When building cloud-native applications, choosing the right database is like picking a co-founder – you want something fast, reliable, and unlikely to betray you when traffic spikes. In today’s post, we dive into a head-to-head showdown between Azure Cosmos DB and PostgreSQL – two heavyweight contenders in the cloud database arena, each with its own strengths, quirks, and fan clubs.

Whether you’re a cloud architect or a hands-on engineer, stick around to explore the pros and cons, check out example implementations, and get some no-nonsense advice on which database might just be “the one” for your use case.

Understanding the Contenders

Azure Cosmos DB

Azure Cosmos DB is a fully managed, globally distributed NoSQL database service designed to handle mission-critical applications with low latency and elastic scalability. Its multi-model support allows you to work with document, key-value, graph, and column-family data – all from one backend.

Key Features:

• Global Distribution: Easily replicate data across multiple regions
• Multi-Model Support: Use various data models with a single API
• Low Latency: Optimized for fast, responsive applications
• Elastic Scalability: Scale throughput and storage dynamically

PostgreSQL

PostgreSQL is a powerful open-source relational database system known for its robustness, ACID compliance, and adherence to SQL standards. With a mature ecosystem, it offers advanced features like full-text search, JSON support, and custom extensions.

Key Features:

• Relational Integrity: Strong ACID-compliance for transactional applications
• Rich Querying: Advanced SQL capabilities and indexing.
• Extensibility: Supports custom functions, procedures, and extensions
• Community-Driven: A vibrant ecosystem with extensive community support

Pros and Cons Comparison

Below is a table summarizing the main advantages and drawbacks of each solution from an implementation and cloud architecture perspective.

Implementation Examples

Example Infrastructure-Part in Terraform

• Creates a Cosmos DB (SQL API) instance
• Sets up a PostgreSQL Flexible Server
• Creates corresponding databases
• Uses Terraform best practices

provider "azurerm" {
  features {}
}

# Create a Resource Group
resource "azurerm_resource_group" "rg" {
  name     = "my-resource-group"
  location = "East US"
}

# Create an Azure Cosmos DB Account
resource "azurerm_cosmosdb_account" "cosmosdb" {
  name                      = "mycosmosdbaccount"
  location                  = azurerm_resource_group.rg.location
  resource_group_name       = azurerm_resource_group.rg.name
  offer_type                = "Standard"
  kind                      = "GlobalDocumentDB"

  consistency_policy {
    consistency_level = "Session"
  }

  geo_location {
    location          = azurerm_resource_group.rg.location
    failover_priority = 0
  }
}

# Create a Cosmos DB SQL Database
resource "azurerm_cosmosdb_sql_database" "cosmos_sql_db" {
  name                = "mycosmosdb"
  resource_group_name = azurerm_resource_group.rg.name
  account_name        = azurerm_cosmosdb_account.cosmosdb.name
}

# Create an Azure PostgreSQL Flexible Server
resource "azurerm_postgresql_flexible_server" "postgres" {
  name                   = "mypostgresserver"
  location               = azurerm_resource_group.rg.location
  resource_group_name    = azurerm_resource_group.rg.name
  administrator_login    = "adminuser"
  administrator_password = "P@ssword1234!" # Use a secure password in production
  sku_name               = "B_Standard_B1ms"
  version                = "14"
}

# Create a PostgreSQL Database
resource "azurerm_postgresql_flexible_server_database" "postgres_db" {
  name      = "mypostgresdb"
  server_id = azurerm_postgresql_flexible_server.postgres.id
  collation = "en_US.utf8"
  charset   = "UTF8"
}

Don’t forget to use a variable file (not included in this basic example).

Example: Azure Cosmos DB

Below is a Python snippet using the Azure Cosmos DB SDK to create a database, container, and insert an item:

from azure.cosmos import CosmosClient, PartitionKey, exceptions

# Initialize the Cosmos client
endpoint = "YOUR_COSMOS_DB_ENDPOINT"
key = "YOUR_COSMOS_DB_KEY"
client = CosmosClient(endpoint, key)

# Create (or get) the database
database_name = 'MyDatabase'
database = client.create_database_if_not_exists(id=database_name)

# Create (or get) the container
container_name = 'MyContainer'
container = database.create_container_if_not_exists(
    id=container_name,
    partition_key=PartitionKey(path="/myPartitionKey"),
    offer_throughput=400
)

# Create a sample item
item = {
    "id": "1",
    "name": "Jane Doe",
    "myPartitionKey": "Partition1"
}

# Insert the item
container.create_item(body=item)
print("Item created in Azure Cosmos DB!")

Example: PostgreSQL

Here’s an example using Python’s psycopg2 library to connect to a PostgreSQL database, create a table, and insert a record:

import psycopg2

# Connect to your PostgreSQL database
connection = psycopg2.connect(
    host="YOUR_POSTGRES_HOST",
    database="yourDatabase",
    user="yourUser",
    password="yourPassword"
)

cursor = connection.cursor()

# Create a table if it doesn't exist
create_table_query = """
CREATE TABLE IF NOT EXISTS users (
    id SERIAL PRIMARY KEY,
    name VARCHAR(100) NOT NULL
);
"""
cursor.execute(create_table_query)

# Insert a sample record
insert_query = "INSERT INTO users (name) VALUES (%s);"
cursor.execute(insert_query, ("Jane Doe",))

# Commit the transaction
connection.commit()

cursor.close()
connection.close()
print("Record inserted into PostgreSQL!")

When to Choose Which?

Azure Cosmos DB is ideal when:

• Global Distribution is Key: If your application needs low-latency access from multiple regions
• Flexible Data Models are Needed: When working with diverse or rapidly evolving data structures
• Fully Managed Operations: If you prefer a managed service that abstracts much of the operational overhead

PostgreSQL is the go-to choice when:

• Relational Data & Complex Queries: Your application benefits from strong ACID properties and the power of SQL
• Cost Efficiency: You’re looking for a robust, open-source solution or a managed service with predictable costs
• Advanced Analytical Requirements: When you need mature support for complex queries and data integrity

For cloud architects and engineers, the choice between Azure Cosmos DB and PostgreSQL should align with both your application’s requirements and operational preferences. Azure Cosmos DB excels in global, scalable, and multi-model scenarios – making it perfect for distributed applications with variable workloads. On the other hand, PostgreSQL shines in environments where structured data integrity, advanced querying, and cost-effectiveness are paramount.

Ultimately, your decision should factor in:

• Application Requirements: Consider latency, data structure, and transaction complexity
• Operational Overhead: Evaluate your team’s expertise and the importance of managed services
• Cost Considerations: Analyze the pricing model relative to expected usage patterns

By assessing these dimensions, you can confidently select the right database solution to empower your cloud architecture.

Happy architecting!

Cheers, Oskar