Introduction
It’s crucial not to generalize databases based on whether they use SQL or not. For example, PostgreSQL and MySQL both use SQL, but PostgreSQL supports advanced features like JSONB for semi-structured data, making it more flexible for certain use cases, whereas MySQL might prioritize performance over such flexibility. On the flip side, some databases like Google’s BigQuery use SQL for querying but operate on a fundamentally different, columnar storage model optimized for analytical queries, which differs from traditional relational databases.
Relational Databases
Example: Think of a city’s train system, where different trains stop at various stations.
Trains Table:
| Train ID | Train Name |
|---|---|
| 1 | Red Line |
| 2 | Blue Line |
| 3 | Pink Line |
| Station Table: |
| Station ID | Station Name |
|---|---|
| 1 | Broadway |
| 2 | Yankee Station |
| 3 | Your Mom :) |
Trains Stations Table (Relationships):
| Train ID | Station ID |
|---|---|
| 1 | 1 |
| 1 | 2 |
| 1 | 3 |
| 2 | 3 |
| 3 | 3 |
Reasoning Behind Foreign Keys and Normalization:
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Foreign keys link records across tables, ensuring that data is organized and consistent. For example, if you delete a station, the system can ensure that it’s removed from all related train routes automatically. This concept, known as referential integrity, is key to maintaining consistency in complex data structures.
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Normalization involves organizing data into tables in such a way that redundancies are minimized (idea is to divide large tables into smaller, related tables and link them using foreign keys like 1NF, 2NF, 3NF).
- This is great for reducing storage costs and ensuring data consistency—since there’s only one place to update a piece of data, you avoid anomalies. However, this can lead to data locality issues, where related data is stored in different physical locations.
Cons of Relational Databases:
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Data Locality Issues:
- When you normalize data, related information (like trains and their stations) is stored in separate tables. In a distributed system, these tables might be on different servers. For example, adding a new station to a train line might require updates to multiple tables on different servers, leading to a distributed transaction. This can slow down operations significantly due to the need for synchronization across the network.
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Joins Across Tables:
- To fetch all stations for a train, a database needs to perform a join operation. If the data is spread across different servers, this operation requires reading from multiple locations, introducing latency. This is why joins can be expensive in distributed relational databases.
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Two-Phase Commit (2PC):
- To maintain consistency across distributed nodes, relational databases often use the Two-Phase Commit protocol. 2PC ensures that either all parts of a transaction are committed, or none are. While this guarantees consistency, it is slow because it requires multiple rounds of communication between nodes, adding overhead to every transaction.
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Slow Reads:
- If a query involves reading data from multiple nodes, it can be slow. For example, fetching train schedules that are distributed across several servers can introduce latency as the database needs to aggregate the data.
Improving Data Locality:
One approach to mitigate these issues is by considering Non-Relational Databases.
Non-Relational Databases (NoSQL)
NoSQL databases are typically non-relational, though there are exceptions. For instance, Amazon Aurora is a relational database compatible with both MySQL and PostgreSQL but is designed to scale in ways more typical of NoSQL databases. Additionally, certain NoSQL databases like OrientDB blur the lines by supporting graph, document, and key-value models within a single system, offering some relational capabilities in a non-relational environment.
The key concept here is denormalization(combining normalized tables into a single table or reducing the number of joins in a query by adding redundant data), where data is stored in a way that minimizes the need for complex joins and distributed transactions by keeping related data together.
Example: Revisiting the train system:
Instead of spreading data across multiple tables, you might store it like this:
Red Line: [Broadway, Yankee Station, Your Mom]Blue Line: [Your Mom]Pink Line: [Your Mom]
Reasoning Behind Denormalization:
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Better Data Locality:
- By keeping all related data together (e.g., all stations for a particular train line), NoSQL databases minimize the need for cross-partition or cross-node reads. This improves performance because all the necessary data is stored together, reducing the need to fetch it from different places.
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Trade-offs with Redundancy:
- The downside is that data can become redundant. For example, “Your Mom” station is repeated for multiple train lines. This means updates can be more complex—if “Your Mom” station changes its name, you have to update it in multiple places. However, this trade-off is often acceptable in scenarios where read performance and data locality are more critical.
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Increased Writes:
- In denormalized systems, writes can be more complicated because you may need to update the same piece of data in multiple places. This can lead to distributed transactions, which NoSQL databases often avoid in favor of eventual consistency models, where updates are propagated asynchronously.
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Data Transmission:
- Depending on the NoSQL database, you might need to transmit entire documents over the network when accessing data. For example, if you’re using a document store like MongoDB, retrieving a single field might involve transferring the entire document, which could be inefficient depending on the size of the document.
Types of NoSQL Databases:
- Document Stores:
- Like MongoDB, where data is stored in documents (JSON-like structures). Each document contains all the related data in one place, which is great for hierarchical or nested data.
- Key-Value Stores:
- Like Redis, where each piece of data is stored as a key-value pair. This is simple and fast for lookup operations but can be limited in functionality.
- Column Stores:
- Like Cassandra, which stores data in columns rather than rows, making it efficient for read-heavy operations that require specific columns from a large dataset.
- Graph Databases:
- Like Neo4j, designed to manage and query data that is interconnected, making it ideal for scenarios like social networks or recommendation engines.
Conclusion:
Non-Relational Databases are ideal for scenarios where data needs to be flexible, highly decoupled, and the structure doesn’t fit well into a traditional relational model. For example:
- Social Media (e.g., Facebook): A graph database like Neo4j efficiently handles complex and evolving relationships between users, posts, likes, and comments.
- E-commerce (e.g., Amazon): A document store like MongoDB provides scalable and responsive management of product catalogs and user preferences.
These databases prioritize data locality and performance over strict consistency. For instance:
- Distributed Caching (e.g., Netflix): Redis ensures fast access to session data, enhancing user experience by prioritizing speed over strict data consistency.
Relational Databases excel in environments where data integrity and consistency are crucial. Examples include:
- Financial Systems (e.g., Banks): Databases like Oracle and PostgreSQL guarantee accurate and consistent transactions across accounts.
- ERP Systems (e.g., SAP): Relational databases maintain consistent relationships between inventory, orders, and customer data across an organization.
Choosing the right database depends on your project’s needs:
- For robust consistency and well-defined relationships: Use a relational database.
- For handling large volumes of data with complex relationships and high performance needs: Opt for a non-relational database.
Real-World Examples:
- Healthcare Systems: Relational databases like MySQL ensure consistent and accurate patient records across departments.
- Real-Time Analytics (e.g., Uber): Non-relational databases like Cassandra handle large-scale, unstructured data efficiently.
By weighing the need for consistency against performance and the complexity of data relationships, you can select the database technology that best aligns with your goals.