/nosql-expert

name: nosql-expert
type: reference
description: "Provides distributed NoSQL patterns for Cassandra and DynamoDB covering query-first modeling, single-table design, and partition key strategies. Use when designing NoSQL schemas or when the user mentions Cassandra, DynamoDB, or NoSQL modeling."
paths: ["**/mongo*", "**/dynamo*", "**/cassandra*", "**/*.json"]
effort: 3
allowed-tools: Read, Glob, Grep, Write, Edit, Bash
user-invocable: true
when_to_use: "When designing NoSQL database schemas with Cassandra or DynamoDB, applying query-first modeling or single-table design"

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). ❌