What datatype to use when storing latitude and longitude data in SQL databases?

Precision and storage: getting it just right

Evaluating the data needs of your application dictates what datatype to use for storing geographical data. DECIMAL(9,6) and DECIMAL(8,6) offer accurate precision up to six decimal places, which is typically more than enough for applications like GPS services, delivery services, or any other geolocation-based services.

If your application doesn't handle data that needs such a high level of accuracy, optimizing for storage can lead you to use DECIMAL(8,5) or even DECIMAL(7,4), minimizing storage costs and computation time.

Avoid data integrity pitfall: the float trap

While tempting, using float datatype can introduce rounding errors, while DECIMAL and NUMERIC datatypes guarantee exact storage of each coordinate point. This casting-iron guarantee of digital integrity is essential when you cannot afford even the tiniest deviation—like in geolocation services, where a minute rounding error can translate to significant off-course diversions in real-world scenarios.

Oracle and beyond: specialized features and techniques

If you're in the Oracle ecosystem, consider the NUMBER datatype and also Function Based Indexes for an enhanced performance. For SQL databases supporting spatial data, like the PostGIS extension for PostgreSQL, they offer dedicated types built specifically for handling geospatial data. Partitioning techniques are also a viable option for optimizing performance in large datasets.

Data storage strategies: alternative approaches

For systems where performance and storage need to have their foot on the acceleration pedal more than precision, storing coordinates as integers can do the trick. This method involves upscaling coordinates by a fixed factor (such as 10^6) and storing the results as integers. Native or third-party libraries can assist you in downscaling these numbers for their normal use.

Example of Integer Conversion:

$latitude = round(floatval($latitude) * 1000000);  // Going up in the world, six zeroes up!
$longitude = round(floatval($longitude) * 1000000);  // Longitude joins the high-value club too!

// Convert back to floats for usage
$latitude = $latitude / 1000000;   // Down to earth now!
$longitude = $longitude / 1000000;  // Longitude too, we aren't biased
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Working with spatial data types in modern SQL databases

More recent SQL platforms come equipped with advanced tools designed to handle spatial data. Geospatial extensions such as the geography data type in SQL Server or spatial data types in MySQL and add-ons like SpatiaLite for SQLite are excellent examples. These dedicated features facilitate complex queries and operations directly on the stored spatial data.

Making trade-off decisions: precision, storage and performance

When precision goes up, usually storage and performance can take a hit. Therefore, it's vital to balance your application's needs; is high precision a must-have, or can you make some trade-offs to optimize storage and performance?

In big data scenarios, a slight reduction in precision (from DECIMAL(9,6) to DECIMAL(8,5) or using integer conversions) can significantly enhance application performance and decrease storage costs.

Understanding and handling precision and scale

Knowledge of precision (p) and scale (s) is crucial. Precision refers to the total number of digits, and scale represents the number of digits after the decimal point. These values can be tailored to optimize storage for your coordinates, giving you control over the storage needs and accuracy requirements of your application.