The most performant timestamp functions in Python

While gathering data for my exploration into the feasability of a real-time price aggregate, I was generating a high amount of timestamps in order to calculate message latencies.

I was facing issues increasing the capacity of my data pipeline and started optimizing certain areas. Because of the high amount of timestamp calls, I wondered what was the performance impact of generating a timestamp.

Is time.time() the fastest? What if you need a datetime object: datetime.now(), datetime.utcnow()? Let's find out!

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Functions tested

import time
time.time()

import datetime
datetime.datetime.now().timestamp()

import datetime
datetime.datetime.utcnow().timestamp()

import datetime
datetime.datetime.utcnow()

import datetime
datetime.datetime.now()

import datetime
import pytz
 
pytz.UTC.localize(datetime.datetime.utcnow())

import datetime
import pytz
 
a_timezone = pytz.timezone('America/Los_Angeles')
a_timezone.localize(datetime.datetime.utcnow())

Results

The results are actually a bit surprising! Some interesting facts:

Generating a UTC timestamp is much faster than generating one for a local timezone
Unless you really need a tz-aware datetime object, you should stick to a UTC-based, tz-naive datetime or epoch timestamp.
The overhead of creating a datetime object is minimal. Compare time.time() and datetime.utcnow(): the difference is about 50% slower. Therefore, there is almost no impact in tracking a datetime object instead of a epoch timestamp in seconds (there might be a memory footprint impact though).
datetime.utcnow() is faster than datetime.now()!

Conclusion

Use time.time() for epoch timestamps.
Use datetime.utcnow() for datetime timestamps.
For tz-aware datetimes, you should do your own research. I only skimmed the surface here.

Code

 
number = 100000
results = {}
 
import timeit
 
results["time.time()"] = timeit.timeit(
    setup="import time", stmt="time.time()", number=number
)
results["datetime.now().timestamp()"] = timeit.timeit(
    setup="import datetime", stmt="datetime.datetime.now().timestamp()", number=number
)
results["datetime.now()"] = timeit.timeit(
    setup="import datetime", stmt="datetime.datetime.now()", number=number
)
results["datetime.utcnow()"] = timeit.timeit(
    setup="import datetime", stmt="datetime.datetime.utcnow()", number=number
)
results["pytz.UTC.localize(datetime.utcnow())"] = timeit.timeit(
    setup="import datetime; import pytz",
    stmt="pytz.UTC.localize(datetime.datetime.utcnow())",
    number=number,
)
results["tz.localize(datetime.utcnow())"] = timeit.timeit(
    setup="import datetime; import pytz; a_timezone = pytz.timezone('America/Los_Angeles')",
    stmt="a_timezone.localize(datetime.datetime.utcnow())",
    number=number,
)
 
import time
 
print(f"{time.time()} -vs- {time.perf_counter()}")
 
results_sorted = sorted(results.items(), key=lambda t: t[1])
 
for name, result_s in results_sorted:
    print(f"{name}: {result_s}")
 
import plotly.io
import plotly.express as px
 
y, x = zip(*results_sorted)
 
fig = px.bar(x=x, y=y, orientation="h", log_x=True)
 
fig.update_layout(
    title=f"Time required for {number} calls (lower is better)",
    xaxis_title="Time(s), log scale",
    yaxis_title="",
)
plotly.io.write_json(fig, "timestamp_functions.json")
fig