技术交流

学习资料
立即试用 商务报价
专业版
社区版 专业版 云服务 Edge Edge PE 网关 授权服务 Trendz分析 Mobile 应用程序 Mobile PE应用程序
ThingsBoard Professional Edition cluster setup with Docker Compose guide
入门 文档 指南 安装 架构 API 常见问题

本页目录

ThingsBoard Professional Edition cluster setup with Docker Compose guide

This guide will help you to setup ThingsBoard in cluster mode with Docker Compose. For this purpose, we will use docker container images available on Docker Hub.

Prerequisites

ThingsBoard Microservices are running in dockerized environment. Before starting please make sure Docker CE and Docker Compose are installed in your system.

Please note that for the deployment of Rule Engine as a separate service, an additional separate License Key is required.

不要忘记添加linux用户到docker组请参阅非root用户管理docker。

Step 1. Pull ThingsBoard PE Images

Run the following commands to verify that you can pull the images from the Docker hub.

1
2
3
4
5
6
7
8
9

docker pull thingsboard/tb-pe-node:3.5.1PE
docker pull thingsboard/tb-pe-web-report:3.5.1PE
docker pull thingsboard/tb-pe-web-ui:3.5.1PE
docker pull thingsboard/tb-pe-js-executor:3.5.1PE
docker pull thingsboard/tb-pe-http-transport:3.5.1PE
docker pull thingsboard/tb-pe-mqtt-transport:3.5.1PE
docker pull thingsboard/tb-pe-coap-transport:3.5.1PE
docker pull thingsboard/tb-pe-lwm2m-transport:3.5.1PE
docker pull thingsboard/tb-pe-snmp-transport:3.5.1PE

Step 2. Clone ThingsBoard PE Docker Compose scripts

1
2

git clone -b release-3.5.1 https://github.com/thingsboard/thingsboard-pe-docker-compose.git tb-pe-docker-compose
cd tb-pe-docker-compose

Step 3. Obtain your 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.

IMPORTANT NOTE: if you decide to use an advanced deployment type, make sure you have purchased a license key for at least four instances of ThingsBoard PE. Otherwise, you need to modify the local copy of docker-compose.yml) to use the number of ThingsBoard instances that you’ve purchased. We will reference the license key you have obtained during this step as PUT_YOUR_LICENSE_SECRET_HERE later in this guide.

Step 4. Configure your license key

1

nano tb-node.env

and put the license secret parameter instead of “PUT_YOUR_LICENSE_SECRET_HERE”:

1
2
3

# ThingsBoard server configuration
...
TB_LICENSE_SECRET=PUT_YOUR_LICENSE_SECRET_HERE

Step 5. Configure deployment type

Starting ThingsBoard v2.2, it is possible to install ThingsBoard cluster using new microservices architecture and docker containers. See microservices architecture page for more details.

The docker compose scripts support three deployment modes. In order to set the deployment mode, change the value of TB_SETUP variable in .env file to one of the following:

  • basic (recommended, set by default) - ThingsBoard Core and Rule Engine are launched inside one JVM (requires only one license). MQTT, CoAP and HTTP transports are launched in separate containers.
  • monolith - ThingsBoard Core and Rule Engine are launched inside one JVM (requires only one license). MQTT, CoAP and HTTP transports are also launched in the same JVM to minimize memory footprint and server requirements.
  • advanced- ThingsBoard Core and Rule Engine are launched in separate containers and are replicated one JVM (requires 4 licenses).

All deployment modes support separate JS executors, Redis, and different queues.

Step 6. Configure ThingsBoard database

在执行初始安装之前可以配置与ThingsBoard一起使用的数据库的类型。 设置数据库类型在.env文件中的DATABASE变量的值更改为以下值：

1

nano .env
  • postgres - 使用PostgreSQL数据库;
  • hybrid - 使用PostgreSQL保存实体Cassandra时间序列数据;

注意：根据数据库类型将部署相应的docker服务（请参见docker-compose.postgres.yml, docker-compose.hybrid.yml）相关详细信息。

Step 7. Choose ThingsBoard queue service

ThingsBoard使用消息系统存储服务之间的通信，请正确选择队列？

  • 内存 适用于开发环境（开发）

  • Kafka 适用于生产环境（集群）

  • RabbitMQ 适用生产环境（单体）

  • AWS SQS 适用生产环境（AWS公有云）

  • Google发布/订阅 适用生产环境（Google公有云）

  • Azure服务总线 适用生产环境（Azure公有云）

  • Confluent云 适用于生产环境（托管）

参见相应的架构页面和规则引擎页面以获取更多详细信息。

Kafka (default, 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)

Apache Kafka是一个开源的流式数据处理平台。

配置环境变量文件：

1

nano .env

检查下面行：

1

TB_QUEUE_TYPE=kafka

AWS SQS配置

首先需要创建AWS账户然后访问AWS SQS服务。

使用此说明创建AWS SQS服务凭证。

  • Access key ID
  • Secret access key

配置环境变量文件：

1

nano .env

检查下面行：

1

TB_QUEUE_TYPE=aws-sqs

配置环境变量文件：

1

nano queue-aws-sqs.env

添加配置将”YOUR_KEY”和”YOUR_SECRET”替换为真实的AWS用户凭证并将”YOUR_REGION”替换成AWS SQS帐户区域：

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32

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

可以使用UI更新默认规则引擎队列配置（轮询间隔和分区）有关ThingsBoard规则引擎队列的更多信息请参阅文档。

Google发布/订阅配置

创建一个Google帐户并访问发布/订阅服务。

使用此说明创建一个项目并使用发布/订阅服务。

使用此说明创建服务帐户凭据并编辑角色或管理员后保存json凭据步骤9的文件此处。

配置环境变量文件：

1

nano .env

检查下面行：

1

TB_QUEUE_TYPE=pubsub

配置环境变量文件：

1

nano queue-pubsub.env

添加下面配置内容使用真正用户密码替换“YOUR_PROJECT_ID”, “YOUR_SERVICE_ACCOUNT”：

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30

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

可以使用UI更新默认规则引擎队列配置（轮询间隔和分区）有关ThingsBoard规则引擎队列的更多信息请参阅文档。

Azure服务总线配置

创建Azure帐户并访问Azure服务总线。

通过使用说明了解并使用总线服务。

使用说明创建共享访问签名。

配置环境变量文件：

1

nano .env

检查下面行：

1

TB_QUEUE_TYPE=service-bus

配置环境变量文件：

1

nano queue-service-bus.env

添加下面配置内容使用真正的服务总线名称空间替换”YOUR_NAMESPACE_NAME”和”YOUR_SAS_KEY_NAME”及”YOUR_SAS_KEY”：

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34

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.
# 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_RE_MAIN_POLL_INTERVAL_MS=1000
TB_QUEUE_RE_MAIN_PARTITIONS=2
TB_QUEUE_RE_HP_POLL_INTERVAL_MS=1000
TB_QUEUE_RE_HP_PARTITIONS=1
TB_QUEUE_RE_SQ_POLL_INTERVAL_MS=1000
TB_QUEUE_RE_SQ_PARTITIONS=1
TB_QUEUE_TRANSPORT_REQUEST_POLL_INTERVAL_MS=1000
TB_QUEUE_TRANSPORT_RESPONSE_POLL_INTERVAL_MS=1000
TB_QUEU_TRANSPORT_NOTIFICATIONS_POLL_INTERVAL_MS=1000

你可以使用官方文档安装RabbitMQ或按照以下说明：

配置环境变量文件：

1

nano .env

检查下面行：

1

TB_QUEUE_TYPE=rabbitmq

配置环境变量文件：

1

nano queue-rabbitmq.env

将以下行添加到配置文件将“YOUR_USERNAME”和“YOUR_PASSWORD”替换为真实的信息将“localhost”和“5672”替换为真实的RabbitMQ主机和端口：

1
2
3
4
5

TB_QUEUE_TYPE=rabbitmq
TB_QUEUE_RABBIT_MQ_HOST=localhost
TB_QUEUE_RABBIT_MQ_PORT=5672
TB_QUEUE_RABBIT_MQ_USERNAME=YOUR_USERNAME
TB_QUEUE_RABBIT_MQ_PASSWORD=YOUR_PASSWORD

Confluent配置

你应该创建一个帐户后访问Confluent云然后创建一个Kafka集群和 API Key。

配置环境变量文件：

1

nano .env

检查下面行：

1

TB_QUEUE_TYPE=confluent

配置环境变量文件：

1

nano queue-confluent-cloud.env

添加下面配置内容使用真正的Confluent云服务器地址替换”CLUSTER_API_KEY”, “CLUSTER_API_SECRET”和”localhost:9092”：

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45

TB_QUEUE_TYPE=kafka

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

可以使用UI更新默认规则引擎队列配置（轮询间隔和分区）有关ThingsBoard规则引擎队列的更多信息请参阅文档。

Step 8. Enable monitoring (optional)

配置文件启用Grafana和Prometheus服务：

1

nano .env

MONITORING_ENABLED变量值为true：

1

MONITORING_ENABLED=true

部署后能够通过Prometheus：http://localhost:9090和Grafana：http://localhost:3000 (默认用户admin和密码foobar)访问。

Step 9. Running

在启动Docker容器之前请运行以下命令以创建用于存储数据和日志的目录然后将其所有者更改为Docker容器用户，以便能够更改用户使用chown命令该命令需要sudo权限（该命令将要求sudo访问的密码）：

1

./docker-create-log-folders.sh

执行以下命令以运行安装：

1

./docker-install-tb.sh --loadDemo

说明:

  • --loadDemo -可选参数用于是否加载演示数据。

执行以下命令以启动服务：

1

./docker-start-services.sh

执行完命令后你可以http://{your-host-ip}在浏览器中打开(例如http://localhost)。

使用以下默认凭据：

  • System Administrator: sysadmin@thingsboard.org / sysadmin

如果使用演示数据（使用--loadDemo标志）安装了数据库则还可以使用以下凭据：

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

如有任何问题可以检查服务日志中是否有错误。 例如要查看ThingsBoard节点日志请执行以下命令：

1

docker compose -f $TB_SETUP/docker-compose.yml logs -f tb-core1 tb-rule-engine1

如果依然使用Docker Compose请执行如下命令：

docker-compose -f $TB_SETUP/docker-compose.yml logs -f tb-core1 tb-rule-engine1

查看所有容器的状态：

1

docker compose -f $TB_SETUP/docker-compose.yml ps

如果依然使用Docker Compose请执行如下命令：

docker-compose -f $TB_SETUP/docker-compose.yml ps

查看运行的服务的日志：

1

docker compose -f $TB_SETUP/docker-compose.yml logs -f

如果依然使用Docker Compose请执行如下命令：

docker-compose -f $TB_SETUP/docker-compose.yml logs -f

请参阅docker-compose命令详细信息。

执行服务停止命令：

1

./docker-stop-services.sh

删除已部署的docker容器：

1

./docker-remove-services.sh

执行服务更新命令：

1

./docker-update-service.sh [SERVICE...]

说明

  • [SERVICE...] - 更新的服务列表(docker-compose文件中定义的服务)如果未指定会更新所有服务。

升级

如果需要升级数据库请编辑.env文件以将“TB_VERSION”设置为目标版本（例如升级到最新版本请将其设置为3.5.13.4.3）然后执行以下命令：

1
2
3

./docker-stop-services.sh
./docker-upgrade-tb.sh --fromVersion=[FROM_VERSION]
./docker-start-services.sh

FROM_VERSION - 有关有效值为开始升级版本升级说明查看fromVersion值。

HTTPS证书

我们使用HAproxy将流量代理到容器默认情况下使用Web UI的80和443端口，将HTTPS与有效证书一起使用请执行以下命令：

1
2

docker exec haproxy-certbot certbot-certonly --domain your_domain --email your_email
docker exec haproxy-certbot haproxy-refresh

注意：当在URL中通过域名访问Web UI时使用的有效证书，通过IP地址访问UI则使用自签名证书。

Next steps

  • 入门指南 - 快速学习ThingsBoard相关功能。

  • 连接设备 - 学习如何根据你的连接方式或解决方案连接设备。

  • 可 视 化 - 学习如何配置复杂的ThingsBoard仪表板说明。

  • 数据处理 - 学习如何使用ThingsBoard规则引擎。

  • 数据分析 - 学习如何使用规则引擎执行基本的分析任务。

  • 硬件样品 - 学习如何将各种硬件平台连接到ThingsBoard。

  • 高级功能 - 学习高级ThingsBoard功能。

  • 开发指南 - 学习ThingsBoard中的贡献和开发。


© 2023 作者：ThingsBoard 蜀ICP备19012996号-2