jrivero

import os

def split(filehandler, delimiter=',', row_limit=10000, 
    output_name_template='output_%s.csv', output_path='.', keep_headers=True):
    """
    Splits a CSV file into multiple pieces.
    
    A quick bastardization of the Python CSV library.

    Arguments:

jboner

Latency Comparison Numbers (~2012)
----------------------------------
L1 cache reference                           0.5 ns
Branch mispredict                            5   ns
L2 cache reference                           7   ns                      14x L1 cache
Mutex lock/unlock                           25   ns
Main memory reference                      100   ns                      20x L2 cache, 200x L1 cache
Compress 1K bytes with Zippy             3,000   ns        3 us
Send 1K bytes over 1 Gbps network       10,000   ns       10 us
Read 4K randomly from SSD*             150,000   ns      150 us          ~1GB/sec SSD

anantn

function go() {
  var userId = prompt('Username?', 'Guest');
  checkIfUserExists(userId);
}

var USERS_LOCATION = 'https://SampleChat.firebaseIO-demo.com/users';

function userExistsCallback(userId, exists) {
  if (exists) {
    alert('user ' + userId + ' exists!');

luke

import logging
import cStringIO
import csv

DEBUG = False

def data2csv(data):
    si = cStringIO.StringIO()
    cw = csv.writer(si, delimiter='\t',lineterminator="\n")
    for row in data:

karpathy

"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

kelvintaywl

import csv
import sys
import os

# example usage: python split.py example.csv 200
# above command would split the `example.csv` into smaller CSV files of 200 rows each (with header included)
# if example.csv has 401 rows for instance, this creates 3 files in same directory:
#   - `example_1.csv` (row 1 - 200)
#   - `example_2.csv` (row 201 - 400)
#   - `example_3.csv` (row 401)

valyala

Optimizing postgresql table for more than 100K inserts per second

• Create UNLOGGED table. This reduces the amount of data written to persistent storage by up to 2x.
• Set WITH (autovacuum_enabled=false) on the table. This saves CPU time and IO bandwidth on useless vacuuming of the table (since we never DELETE or UPDATE the table).
• Insert rows with COPY FROM STDIN. This is the fastest possible approach to insert rows into table.
• Minimize the number of indexes in the table, since they slow down inserts. Usually an index on time timestamp with time zone is enough.
• Add synchronous_commit = off to postgresql.conf.
• Use table inheritance for fast removal of old data:

tomysmile

type below:

brew update
brew install redis

To have launchd start redis now and restart at login:

brew services start redis

navicore

def sha256Hash(text: String) : String = java.security.MessageDigest.getInstance("SHA-256").digest(text.getBytes()).map(0xFF & ).map { "%02x".format() }.foldLeft(""){_ + _}

def sha256Hash(text: String) : String = String.format("%064x", new java.math.BigInteger(1, java.security.MessageDigest.getInstance("SHA-256").digest(text.getBytes("UTF-8"))))

verify via:

scala> sha256Hash("Rusty is a cowboy!")