Building a DynamoDB-like Database

Introduction

Building a database like DynamoDB from scratch is a complex task, but it can be done by carefully designing and implementing the distributed architecture, data model, storage engine, and API. In this article, we will discuss each component in detail and provide mermaid diagrams to visualize the process.

Distributed Architecture

The distributed architecture consists of the following components:

• Storage nodes: Servers that store data and handle read and write operations.
• Coordinator nodes: Servers that receive client requests and coordinate read and write operations with storage nodes.
• Load balancer: Distributes incoming requests to coordinator nodes.

Data Model

Design a schema-less data model with the following primary data structures:

• Tables: Collections of items, identified by a unique table name.
• Items: Collections of attributes, identified by a primary key.
• Attributes: Key-value pairs that represent the data in an item.

The primary key for each item consists of a partition key and an optional sort key. The partition key is used to distribute data evenly across storage nodes, while the sort key is used to sort items within a partition.

Storage Engine

The storage engine is responsible for managing data storage, retrieval, and consistency. Building a storage engine involves implementing the following components and data structures:

a. LSM Tree (Log-Structured Merge Tree)

LSM trees are efficient for write-heavy workloads and can support high write and read throughput. They comprise two main components:

• Memtable: An in-memory data structure to buffer incoming writes.
• SSTables: Sorted and immutable files on disk containing flushed data from the Memtable.

API Design

We’ll use Go and the Gin Web Framework to design the API. First, install the Gin package:

go get -u github.com/gin-gonic/gin

Next, create a main.go file with the following content:

package main
import (
 "github.com/gin-gonic/gin"
)
func main() {
 router := gin.Default()
 router.POST("/tables", createTable)
 router.DELETE("/tables/:tableName", deleteTable)
 router.PUT("/tables/:tableName/items", putItem)
 router.GET("/tables/:tableName/items/:key", getItem)
 router.POST("/tables/:tableName/items/:key", updateItem)
 router.DELETE("/tables/:tableName/items/:key", deleteItem)
 router.GET("/tables/:tableName/query", queryItems)
 router.GET("/tables/:tableName/scan", scanItems)
 router.Run()
}
func createTable(c *gin.Context) {
 // Implement CreateTable logic
}
func deleteTable(c *gin.Context) {
 // Implement DeleteTable logic
}
func putItem(c *gin.Context) {
 // Implement PutItem logic
}
func getItem(c *gin.Context) {
 // Implement GetItem logic
}
func updateItem(c *gin.Context) {
 // Implement UpdateItem logic
}
func deleteItem(c *gin.Context) {
 // Implement DeleteItem logic
}
func queryItems(c *gin.Context) {
 // Implement Query logic
}
func scanItems(c *gin.Context) {
 // Implement Scan logic
}

This code sets up the API with the necessary endpoints for each operation. You’ll need to implement the logic for each function (e.g., createTable, deleteTable, etc.) to interact with your custom storage engine and data model.

By carefully designing and implementing these components and data structures, you can build a distributed, scalable, and fault-tolerant database similar to DynamoDB. Keep in mind that building a production-ready database from scratch is a complex and time-consuming task. It’s essential to thoroughly test and optimize the system to ensure it meets the desired performance and reliability requirements.