benchmarking

I'm working on a simulation model, where I want to determine when the storage IOPS capacity becomes a bottleneck (e.g. and HDD has ~150 IOPS, while an SSD can have 150,000). So I'm trying to come up with a way to benchmark IOPS in a command (git) for some of it's different operations (push, pull, merge, clone). So far, I have found tools like iostat, however, I am not sure how to limit the report to what a single command does. The best idea I can come up with is to determine my HDD IOPS capacity, use time on the actual command, see how long it lasts, multiply that by IOPS and those are my IOPS

I'd like to time a block of code without putting it in a separate function. for example: def myfunc: # some code here t1 = time.time() # block of code to time here t2 = time.time() print "Code took %s seconds." %(str(t2-t1)) however, I'd like to do this with the timeit module in a cleaner way, but I don't want to make a separate function for the block of code. thanks. interjay You can do this with the with statement. For example: import time from contextlib import contextmanager @contextmanager def measureTime(title): t1 = time.clock() yield t2 = time.clock() print '%s: %0.2f seconds elapsed'

I was interested in whether it would be faster to sort my classes using LINQ, or by implementing the IComparable interface and List.Sort. I was quite surprised when the LINQ code was faster. To do the test, I made a very simple class with the not-so-apt name of TestSort, implementing IComparable. class TestSort: IComparable<TestSort> { private int age; private string givenName; public int Age { get { return age; } set { age = value; } } public string GivenName { get { return givenName; } set { givenName = value; } } public TestSort(int age, string name) { this.age = age; this.givenName = name;

I am using data grids as my primary "database". I noticed a drastic difference between Hazelcast and Ignite query performance. I optimized my data grid usage by the proper custom serialization and indexes, but the difference is still noticeable IMO. Since no one asked it here, I am going to answer my own question for all future references. This is not an abstract (learning) exercise, but a real-world benchmark, that models my data grid usage in large SaaS systems - primarily to display sorted and filtered paginated lists. I primarily wanted to know how much overhead my universal JDBC-ish data

We are trying to chose between technologies at my work. And I thought I'd run a benchmark using both libraries (aiohttp and requests). I want it to be as fair / unbiased as possible, and would love a look from the community into this. So this is my current code : import asyncio as aio import aiohttp import requests import time TEST_URL = "https://a-domain-i-can-use.tld" def requests_fetch_url(url): with requests.Session() as session: with session.get(url) as resp: html = resp.text async def aio_fetch_url(url): async with aiohttp.ClientSession() as session: async with session.get(url) as resp: