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Installing ThingsBoard PE using Docker (Windows)

This guide will help you to install and start ThingsBoard Professional Edition (PE) using Docker on Windows. This guide covers standalone ThingsBoard PE installation. If you are looking for a cluster installation instruction, please visit cluster setup page.

Prerequisites

Step 1. Pull ThingsBoard PE Image

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docker pull thingsboard/tb-pe:3.4.3PE

Step 2. Obtain the license key

We assume you have already chosen your subscription plan or decided to purchase a perpetual license. If not, please navigate to pricing page to select the best license option for your case and get your license. See How-to get pay-as-you-go subscription or How-to get perpetual license for more details.

Note: We will reference the license key you have obtained during this step as PUT_YOUR_LICENSE_SECRET_HERE later in this guide.

Step 3. 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.

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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docker-compose.yml

Add the following line to the yml file. Don’t forget to replace “PUT_YOUR_LICENSE_SECRET_HERE” with your license secret obtained on the first step:

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version: '3.0'
services:
  mytbpe:
    restart: always
    image: "thingsboard/tb-pe:3.4.3PE"
    ports:
      - "8080:8080"
      - "1883:1883"
      - "7070:7070"
      - "5683-5688:5683-5688/udp"
    environment:
      TB_QUEUE_TYPE: in-memory
      SPRING_DATASOURCE_URL: jdbc:postgresql://postgres:5432/thingsboard
      TB_LICENSE_SECRET: PUT_YOUR_LICENSE_SECRET_HERE
      TB_LICENSE_INSTANCE_DATA_FILE: /data/license.data
    volumes:
      - mytbpe-data:/data
      - mytbpe-logs:/var/log/thingsboard
  postgres:
    restart: always
    image: "postgres:12"
    ports:
    - "5432"
    environment:
      POSTGRES_DB: thingsboard
      POSTGRES_PASSWORD: postgres
    volumes:
      - mytbpe-data-db:/var/lib/postgresql/data
volumes:
  mytbpe-data:
    external: true
  mytbpe-logs:
    external: true
  mytbpe-data-db:
    external: true

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

Create docker compose file for ThingsBoard queue service:

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docker-compose.yml

Add the following line to the yml file. Don’t forget to replace “PUT_YOUR_LICENSE_SECRET_HERE” with your license secret obtained on the first step:

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version: '3.0'
services:
  zookeeper:
    restart: always
    image: "zookeeper:3.5"
    ports:
      - "2181:2181"
    environment:
      ZOO_MY_ID: 1
      ZOO_SERVERS: server.1=zookeeper:2888:3888;zookeeper:2181
  kafka:
    restart: always
    image: wurstmeister/kafka
    depends_on:
      - zookeeper
    ports:
      - "9092:9092"
    environment:
      KAFKA_ZOOKEEPER_CONNECT: zookeeper:2181
      KAFKA_LISTENERS: INSIDE://:9093,OUTSIDE://:9092
      KAFKA_ADVERTISED_LISTENERS: INSIDE://:9093,OUTSIDE://kafka:9092
      KAFKA_LISTENER_SECURITY_PROTOCOL_MAP: INSIDE:PLAINTEXT,OUTSIDE:PLAINTEXT
      KAFKA_INTER_BROKER_LISTENER_NAME: INSIDE
    volumes:
      - /var/run/docker.sock:/var/run/docker.sock
  mytbpe:
    restart: always
    image: "thingsboard/tb-pe:3.4.3PE"
    depends_on:
      - kafka
    ports:
      - "8080:8080"
      - "1883:1883"
      - "7070:7070"
      - "5683-5688:5683-5688/udp"
    environment:
      TB_QUEUE_TYPE: kafka
      SPRING_DATASOURCE_URL: jdbc:postgresql://postgres:5432/thingsboard
      TB_KAFKA_SERVERS: kafka:9092
      TB_LICENSE_SECRET: PUT_YOUR_LICENSE_SECRET_HERE
      TB_LICENSE_INSTANCE_DATA_FILE: /data/license.data
    volumes:
      - mytbpe-data:/data
      - mytbpe-logs:/var/log/thingsboard
  postgres:
    restart: always
    image: "postgres:12"
    ports:
    - "5432"
    environment:
      POSTGRES_DB: thingsboard
      POSTGRES_PASSWORD: postgres
    volumes:
      - mytbpe-data-db:/var/lib/postgresql/data
volumes:
  mytbpe-data:
    external: true
  mytbpe-logs:
    external: true
  mytbpe-data-db:
    external: true

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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docker-compose.yml

Add the following line 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, and “PUT_YOUR_LICENSE_SECRET_HERE” with your license secret obtained on the first step:

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version: '3.0'
services:
  mytbpe:
    restart: always
    image: "thingsboard/tb-pe:3.4.3PE"
    ports:
      - "8080:8080"
      - "1883:1883"
      - "7070:7070"
      - "5683-5688:5683-5688/udp"
    environment:
      TB_QUEUE_TYPE: aws-sqs
      SPRING_DATASOURCE_URL: jdbc:postgresql://postgres:5432/thingsboard
      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
      TB_LICENSE_SECRET: PUT_YOUR_LICENSE_SECRET_HERE
      TB_LICENSE_INSTANCE_DATA_FILE: /data/license.data

      # 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:
      - mytbpe-data:/data
      - mytbpe-logs:/var/log/thingsboard
  postgres:
    restart: always
    image: "postgres:12"
    ports:
    - "5432"
    environment:
      POSTGRES_DB: thingsboard
      POSTGRES_PASSWORD: postgres
    volumes:
      - mytbpe-data-db:/var/lib/postgresql/data
volumes:
  mytbpe-data:
    external: true
  mytbpe-logs:
    external: true
  mytbpe-data-db:
    external: true

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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docker-compose.yml

Add the following line 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), and “PUT_YOUR_LICENSE_SECRET_HERE” with your **license secret obtained on the first step:

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version: '3.0'
services:
  mytbpe:
    restart: always
    image: "thingsboard/tb-pe:3.4.3PE"
    ports:
      - "8080:8080"
      - "1883:1883"
      - "7070:7070"
      - "5683-5688:5683-5688/udp"
    environment:
      TB_QUEUE_TYPE: pubsub
      SPRING_DATASOURCE_URL: jdbc:postgresql://postgres:5432/thingsboard
      TB_QUEUE_PUBSUB_PROJECT_ID: YOUR_PROJECT_ID
      TB_QUEUE_PUBSUB_SERVICE_ACCOUNT: YOUR_SERVICE_ACCOUNT
      TB_LICENSE_SECRET: PUT_YOUR_LICENSE_SECRET_HERE
      TB_LICENSE_INSTANCE_DATA_FILE: /data/license.data

      # 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:
      - mytbpe-data:/data
      - mytbpe-logs:/var/log/thingsboard
  postgres:
    restart: always
    image: "postgres:12"
    ports:
    - "5432"
    environment:
      POSTGRES_DB: thingsboard
      POSTGRES_PASSWORD: postgres
    volumes:
      - mytbpe-data-db:/var/lib/postgresql/data
volumes:
  mytbpe-data:
    external: true
  mytbpe-logs:
    external: true
  mytbpe-data-db:
    external: true

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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docker-compose.yml

Add the following line 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”, and “PUT_YOUR_LICENSE_SECRET_HERE” with your license secret obtained on the first step:

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version: '3.0'
services:
  mytbpe:
    restart: always
    image: "thingsboard/tb-pe:3.4.3PE"
    ports:
      - "8080:8080"
      - "1883:1883"
      - "7070:7070"
      - "5683-5688:5683-5688/udp"
    environment:
      TB_QUEUE_TYPE: service-bus
      SPRING_DATASOURCE_URL: jdbc:postgresql://postgres:5432/thingsboard
      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
      TB_LICENSE_SECRET: PUT_YOUR_LICENSE_SECRET_HERE
      TB_LICENSE_INSTANCE_DATA_FILE: /data/license.data

      # 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:
      - mytbpe-data:/data
      - mytbpe-logs:/var/log/thingsboard
  postgres:
    restart: always
    image: "postgres:12"
    ports:
    - "5432"
    environment:
      POSTGRES_DB: thingsboard
      POSTGRES_PASSWORD: postgres
    volumes:
      - mytbpe-data-db:/var/lib/postgresql/data
volumes:
  mytbpe-data:
    external: true
  mytbpe-logs:
    external: true
  mytbpe-data-db:
    external: true

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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docker-compose.yml

Add the following line 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, and “PUT_YOUR_LICENSE_SECRET_HERE” with your license secret obtained on the first step:

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version: '3.0'
services:
  mytbpe:
    restart: always
    image: "thingsboard/tb-pe:3.4.3PE"
    ports:
      - "8080:8080"
      - "1883:1883"
      - "7070:7070"
      - "5683-5688:5683-5688/udp"
    environment:
      TB_QUEUE_TYPE: rabbitmq
      SPRING_DATASOURCE_URL: jdbc:postgresql://postgres:5432/thingsboard
      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
      TB_LICENSE_SECRET: PUT_YOUR_LICENSE_SECRET_HERE
      TB_LICENSE_INSTANCE_DATA_FILE: /data/license.data
    volumes:
      - mytbpe-data:/data
      - mytbpe-logs:/var/log/thingsboard
  postgres:
    restart: always
    image: "postgres:12"
    ports:
    - "5432"
    environment:
      POSTGRES_DB: thingsboard
      POSTGRES_PASSWORD: postgres
    volumes:
      - mytbpe-data-db:/var/lib/postgresql/data
volumes:
  mytbpe-data:
    external: true
  mytbpe-logs:
    external: true
  mytbpe-data-db:
    external: true

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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docker-compose.yml

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

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version: '3.0'
services:
  mytbpe:
    restart: always
    image: "thingsboard/tb-pe:3.4.3PE"
    ports:
      - "8080:8080"
      - "1883:1883"
      - "7070:7070"
      - "5683-5688:5683-5688/udp"
    environment:
      TB_QUEUE_TYPE=kafka
      SPRING_DATASOURCE_URL: jdbc:postgresql://postgres:5432/thingsboard
    
      TB_KAFKA_SERVERS:confluent.cloud:9092
      TB_QUEUE_KAFKA_REPLICATION_FACTOR:3
    
      TB_QUEUE_KAFKA_USE_CONFLUENT_CLOUD:true
      TB_QUEUE_KAFKA_CONFLUENT_SSL_ALGORITHM:https
      TB_QUEUE_KAFKA_CONFLUENT_SASL_MECHANISM:PLAIN
      TB_QUEUE_KAFKA_CONFLUENT_SASL_JAAS_CONFIG:org.apache.kafka.common.security.plain.PlainLoginModule required username="CLUSTER_API_KEY" password="CLUSTER_API_SECRET";
      TB_QUEUE_KAFKA_CONFLUENT_SECURITY_PROTOCOL:SASL_SSL
      TB_QUEUE_KAFKA_CONFLUENT_USERNAME:CLUSTER_API_KEY
      TB_QUEUE_KAFKA_CONFLUENT_PASSWORD:CLUSTER_API_SECRET
    
      TB_QUEUE_KAFKA_RE_TOPIC_PROPERTIES:retention.ms:604800000;segment.bytes:52428800;retention.bytes:1048576000
      TB_QUEUE_KAFKA_CORE_TOPIC_PROPERTIES:retention.ms:604800000;segment.bytes:52428800;retention.bytes:1048576000
      TB_QUEUE_KAFKA_TA_TOPIC_PROPERTIES:retention.ms:604800000;segment.bytes:52428800;retention.bytes:1048576000
      TB_QUEUE_KAFKA_NOTIFICATIONS_TOPIC_PROPERTIES:retention.ms:604800000;segment.bytes:52428800;retention.bytes:1048576000
      TB_QUEUE_KAFKA_JE_TOPIC_PROPERTIES:retention.ms:604800000;segment.bytes:52428800;retention.bytes:104857600

      # 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:
      - mytbpe-data:/data
      - mytbpe-logs:/var/log/thingsboard
  postgres:
    restart: always
    image: "postgres:12"
    ports:
    - "5432"
    environment:
      POSTGRES_DB: thingsboard
      POSTGRES_PASSWORD: postgres
    volumes:
      - mytbpe-data-db:/var/lib/postgresql/data
volumes:
  mytbpe-data:
    external: true
  mytbpe-logs:
    external: true
  mytbpe-data-db:
    external: true

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

Where:

  • 8080:8080 - connect local port 8080 to exposed internal HTTP port 8080
  • 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
  • mytbpe-data:/data - mounts the volume mytb-data to ThingsBoard data directory
  • mytbpe-data-db:/var/lib/postgresql/data - mounts the volume mytbpe-data-db to Postgres data directory;
  • mytb-logs:/var/log/thingsboard - mounts the volume mytb-logs to ThingsBoard logs directory
  • mytbpe - 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-pe:3.4.3PE - docker image.

Step 4. Running

Windows users should use docker managed volume for ThingsBoard DataBase. Create docker volume (for ex. mytbpe-data) before executing docker run command: Open “Docker Quickstart Terminal”. Execute the following command to create docker volume:

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docker volume create mytbpe-data
docker volume create mytbpe-data-db
docker volume create mytbpe-logs

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 mytbpe

ThingsBoard supports Docker Compose V2 (Docker Desktop or Compose plugin) starting from 3.4.2 release, because docker-compose as standalone setup is no longer supported by Docker.

We strongly recommend to update to Docker Compose V2 and use it.

If you still rely on using Docker Compose as docker-compose (with a hyphen), then please execute the following commands to start ThingsBoard:
docker-compose up -d
docker-compose logs -f mytbpe

In order to get access to necessary resources from external IP/Host on Windows machine, please execute the following commands:

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set PATH=%PATH%;"C:\Program Files\Oracle\VirtualBox"
VBoxManage controlvm "default" natpf1 "tcp-port8080,tcp,,8080,,8080"  
VBoxManage controlvm "default" natpf1 "tcp-port1883,tcp,,1883,,1883"
VBoxManage controlvm "default" natpf1 "tcp-port7070,tcp,,7070,,7070"
VBoxManage controlvm "default" natpf1 "udp-port5683,udp,,5683,,5683"
VBoxManage controlvm "default" natpf1 "udp-port5684,udp,,5684,,5684"
VBoxManage controlvm "default" natpf1 "udp-port5685,udp,,5685,,5685"
VBoxManage controlvm "default" natpf1 "udp-port5686,udp,,5686,,5686"
VBoxManage controlvm "default" natpf1 "udp-port5687,udp,,5687,,5687"
VBoxManage controlvm "default" natpf1 "udp-port5688,udp,,5688,,5688"

Where:

  • C:\Program Files\Oracle\VirtualBox - path to your VirtualBox installation directory

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

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 PE container logs execute the following command:

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docker compose logs -f mytbpe

To stop the container:

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docker compose stop mytbpe

To start the container:

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docker compose start mytbpe

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 mytbpe
docker-compose stop mytbpe
docker-compose start mytbpe

Upgrading

In case when database upgrade is needed, execute the following commands:

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$ docker compose stop tb-node
$ docker compose run mytbpe upgrade-tb.sh
$ docker compose start mytbpe

If you still rely on Docker Compose as docker-compose (with a hyphen) here is the list of the above commands:
docker-compose stop tb-node
docker-compose run mytbpe upgrade-tb.sh
docker-compose start mytbpe

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

Next steps