Tin Nguyen Tin-Nguyen

## docker-compose.yml
version: '2'
services:
  postgres:
    image: postgres:9.6
    container_name: postgres
    ports:
      - "5432:5432"
    environment:
      POSTGRES_PASSWORD: postgres_password
      POSTGRES_USER: postgres_user

## KSQL Notes
CREATE TABLE jobs \
  (id INT, \
   configuration VARCHAR, \
   extraParameters VARCHAR, \
   jobInstanceId VARCHAR, \
   network VARCHAR, \
   duration INT,
   durationUnit VARCHAR) \
  WITH (KAFKA_TOPIC='jobs', \
        VALUE_FORMAT='JSON', \

## pacing_test.csv

          
            brand_id
            brand_name
            publisher
            lineitem_id
            media_budget
            end_date
            start_date
            days_remain
            is_5days_end
            lineitem_foreign_id
            lineitem_name
            adaccount_id
            adaccount_name
            actual_days_num
            avg_3day_media_spend
            yesterday_media_spent
            spent_today
            spent_through_yesterday
            budget_remaining_as_of_yesterday
            true_pacing_pct
            pacing_status_key
            pacing_status
            is_underpasing
            is_overpacing
            pacing_trend_pct
            spent_to_date
            budget_remain
            budget_complete_pct
            budget_at_risk
            avg_daily_spend_target
            open_media_budget
            open_media_budget_pct

            
              0f543841-6eba-4029-9a34-d735215111df
              Spotify
              facebook
              195dd03a-86cb-43e0-b5ac-7418c9dd1deb
              100000000
              2018-01-01 00:00:00
              2017-01-01 00:00:00
              -407
              0
              
              PM - Subs Evergreen 2
              1437331579719236
              PM - Subs Evergreen 2
              3
              0.00
              0.00
              0.00
              0.00
              100000000.00
              0
              1
              Underpacing
              1
              0
              0
              0.00
              100000000
              0
              100000000.00
              0
              0.00
              0.00

## request-issue-jessica-starbucks
{
    "breakdowns": null,
    "default_summary": true,
    "action_attribution_windows": "['1d_view', '28d_click']",
    "level": "ad",
    "line_item_id": "5f6b9a02-ab27-4c6b-8a19-8652273c218f",
    "fields": "reach,cpp,frequency,unique_actions,total_unique_actions,estimated_ad_recallers,cost_per_estimated_ad_recallers,estimated_ad_recall_rate,unique_inline_link_clicks,video_avg_percent_watched_actions,video_avg_time_watched_actions,canvas_avg_view_time,canvas_avg_view_percent,unique_clicks,unique_inline_link_click_ctr,cost_per_unique_inline_link_click",
    "ids": "23842713111030454,23842713111100454,23842713111110454,23842713111120454,23842713111130454,23842713111140454,23842713111150454,23842713111160454,23842713111170454,23842713111180454,23842713111190454,23842713111200454,23842713111210454,23842713111220454,23842713111230454,23842713111240454,23842713111250454,23842713111260454,23842713111270454,23842713111280454,23842713111290454,23842713111300454,23842713111310454,23842713111320454,23842713111330454,

## test-actions-example.js
it('should dispatch action', (done) => {
  const getState = {}
  const action = { type: 'ADD_TODO' }
  const expectedActions = [action]

  const store = mockStore(getState, expectedActions, done)
  store.dispatch(action)
})

## test-reducer-example.js
it('should set token', () => {
  const nextState = reducer(undefined, {
    type: USER_SET_TOKEN,
    token: 'my-token'
  })

  // immutable.js state output
  expect(nextState.toJS()).to.be.eql({
    token: 'my-token'
  })

## using-shallow-test-enzyme-example.js
it('simulates click events', () => {
  const onButtonClick = sinon.spy()
  const wrapper = shallow(
    <Foo onButtonClick={onButtonClick} />
  )
  wrapper.find('button').simulate('click')
  expect(onButtonClick.calledOnce).to.be.true
})

## functional-component-example.js
const LatestPostsComponent = props => {
  const postPreviews = renderPostPreviews(props.posts);

  return (
    <section>
      <div><h1>Latest posts</h1></div>
      <div>
        { postPreviews }
      </div>
    </section>

## condition-in-jsx-example.js
// bad
{
  isTrue
   ? <p>True!</p>
   : <none/>
}

// good
{
  isTrue &&

## do-more-in-render-example.js
// bad
componentWillMount() {
    this.setState({
        computedFoo: compute(this.props.foo)
    });
},
componentWillReceiveProps(nextProps) {
    this.setState({
        computedFoo: compute(nextProps.foo)
    });
	version: '2'
	services:
	postgres:
	image: postgres:9.6
	container_name: postgres
	ports:
	- "5432:5432"
	environment:
	POSTGRES_PASSWORD: postgres_password
	POSTGRES_USER: postgres_user
	CREATE TABLE jobs \
	(id INT, \
	configuration VARCHAR, \
	extraParameters VARCHAR, \
	jobInstanceId VARCHAR, \
	network VARCHAR, \
	duration INT,
	durationUnit VARCHAR) \
	WITH (KAFKA_TOPIC='jobs', \
	VALUE_FORMAT='JSON', \
	brand_id	brand_name	publisher	lineitem_id	media_budget	end_date	start_date	days_remain	is_5days_end	lineitem_foreign_id	lineitem_name	adaccount_id	adaccount_name	actual_days_num	avg_3day_media_spend	yesterday_media_spent	spent_today	spent_through_yesterday	budget_remaining_as_of_yesterday	true_pacing_pct	pacing_status_key	pacing_status	is_underpasing	is_overpacing	pacing_trend_pct	spent_to_date	budget_remain	budget_complete_pct	budget_at_risk	avg_daily_spend_target	open_media_budget	open_media_budget_pct
	0f543841-6eba-4029-9a34-d735215111df	Spotify	facebook	195dd03a-86cb-43e0-b5ac-7418c9dd1deb	100000000	2018-01-01 00:00:00	2017-01-01 00:00:00	-407	0		PM - Subs Evergreen 2	1437331579719236	PM - Subs Evergreen 2	3	0.00	0.00	0.00	0.00	100000000.00	0	1	Underpacing	1	0	0	0.00	100000000	0	100000000.00	0	0.00	0.00
	{
	"breakdowns": null,
	"default_summary": true,
	"action_attribution_windows": "['1d_view', '28d_click']",
	"level": "ad",
	"line_item_id": "5f6b9a02-ab27-4c6b-8a19-8652273c218f",
	"fields": "reach,cpp,frequency,unique_actions,total_unique_actions,estimated_ad_recallers,cost_per_estimated_ad_recallers,estimated_ad_recall_rate,unique_inline_link_clicks,video_avg_percent_watched_actions,video_avg_time_watched_actions,canvas_avg_view_time,canvas_avg_view_percent,unique_clicks,unique_inline_link_click_ctr,cost_per_unique_inline_link_click",
	"ids": "23842713111030454,23842713111100454,23842713111110454,23842713111120454,23842713111130454,23842713111140454,23842713111150454,23842713111160454,23842713111170454,23842713111180454,23842713111190454,23842713111200454,23842713111210454,23842713111220454,23842713111230454,23842713111240454,23842713111250454,23842713111260454,23842713111270454,23842713111280454,23842713111290454,23842713111300454,23842713111310454,23842713111320454,23842713111330454,
	it('should dispatch action', (done) => {
	const getState = {}
	const action = { type: 'ADD_TODO' }
	const expectedActions = [action]

	const store = mockStore(getState, expectedActions, done)
	store.dispatch(action)
	})
	it('should set token', () => {
	const nextState = reducer(undefined, {
	type: USER_SET_TOKEN,
	token: 'my-token'
	})

	// immutable.js state output
	expect(nextState.toJS()).to.be.eql({
	token: 'my-token'
	})
	it('simulates click events', () => {
	const onButtonClick = sinon.spy()
	const wrapper = shallow(
	<Foo onButtonClick={onButtonClick} />
	)
	wrapper.find('button').simulate('click')
	expect(onButtonClick.calledOnce).to.be.true
	})
	const LatestPostsComponent = props => {
	const postPreviews = renderPostPreviews(props.posts);

	return (
	<section>
	<div><h1>Latest posts</h1></div>
	<div>
	{ postPreviews }
	</div>
	</section>
	// bad
	componentWillMount() {
	this.setState({
	computedFoo: compute(this.props.foo)
	});
	},
	componentWillReceiveProps(nextProps) {
	this.setState({
	computedFoo: compute(nextProps.foo)
	});