FeepingCreature

You are doing JSON APIs wrong.

When you use JSON to call an API - not a REST API, but something like JSON-RPC - you will usually want to encode one of several possible messages.

Your request body looks like this:

{
 "type": "MessageWithA",

luistung

#include <iostream>
#include <fstream>
#include <string>
#include <vector>
#include <unordered_map>
#include <boost/algorithm/string.hpp>
#include <utf8proc.h>

//https://unicode.org/reports/tr15/#Norm_Forms
//https://ssl.icu-project.org/apiref/icu4c/uchar_8h.html

stefan-it

from __future__ import absolute_import, division, print_function

import argparse
import glob
import logging
import os
import random

import numpy as np
import torch

JoeyBurzynski

58 bytes of CSS to look great nearly everywhere

When making this website, i wanted a simple, reasonable way to make it look good on most displays. Not counting any minimization techniques, the following 58 bytes worked well for me:

main {
  max-width: 38rem;
  padding: 2rem;
  margin: auto;
}

fnky

ANSI Escape Sequences

Standard escape codes are prefixed with Escape:

• Ctrl-Key: ^[
• Octal: \033
• Unicode: \u001b
• Hexadecimal: \x1B
• Decimal: 27

suxue

#include <boost/flyweight.hpp>
#include <boost/multi_index_container.hpp>
#include <boost/multi_index/member.hpp>
#include <string>
#include <cstdint>
#include <vector>
#include <iostream>
#include <tuple>

typedef std::tuple<short, std::uint8_t, std::uint8_t> Date;

robertdale

// This can be imported via ./bin/gremlin.sh -i describe.groovy
// A variable 'graph' must be defined with a JanusGraph graph
// Run it as a plugin command ':schema'
// :schema describe
// 

import org.janusgraph.graphdb.database.management.MgmtLogType
import org.codehaus.groovy.tools.shell.Groovysh
import org.codehaus.groovy.tools.shell.CommandSupport

yurishkuro

• What do Etcd, Consul, and Zookeeper do?
  • Service Registration:
    • Host, port number, and sometimes authentication credentials, protocols, versions numbers, and/or environment details.
  • Service Discovery:
    • Ability for client application to query the central registry to learn of service location.
  • Consistent and durable general-purpose K/V store across distributed system.
    • Some solutions support this better than others.
    • Based on Paxos or some derivative (i.e. Raft) algorithm to quickly converge to a consistent state.
• Centralized locking can be based on this K/V store.

dusenberrymw

Spark Tips & Tricks

Misc. Tips & Tricks

• If values are integers in [0, 255], Parquet will automatically compress to use 1 byte unsigned integers, thus decreasing the size of saved DataFrame by a factor of 8.
• Partition DataFrames to have evenly-distributed, ~128MB partition sizes (empirical finding). Always err on the higher side w.r.t. number of partitions.
• Pay particular attention to the number of partitions when using flatMap, especially if the following operation will result in high memory usage. The flatMap op usually results in a DataFrame with a [much] larger number of rows, yet the number of partitions will remain the same. Thus, if a subsequent op causes a large expansion of memory usage (i.e. converting a DataFrame of indices to a DataFrame of large Vectors), the memory usage per partition may become too high. In this case, it is beneficial to repartition the output of flatMap to a number of partitions that will safely allow for appropriate partition memory sizes, based upon the

msmfsd

// Async/Await requirements: Latest Chrome/FF browser or Babel: https://babeljs.io/docs/plugins/transform-async-to-generator/
// Fetch requirements: Latest Chrome/FF browser or Github fetch polyfill: https://github.com/github/fetch

// async function
async function fetchAsync () {
  // await response of fetch call
  let response = await fetch('https://api.github.com');
  // only proceed once promise is resolved
  let data = await response.json();
  // only proceed once second promise is resolved