To ensure that the code can start properly, please configure the local host mapping first, add the following mapping to the configuration file.
```shell
# for integrated-example
127.0.0.1 integrated-mysql
127.0.0.1 nacos-server
127.0.0.1 seata-server
127.0.0.1 rocketmq
127.0.0.1 gateway-service
127.0.0.1 integrated-frontend
```
### Preparing jar packages
Go to the `spring-cloud-alibaba-examples` directory and run the `mvn package` command to compile the project and generate the jar package, so as to prepare for the subsequent construction of the docker service image.
## Quickly start
### Component start
Enter `spring-cloud-alibaba-examples/integration-example` directory, run the following command in the terminal to quickly deploy the components required to run example: `docker-compose -f ./docker-compose/docker-compose-env.yml up -d`.
### Add configuration
After docker-compose-env.yml is run successfully, add the Nacos configuration:
- Enter `spring-cloud-alibaba-examples/integration-example` directory;
- Execute the `config-init/scripts/nacos-config-quick.sh` script file in the terminal.
The one-click import of all micro-service configurations is complete.
> Note: windows operating systems can use `git bash` to execute shell script files to complete the configuration import.
### Service start
Enter `spring-cloud-alibaba-examples/integration-example` directory, Run the following command in the terminal to quickly deploy the services required for running example: `docker-compose -f ./docker-compose/docker-compose-service.yml up -d`.
## Stop all containers
### Stops the service container
Enter `spring-cloud-alibaba-examples/integration-examplee` directory, Run the following command in the terminal to `docker-compose -f ./docker-compose/docker-compose-service.yml down` to stop the running example service container.
### Stops the component container
Enter `spring-cloud-alibaba-examples/integration-example` directory, Run the following command in the terminal to `docker-compose -f ./docker-compose/docker-compose-env.yml down` to stop the running example component container.
In this demo example, the unit price of each item is 2 for demonstration purposes.
And in the previous preparation, **initialize business database table** we created a new user userId = admin with a balance of $3, and a new item numbered 1 with 100 units in stock.
So by doing the above, we will create an order, deduct the number of items in stock corresponding to item number 1 (100-1=99), and deduct the balance of the admin user (3-2=1).
If the same interface is requested again, again the inventory is deducted first (99-1=98), but an exception is thrown because the admin user's balance is insufficient and is caught by Seata, which performs a two-stage commit of the distributed transaction and rolls back the transaction.
You can see that the database still has 99 records in stock because of the rollback.
### Fused flow limiting, peak shaving capability
#### Scenario Description
For service fusion limiting and peak and valley cutting in the context of high traffic, we provide a scenario **where users make likes for products**. In this scenario, we provide two ways to deal with high traffic.
- Sentinel binds specified gateway routes on the gateway side for fusion degradation of services.
- RocketMQ performs traffic clipping, where the producer sends messages to RocketMQ under high traffic requests, while the consumer pulls and consumes through a configurable consumption rate, reducing the pressure of high traffic direct requests to the database to increase the number of likes requests.
The Gateway routing point service has a flow limit rule of 5, while 10 concurrent requests are simulated on the front end through asynchronous processing.
Therefore, we can see that Sentinel performs a service fusion on the Gateway side to return the fallback to the client for the extra traffic, while the number of likes in the database is updated (+5).
- RocketMQ is performing peak and valley reduction
Visit `http://integrated-frontend:8080/rocketmq` to experience the corresponding scenario.
Since we previously configured the consumption rate and interval of the `integrated-praise-consumer` consumer module in Nacos, we simulate 1000 requests for likes at the click of a button, and the `integrated-praise-provider`
will deliver 1000 requests to the Broker, and the consumer module will consume them according to the configured consumption rate, and update the database with the product data of the likes, simulating the characteristics of RocketMQ to cut the peaks and fill the valleys under high traffic.
You can see that the number of likes in the database is being dynamically updated.
