NewSQL databases represent an innovative category of relational databases that provide a familiar SQL interface alongside ACID compliance, while being optimized for cloud-scale performance. This guide is essential for beginner developers, operations personnel, and students who are familiar with basic SQL and traditional relational databases (RDBMS). You will gain insights into the core principles of NewSQL, its architectural components, practical applications, and how to start working with popular NewSQL systems. By the end of this article, you will be equipped to determine if NewSQL is suitable for your projects and understand how to implement it effectively.

What is NewSQL?

Core Definition and Analogy

NewSQL combines the relational data model and SQL query language with the ability to scale horizontally across many machines, similar to modern distributed systems. Think of NewSQL as a contemporary, distributed warehouse that maintains the use of SQL: you benefit from the same querying capabilities and strict transaction guarantees, but with an architecture that can expand by adding more nodes.

Differences from Traditional RDBMS and NoSQL

• Traditional RDBMS (e.g., Postgres/MySQL): These systems excel in features and reliability but generally scale vertically, which can lead to challenges at large volumes. NewSQL aims for horizontal scalability to accommodate larger datasets and higher throughput.
• NoSQL (e.g., Cassandra, MongoDB): While NoSQL solutions focus on availability and partition tolerance, many sacrifice consistency and limit transactional capabilities. NewSQL merges the scalability of NoSQL with strong consistency and full transactional integrity.

In summary, if your project requires SQL and strict transactional integrity at scale, NewSQL systems offer the solutions you need.

Why NewSQL Emerged

History and Motivation

Traditional relational databases function on a single primary node for writing and usually leverage replicas for reads. This vertical approach can lead to hardware limitations and high operational costs at scale, making them cumbersome to manage across multiple regions.

NoSQL’s Limitations for Transactional Workloads

Initially, NoSQL databases prioritized availability, which resulted in trade-offs for multi-row ACID transactions and SQL usability. Applications needing rigorous consistency—such as payment processing—found these trade-offs unacceptable.

Market Drivers

Industry needs, especially in sectors like global payments and e-commerce, called for high throughput with stringent transactional accuracy. Research conducted by Google on Spanner (reference to the Spanner paper) showed that a globally distributed database could achieve strong consistency, laying the groundwork for many NewSQL designs.

Core Principles and Architecture of NewSQL Systems

Architectural Building Blocks

• Sharding/Partitioning: Distributes data across nodes using methods like range and hash partitioning to balance the load.
• Replication: Ensures fault tolerance by keeping copies of partitions, with options for synchronous (strong consistency) and asynchronous methods (eventual consistency).
• Consensus Protocols: Raft and Paxos protocols elect leaders to coordinate replication across different nodes.

Transaction and Consistency Models

NewSQL systems deliver ACID transactions across partitions, which requires support for distributed transactions:

• Concurrency Control: Approaches vary from optimistic methods with retries to locks.
• Two-Phase Commit (2PC): Often optimized to minimize operational costs in NewSQL architectures.
• Isolation Levels: Many databases aim for serializability or externally consistent semantics.

Clock and Ordering Techniques

Accurate event ordering is crucial. Technologies like Google’s TrueTime utilize atomic clocks to manage uncertainty, while other systems may rely on logical clocks for causal ordering without needing highly precise physical clocks.

Query Processing and SQL Support

Executing SQL queries over partitions often necessitates sophisticated distributed processing to minimize data movement across nodes.

Popular NewSQL Systems and Quick Comparisons

Short Profiles

• Google Spanner: A globally distributed system using TrueTime for external consistency. Designed for global financial systems and multi-region services. Its public version is Cloud Spanner.
• CockroachDB: Known for its Raft-based replication and PostgreSQL wire compatibility, making it suitable for cloud-native applications. Documentation is available here.
• TiDB: Features a MySQL-compatible interface and is designed for hybrid transactional plus analytical processing (HTAP).
• VoltDB: An excellent choice for ultra-low latency transactional applications, ideal for environments like high-frequency trading.
• YugabyteDB: A PostgreSQL-compatible system that emphasizes strong distributed transactions.

Comparison Table

Use Cases — When to Choose NewSQL

Suitable Workloads and Industries

• Use NewSQL for high-throughput OLTP systems requiring strong correctness (e.g., payment processing or inventory management).
• Consider it for multi-region SaaS applications demanding consistent transactions across geographies.
• Ideal for platforms that necessitate both transactional and operational analytics through HTAP.

When Not to Use NewSQL

• For small-scale projects, a single-node RDBMS might be more cost-effective and easier to manage.
• Avoid NewSQL for heavily analytical workloads with complex OLAP queries; specialized OLAP systems are preferable.
• Extreme schema-less requirements might better suit NoSQL solutions.

Choosing technology should be based on access patterns, latency needs, and team expertise. NewSQL is particularly valuable for projects demanding scale or multi-region consistency.

Getting Started — Picking a NewSQL and a Simple Example

Selection Checklist

• Check SQL compatibility (Postgres/MySQL) and portability of existing applications.
• Assess transaction guarantees (serializability vs snapshot isolation).
• Consider multi-region needs and cross-region latency.
• Evaluate the operational complexity and available managed options.
• Look into ecosystem support: drivers, ORMs, and monitoring integrations.

Beginner-Friendly Picks

• CockroachDB: Best for those wanting a Postgres-like experience.
• TiDB: Great for teams migrating from MySQL workloads.
• VoltDB: Ideal for an experimental setup requiring ultra-low latency performance.

Quick Hands-on: Start a 3-Node CockroachDB Cluster in Docker

1. Prerequisites: Ensure Docker is installed.
2. Begin by creating a network and starting three nodes:

   # Create a network
   docker network create cockroach-net
   
   # Start 3 Cockroach nodes (insecure, for local testing only)
   docker run -d --name=cockroach1 --hostname=cockroach1 --net=cockroach-net -p 26257:26257 -p 8080:8080 cockroachdb/cockroach:v22.2.8 start --insecure
   

docker run -d —name=cockroach2 —hostname=cockroach2 —net=cockroach-net cockroachdb/cockroach:v22.2.8 start —insecure —join=cockroach1:26257

docker run -d —name=cockroach3 —hostname=cockroach3 —net=cockroach-net cockroachdb/cockroach:v22.2.8 start —insecure —join=cockroach1:26257

Initialize the cluster

docker exec -it cockroach1 ./cockroach init —insecure

Start SQL shell (from node1)

docker exec -it cockroach1 ./cockroach sql —insecure

3. In the SQL shell, create a database and execute a multi-statement transaction:
```sql
CREATE DATABASE bank;
USE bank;

CREATE TABLE accounts (
  id INT PRIMARY KEY,
  balance DECIMAL
);

INSERT INTO accounts VALUES (1, 100.00), (2, 75.00);

BEGIN;
UPDATE accounts SET balance = balance - 20.00 WHERE id = 1;
UPDATE accounts SET balance = balance + 20.00 WHERE id = 2;
COMMIT;

SELECT * FROM accounts;

This transaction simulates a money transfer that adheres to ACID properties, maintaining correctness even when data is distributed across different shards.

Official Quickstarts and Documentation

• For further details, refer to the CockroachDB documentation. For guidance on other systems, consult their respective vendor documentation like TiDB or VoltDB.

Operational Considerations and Best Practices

Monitoring, Backups, and Disaster Recovery

• Track key metrics like latency, throughput, and replication health using tools like Prometheus and Grafana.
• Implement regular backups and test restoration processes—to avoid relying solely on replication as a safety net.
• Establish a disaster recovery strategy that includes cross-region replication.

Schema Design and Partitioning Strategies

• Choose partition keys wisely to colocate frequently accessed data, minimizing cross-shard transactions.
• Avoid hot-shard conditions—implement hashed or composite keys where necessary.

Testing and Benchmarking

• Simulate realistic workloads during benchmark tests to reflect typical read/write patterns.
• Conduct chaos engineering by simulating node failures to ensure system resilience.

Security Basics

• Utilize TLS for secure connections, ensure proper authentication, and adhere to least-privilege access for service accounts.
• Follow OS hardening best practices—further details can be found in resources like the AppArmor guide.

Storage and Hardware Considerations

• Use SSDs with high endurance for write-heavy OLTP workloads. Reference the SSD guide for optimal selection.
• For on-premise setups, ensure proper RAID configuration—consider the storage RAID configuration guide for best practices.

Common Pitfalls and FAQ

Common Mistakes

• Expecting linear scaling for all cases; cross-partition transactions add complexity.
• Poor choice of partition keys leading to hot shards and uneven loads.
• Neglecting backup strategies assuming replication suffices.

FAQ (Short Answers)

1. Can I use standard SQL clients and ORMs?

   • Yes, many NewSQL databases offer compatibility with standard SQL interfaces, including Postgres and MySQL.

2. Do I need backups even with replication in place?

   • Absolutely, as replication alone does not protect against logical errors or accidental deletions.

3. Is schema design still important with NewSQL?

   • Definitely; good design and partitioning strategies are vital for optimal performance.

4. Are distributed transactions always slower?

   • Generally, but NewSQL strategies aim to minimize this issue through smart partitioning.

5. How do I choose between managed and self-hosted options?

   • Consider operational overhead, regulatory needs, and customization potential.

6. Is NewSQL suitable for production?

   • Yes, many systems are well-established in production; conduct tests with real workloads to confirm suitability.

7. Will NewSQL replace traditional RDBMS?

   • Not necessarily; single-node RDBMS may often be simpler and more cost-effective for many use cases.

Further Reading and Authoritative Resources

• Spanner: Google’s Globally-Distributed Database Research Paper
• CockroachDB Architecture
• TiDB Documentation and Quickstart
• VoltDB Documentation
• YugabyteDB Docs and Quickstart

Glossary

• ACID — Atomicity, Consistency, Isolation, Durability
• Serializability — The highest isolation level, ensuring transactions appear to execute in a serial order.
• Raft / Paxos — Consensus protocols for distributed coordination.
• Sharding / Partitioning — Dividing data across multiple nodes.
• TrueTime — Google’s clock uncertainty management API utilized by Spanner.
• HTAP — Hybrid Transactional/Analytical Processing
• OLTP vs OLAP — Transactional versus analytical workloads.

By engaging with the mini-lab and experimenting with systems like CockroachDB or TiDB, you can observe firsthand the behavior of these advanced databases under various conditions. Understanding the implications of operational trade-offs will prepare you for effectively leveraging NewSQL in your next project.