How to Work Optimally with Relational Databases

Relational databases have been a mainstay of software development for decades, powering applications across virtually every industry. Despite the rise of NoSQL databases in recent years, RDBMSs remain the most widely used databases today. In the Stack Overflow 2021 Developer Survey, 50% of respondents use MySQL, 40% use PostgreSQL, and 35% use SQL Server, while leading NoSQL databases like MongoDB and Redis are used by 25% and 12% respectively.

The dominance of relational databases is due to their reliability, consistency, flexibility, and ability to scale to handle large workloads. However, to leverage RDBMSs optimally and maintain application performance as data and traffic grows requires understanding key database concepts and optimization techniques. Improperly used, databases can quickly become a performance bottleneck, crippling application responsiveness.

In this in-depth guide, we‘ll explore how to work effectively with relational databases from the perspective of a full-stack developer. Whether you‘re a database beginner or a seasoned application architect, understanding how to design schemas efficiently, write performant queries, and scale databases is essential. Let‘s dive in!

Relational Database Fundamentals

At their core, relational databases organize data into tables composed of rows and columns. Tables are defined by a schema that specifies column names, data types, and any constraints. Relationships between tables are represented through primary key-foreign key references.

The relational model, proposed by Edgar Codd in 1970, provided a mathematical foundation for representing data and querying it declaratively using relational operators. This gave rise to SQL (Structured Query Language), standardized in 1986 and still the lingua franca for interacting with relational databases today.

The ACID (Atomicity, Consistency, Isolation, Durability) transaction model is another pillar of RDBMSs, ensuring that database operations are processed reliably and independently. ACID is crucial for maintaining data integrity, especially in systems with concurrent users and complex business logic.

The SQL Performance Imperative

SQL makes it easy to write queries to retrieve, filter, and aggregate data flexibly. However, not all queries are created equal from a performance standpoint. As data size grows, poorly written queries can bring database performance to its knees.

The main enemy of database performance is the full table scan – when the database must read every row in a table to find matches for a query. For small tables this is trivial, but for tables with millions of rows, full scans become prohibitively expensive.

Consider an e-commerce database with a products table containing 10 million rows. A query to find a product by name like:

SELECT * FROM products
WHERE name = ‘Wireless Bluetooth Headphones‘;

Without any optimizations, the database must scan all 10 million rows to find matches, even though logically there may only be a handful of products with that exact name. At a disk read speed of 100 MB/s, this would take over 20 seconds! Clearly unacceptable for a user-facing application.

Indexes: The Ubiquitous Database Optimizer

The single most important tool for improving database query speed is indexing. An index is a data structure that allows the database to quickly locate rows with specific column values without having to scan the entire table.

The most common index type is a B-tree, a self-balancing tree data structure. When you create a B-tree index on a column, the database builds a tree containing the column‘s sorted values and pointers to the corresponding table rows. Searching a B-tree is very fast, requiring only a logarithmic number of node traversals to find a given value.

With a B-tree index on the "name" column, our earlier query can be transformed from a full scan to an efficient index lookup. The database simply searches the index tree for "Wireless Bluetooth Headphones", finds the handful of matching row pointers, and returns those rows directly. This can take milliseconds instead of seconds!

There are several types of indexes available:

• Unique indexes enforce uniqueness on the indexed column(s). Attempting to insert a duplicate value will fail.
• Non-unique indexes allow duplicates in the indexed columns. These are useful for speeding up queries with selective WHERE or JOIN conditions.
• Composite indexes span multiple columns, allowing queries that filter on those columns together to search efficiently. Column order matters for performance.
• Partial indexes only index a subset of a table‘s rows matching some condition. This can save space and improve write speed for large, sparsely accessed tables.
• Covering indexes contain all columns needed to satisfy a query, avoiding the need to fetch the row data separately. These can dramatically speed up queries that only select indexed columns.

As a general rule, you should index columns that are frequently used for filtering, joining, and sorting in queries. However, it‘s important to use indexes judiciously. Each index consumes additional storage and slows write operations on the table. Over-indexing can actually harm performance by flooding memory with seldom-used index pages.

Analyzing and Optimizing Query Performance

The foundation of any database optimization effort is understanding how the database executes queries. Most RDBMSs provide tools for viewing the query execution plan, which shows the step-by-step process the database uses to return query results.

Execution plans show which indexes (if any) are used, in what order tables are joined, how intermediate results are sorted or aggregated, and critically, the estimated number of rows processed at each step. By analyzing plans for slow queries, you can identify bottlenecks like missing indexes, inefficient JOIN orders, or expensive operations like sorting large result sets.

Some common query anti-patterns to watch for:

• Avoid SELECT * and only retrieve needed columns. This reduces I/O between the database and application.
• Use LIMIT to avoid returning too many rows at once. It‘s better to paginate results in application code.
• Avoid leading wildcards (LIKE ‘%foo%‘) since they can‘t use normal B-tree indexes. Consider full-text search instead.
• Prefer indexed columns for filtering, joining, and sorting. The query optimizer can better leverage indexes in these cases.
• In multi-table JOINs, put the most selective tables first to reduce intermediate result size.
• Avoid OR conditions that can‘t use indexes efficiently. Compound AND conditions are preferred.
• Aggregate data in the database with GROUP BY instead of in application code when possible.

There are also more advanced techniques for speeding up specific queries:

• Materialized views are pre-computed result sets that can be queried like tables. They excel for expensive aggregation queries that don‘t need real-time data.
• Temporary tables allow complex subqueries to be computed once and referenced multiple times, avoiding repeated work.
• Denormalizing data by storing redundant data across tables can drastically reduce JOIN complexity for read-heavy workloads. Of course, this makes maintaining consistency on writes more challenging.

Ultimately, achieving optimal query performance requires a deep understanding of the data model, common access patterns, and database‘s query optimizer. Continuously profiling queries in production and experimenting with different optimization approaches is key.

Vertical and Horizontal Scaling

As data and traffic grows, it‘s often necessary to scale the database beyond a single machine. There are two fundamental scaling approaches:

• Vertical scaling adds more CPU, memory, and I/O resources to an existing database server. This can be a simple and effective way to boost performance up to a point, but is ultimately limited by the size of the beefiest server available.
• Horizontal scaling, or scale-out, distributes the database across multiple machines. While more complex to manage, this allows near-linear scaling as nodes are added to the cluster.

Two horizontal scaling techniques are especially important for RDBMSs: replication and sharding.

Replication: Scaling Reads

Replication involves creating one or more copies of a database that synchronize changes from a primary server. Reads can then be distributed across replicas, greatly increasing read throughput.

There are several replication topologies like single-primary, multi-primary, and cascading replication that offer different trade-offs in consistency and performance. Replicas can be located in different data centers to improve read latency in distributed deployments.

While replicas are traditionally used for read-only queries, some modern RDBMSs like CockroachDB support distributed transactions across replicas for geographically distributed writes.

Sharding: Scaling Writes

For write-heavy workloads, database sharding is a powerful horizontal scaling technique. With sharding, the data is split across multiple independent database instances by some shard key, like user ID or geographic region.

Each shard is responsible for a disjoint subset of the data, allowing writes to be distributed across the cluster. Queries must be routed to the appropriate shard based on the shard key, which can add complexity to the application.

Advanced sharding approaches like sub-partitioning and directory-based sharding can further distribute load across larger cluster sizes. However, sharding does come with operational challenges in balancing load, managing failover, and maintaining cross-shard consistency.

Looking Ahead: The NewSQL Revolution

Traditional RDBMSs have been burdened by a perception of limited scalability compared to NoSQL databases. However, a new class of relational databases dubbed "NewSQL" are challenging this assumption.

Databases like Google Spanner, CockroachDB, and VoltDB offer the scalability and flexibility of NoSQL while still maintaining ACID transactions and relational semantics. They achieve this through advanced architectures leveraging distributed consensus protocols, lockless commit algorithms, and automatic sharding.

While it will take time for NewSQL to challenge the entrenched positions of legacy databases, they point an exciting way forward for relational databases in the cloud-native era. Developers may soon be freed from the choice between SQL consistency and NoSQL scalability.

Conclusion

Relational databases are a critical foundation for most applications, but optimally leveraging them requires understanding their strengths, limitations, and optimization techniques. By designing efficient schemas, creating appropriate indexes, writing optimized queries, and scaling horizontally when needed, developers can keep their databases humming even under heavy load.

While NoSQL databases offer enticing scalability and flexibility, RDBMSs remain the best choice for many use cases requiring strong consistency and ACID transactions. And with the rise of NewSQL, the scalability gap is closing rapidly.

As a full-stack developer, investing in your relational database skills is sure to pay long-term dividends. Don‘t be afraid to dive deep into query tuning, schema design, and scaling approaches. Your users (and your future self) will thank you!