| -- first query all the users | |
| WITH offsets AS (SELECT a.*, | |
| EXTRACT(hour FROM ptn.utc_offset) AS utc_offset | |
| FROM bootcamp.attendees a | |
| JOIN pg_timezone_names ptn ON a.timezone = ptn.name | |
| WHERE a.bootcamp_version = 3 | |
| AND a.timezone IS NOT NULL | |
| AND a.content_delivery = 'Live'::text | |
| ), | |
| -- then aggregate the users by track and offset, we want matching timezones to fill up first |
| import threading | |
| import asyncio | |
| from queue import Queue as BlockingQueue | |
| class TwoSidedQueue: | |
| """ | |
| Behaves like an `asyncio.Queue`, but `get` and `put` act on different ends. | |
| """ | |
| def __init__(self, queue_in, queue_out): |
I liked the way Grokking the coding interview organized problems into learnable patterns. However, the course is expensive and the majority of the time the problems are copy-pasted from leetcode. As the explanations on leetcode are usually just as good, the course really boils down to being a glorified curated list of leetcode problems.
So below I made a list of leetcode problems that are as close to grokking problems as possible.
| #!/usr/bin/env python3 | |
| import atexit | |
| import json | |
| import requests | |
| import subprocess | |
| import sys | |
| namespace = sys.argv[1] | |
| proxy_process = subprocess.Popen(['kubectl', 'proxy']) | |
| atexit.register(proxy_process.kill) |
| version = 2 | |
| root = "/var/lib/containerd" | |
| state = "/run/containerd" | |
| plugin_dir = "" | |
| disabled_plugins = [] | |
| required_plugins = [] | |
| oom_score = 0 | |
| [grpc] | |
| address = "/run/containerd/containerd.sock" |
First, you have to enable profiling
> db.setProfilingLevel(1)
Now let it run for a while. It collects the slow queries ( > 100ms) into a capped collections, so queries go in and if it's full, old queries go out, so don't be surprised that it's a moving target...
| #cython: boundscheck=False, wraparound=False | |
| import numpy as np | |
| cimport numpy as np | |
| from cython.parallel cimport prange | |
| def dot(np.ndarray[np.float32_t, ndim=2] a not None, | |
| np.ndarray[np.float32_t, ndim=2] b not None, | |
| np.ndarray[np.float32_t, ndim=2] out=None): | |
| """Naive O(N**3) 2D np.dot() implementation.""" |
