---
name: nosql-expert
description: "Expert guidance for distributed NoSQL databases (Cassandra, DynamoDB). Focuses on mental models, query-first modeling, single-table design, and avoiding hot partitions in high-scale systems."
---

# NoSQL Expert Patterns (Cassandra & DynamoDB)

## Overview

This skill provides professional mental models and design patterns for **distributed wide-column and key-value stores** (specifically Apache Cassandra and Amazon DynamoDB).

Unlike SQL (where you model data entities), or document stores (like MongoDB), these distributed systems require you to **model your queries first**.

## When to Use

- **Designing for Scale**: Moving beyond simple single-node databases to distributed clusters.
- **Technology Selection**: Evaluating or using **Cassandra**, **ScyllaDB**, or **DynamoDB**.
- **Performance Tuning**: Troubleshooting "hot partitions" or high latency in existing NoSQL systems.
- **Microservices**: Implementing "database-per-service" patterns where highly optimized reads are required.

## The Mental Shift: SQL vs. Distributed NoSQL

| Feature | SQL (Relational) | Distributed NoSQL (Cassandra/DynamoDB) |
| :--- | :--- | :--- |
| **Data modeling** | Model Entities + Relationships | Model **Queries** (Access Patterns) |
| **Joins** | CPU-intensive, at read time | **Pre-computed** (Denormalized) at write time |
| **Storage cost** | Expensive (minimize duplication) | Cheap (duplicate data for read speed) |
| **Consistency** | ACID (Strong) | **BASE (Eventual)** / Tunable |
| **Scalability** | Vertical (Bigger machine) | **Horizontal** (More nodes/shards) |

> **The Golden Rule:** In SQL, you design the data model to answer *any* query. In NoSQL, you design the data model to answer *specific* queries efficiently.

## Core Design Patterns

### 1. Query-First Modeling (Access Patterns)

You typically cannot "add a query later" without migration or creating a new table/index.

**Process:**
1.  **List all Entities** (User, Order, Product).
2.  **List all Access Patterns** ("Get User by Email", "Get Orders by User sorted by Date").
3.  **Design Table(s)** specifically to serve those patterns with a single lookup.

### 2. The Partition Key is King

Data is distributed across physical nodes based on the **Partition Key (PK)**.
-   **Goal:** Even distribution of data and traffic.
-   **Anti-Pattern:** Using a low-cardinality PK (e.g., `status="active"` or `gender="m"`) creates **Hot Partitions**, limiting throughput to a single node's capacity.
-   **Best Practice:** Use high-cardinality keys (User IDs, Device IDs, Composite Keys).

### 3. Clustering / Sort Keys

Within a partition, data is sorted on disk by the **Clustering Key (Cassandra)** or **Sort Key (DynamoDB)**.
-   This allows for efficient **Range Queries** (e.g., `WHERE user_id=X AND date > Y`).
-   It effectively pre-sorts your data for specific retrieval requirements.

### 4. Single-Table Design (Adjacency Lists)

*Primary use: DynamoDB (but concepts apply elsewhere)*

Storing multiple entity types in one table to enable pre-joined reads.

| PK (Partition) | SK (Sort) | Data Fields... |
| :--- | :--- | :--- |
| `USER#123` | `PROFILE` | `{ name: "Ian", email: "..." }` |
| `USER#123` | `ORDER#998` | `{ total: 50.00, status: "shipped" }` |
| `USER#123` | `ORDER#999` | `{ total: 12.00, status: "pending" }` |

-   **Query:** `PK="USER#123"`
-   **Result:** Fetches User Profile AND all Orders in **one network request**.

### 5. Denormalization & Duplication

Don't be afraid to store the same data in multiple tables to serve different query patterns.
-   **Table A:** `users_by_id` (PK: uuid)
-   **Table B:** `users_by_email` (PK: email)

*Trade-off: You must manage data consistency across tables (often using eventual consistency or batch writes).*

## Specific Guidance

### Apache Cassandra / ScyllaDB

-   **Primary Key Structure:** `((Partition Key), Clustering Columns)`
-   **No Joins, No Aggregates:** Do not try to `JOIN` or `GROUP BY`. Pre-calculate aggregates in a separate counter table.
-   **Avoid `ALLOW FILTERING`:** If you see this in production, your data model is wrong. It implies a full cluster scan.
-   **Writes are Cheap:** Inserts and Updates are just appends to the LSM tree. Don't worry about write volume as much as read efficiency.
-   **Tombstones:** Deletes are expensive markers. Avoid high-velocity delete patterns (like queues) in standard tables.

### AWS DynamoDB

-   **GSI (Global Secondary Index):** Use GSIs to create alternative views of your data (e.g., "Search Orders by Date" instead of by User).
    -   *Note:* GSIs are eventually consistent.
-   **LSI (Local Secondary Index):** Sorts data differently *within* the same partition. Must be created at table creation time.
-   **WCU / RCU:** Understand capacity modes. Single-table design helps optimize consumed capacity units.
-   **TTL:** Use Time-To-Live attributes to automatically expire old data (free delete) without creating tombstones.

## Expert Checklist

Before finalizing your NoSQL schema:

-   [ ] **Access Pattern Coverage:** Does every query pattern map to a specific table or index?
-   [ ] **Cardinality Check:** Does the Partition Key have enough unique values to spread traffic evenly?
-   [ ] **Split Partition Risk:** For any single partition (e.g., a single user's orders), will it grow indefinitely? (If > 10GB, you need to "shard" the partition, e.g., `USER#123#2024-01`).
-   [ ] **Consistency Requirement:** Can the application tolerate eventual consistency for this read pattern?

## Common Anti-Patterns

❌ **Scatter-Gather:** Querying *all* partitions to find one item (Scan).
❌ **Hot Keys:** Putting all "Monday" data into one partition.
❌ **Relational Modeling:** Creating `Author` and `Book` tables and trying to join them in code. (Instead, embed Book summaries in Author, or duplicate Author info in Books).
