAAS_CONFIG: 'org.apache.kafka.common.security.plain.PlainLoginModule required username="CLUSTER_API_KEY" password="CLUSTER_API_SECRET";'

      # These params affect the number of requests per second from each partitions per each queue.
      # Number of requests to particular Message Queue is calculated based on the formula:
      # ((Number of Rule Engine and Core Queues) * (Number of partitions per Queue) + (Number of transport queues)
      #  + (Number of microservices) + (Number of JS executors)) * 1000 / POLL_INTERVAL_MS
      # For example, number of requests based on default parameters is:
      # Rule Engine queues:
      # Main 10 partitions + HighPriority 10 partitions + SequentialByOriginator 10 partitions = 30
      # Core queue 10 partitions
      # Transport request Queue + response Queue = 2
      # Rule Engine Transport notifications Queue + Core Transport notifications Queue = 2
      # Total = 44
      # Number of requests per second = 44 * 1000 / 25 = 1760 requests
      # 
      # Based on the use case, you can compromise latency and decrease number of partitions/requests to the queue, if the message load is low.
      # By UI set the parameters - interval (1000) and partitions (1) for Rule Engine queues.
      # Sample parameters to fit into 10 requests per second on a "monolith" deployment: 
      TB_QUEUE_CORE_POLL_INTERVAL_MS: 1000
      TB_QUEUE_CORE_PARTITIONS: 2
      TB_QUEUE_RULE_ENGINE_POLL_INTERVAL_MS: 1000
      TB_QUEUE_TRANSPORT_REQUEST_POLL_INTERVAL_MS: 1000
      TB_QUEUE_TRANSPORT_RESPONSE_POLL_INTERVAL_MS: 1000
      TB_QUEUE_TRANSPORT_NOTIFICATIONS_POLL_INTERVAL_MS: 1000
      TB_QUEUE_VC_INTERVAL_MS: 1000
      TB_QUEUE_VC_PARTITIONS: 1
    volumes:
      - ~/.mytb-data:/data
      - ~/.mytb-logs:/var/log/thingsboard

You can update default Rule Engine queues configuration using UI. More about ThingsBoard Rule Engine queues see in documentation.

Where:

  • 8080:9090 - connect local port 8080 to exposed internal HTTP port 9090
  • 1883:1883 - connect local port 1883 to exposed internal MQTT port 1883
  • 7070:7070 - connect local port 7070 to exposed internal Edge RPC port 7070
  • 5683-5688:5683-5688/udp - connect local UDP ports 5683-5688 to exposed internal COAP and LwM2M ports
  • ~/.mytb-data:/data - mounts the host’s dir ~/.mytb-data to ThingsBoard DataBase data directory
  • ~/.mytb-logs:/var/log/thingsboard - mounts the host’s dir ~/.mytb-logs to ThingsBoard logs directory
  • mytb - friendly local name of this machine
  • restart: always - automatically start ThingsBoard in case of system reboot and restart in case of failure.
  • image: thingsboard/tb-postgres - docker image, can be also thingsboard/tb-cassandra or thingsboard/tb

Before starting your Docker containers, execute the following commands to create directories for data storage and logs. These commands will also change the ownership of the newly created directories to the Docker container user.

The chown command is used to change the owner of the directories, and it requires sudo permissions. You may be prompted to enter a password to grant sudo access:

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mkdir -p ~/.mytb-data && sudo chown -R 799:799 ~/.mytb-data
mkdir -p ~/.mytb-logs && sudo chown -R 799:799 ~/.mytb-logs

NOTE: Replace directory ~/.mytb-data and ~/.mytb-logs with directories you’re planning to use in docker-compose.yml.

Set the terminal in the directory which contains the docker-compose.yml file and execute the following commands to up this docker compose directly:

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docker compose up -d && docker compose logs -f mytb

After executing this command you can open http://{your-host-ip}:8080 in your browser (for ex. http://localhost:8080). You should see ThingsBoard login page. Use the following default credentials:

  • System Administrator: sysadmin@thingsboard.org / sysadmin
  • Tenant Administrator: tenant@thingsboard.org / tenant
  • Customer User: customer@thingsboard.org / customer

You can always change passwords for each account in account profile page.

Detaching, stop and start commands

You can detach from session terminal using Ctrl-p Ctrl-q key sequence - the container will keep running in the background.

In case of any issues you can examine service logs for errors. For example to see ThingsBoard container logs execute the following command:

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docker compose logs -f mytb

To stop the container:

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docker compose stop mytb

To start the container:

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docker compose start mytb
Doc info icon

Docker Compose as docker-compose (with a hyphen) is deprecated. It is recommended to use Docker Compose V2 instead.
If you still rely on docker compose as standalone here is the list of the above commands:
docker-compose logs -f mytb
docker-compose stop mytb
docker-compose start mytb

Upgrading

In order to update to the latest image, execute the following commands:

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docker pull thingsboard/tb-postgres
docker compose stop
docker run -it -v ~/.mytb-data:/data --rm thingsboard/tb-postgres upgrade-tb.sh
docker compose rm mytb
docker compose up

NOTE: if you use different database change image name in all commands from thingsboard/tb-postgres to thingsboard/tb-cassandra or thingsboard/tb correspondingly.

NOTE: replace host’s directory ~/.mytb-data with directory used during container creation.

NOTE: if you have used one database and want to try another one, then remove the current docker container using docker-compose rm command and use different directory for ~/.mytb-data in docker-compose.yml.

Doc info icon

If you still rely on Docker Compose as docker-compose (with a hyphen) here is the list of the above commands:
docker pull thingsboard/tb-postgres
docker-compose stop
docker run -it -v ~/.mytb-data:/data –rm thingsboard/tb-postgres upgrade-tb.sh
docker-compose rm mytb
docker-compose up

Troubleshooting

DNS issues

NOTE If you observe errors related to DNS issues, for example

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127.0.1.1:53: cannot unmarshal DNS message

You may configure your system to use Google public DNS servers. See corresponding Linux and Mac OS instructions.

Next steps