Prefer Python time.time_ns(): Integer Nanosecond Timestamps over Floats for Precision and Database Storage

Learn why Python float timestamps lose subsecond precision and how time.time_ns() gives integer nanoseconds ideal for BigInt databases, logging, tracing.

Python’s float-based timestamps are convenient, but they come with two hard edges that matter in production systems. Precision erodes as values grow, and a float is an awkward fit for BigInt storage in databases. If you need a durable, precise, and database-friendly time representation, there’s a better option in the standard library.

Problem

Working with the seconds-based float timestamp and then coercing it to an integer discards all subsecond resolution. That’s often not acceptable for logging, ordering events, or tracing latency-sensitive operations.

import datetime

# Float seconds since the Unix epoch
moment_f = datetime.datetime.now().timestamp()

# Integer seconds; fractional detail lost
moment_s = int(moment_f)

It would be nice to have an integer timestamp without manual casting, something like a direct call returning an integer with subsecond precision. The real issue isn’t the cast itself; it’s that the float representation starts losing granularity over time.

Why float timestamps become problematic

The core of the issue is how floating-point numbers encode values. As timestamps grow, the spacing between representable floats also grows, which means you gradually lose precision in the fractional part. That’s not a theoretical nit—subsecond fidelity genuinely degrades the farther you get from the epoch.

Several practical observations clarify the trade-offs:

When using floating point, the fact that the available precision diminishes the farther you go into the future is admittedly somewhat of a wart. You only get nanosecond precision for a few months, losing it at about 2am on April 6, 1970. Microsecond precision, on the other hand, you can have 'til the year 2242. And if you use time.time_ns(), you sidestep this issue and get the underlying platform's time resolution uniformly.

The timestamp is a 64 bit floating point number. That can represent integers accurately up to 2**53.

In other words, a float can exactly represent integers up to 2**53, but once you rely on its fractional part for subsecond detail, you’re at the mercy of growing step sizes. That is precisely what makes floats a fragile choice for high-resolution time capture over long horizons.

Solution

Use the standard-library function that already solves both problems—precision and integer form—at once. It returns nanoseconds since the Unix epoch as an int.

import time

# Integer nanoseconds since the Unix epoch
epoch_ns_val = time.time_ns()

This avoids the precision loss caused by the float type and keeps time values directly usable with BigInt database columns. If what you need are integer seconds, you can still derive them from the nanosecond integer while staying in integer arithmetic.

import time

# Capture once as integer nanoseconds
now_ns_val = time.time_ns()

# Derive integer seconds without floating point
now_sec_i = now_ns_val // 1_000_000_000

This approach retains uniform subsecond precision at capture time and lets you downsample on your terms.

Why this matters

Many systems must order events, correlate traces, or persist timestamps across services reliably. Floats leak precision as values grow, making them a risky basis for long-lived data. Integer nanoseconds keep your data stable and portable. You also avoid juggling extra structures for subsecond precision.

Arguably, it’s “everyone else” who’s got it wrong, since measuring time with subsecond precision is a frequent need. Everyone else has to use clumsy structures.

In ecosystems where a conversion function is perfectly fine, having a single canonical capture method and converting as needed is simpler and less error-prone. It also plays well with Python’s integer model.

For language like Python, using a conversion function is not a problem … we just adapt the output. Note: Python integers are also not what people in other language think. (they are BigInt so “infinite precision”)

Takeaways and guidance

If you’re still using float timestamps purely out of habit, consider the implications for precision and storage. For precise, future-proof, and database-friendly time values, prefer integer nanoseconds from the standard library. Capture once as an int via time.time_ns(), then convert to whatever granularity you need—without ever reintroducing float rounding behavior.