Google Cloud Spanner vs PostgreSQL: What are the differences?

Introduction:

In this comparison, we will explore the key differences between Google Cloud Spanner and PostgreSQL, focusing on specific aspects that set them apart from each other in terms of their features and functionality.

1. Scalability: Google Cloud Spanner is designed to be a globally distributed and horizontally scalable database, allowing it to handle massive amounts of data across multiple regions. It offers automatic scaling and provides consistent performance even under heavy workloads. On the other hand, PostgreSQL is a more traditional relational database management system (RDBMS) that is generally limited to vertical scaling, meaning it can be scaled up by adding more resources to a single server rather than distributing the workload across multiple servers.

2. ACID Compliance: Both Google Cloud Spanner and PostgreSQL support ACID (Atomicity, Consistency, Isolation, Durability) properties, ensuring the reliability and consistency of database transactions. However, Google Cloud Spanner goes a step further by providing externally consistent reads, which means it can provide the most up-to-date data across distributed systems even at a global scale. This level of consistency is not easily achievable in PostgreSQL.

3. Distributed Transactions: Google Cloud Spanner allows for distributed transactions across multiple regions, ensuring data integrity and consistency even in a highly distributed environment. It provides a built-in mechanism to handle transactions that span multiple nodes, which is especially useful for global deployments. In contrast, PostgreSQL relies on two-phase commit protocols or external tools to achieve distributed transactions, making the setup and management more complex.

4. Secondary Indexes: While both Google Cloud Spanner and PostgreSQL support secondary indexes for improved query performance, there is a notable difference in their implementation. Google Cloud Spanner automatically maintains and synchronizes secondary indexes, ensuring consistency between indexes and the underlying data. In PostgreSQL, secondary indexes need to be explicitly created and updated by the user, which requires more manual effort and can lead to potential inconsistencies if not managed correctly.

5. SQL Dialect: PostgreSQL uses the SQL standard as its primary query language, making it compatible with various SQL-based applications and tools. It supports a wide range of SQL features and has a rich ecosystem of extensions and addons. Google Cloud Spanner, on the other hand, uses a slightly modified version of SQL called SQL Extended for Spanner (SQLx). While it is similar to SQL, there are some differences in syntax, functions, and available features compared to PostgreSQL.

6. Cost Model: The cost model for Google Cloud Spanner is based on a combination of factors, including the amount of storage used, network egress, and processing power required. It offers pricing tiers based on the level of performance and availability desired. PostgreSQL, being open-source software, has no direct cost associated with it, but it may require additional expenses for hosting, maintenance, and scaling of the infrastructure. The cost comparison between the two depends on the specific use case and requirements.

In summary, Google Cloud Spanner and PostgreSQL differ significantly in terms of scalability, distributed transactions, secondary indexes, SQL dialect, and cost model. Google Cloud Spanner offers global scalability, distributed transactions, automatic index maintenance, a modified SQL dialect, and a flexible cost model. PostgreSQL, on the other hand, relies on vertical scaling, external tools for distributed transactions, manual index management, standard SQL dialect, and has potential infrastructure costs.