Pros of CockroachDB

• Designed for horizontal scalability and distributed operations
• Built-in support for multi-region deployments and geo-partitioning
• Strong consistency model with serializable isolation by default

Cons of CockroachDB

• Less mature ecosystem and community compared to PostgreSQL
• Some PostgreSQL features and extensions are not supported
• Higher resource consumption for small-scale deployments

Code Comparison

PostgreSQL:

CREATE TABLE users (
  id SERIAL PRIMARY KEY,
  name TEXT,
  created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

CockroachDB:

CREATE TABLE users (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  name STRING,
  created_at TIMESTAMP DEFAULT current_timestamp()
);

The main differences in this example are:

1. CockroachDB uses UUID as the default primary key type instead of SERIAL.
2. CockroachDB uses STRING instead of TEXT for string data.
3. The gen_random_uuid() function is used to generate unique IDs in CockroachDB.

Both databases support SQL syntax, but CockroachDB has some unique features and syntax differences to accommodate its distributed nature and scalability focus. While PostgreSQL is a more established and feature-rich database, CockroachDB offers advantages in terms of horizontal scaling and distributed operations, making it suitable for certain use cases where these features are critical.

Pros of MySQL

• Generally faster for read-heavy workloads and simple queries
• Easier to set up and manage for beginners
• More widespread adoption, especially in web applications

Cons of MySQL

• Less ACID-compliant in certain configurations
• Limited support for complex queries and advanced features
• Less adherence to SQL standards compared to PostgreSQL

Code Comparison

MySQL:

CREATE TABLE users (
  id INT AUTO_INCREMENT PRIMARY KEY,
  name VARCHAR(50),
  created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

PostgreSQL:

CREATE TABLE users (
  id SERIAL PRIMARY KEY,
  name VARCHAR(50),
  created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

The main difference in this example is the use of AUTO_INCREMENT in MySQL versus SERIAL in PostgreSQL for creating an auto-incrementing primary key. Both achieve similar results, but PostgreSQL's SERIAL is more standards-compliant.

MySQL also tends to be more lenient with data types, while PostgreSQL is stricter. For instance, MySQL might allow implicit type conversions that PostgreSQL would reject, requiring more explicit casting.

Overall, both databases have their strengths and are suitable for different use cases. MySQL excels in simple, read-heavy scenarios, while PostgreSQL offers more advanced features and better standards compliance.

Pros of MariaDB server

• More storage engines, including specialized ones like ColumnStore for analytics
• Galera Cluster for multi-master replication out-of-the-box
• Generally faster performance for write-intensive workloads

Cons of MariaDB server

• Smaller community and ecosystem compared to Postgres
• Less advanced support for JSON and other NoSQL-like features
• Some compatibility issues with MySQL, despite being a fork

Code comparison

MariaDB server (SQL syntax):

CREATE TABLE users (
  id INT AUTO_INCREMENT PRIMARY KEY,
  name VARCHAR(50),
  created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
) ENGINE=InnoDB;

Postgres (SQL syntax):

CREATE TABLE users (
  id SERIAL PRIMARY KEY,
  name VARCHAR(50),
  created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

The main difference in this example is the AUTO_INCREMENT vs SERIAL for auto-incrementing primary keys, and the explicit engine specification in MariaDB. Postgres uses SERIAL as a shorthand for an auto-incrementing integer column.

Both projects are open-source relational database management systems with active development. MariaDB offers some unique features and potentially better performance for certain workloads, while Postgres has a larger community and more advanced features in some areas. The choice between them often depends on specific project requirements and existing infrastructure.

Pros of YugabyteDB

• Distributed architecture for high availability and horizontal scalability
• Built-in support for multi-region deployments and geo-distribution
• ACID-compliant transactions across multiple nodes and regions

Cons of YugabyteDB

• Relatively newer project with a smaller community compared to Postgres
• Some advanced Postgres features may not be fully supported or implemented
• Potential learning curve for teams familiar with traditional RDBMS systems

Code Comparison

YugabyteDB extends Postgres syntax with additional features:

-- YugabyteDB: Creating a table with hash-based sharding
CREATE TABLE users (
  id INT PRIMARY KEY,
  name TEXT
) SPLIT INTO 4 TABLETS;

-- Postgres: Standard table creation
CREATE TABLE users (
  id SERIAL PRIMARY KEY,
  name TEXT
);

YugabyteDB supports distributed transactions across nodes:

-- YugabyteDB: Multi-node transaction
BEGIN TRANSACTION;
INSERT INTO users (id, name) VALUES (1, 'Alice') ON TABLET 1;
INSERT INTO users (id, name) VALUES (2, 'Bob') ON TABLET 2;
COMMIT;

-- Postgres: Standard transaction
BEGIN;
INSERT INTO users (name) VALUES ('Alice');
INSERT INTO users (name) VALUES ('Bob');
COMMIT;

Both projects use C for core database functionality, but YugabyteDB incorporates additional languages like C++ for its distributed components.

Pros of Citus

• Enables horizontal scaling of PostgreSQL for improved performance on large datasets
• Provides built-in sharding capabilities for distributed query processing
• Offers real-time analytics on large-scale data with minimal latency

Cons of Citus

• Adds complexity to database management and maintenance
• May require application changes to fully leverage distributed capabilities
• Limited compatibility with some PostgreSQL extensions and features

Code Comparison

PostgreSQL (standard query):

SELECT * FROM users WHERE city = 'New York';

Citus (distributed query):

SELECT * FROM users_distributed WHERE city = 'New York';

The main difference is that Citus requires tables to be distributed across nodes, which is reflected in the table name and underlying query execution.

Key Differences

• Postgres is a traditional relational database, while Citus extends Postgres for distributed computing
• Citus focuses on scalability and performance for large-scale applications
• Postgres offers a wider range of features and extensions, while Citus specializes in distributed workloads

Use Cases

• Postgres: General-purpose database for various application types
• Citus: High-performance analytics, real-time dashboards, and large-scale multi-tenant applications

Pros of TiDB

• Designed for horizontal scalability and distributed architecture
• Supports both OLTP and OLAP workloads
• Compatible with MySQL protocol, making migration easier

Cons of TiDB

• Relatively newer project with a smaller community compared to Postgres
• May have fewer extensions and third-party tools available
• Performance can be less predictable in certain scenarios due to distributed nature

Code Comparison

TiDB (SQL syntax):

CREATE TABLE users (
  id INT PRIMARY KEY,
  name VARCHAR(255),
  created_at TIMESTAMP
);

Postgres (SQL syntax):

CREATE TABLE users (
  id SERIAL PRIMARY KEY,
  name VARCHAR(255),
  created_at TIMESTAMP
);

The SQL syntax for basic operations is similar between TiDB and Postgres, with minor differences in data types and default behaviors. TiDB aims for MySQL compatibility, while Postgres follows the SQL standard more closely.

TiDB is built for distributed scenarios and horizontal scaling, making it suitable for large-scale applications with high concurrency. Postgres excels in traditional relational database use cases and offers a more mature ecosystem.

Both projects are open-source and actively maintained, with TiDB focusing on distributed SQL capabilities and Postgres continuing to enhance its robust feature set and performance for single-node deployments.