SQL: How to Remove Time from DateTime – A Deep Dive

Dates and times are intricately woven into most business applications and analytics. SQL databases provide robust data types to store combined date and time values efficiently for convenient processing and retrieval.

But we often need to separate the date and time portions for simpler analysis and reporting. This guide takes a comprehensive, code-focused look at how to extract just the date parts from datetimes across major RDBMS systems.

The SQL DateTime Data Model

SQL Server, Oracle, PostgreSQL and MySQL all utilize a similar internal data model for storing datetimes:

• Dates are stored as a 4-byte integer signifying days since January 1, 0001.
• Times are stored as nanoseconds since midnight.
• In memory, datetimes are compiled to 8-byte binary floating point representing the number of days since the epoch date January 1, 1900.

This compact storage allows efficient comparisons, sorting, math operations and conversions.

But times still need to be separated from dates where required.

Use Cases for Removing Times

Here are some common scenarios where stripping the time provides simpler date-only values:

Simpler Analysis and Reports

Grouping, aggregation, classifications etc can be made purely based on dates instead of full timestamps:

SELECT DATE(order_date), SUM(quantity)
FROM orders
GROUP BY DATE(order_date);

Date-based Charting and Visuzalization

Times would clutter graphs and charts focused on date-wise trends:

Application UIs and Outputs

Displaying just the date keeps user interfaces clean and understandable:

Order Date: 2022-02-14

Date Partitioning for Performance

Databases can natively partition data by dates for faster query performance.

The Cost of Carrying Times

Although datetimes provide greater precision, carrying unnecessary time portions has downsides:

• Storage Overhead: Times expand the storage footprint especially for history tables containing billions of rows.

• Processing Overhead: Comparing, sorting and filtering full datetimes takes more CPU cycles versus date-only values.

• Code Complexity: Application logic involves additional steps to strip times for every user-facing output.

So it‘s essential to remove times where possible via SQL queries rather than later downstream.

Built-in Library Support

Across programming languages like JavaScript, Python, Java etc that application developers use to interact with databases, rich support exists for dealing with dates and times.

For instance:

// JavaScript date handling
const orderDate = new Date("2023-02-14T14:23:45Z");
orderDate.toDateString(); // Tue Feb 14 2023

However, for best performance and simplicity, the RDBMS is still the ideal place for converting datetimes to dates.

Keeping datetimes intact until the latest responsible moment allows maximum flexibility too.

Watch Out for These Pitfalls!

Working with datetimes quickly gets complex due to many intricacies around timezones, daylight savings changes, leap years handling etc.

Some challenges faced:

• Inaccurate assumptions about timezone offsets
• Daylight savings gaps not handled in custom logic
• Comparing / grouping across timezone differences
• Mixing date, time and timestamp datatypes
• Leap days affecting date math, intervals and comparisons

Getting datetime handling right is truly harder than it looks!

Regional Date Representations

Once stripped, date values may need specific display formats:

-- German
SELECT FORMAT(order_date, ‘d‘, ‘de-de‘) AS order_date; 

-- Chinese
SELECT DATE_FORMAT(order_date, ‘%Y年%m月%d日‘);

Accurately processing user-provided string inputs is another challenge:

‘14/2/2023‘  -- Some European format
‘2023年2月14日‘ -- Chinese
‘02/14/2023‘  -- US

These need language-aware parsing before storing into database date/time types.

Store Only What You Need

An alternative design approach is minimizing use of datetimes from the start:

CREATE TABLE orders (
  order_id INT,
  order_date DATE,  -- just date
  order_time TIME -- just time
);

Splitting columns avoids constant extraction later.

But rebuilding timestamps during retrievals and joins may then be needed:

SELECT *, order_date + order_time AS order_datetime
FROM orders; 

So there‘s always an associated processing tradeoff!

Temporal Data Models

Special techniques like temporal databases retain historical data against records, storing exact datetimes to track previous states and changes:

Effective	Name	Status
2020-01-01	Alice	Active
2021-06-14	Alice	Inactive

But application-level logic is still required to query specific points in history.

Matching Business Needs

Ultimately the importance of time precision depends on the use case. Order timestamps with millisecond granularity are crucial for real-time stock trading systems but overkill for supply chain analytics.

So choose appropriate date vs time datatypes matching the business domain needs rather than blindly defaulting to datetimes everywhere.

In Summary

• Datetimes enable precise recording of business events with full chronology but can complicate analysis.
• Removing times is essential for practical reports and visualization.
• Built-in SQL functions efficiently extract just the date portions from datetimes.
• However, hidden pitfalls exist when processing dates and times.
• Where possible, directly store data matching the precise needs.
• Apply judgement around datetime usage based on reporting needs rather than blind consistency.

Hope you found this guide useful! Please share any date/time handling challenges faced in your projects.