Pros of Cassandra

• Mature and battle-tested with a large community and extensive documentation
• Highly scalable and fault-tolerant, designed for large-scale distributed systems
• Rich ecosystem of tools and integrations

Cons of Cassandra

• Written in Java, which can lead to higher memory usage and longer garbage collection pauses
• Generally slower performance compared to ScyllaDB, especially for read-heavy workloads
• More complex configuration and tuning required for optimal performance

Code Comparison

Cassandra (CQL):

CREATE TABLE users (
  user_id uuid PRIMARY KEY,
  username text,
  email text
);

ScyllaDB (CQL):

CREATE TABLE users (
  user_id uuid PRIMARY KEY,
  username text,
  email text
) WITH compression = { 'sstable_compression' : 'LZ4Compressor' };

Both ScyllaDB and Cassandra use CQL (Cassandra Query Language) for data manipulation, making them syntactically similar. However, ScyllaDB offers some additional options for performance tuning, such as specifying compression algorithms at the table level.

ScyllaDB aims to be a drop-in replacement for Cassandra, focusing on improved performance and reduced operational complexity. While Cassandra has a longer history and wider adoption, ScyllaDB leverages its C++ implementation and shard-per-core architecture to achieve better resource utilization and lower latencies, especially for larger datasets and read-intensive workloads.

Pros of CockroachDB

• Stronger consistency model with serializable isolation
• Built-in multi-region support for global deployments
• More mature SQL support with advanced features

Cons of CockroachDB

• Higher resource consumption and overhead
• Steeper learning curve for operations and tuning
• Less predictable performance under high concurrency

Code Comparison

ScyllaDB (CQL):

CREATE TABLE users (
  id UUID PRIMARY KEY,
  name TEXT,
  email TEXT
);

CockroachDB (SQL):

CREATE TABLE users (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  name STRING,
  email STRING
);

Both databases use similar syntax for basic operations, but CockroachDB follows standard SQL more closely. ScyllaDB uses CQL (Cassandra Query Language), which is similar to SQL but with some differences in data types and features.

CockroachDB offers more advanced SQL features like foreign keys, indexes, and joins out of the box, while ScyllaDB focuses on high-performance, low-latency operations for simpler data models.

ScyllaDB's approach is generally more suitable for write-heavy workloads and time-series data, while CockroachDB excels in scenarios requiring strong consistency and complex queries across distributed data.

Pros of MongoDB

• More mature and widely adopted ecosystem with extensive documentation and community support
• Flexible schema design allows for easier adaptation to changing data structures
• Rich query language and aggregation framework for complex data operations

Cons of MongoDB

• Generally slower performance compared to ScyllaDB, especially for write-heavy workloads
• Less efficient use of system resources, potentially requiring more hardware
• Scaling can be more complex and costly, particularly for large datasets

Code Comparison

MongoDB query example:

db.users.find({
  age: { $gte: 18 },
  status: "active"
}).sort({ name: 1 })

ScyllaDB query example (using CQL):

SELECT * FROM users
WHERE age >= 18 AND status = 'active'
ORDER BY name ASC;

Both databases offer different query languages, with MongoDB using a JSON-like syntax and ScyllaDB using CQL, which is similar to SQL. MongoDB's query language is often considered more flexible, while ScyllaDB's CQL is more familiar to those with SQL experience.

ScyllaDB is designed for high performance and scalability, particularly suited for large-scale, write-intensive applications. MongoDB, on the other hand, offers more flexibility in data modeling and querying, making it a popular choice for a wide range of applications, especially those with evolving schemas.

Pros of Elasticsearch

• More mature and widely adopted, with a larger ecosystem and community support
• Powerful full-text search capabilities and advanced querying options
• Extensive documentation and learning resources available

Cons of Elasticsearch

• Higher resource consumption and slower performance for large-scale deployments
• More complex setup and configuration process
• Licensing changes have caused concerns in the open-source community

Code Comparison

Elasticsearch query example:

GET /my_index/_search
{
  "query": {
    "match": {
      "title": "elasticsearch"
    }
  }
}

ScyllaDB query example:

SELECT * FROM my_table
WHERE title LIKE '%scylladb%';

ScyllaDB focuses on high-performance, low-latency operations for large datasets, while Elasticsearch excels in full-text search and complex querying. ScyllaDB uses a CQL-like syntax, similar to SQL, making it more familiar for developers with relational database experience. Elasticsearch uses a JSON-based query DSL, which is powerful but may require a steeper learning curve.

Both databases have their strengths and are suited for different use cases. Elasticsearch is ideal for search-heavy applications, while ScyllaDB is better for high-throughput, low-latency workloads with large amounts of data.

Pros of TiDB

• SQL compatibility: TiDB offers MySQL compatibility, making it easier for users familiar with SQL databases
• Horizontal scalability: Designed for distributed scaling across multiple nodes
• HTAP capabilities: Supports both OLTP and OLAP workloads in a single system

Cons of TiDB

• Higher resource consumption: Generally requires more resources compared to ScyllaDB
• Complexity: More complex architecture and setup process than ScyllaDB
• Learning curve: Steeper learning curve for optimization and management

Code Comparison

TiDB SQL query example:

SELECT * FROM users
WHERE age > 25 AND city = 'New York'
ORDER BY name
LIMIT 10;

ScyllaDB CQL query example:

SELECT * FROM users
WHERE age > 25 AND city = 'New York'
ORDER BY name
LIMIT 10;

While the syntax looks similar, TiDB supports a wider range of SQL features and functions compared to ScyllaDB's CQL. ScyllaDB focuses on high-performance, low-latency operations for specific use cases, while TiDB aims to provide a more comprehensive SQL-compatible distributed database solution.