Mastering Techniques to Get Column Names in SQL

As a full-stack developer, understanding SQL schema metadata is a crucial skill for building applications and analyzing data. A key aspect is retrieving the list of column names for tables in a database. This guide will provide an in-depth walkthrough of multiple methods, best practices, use cases and expert considerations for getting column names in various SQL dialects.

Real-World Significance of Getting Column Names

Before diving into the syntax details, it‘s important to step back and motivate why a developer needs to get column names programmatically at runtime.

Building Generic Applications

Modern applications often use ORM frameworks or query builders to interface with the database. These tools work best when generic – able to adapt to any table schema rather than hardcoded assumptions.

By querying column metadata, they construct dynamic SQL statements on the fly based on the actual table structure. This allows supporting wider range of database schemas without reconfiguration.

Schema Evolution Over Time

As analytics requirements evolve, database administrators refine schemas to add/remove columns or change data types. Hardcoding column names in queries will break whenever such schema evolution occurs.

But using metadata lookup allows adapting to underlying changes automatically – providing insulation from schema evolution and avoiding fragile hardcoding.

Data Discovery and Exploration

During data science and exploration, analysts often work with unfamiliar datasets. To understand available data, first step is retrieving column names rather than assuming what exists. Metadata lookup provides this data discovery capability.

Database Administration Automation

Tools for auto-generating schema documentation or building testing suites rely on programmatic access to column metadata to support generality across different databases.

As is clear from these examples, the technique of looking up column names dynamically is extremely useful for realistic development scenarios.

SQL Offers Metadata Tables and Views

A key capability enabling the above use cases is – SQL database systems provide in-built metadata i.e. data about the data. Special schema objects store information like database/table names, column data types, constraints etc.

This metadata exists in tables for some databases, and in-memory information schema views in others. But conceptually, it serves as a centralized catalog of metadata available to query at runtime.

This is what enables dynamically inspecting the schema layout to retrieve column names. Now let‘s explore common techniques to actually query this metadata.

Approaches To Get Column Names

While all major SQL databases offer metadata access, the exact objects and syntax varies:

DESCRIBE

The DESCRIBE <table_name> statement provides a quick way to get high-level details:

DESCRIBE users;

It outputs a result set listing each column, data types and nullability constraint. Simple and quick, but only available in certain databases.

SHOW COLUMNS

The SHOW COLUMNS FROM <table_name> statement available in MySQL returns something similar:

SHOW COLUMNS FROM users; 

Information Schema

The SQL standard INFORMATION_SCHEMA views provide metadata consistently across databases:

SELECT COLUMN_NAME 
FROM INFORMATION_SCHEMA.COLUMNS
WHERE TABLE_NAME = ‘users‘;

Dictionary Views

Some databases offer custom dictionary views – for example, Oracle‘s ALL_TAB_COLUMNS contains metadata on columns:

SELECT * FROM ALL_TAB_COLUMNS WHERE TABLE_NAME = ‘USERS‘;  

Beyond Column Names

The above returned just the column names. But the metadata exposes much more – such as data types, key constraints, triggers etc.

Having this centralized catalog enables easily extracting incredibly rich schema detail programmatically.

Now that we have seen the common techniques, let‘s put them in action across popular SQL databases:

Get Column Names in MySQL

As a popular open source database, MySQL offers 3 main options for developers to get column names:

DESCRIBE

DESCRIBE users;  

SHOW COLUMNS

SHOW COLUMNS FROM users;

INFORMATION_SCHEMA

SELECT COLUMN_NAME 
FROM INFORMATION_SCHEMA.COLUMNS
WHERE TABLE_SCHEMA = ‘my_database‘
AND TABLE_NAME = ‘users‘;

The INFORMATION_SCHEMA provides greatest flexibility – for example to query specific database or table names.

Get Column Names in PostgreSQL

PostgreSQL has excellent metadata functionality through:

\d command

\d users;  

\d+

Even more detail:

\d+ users;

INFORMATION_SCHEMA

Central standard for cross-database metadata:

SELECT * FROM INFORMATION_SCHEMA.COLUMNS
WHERE TABLE_NAME = ‘users‘;

Get Column Names in SQL Server

As a leading commercial database, SQL Server enables getting column names through:

sp_columns

Classic stored procedure to return column info:

EXEC sp_columns @table_name = ‘users‘; 

INFORMATION_SCHEMA

Fully standards-compliant views on metadata:

SELECT COLUMN_NAME 
FROM INFORMATION_SCHEMA.COLUMNS
WHERE TABLE_NAME = ‘users‘;  

The INFORMATION_SCHEMA support provides great consistency with other databases.

Get Column Names in Oracle

Oracle has long been the enterprise database standard. Developers can leverage:

DESCRIBE

Builtin DESCRIBE command reveals table structure:

DESCRIBE users;

Dictionary Views

SELECT * FROM ALL_TAB_COLUMNS
WHERE TABLE_NAME = ‘USERS‘;  

Rich catalog of dictionary views return metadata as result sets for querying.

So Oracle enables full programmatic access to catalog metadata just like other databases.

Get Column Names in More Databases

Beyond the major vendors above, most databases implement SQL INFORMATION_SCHEMA to some degree:

Database	Metadata Technique
DB2	SYSCAT views
SQLite	PRAGMA table_info()
Elasticsearch	_mapping API
MongoDB	Collection introspection

So even NoSQL stores allow discovering schema programmatically. The syntax may vary, but principles are consistent.

Performance Considerations

Looking up schema metadata can have overheads in certain cases:

• Repeated access when data warehouse queries join large numbers of columns
• Querying virtual views that require expensive aggregation

Caching frequently accessed metadata in application layer may help mitigate such bottlenecks when they arise.

Best Practices

• Prefer INFORMATION_SCHEMA for portability across different SQL databases
• Use database-specific metadata views for added functionality
• Cache commonly used metadata when performance sensitive
• Build applications generically using metadata instead of hardcoding table/column names
• Reduce need for rework when schema changes by relying on metadata

The Essential Tool in the Toolbelt

This demonstrates the wealth of functionality SQL offers to get metadata on column names dynamically – a key tool for developer productivity.

Mastering the skill of manipulating metadata opens up wider possibilities:

• Auto-generate SQL scripts optimized for particular schema
• Analyze schema for potential improvements
• Discover hidden datasets without documentation
• Build reusable libraries that adapt to evolving schema

I encourage readers to explore further – and leverage metadata capabilities in your own applications.