A comparison of two very different approaches to scale

CockroachDB vs MongoDB

/* Distributed relational SQL or the document model? Which will get you further, faster, better? */

Why developers choose CockroachDB over MongoDB

Consistent Transactions

CockroachDB guarantees consistent and high performant ACID transactions at global scale and is never eventual

Development Ease

CockroachDB ensures data integrity, provides joins, and eliminates complex, error-prone application code

Business Workloads

CockroachDB delivers a relational database built for operational workloads and more complex data models

CockroachDB and MongoDB are both used for scale-out applications; however, you should use CockroachDB for operational and transactional workloads.

Schema

Document Model: JSON, partial SQL coverage through connectors

Relational Model and JSON, native SQL language

Data model complexity

Optimized for non-relational data

Simple and complex, deep operational data models

Data integrity

Optional schema validation lacks referential integrity

Relational model enforces referential integrity and strict data types

Schema changes

Agile with limited schema

Online, orchestrated schema changes

Availability

Raft protocol ensures availability during node failure

Resilience

With Atlas, failed nodes restart.

Up-replicates data if nodes do not restart

Query routing

Router processes act as gateways to the entirety of the database for unlimited reads and writes in any region

Every node is a gateway to the entirety of the database for unlimited reads and writes in any region

Transactional consistency

Consistent transactions not guaranteed by default. Eventually consistent

Serializable, guaranteed consistent transactions

Transaction performance

Sub-optimal for transactional workloads

Optimized for transactional workloads

Multi-region writes

Not by default, but possible with complex, careful setup

Native, all nodes can accept reads and writes even across regions

Data residency

Tie data to a node to optimize performance or comply with data regulations

Developer tools

Closed system with limited tooling, mostly created by vendor

Rich ecosystem of PostgreSQL developer tools

Multi-cloud

Deploy in any environment

Deploy in any environment and even run a single instance across envs

Ideal workloads

Simple hierarchical data models, access forward with minimal transactions (if any)

Optimized for relationship based, simple and complex transactional workloads

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Relational (SQL) vs document model (NoSQL) databases

Relational databases are founded in relational algebra and deliver a highly efficient store that enforces data integrity in the database as opposed to in code.

Referential Integrity

With a relational database, maintaining parent/child, data types, and cardinality of relationships is enforced by the database. With NoSQL, the developer needs to add complex code to the application to ensure data integrity and relationships.

Complex Joins

A relational database implements complex joins and aggregate views as part of its execution layer and offers simple DDL for making requests. With NoSQL, joins are often the task of a developer who has to code these routines in their application logic.

Workloads

A relational database is optimal for workloads where data relationships are paramount such as transactional system of record workloads. NoSQL is typically used when you have an established access pattern that will not evolve, with simple data models and minimal transactions (if any).

Related resources

Cloud Database Architecture | CockroachDB vs. Amazon Aurora

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