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Update edge install instructions

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- Minor instruction adjustments
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yevhenii 2025-05-20 18:22:24 +03:00
parent a6cc03fb64
commit ebf41af1a2
3 changed files with 14 additions and 7 deletions
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4
application/src/main/data/json/edge/instructions/install/centos/instructions.md
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@ -41,6 +41,8 @@ OpenJDK 64-Bit Server VM (build ...)
#### Step 2. Configure ThingsBoard Database
ThingsBoard Edge supports SQL and hybrid database approaches.
In this guide we will use SQL only.
For hybrid details please follow official installation instructions from the ThingsBoard documentation site.
### PostgresSql
ThingsBoard Edge uses PostgreSQL database as a local storage.
@ -111,7 +113,7 @@ sudo systemctl restart postgresql-16.service && psql -U postgres -d postgres -h
#### Step 3. Choose Queue Service
ThingsBoard Edge is able to use different messaging systems/brokers for storing the messages and communication between ThingsBoard services.
ThingsBoard Edge supports only Kafka or in-memory queue (since v4.0) for message storage 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.

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application/src/main/data/json/edge/instructions/install/docker/instructions.md
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@ -12,15 +12,18 @@ Here you can find ThingsBoard Edge docker image:
#### Step 2. Choose Queue and/or Database Services
ThingsBoard Edge is able to use different messaging systems/brokers for storing the messages and communication between ThingsBoard services.
ThingsBoard Edge supports only Kafka or in-memory queue (since v4.0) for message storage and communication between ThingsBoard services.
ThingsBoard Edge supports SQL and hybrid database approaches.
In this guide we will use SQL only.
For hybrid details please follow official installation instructions from the ThingsBoard documentation site.
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.
Hybrid implementation combines PostgreSQL and Cassandra databases with Kafka queue service. It is recommended if you plan to manage 1M+ devices in production or handle high data ingestion rate (more than 5000 msg/sec).
Create a docker compose file for the ThingsBoard Edge service:
##### In Memory
@ -54,7 +57,7 @@ services:
- tb-edge-logs:/var/log/tb-edge
postgres:
restart: always
image: "postgres:15"
image: "postgres:16"
ports:
- "5432"
environment:

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application/src/main/data/json/edge/instructions/install/ubuntu/instructions.md
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@ -32,7 +32,9 @@ OpenJDK 64-Bit Server VM (...)
#### Step 2. Configure ThingsBoard Edge Database
ThingsBoard Edge supports SQL and hybrid database approaches. See the architecture page for details.
ThingsBoard Edge supports SQL and hybrid database approaches.
In this guide we will use SQL only.
For hybrid details please follow official installation instructions from the ThingsBoard documentation site.
### Configure PostgreSQL
ThingsBoard Edge uses PostgreSQL database as a local storage.
@ -71,7 +73,7 @@ echo "CREATE DATABASE tb_edge;" | psql -U postgres -d postgres -h 127.0.0.1 -W
#### Step 3. Choose Queue Service
ThingsBoard Edge can use different messaging systems and brokers for storing messages and enabling communication between its services. Choose the appropriate queue implementation based on your specific business needs:
ThingsBoard Edge supports only Kafka or in-memory queue (since v4.0) for message storage and communication between ThingsBoard services. Choose the appropriate queue implementation based on your specific business needs:
In Memory: The built-in and default queue implementation. It is useful for development or proof-of-concept (PoC) environments, but is not recommended for production or any type of clustered deployments due to limited scalability.