February 27, 2026 at 10:22 am
SQL Server is typically viewed as a transactional or analytical database engine. However, it is also a deterministic numerical computation environment capable of handling large-scale scientific data.
This article demonstrates how Microsoft SQL Server can:
Store astronomical datasets
Compute derived physical quantities
Reconstruct velocity from an algebraic invariant
Compare simulation results against real observational data
Perform statistical validation entirely in T-SQL
The dataset used in this example consists of publicly available orbital data for Mercury (2024–2025).
1. Designing the Data Model
We begin by storing the 2024 reference dataset.
CREATE TABLE Mercury_2024 (
ObsDate DATE PRIMARY KEY,
Position_m FLOAT,
Velocity_ms FLOAT,
Mass_kg FLOAT
);
INSERT INTO Mercury_2024 VALUES
('2024-01-01', 5.16E+10, 5.33E+04, 3.30E+23),
('2024-04-01', 6.97E+10, 3.90E+04, 3.30E+23),
('2024-07-01', 5.36E+10, 5.20E+04, 3.30E+23),
('2024-10-01', 6.95E+10, 3.92E+04, 3.30E+23),
('2024-12-31', 4.64E+10, 5.81E+04, 3.30E+23);
This table stores:
Position (meters)
Velocity (m/s)
Mass (kg)
2. Computing Derived Quantities
We compute two derived values:
Momentum: p = m × v
Invariant quantity: C = x × p
SELECT
ObsDate,
Position_m,
Velocity_ms,
Mass_kg,
Mass_kg * Velocity_ms AS Momentum,
Position_m * (Mass_kg * Velocity_ms) AS InvariantValue
FROM Mercury_2024;
This establishes a large-scale constant on the order of 10^38.
3. Reconstructing Velocity Algebraically
If we define a target invariant value:
DECLARE @Invariant FLOAT = 8.90E+38;
Velocity can be reconstructed using:
v=Invariantx·mv = \frac{Invariant}{x \cdot m}v=x·mInvariant?Implementation:
SELECT
ObsDate,
@Invariant / (Position_m * Mass_kg) AS SimulatedVelocity
FROM Mercury_2024;
This demonstrates that SQL Server can directly perform high-magnitude scientific calculations.
4. Loading Observed 2025 Data
CREATE TABLE Mercury_2025_NASA (
ObsDate DATE PRIMARY KEY,
Position_m FLOAT,
Velocity_ms FLOAT,
Mass_kg FLOAT
);
INSERT INTO Mercury_2025_NASA VALUES
('2025-01-01', 5.16E+10, 53400, 3.30E+23),
('2025-04-01', 6.97E+10, 38900, 3.30E+23),
('2025-07-01', 5.49E+10, 50400, 3.30E+23),
('2025-10-01', 6.83E+10, 39800, 3.30E+23),
('2025-12-31', 4.61E+10, 58900, 3.30E+23);
5. Validation Against Observed Data
We now compute relative error directly in SQL:
DECLARE @Invariant FLOAT = 8.90E+38;
SELECT
N.ObsDate,
N.Velocity_ms AS NASA_Velocity,
@Invariant / (N.Position_m * N.Mass_kg) AS Simulated_Velocity,
(
(@Invariant / (N.Position_m * N.Mass_kg) - N.Velocity_ms)
/ N.Velocity_ms
) * 100 AS Relative_Error_Percent
FROM Mercury_2025_NASA N
ORDER BY N.ObsDate;
Average deviation:
SELECT
AVG(
ABS(
(@Invariant / (Position_m * Mass_kg) - Velocity_ms)
/ Velocity_ms
) * 100
) AS AvgRelativeErrorPercent
FROM Mercury_2025_NASA;
Observed average relative deviation: approximately 1–2%.
6. Precision Considerations: FLOAT vs DECIMAL
Because values approach 10^38:
FLOAT provides performance and simplicity
DECIMAL(38,10) increases determinism
Large-scale scientific workloads may benefit from DECIMAL
Example alteration:
ALTER TABLE Mercury_2025_NASA
ALTER COLUMN Position_m DECIMAL(38,10);
7. Encapsulating Logic in a Stored Procedure
CREATE OR ALTER PROCEDURE SimulateMercury
@Invariant FLOAT
AS
BEGIN
SET NOCOUNT ON;
SELECT
ObsDate,
Velocity_ms AS NASA_Velocity,
@Invariant / (Position_m * Mass_kg) AS Simulated_Velocity,
(
(@Invariant / (Position_m * Mass_kg) - Velocity_ms)
/ Velocity_ms
) * 100 AS Relative_Error_Percent
FROM Mercury_2025_NASA
ORDER BY ObsDate;
END;
Execution:
EXEC SimulateMercury @Invariant = 8.90E+38;
8. Performance and Execution Plan
Key characteristics:
Clustered index seek on primary key
Scalar arithmetic operations
Linear complexity O(n)
No external computation engine required
SQL Server handles large-magnitude arithmetic efficiently within standard query execution.
9. Why This Matters
This exercise demonstrates that SQL Server can serve as:
A deterministic scientific calculation environment
A validation engine for observational datasets
A reproducible numerical workflow system
A self-contained simulation platform
All computations, validation, and statistics are performed directly inside T-SQL without external scripting languages.
Conclusion
Relational databases are often underestimated as scientific tools. This example shows that SQL Server can:
Store astronomical-scale data
Perform algebraic reconstruction
Validate real observational measurements
Maintain reproducibility within a structured query environment
While SQL Server is not a physics engine, it is fully capable of supporting structured scientific computation pipelines when problems are algebraically defined.
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