AWS Cognito Identity ID 是否适合作为MySQL收藏表的主键？

Alright, let's tackle this favorites table design for your MySQL platform—since you're dealing with Cognito Identity IDs and potentially millions of rows, we need to make sure it's efficient, scalable, and fits your use case perfectly.

First, let's start with a production-ready table structure tailored to your requirements:

CREATE TABLE user_favorites (
    user_id VARCHAR(60) NOT NULL,
    item_id BIGINT NOT NULL, -- Adjust this data type to match your items table's itemID
    date_added TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
    PRIMARY KEY (user_id, item_id),
    INDEX idx_item_user (item_id, user_id)
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4;

Let's break down why each part matters:

Data Type Choices #

• user_id: Cognito's Identity ID follows the format us-east-1:128d0a74-c82f-4553-916d-90053e4a8b0f, which is 46 characters long. Using VARCHAR(60) gives you plenty of buffer in case AWS ever adjusts the ID format, avoiding truncation issues. Marking it NOT NULL ensures every favorite record is tied to a valid user.
• item_id: I assumed your items table uses a numeric BIGINT for item IDs—if your item IDs are string-based (like UUIDs), swap this for a VARCHAR with a length matching your items table. Again, NOT NULL enforces that each favorite links to a valid item.
• date_added: Using TIMESTAMP with DEFAULT CURRENT_TIMESTAMP automatically sets the timestamp when a favorite is added, perfect for sorting a user's favorites by recency. It also uses half the storage of DATETIME (4 bytes vs 8 bytes), which adds up with millions of rows.

Primary Key Design #

Setting PRIMARY KEY (user_id, item_id) is exactly the right call here:

• It enforces uniqueness: A user can't favorite the same item more than once (no duplicate records).
• It creates a clustered index (InnoDB uses clustered indexes for primary keys) that blazes through the most common query: "Get all favorites for a specific user". The index is ordered by user_id first, so MySQL can quickly locate all rows for a given user without scanning the entire table.

Optional Index for Reverse Queries #

The idx_item_user (item_id, user_id) index is useful if you ever need to run queries like:

• "How many users have favorited this item?"
• "Which users have favorited this item?"

If you don't plan on running these kinds of reverse queries, you can skip this index to save a bit of storage and reduce write overhead (every insert/update will update this index too). But it's cheap insurance if your requirements might expand later.

Additional Tips for Scalability #

• Use InnoDB: It's the default MySQL engine for good reason—supports transactions, row-level locking (critical for high write throughput), and crash recovery, essential for a table with frequent inserts/deletes.
• Batch Operations: When users bulk-add favorites, use INSERT ... ON DUPLICATE KEY UPDATE to avoid duplicate entry errors. For example, if you want to update the date_added when a user re-favorites an item:

  INSERT INTO user_favorites (user_id, item_id)
  VALUES ('us-east-1:128d0a74-c82f-4553-916d-90053e4a8b0f', 12345)
  ON DUPLICATE KEY UPDATE date_added = CURRENT_TIMESTAMP;
  

• Clean Up Stale Data: Periodically delete records for items that are no longer in your products table, or for users that have been inactive long-term. This keeps the table lean and queries fast.

内容的提问来源于stack exchange，提问作者OhNoNotALinkerError