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## 2. `OpenPedCan-api` server deployment

`OpenPedCan-api` server is deployed using Amazon Web Services (AWS). `OpenPedCan-api` HTTP server is deployed using Amazon Elastic Container Registry (ECR), Elastic Container Service (ECS), and Fargate. The HTTP server queries `OpenPedCan-api` database server, and the database is deployed using Amazon Relational Database Service (RDS).
`OpenPedCan-api` server is deployed using Amazon Web Services (AWS). `OpenPedCan-api` HTTP server is deployed using Amazon Elastic Container Registry (ECR), Elastic Container Service (ECS), and Fargate. The HTTP server queries `OpenPedCan-api` database server, and the database server is deployed using Amazon Relational Database Service (RDS).

<https://openpedcan-api-qa.d3b.io/__docs__/> is the URL of `OpenPedCan-api` QA server. The QA server will only deploy the `main` branch of the repository.

Expand Down Expand Up @@ -289,7 +289,7 @@ The `OpenPedCan-api` server system has the following layers:

For more details about implementations, see [Test run `OpenPedCan-api` server locally](#test-run-openpedcan-api-server-locally) section.

The root directory of this repository should only contain starting points of different layer and configuration files.
The root directory of this repository should only contain starting points of different layers and configuration files.

### 4.1. Data model layer

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`db/load_db.sh` loads local or remote pre-built data model files to the HTTP server layer.

`db/r_interfaces` directory contains files that define data model interfaces for R runtime.

### 4.2. Analysis logic layer

`src` directory contains files that implement the analysis logic layer.
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### 4.6. Deployment layer

Jenkinsfile and Dockerfile specify the procedures to deploy the `OpenPedCan-api` server.
`Jenkinsfile` and `Dockerfile` specify the procedures to deploy the `OpenPedCan-api` server.

## 5. API Development roadmap

- #27
- #22 and #23
- Optimize HTTP server deployment task scaling rules and memory/CPU resource allocations to balance performance and cost.
- Optimize database server deployment memory/CPU resource allocations and DBMS runtime configurations, e.g. `shared_buffers` and `work_mem`, to balance performance and cost.
- Add bulk tissue differential expression table and plot endpoints.

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