D1

D1 is a serverless SQL database (SQL = the standard language for storing data in tables of rows and columns; serverless = you never set up or manage a database server yourself). It runs on SQLite, a tiny, rock-solid database engine used in billions of devices.

You define tables, write familiar SQL queries (SELECT, INSERT, UPDATE), and D1 handles the storage, backups, and scaling for you. It is designed to work hand-in-hand with Cloudflare Workers.

Running your own database means provisioning servers, patching them, and paying even when idle. D1 removes all of that and bills only for the queries and storage you actually use.

These steps create a database, define a table, run a query from the terminal, and read it from a Worker. The --remote flag means 'run against the real cloud database' (leave it off to test locally).

1. Create a database

   Wrangler prints a database_id — copy it for the next step.

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   npx wrangler d1 create my-app-db

2. Bind the database

   Add this to wrangler.jsonc so your Worker can reach it as env.DB.

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   {
     "d1_databases": [
       {
         "binding": "DB",
         "database_name": "my-app-db",
         "database_id": "<paste-your-id-here>"
       }
     ]
   }

3. Create a table & add data

   Run SQL straight from the terminal with --command.

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   npx wrangler d1 execute my-app-db --remote \
     --command "CREATE TABLE customers (id INTEGER PRIMARY KEY, name TEXT, city TEXT);"
   
   npx wrangler d1 execute my-app-db --remote \
     --command "INSERT INTO customers (name, city) VALUES ('Mei', 'Taipei');"

4. Query from a Worker

   prepare() builds a safe query, bind() fills in the ? placeholder (this prevents SQL injection), and all() returns the rows.

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   export default {
     async fetch(request, env) {
       const { results } = await env.DB
         .prepare("SELECT * FROM customers WHERE city = ?")
         .bind("Taipei")
         .all();
       return Response.json(results);
     },
   };

5. Deploy

   Publish the Worker and your database-backed API is live.

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   npx wrangler deploy

• Add indexes on columns you filter by often to read fewer rows (and save money).
• Keep schema changes in a schema.sql file and apply with --file=./schema.sql.
• Paid plan includes 25 billion rows read and 50 million rows written per month before extra charges.
• Use D1 for relational data; use KV for simple key lookups and R2 for large files.