mickdelaney

import { Animated, FlatList } from 'react-native';
import React, { FC, Suspense, useState } from 'react';
import type {
  SearchUsersPaginationQuery,
  SearchUsersPaginationQueryResponse,
  SearchUsersPaginationQueryVariables,
} from '../../__generated__/SearchUsersPaginationQuery.graphql';
import {
  graphql,
  useLazyLoadQuery,

mickdelaney

const path = require('path');

const getWorkspaces = require('get-yarn-workspaces');
const blacklist = require('metro-config/src/defaults/blacklist');

const workspaces = getWorkspaces(__dirname);

module.exports = {
  projectRoot: path.resolve(__dirname, 'packages/app'),

mickdelaney

import { promises } from "fs";
import crypto from "crypto";
import path from "path";
import { print, parse } from "graphql";

const plugin = {
	name: "relay",
	setup: build => {
		build.onLoad({ filter: /\.tsx$/, namespace: "" }, async args => {
			let contents = await promises.readFile(args.path, "utf8");

mickdelaney

module.exports = {
  schema: './data/schema.graphql',
  language: 'typescript',
  src: '../.',
  include: [
    './web/src/**',
    './shared/src/**',
  ],
};

mickdelaney

<div ng-app="stepWizardApp" ng-controller="stepWizardCtrl">
  <ol class="step-wizard">
    <li ng-repeat="step in steps" ng-class="stepStyle($index)"ng-click="">
      <span class="number-circle" ng-bind="step.number"></span>
      <span class="step-name" ng-bind="step.name"></span>
    </li>
  </ol>
</div>

mickdelaney

upload-file angular-formly type

This is my upload-file type for what I use at work. We use angular-upload for the upload service to do the actual file uploading. We also have several abstractions and use ES6 that may confuse you a little bit (sorry about that). Hopefully this gets you started though.

mickdelaney

-- show running queries (pre 9.2)
SELECT procpid, age(query_start, clock_timestamp()), usename, current_query 
FROM pg_stat_activity 
WHERE current_query != '<IDLE>' AND current_query NOT ILIKE '%pg_stat_activity%' 
ORDER BY query_start desc;

-- show running queries (9.2)
SELECT pid, age(query_start, clock_timestamp()), usename, query 
FROM pg_stat_activity 
WHERE query != '<IDLE>' AND query NOT ILIKE '%pg_stat_activity%' 

mickdelaney

from pyspark.sql.types import StringType
from pyspark.sql.functions import udf

maturity_udf = udf(lambda age: "adult" if age >=18 else "child", StringType())

df = sqlContext.createDataFrame([{'name': 'Alice', 'age': 1}])
df.withColumn("maturity", maturity_udf(df.age))

mickdelaney

1. General Background and Overview

• Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
• Models and Issues in Data Stream Systems
• Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
• Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
• [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep

mickdelaney

// Use Gists to store code you would like to remember later on
console.log(window); // log the "window" object to the console