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Installing SENTIENT Professional Edition using Docker (Linux or MacOS)

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

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.

Step 1. 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 2. Choose SENTIENT queue service

SENTIENT platform currently supports two type of messaging brokers for storing the messages and communication between SENTIENT services: In-memory and Kafka-based brokers.

  • 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 SENTIENT 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.

  • 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)
Confluent Cloud (Event Streaming Platform based on Kafka)

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

Create docker compose file for SENTIENT queue service:

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nano docker-compose.yml

Add the following lines 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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services:
  postgres:
    restart: always
    image: "postgres:16"
    ports:
      - "5432"
    environment:
      POSTGRES_DB: sentient
      POSTGRES_PASSWORD: postgres
    volumes:
      - postgres-data:/var/lib/postgresql/data
  sentient-pe:
    restart: always
    image: "sentient/tb-pe-node:4.3.1.1PE"
    ports:
      - "8080:8080"
      - "1883:1883"
      - "8883:8883"
      - "9090:9090"
      - "7070:7070"
      - "5683-5688:5683-5688/udp"
    logging:
      driver: "json-file"
      options:
        max-size: "100m"
        max-file: "10"
    environment:
      TB_SERVICE_ID: tb-pe-node
      TB_LICENSE_SECRET: PUT_YOUR_LICENSE_SECRET_HERE
      TB_LICENSE_INSTANCE_DATA_FILE: /data/license.data
      REPORTS_SERVER_ENDPOINT_URL: http://tb-web-report:8383
      SPRING_DATASOURCE_URL: jdbc:postgresql://postgres:5432/sentient
      DEFAULT_SENTIENT_ANALYTICS_URL: http://sentient-analytics:8888
      DEFAULT_TB_URL: http://sentient-pe:8080
    volumes:
      - license-data:/data
    depends_on:
      - postgres
  tb-web-report:
    restart: always
    image: "sentient/tb-pe-web-report:4.3.1.1PE"
    ports:
      - "8383"
    depends_on:
      - sentient-pe
    environment:
      HTTP_BIND_ADDRESS: 0.0.0.0
      HTTP_BIND_PORT: 8383
      LOGGER_LEVEL: info
      LOG_FOLDER: logs
      LOGGER_FILENAME: tb-web-report-%DATE%.log
      DOCKER_MODE: true
      DEFAULT_PAGE_NAVIGATION_TIMEOUT: 120000
      DASHBOARD_IDLE_WAIT_TIME: 3000
      USE_NEW_PAGE_FOR_REPORT: true
volumes:
  postgres-data:
    name: tb-postgres-data
    driver: local
  license-data:
    name: tb-pe-license-data
    driver: local

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

Create docker compose file for SENTIENT queue service:

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nano docker-compose.yml

Add the following lines 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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services:
  postgres:
    restart: always
    image: "postgres:16"
    ports:
      - "5432"
    environment:
      POSTGRES_DB: sentient
      POSTGRES_PASSWORD: postgres
    volumes:
      - postgres-data:/var/lib/postgresql/data
  kafka:
    restart: always
    image: bitnamilegacy/kafka:4.0
    ports:
      - 9092:9092 #to localhost:9092 from host machine
      - 9093 #for Kraft
    environment:
      ALLOW_PLAINTEXT_LISTENER: "yes"
      KAFKA_CFG_LISTENERS: "PLAINTEXT://:9092,CONTROLLER://:9093"
      KAFKA_CFG_ADVERTISED_LISTENERS: "PLAINTEXT://:9092"
      KAFKA_CFG_LISTENER_SECURITY_PROTOCOL_MAP: "CONTROLLER:PLAINTEXT,PLAINTEXT:PLAINTEXT"
      KAFKA_CFG_INTER_BROKER_LISTENER_NAME: "PLAINTEXT"
      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
      KAFKA_CFG_LOG_RETENTION_MS: "300000"
      KAFKA_CFG_SEGMENT_BYTES: "26214400"
    volumes:
      - kafka-data:/bitnami
  sentient-pe:
    restart: always
    image: "sentient/tb-pe-node:4.3.1.1PE"
    ports:
      - "8080:8080"
      - "1883:1883"
      - "8883:8883"
      - "9090:9090"
      - "7070:7070"
      - "5683-5688:5683-5688/udp"
    logging:
      driver: "json-file"
      options:
        max-size: "100m"
        max-file: "10"
    environment:
      TB_SERVICE_ID: tb-pe-node
      TB_LICENSE_SECRET: PUT_YOUR_LICENSE_SECRET_HERE
      TB_LICENSE_INSTANCE_DATA_FILE: /data/license.data
      REPORTS_SERVER_ENDPOINT_URL: http://tb-web-report:8383
      SPRING_DATASOURCE_URL: jdbc:postgresql://postgres:5432/sentient
      DEFAULT_SENTIENT_ANALYTICS_URL: http://sentient-analytics:8888
      DEFAULT_TB_URL: http://sentient-pe:8080
      TB_QUEUE_TYPE: kafka
      TB_KAFKA_SERVERS: kafka:9092
    volumes:
      - license-data:/data
    depends_on:
      - postgres
  tb-web-report:
    restart: always
    image: "sentient/tb-pe-web-report:4.3.1.1PE"
    ports:
      - "8383"
    depends_on:
      - sentient-pe
    environment:
     HTTP_BIND_ADDRESS: 0.0.0.0
     HTTP_BIND_PORT: 8383
     LOGGER_LEVEL: info
     LOG_FOLDER: logs
     LOGGER_FILENAME: tb-web-report-%DATE%.log
     DOCKER_MODE: true
     DEFAULT_PAGE_NAVIGATION_TIMEOUT: 120000
     DASHBOARD_IDLE_WAIT_TIME: 3000
     USE_NEW_PAGE_FOR_REPORT: true
volumes:
  postgres-data:
    name: tb-postgres-data
    driver: local
  license-data:
    name: tb-pe-license-data
    driver: local
  kafka-data:
    name: tb-pe-kafka-data
    driver: local

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 SENTIENT queue service:

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nano docker-compose.yml

Add the following lines 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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services:
  postgres:
    restart: always
    image: "postgres:16"
    ports:
      - "5432"
    environment:
      POSTGRES_DB: sentient
      POSTGRES_PASSWORD: postgres
    volumes:
      - postgres-data:/var/lib/postgresql/data
  sentient-pe:
    restart: always
    image: "sentient/tb-pe-node:4.3.1.1PE"
    ports:
      - "8080:8080"
      - "1883:1883"
      - "8883:8883"
      - "9090:9090"
      - "7070:7070"
      - "5683-5688:5683-5688/udp"
    logging:
      driver: "json-file"
      options:
        max-size: "100m"
        max-file: "10"
    environment:
      TB_SERVICE_ID: tb-pe-node
      TB_LICENSE_SECRET: PUT_YOUR_LICENSE_SECRET_HERE
      TB_LICENSE_INSTANCE_DATA_FILE: /data/license.data
      REPORTS_SERVER_ENDPOINT_URL: http://tb-web-report:8383
      SPRING_DATASOURCE_URL: jdbc:postgresql://postgres:5432/sentient
      DEFAULT_SENTIENT_ANALYTICS_URL: http://sentient-analytics:8888
      DEFAULT_TB_URL: http://sentient-pe:8080
      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      
    depends_on:
      - postgres
    volumes:
      - license-data:/data
  tb-web-report:
    restart: always
    image: "sentient/tb-pe-web-report:4.3.1.1PE"
    ports:
      - "8383"
    depends_on:
      - sentient-pe
    environment:
      HTTP_BIND_ADDRESS: 0.0.0.0
      HTTP_BIND_PORT: 8383
      LOGGER_LEVEL: info
      LOG_FOLDER: logs
      LOGGER_FILENAME: tb-web-report-%DATE%.log
      DOCKER_MODE: true
      DEFAULT_PAGE_NAVIGATION_TIMEOUT: 120000
      DASHBOARD_IDLE_WAIT_TIME: 3000
      USE_NEW_PAGE_FOR_REPORT: true
volumes:
  postgres-data:
    name: tb-postgres-data
    driver: local
  license-data:
    name: tb-pe-license-data
    driver: local

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

Where:

  • PUT_YOUR_LICENSE_SECRET_HERE - placeholder for your license secret obtained on the third step
  • 8080:8080 - connect local port 8080 to exposed internal HTTP port 8080
  • 1883:1883 - connect local port 1883 to exposed internal MQTT port 1883
  • 8883:8883 - connect local port 8883 to exposed internal MQTT over SSL port 8883
  • 7070:7070 - connect local port 7070 to exposed internal Edge RPC port 7070
  • 9090:9090 - connect local port 9090 to exposed internal Remote Integration port 9090
  • 5683-5688:5683-5688/udp - connect local UDP ports 5683-5688 to exposed internal COAP and LwM2M ports
  • tb-pe-license-data - name of the docker volume that stores the SENTIENT’s license instance data file
  • tb-postgres-data - name of the docker volume that stores the PostgreSQL’s data
  • sentient-pe - friendly local name of this machine
  • restart: always - automatically start SENTIENT in case of system reboot and restart in case of failure.
  • sentient/tb-pe-node:4.3.1.1PE - docker image.

Step 3. Initialize database schema & system assets

Before you start SENTIENT, initialize the database schema and load built-in assets by running:

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docker compose run --rm -e INSTALL_TB=true -e LOAD_DEMO=true sentient-pe

Environment variables:

  • INSTALL_TB=true - Installs the core database schema and system resources (widgets, images, rule chains, etc.).
  • LOAD_DEMO=true - Loads sample tenant account, dashboards and devices for evaluation and testing.

Step 4. Start the platform & tail logs

Bring up all core containers in detached mode, then follow the SENTIENT logs:

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docker compose up -d && docker compose logs -f sentient-pe

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

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Note that web-reports will generate only if you access SENTIENT via external IP address or domain name.

Web-report will not generate if you access SENTIENT by http://localhost:8080

Use the following default credentials:

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

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

You can safely detach from the log stream (e.g. Ctrl+C); containers will continue running.

Inspect logs & control container lifecycle

If something goes wrong, you can stream the SENTIENT container logs in real time:

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docker compose logs -f sentient-pe

Bring down every container defined in your Compose file:

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docker compose down

Launch all services in detached mode:

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docker compose up -d

Post-installation steps

Install SENTIENT ANALYTICS

You may optionally install SENTIENT ANALYTICS any time using the following steps:

Create a SENTIENT ANALYTICS database in Postgres:

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docker compose -f docker-compose.yml exec -it postgres psql -U postgres -c "CREATE DATABASE sentient-analytics;"

Create a separate docker compose file for SENTIENT ANALYTICS:

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nano docker-compose-sentient-analytics.yml

Add the following lines to the yml file.

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services:
  sentient-analytics:
    restart: always
    image: "sentient/sentient-analytics:1.15.0.4"
    ports:
      - "8888:8888"
    environment:
      TB_API_URL: http://sentient-pe:8080
      SPRING_DATASOURCE_URL: jdbc:postgresql://postgres:5432/sentient-analytics
      SPRING_DATASOURCE_USERNAME: postgres
      SPRING_DATASOURCE_PASSWORD: postgres
      SCRIPT_ENGINE_DOCKER_PROVIDER_URL: sentient-analytics-python-executor:8181
      SCRIPT_ENGINE_TIMEOUT: 30000
    volumes:
      - sentient-analytics-conf:/sentient-analytics-config-files
      - sentient-analytics-data:/data
    depends_on:
      - postgres
  sentient-analytics-python-executor:
    restart: always
    image: "sentient/sentient-analytics-python-executor:1.15.0.4"
    ports:
      - "8181:8181"
    environment:
      EXECUTOR_MANAGER: 1
      EXECUTOR_SCRIPT_ENGINE: 6
      THROTTLING_QUEUE_CAPACITY: 10
      THROTTLING_THREAD_POOL_SIZE: 6
      NETWORK_BUFFER_SIZE: 5242880
    volumes:
      - sentient-analytics-python-executor-conf:/python-executor-config-files
      - sentient-analytics-python-executor-data:/data
volumes:
  sentient-analytics-conf:
    name: sentient-analytics-conf
    driver: local
  sentient-analytics-data:
    name: sentient-analytics-data
    driver: local
  sentient-analytics-python-executor-conf:
    name: sentient-analytics-python-executor-conf
    driver: local
  sentient-analytics-python-executor-data:
    name: sentient-analytics-python-executor-data
    driver: local

Where:

  • 8888:8888 - connect local port 8888 to exposed internal HTTP port 8888
  • sentient-analytics-conf - name of the docker volume that stores the SENTIENT ANALYTICS’s configuration files
  • sentient-analytics-data - name of the docker volume that stores the SENTIENT ANALYTICS’s data
  • sentient-analytics-python-executor-conf - name of the docker volume that stores the SENTIENT ANALYTICS python executor configuration files
  • sentient-analytics-python-executor-data - name of the docker volume that stores the SENTIENT ANALYTICS python executor data
  • sentient-analytics-postgres-data - name of the docker volume that stores the SENTIENT ANALYTICS PostgreSQL’s data

You can read more about SENTIENT ANALYTICS here.

Start the platform with SENTIENT ANALYTICS & tail logs

Bring up all containers (including SENTIENT ANALYTICS containers) as a single Compose project in detached mode, then follow the SENTIENT logs:

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docker compose -f docker-compose.yml -f docker-compose-sentient-analytics.yml up -d
docker compose -f docker-compose.yml -f docker-compose-sentient-analytics.yml logs -f sentient-pe

Inspect logs & control container lifecycle with SENTIENT ANALYTICS

If something goes wrong, you can stream the SENTIENT container logs in real time:

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docker compose -f docker-compose.yml -f docker-compose-sentient-analytics.yml logs -f sentient-pe

Stream the SENTIENT ANALYTICS container logs in real time:

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docker compose -f docker-compose.yml -f docker-compose-sentient-analytics.yml logs -f sentient-analytics

Bring down every container defined in your Compose files:

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docker compose -f docker-compose.yml -f docker-compose-sentient-analytics.yml down

Launch all services in detached mode:

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docker compose -f docker-compose.yml -f docker-compose-sentient-analytics.yml up -d

Upgrading to new SENTIENT ANALYTICS version

SENTIENT ANALYTICS has a different version system, and should be updated separately from SENTIENT platform main services.

You can read how to upgrade SENTIENT ANALYTICS here.

Upgrading to new SENTIENT version

Keep Your Platform Secure and Up-to-Date

When a new SENTIENT release becomes available, we provide a streamlined update process to ensure your system benefits from the latest features and security patches without any risk to your data.

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To maintain database integrity and system stability, please perform upgrades sequentially (e.g., v4.0.2 → v4.1.0 → v4.2.0).

Please refer to our official Upgrade Instructions for detailed steps tailored to your current deployment environment.

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