reference.txt

To develop the application you described, where you have a product service, recommendation system, and integration with Kafka or another streaming service, you need a comprehensive architecture and clear roadmap. I'll walk you through the key steps, technologies, and design principles you should follow from data ingestion to serving product recommendations to the end user.

### **System Overview**

You have the following key components:
1. **Auth Service** – Handles user authentication and authorization.
2. **Gateway Service** – Acts as an API gateway to route requests to different services.
3. **Service Registry** – Keeps track of all microservices for service discovery.
4. **Product Service** – Manages product data and product recommendations.
5. **Recommendation Service** – Receives product data and user behavior data for recommending products.
6. **Big Query/Big Table** – Stores large amounts of product data for querying and analytics.
7. **Kafka or Stream Service** – Used for real-time streaming of data between services, especially for sending user interactions (like views or clicks) to the recommendation system.
8. **ML Service** – Uses machine learning models to generate personalized recommendations.

Now let's break down the steps:

---

### **Roadmap and Design**

---

#### **1. Data Loading into Big Table (Big Query)**

- **Step 1: Data Extraction** – 
  - First, you'll need to load the data from your Excel sheet (2M rows) into a scalable storage solution. In this case, you mentioned Big Query (Google Cloud) or BigTable, both of which can handle large amounts of data.
  - Use a tool like **Apache POI** (Java) or **Pandas** (Python) to read the data from the Excel file.
  - Transform the data if necessary (e.g., cleaning, removing duplicates, handling missing values).

- **Step 2: Data Loading** –
  - **BigQuery**: Load data into BigQuery tables, either directly from your local or using tools like **Cloud Storage**.
  - **BigTable**: If you are using BigTable (recommended for large, fast reads), you may want to use the **HBase** client or **Dataflow** to transform and load data into BigTable.

- **Step 3: Storage and Querying** – 
  - Once the data is in BigQuery or BigTable, create appropriate indexes and partitions to optimize queries (e.g., by category, brand, etc.).
  - Design the schema such that it allows for fast lookups by product ID and other product attributes.
  
  For example, your product table could have columns like:
  - `id`, `name`, `description`, `price`, `category`, `brand`, `image_url`, `created_at`, etc.

---

#### **2. Product Service Implementation**

The product service will expose APIs for product details, recommendations, and handling search queries.

**Key Endpoints:**
1. **GET /product/{id}**:
   - Fetch product details by ID.
   - If the user is **not logged in**, return product details as usual.
   - If the user **is logged in**, you need to send the product interaction to the **Kafka** (or other stream service) for real-time processing by the recommendation service.

2. **GET /products/recommendations**:
   - Fetch recommended products based on user preferences and machine learning models.
   - This can query a recommendation engine or fetch from the **Big Query** based on recent user activity.
   - You can also set up a periodic job (like a cron job) to update the recommendations based on new data.

3. **GET /products/latest**:
   - Fetch the latest products (for example, 10 products).
   - This can be a simple query to BigQuery or BigTable to get the latest products based on the `created_at` timestamp.

4. **POST /product/viewed**:
   - When a user views a product, send this event to Kafka (or a similar streaming service) to update the recommendation system in real-time.

**Kafka Integration**:
- You can use Kafka to stream user behavior data (like product views) to the **Recommendation Service**.
- Every time a user views a product, this event (with product ID, user ID, and timestamp) will be sent to a Kafka topic.
  
---

#### **3. Kafka or Streaming Service Integration**

- **Kafka Setup**: Set up a Kafka cluster with multiple topics. One topic will handle user product views (`product-view-topic`), and another could be used for other user actions (`user-interaction-topic`).
  
- **Producer**: The **Product Service** will act as a Kafka producer that sends data (e.g., product views) to Kafka topics.
  
- **Consumer**: The **Recommendation Service** will be a Kafka consumer, listening for incoming product view events, processing them, and updating its recommendation database or cache.

**Kafka Event Example**:
```json
{
  "user_id": "123",
  "product_id": "456",
  "timestamp": "2024-12-13T15:00:00Z",
  "event_type": "view"
}
```

---

#### **4. Recommendation Service**

The recommendation service will consume product view events from Kafka and update its internal model. There are two main types of recommendations to consider:

1. **Collaborative Filtering**: This method suggests products based on similar user behavior.
2. **Content-Based Filtering**: This method suggests products similar to what the user has previously interacted with (based on product attributes like category, brand, etc.).

You can start by implementing collaborative filtering or use a **Matrix Factorization** model (e.g., ALS in Apache Spark).

**Steps for the Recommendation Service**:
- **Consume Kafka Events**: The service listens to Kafka topics for product view events.
- **Process Events**: Based on the product views, it generates recommendations for the user (based on collaborative filtering, content-based filtering, or both).
- **Store Recommendations**: Store recommendations in a fast cache (e.g., **Redis** or **BigQuery**), so they can be quickly retrieved by the product service.
- **Serve Recommendations**: Create an endpoint that serves personalized recommendations for the user, such as `GET /recommendations/user/{id}`.

---

#### **5. Machine Learning for Recommendations**

For personalized recommendations, you can integrate an **ML model** that generates recommendations based on user behavior (e.g., TensorFlow, Scikit-learn, etc.). Here’s how you could integrate ML:

- **Train a Model**: Use user interaction data (e.g., product views, searches, etc.) to train a machine learning model. You can start by using collaborative filtering (e.g., Matrix Factorization) or even deep learning models (e.g., Neural Collaborative Filtering).
  
- **Deploy the Model**: Once the model is trained, deploy it in a service that can receive user interaction data and generate recommendations in real time.

- **Use ML for Dynamic Recommendations**: Periodically retrain your model with new data to ensure the recommendations stay relevant.

---

#### **6. Gateway Service and Service Registry**

- **API Gateway**: The **Gateway Service** will route requests to appropriate services like the Product Service, Recommendation Service, and others.
  - For example, when a user requests product details, the gateway will route the request to the **Product Service**.
  - If the user is authenticated, it will also trigger Kafka events for recommendation updates.

- **Service Registry**: Use **Spring Cloud Eureka** or **Consul** to manage service discovery for your microservices. This ensures that your gateway can dynamically find and route requests to services like the Product Service, Recommendation Service, and others.

---

### **Technology Stack**

1. **Backend**:
   - **Spring Boot**: For building your REST APIs.
   - **Spring Security**: For authentication and authorization.
   - **Spring Cloud**: For service discovery, API Gateway, and distributed configuration.
   - **Kafka**: For real-time streaming of product view events.
   - **ML Framework**: TensorFlow, Scikit-learn, or Apache Spark for recommendations.
   - **BigQuery or BigTable**: For storing product data and querying large datasets.
   - **Redis**: For caching product recommendations.

2. **Frontend**:
   - React or Angular for building the UI.
   - Use libraries like **Axios** for making API calls to the product and recommendation services.

---

### **High-Level Architecture**

1. **Data Load**: 
   - Load Excel data into BigQuery/BigTable.
   
2. **Product Service**:
   - Exposes product APIs (view, latest, recommendations).
   - Sends product interaction events to Kafka for real-time recommendations.

3. **Recommendation Service**:
   - Consumes events from Kafka, updates the recommendation system, and provides personalized recommendations.

4. **ML Service**:
   - Uses machine learning to dynamically generate recommendations.

5. **API Gateway**:
   - Routes user requests to the correct service (Product or Recommendation).

6. **Kafka**:
   - Stream product interactions from Product Service to Recommendation Service.

---

### **Next Steps**

1. **Set up BigQuery/BigTable** and load your product data.
2. **Implement Product Service** with endpoints for product detail, latest products, and product views.
3. **Integrate Kafka** for real-time streaming of product interactions.
4. **Develop Recommendation Service** that listens to Kafka topics and updates user recommendations.
5. **Train and deploy ML models** for generating personalized recommendations.
6. **Integrate all services using Spring Cloud** for service discovery and API Gateway.
7. **Test and optimize** the entire flow, from data loading to real-time recommendations.

This roadmap should provide you with a clear direction for implementing your product and recommendation services.


  CREATE EXTERNAL TABLE amazon_bigdata.products
  OPTIONS (
    format = 'CLOUD_BIGTABLE',
    uris = ['https://googleapis.com/bigtable/projects/virtualization-and-cloud/instances/product-recommendation/tables/products'],
    bigtable_options =
      """
      {
        "columnFamilies": [
          {
            "familyId": "metadata",
            "type": "STRING",
            "encoding": "BINARY"
          },
          {
            "familyId": "attributes",
            "type": "STRING",
            "encoding": "BINARY"
          },
          {
            "familyId": "links",
            "type": "STRING",
            "encoding": "BINARY"
          }
        ],
        "readRowkeyAsString": true
      }
      """
  );


SELECT
  rowkey,
  -- Access the 'categoryName' and 'title' from metadata column family
  (SELECT col.cell[OFFSET(0)].value 
   FROM UNNEST(metadata.column) AS col 
   WHERE col.name = 'categoryName' 
   LIMIT 1) AS categoryName,
   
  (SELECT col.cell[OFFSET(0)].value 
   FROM UNNEST(metadata.column) AS col 
   WHERE col.name = 'title' 
   LIMIT 1) AS title,
   
  -- Access 'boughtInLastMonth', 'isBestSeller', 'price', etc. from attributes column family
  (SELECT col.cell[OFFSET(0)].value 
   FROM UNNEST(attributes.column) AS col 
   WHERE col.name = 'boughtInLastMonth' 
   LIMIT 1) AS boughtInLastMonth,
   
  (SELECT col.cell[OFFSET(0)].value 
   FROM UNNEST(attributes.column) AS col 
   WHERE col.name = 'isBestSeller' 
   LIMIT 1) AS isBestSeller,
   
  (SELECT col.cell[OFFSET(0)].value 
   FROM UNNEST(attributes.column) AS col 
   WHERE col.name = 'price' 
   LIMIT 1) AS price,
   
  (SELECT col.cell[OFFSET(0)].value 
   FROM UNNEST(attributes.column) AS col 
   WHERE col.name = 'reviews' 
   LIMIT 1) AS reviews,

  (SELECT col.cell[OFFSET(0)].value 
   FROM UNNEST(attributes.column) AS col 
   WHERE col.name = 'stars' 
   LIMIT 1) AS stars,
   
  -- Access 'imgUrl' and 'productURL' from links column family
  (SELECT col.cell[OFFSET(0)].value 
   FROM UNNEST(links.column) AS col 
   WHERE col.name = 'imgUrl' 
   LIMIT 1) AS imgUrl,
   
  (SELECT col.cell[OFFSET(0)].value 
   FROM UNNEST(links.column) AS col 
   WHERE col.name = 'productURL' 
   LIMIT 1) AS productURL
FROM
  `virtualization-and-cloud.amazon_bigdata.products`
LIMIT 1;