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In April 2014, Pinterest engineers presented to members of the engineering community at a series of Tech Talks held at the Pinterest offices in San Francisco. Topics included: - Mobile & Growth: Scaling user education on mobile, and a deep dive into the new user experience (with engineers Dannie Chu and Wendy Lu) - Monetization & Data: The open sourcing of Pinterest Secor and a look at zero data loss log persistence services (with engineer Pawel Garbacki) - Developing & Shipping Code at Pinterest: The tools and technologies Pinterest uses to build quickly and deploy confidently. You can find more at: engineering.pinterest.com and facebook.com/pinterestengineering
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Discover Pinterest Engineering
Agenda
!
Mobile & Growth: Monetization & Data:
Deploying & Shipping Code:
Wendy & Dannie
Pawel
Chris & Jeremy
Wendy LuMobile
Scaling User Education on MobileThe Experience Framework
Dannie ChuGrowth
User Education
Motivation?
User Education leads to Engagement
First Pin Education
Challenges
1. Preserve a great user experience
Conflicting Education
Never-ending Stream of Nags
Nag that never goes away
2. Targeting
Are you a new user? Invite some friends!
Do you have an empty homefeed? Make it better!
3. Rapid Experimentation
Experiment with messaging w/o requiring a release
4. Keep the client code clean
Solution?
The Experience Framework
Conflicting Experiences
Experiences and PlacementsNag Placement Home view Placement
Pin Tutorial Card Placement
AB Experimentation, Simple Client Code
Experiences are delivered to the client at runtime
Experience Framework
Decision Engine
Configuration
Handlers
Admin DashboardAPI Servers
AB Experiments Framework Kafka (logging)
HBase (User States,
Experience States)
API ServersAPI ServersCascading/ MapRed
User State
Batch Loader
Clients
SDK
SDK
What experience should the user see?
Experience Framework
Deep Dive: Nags
What are nags? Important information which we
occasionally display at the top of a users feed
Can be a Call to Action Confirm your email or an announcement You can now add a map to any board!
Enter: The Experience Framework
Enter: The Experience FrameworkEach time we reload the home
feed, ask the experience framework: What should I show in this nag?
Single Nag Manager that relies on the experience framework to give it the contents to render
Nag Manager
Experiment with nag messaging, call to actions, images
Add any new nag dynamically, controlled from the backend.
Cool-down management - should not see more than one nag in a set
period of time
What does that nag data look like?Handling actions
"bg_img_url_2x" = "http://mobile-assets.pinterest.com/iphone/nags/[email protected]"
"title_text" = "Pinspire your friends!"
"detailed_text" = "Know someone who'd like Pinterest? Invite them along."
"button1_text" = "No, Thanks!"button1_uri" = ""!
"button2_text" = "Invite Friends"!"button2_uri" = "pinterest://invite_friends"
Handling ActionsAll initialization and presentation of view controllers is handled through a
central Navigation Manager.
Centralizes code to create and present view controllers
Consistency to other platforms for deep links
Allows dynamic insertion of nags from the backend without having to write
new client code and submit a new release
[[NavigationManager sharedManager] handleURL:[NSURL URLWithString:@pinterest://invite_friends]];
Navigation Manager
self.presentationDelegate
presentInviteFriends
Deep Dive: New User Experience
New User ExperienceWhat is NUX?
!
The set of initial tutorials and education presented to a user after they register
Personalize a users content immediately after signupConnect with Friends Choose Interests
Next, teach users how to Pin
Experiment between classic user ed and NUX
Experiment between classic user ed and NUXWhen doing experiments where we need to call the network to get the
users treatment group, need to make sure were not adding perceived latency
Structure view controllers in a way that you can asynchronously load in the modules
dependent on the treatment group
If need to transition to dierent view controllers, set a time out in which we transition
to the control treatment
Be Fast or Fail Fast
1 step vs. 2 steps vs. 3 stepsExperiment with dierent versions of NUX
Experiment with dierent versions of NUXBackend controls all strings, allowing us to dynamically experiment with
dierent text (Messaging, titles, calls-to-action)
steps = ( { "continue_button_text" = Continue; "detailed_text" = "Tap a network to find people who share your interests."; "follow_button_text" = "Follow selected people"; step = 1; "title_text" = "First things first"; "total_steps" = 2; }, { "completion_message" = "Finding Pins for you..."; "continue_button_text" = "Tap at least {0} more to continue"; "detailed_text" = "Tap whatever you're interested in these days."; "finish_text" = Finish; "num_interests" = 5; "skip_text" = "Pinterest is much more interesting when you tell us what you like."; step = 2; "title_text" = "Pick 5 interests"; "total_steps" = 2; } );
After signup, request all data for NUXEnter: Experience Framework
Supports dynamic number and order of stepsNUXViewController : UINavigationController
Maps an array of display data to an array of view controllers
Protocol method advanceToNextStep called by each child view controller
Checks the array it keeps for the next view controller to push
JSON dict for Intro
JSON dict for Friend Selector
JSON dict for Interests Selector
NUXIntroViewController
NUXConnectViewController
NUXInterestsViewController
Wins from Experience FrameworkSingle place in the backend that manages all experiences for all platforms
Dynamically trigger display of content
Conflict resolution for educations that touch the same views
Experiment with flows, messaging, and images
engineering.pinterest.com
Pawel GarbackiSoftware Engineer, Monetization
Pinterest SecorZero-data-loss log persistence service
Pinterest is a data driven companyData matters
100+ experiments active at a given point in time
1500+ tracked metrics
200+ log types
We produce a lot of dataWe produce a lot of data
PBs of data in S3, growing by Tens of TB a day
Hundreds of production hadoop jobs, processing about half a PB of data each day
Singer (logging agent)
App
Data pipeline
Local Disk
S3Kafka (log collector)Secor
(log saver)
Storm (realtime
stats)Hive (hadoop
analytics)Redshift (ad hoc queries)
Singer (logging agent)
App
Local Disk
S3Kafka (log collector)
Secor (log saver)
Storm (realtime
stats)Hive (hadoop
analytics)Redshift (ad hoc queries)
Data pipeline20B messages/day
Kafka 101Distributed pub-sub service
Designed for high throughput Producer Producer Producer
Consumer Consumer Consumer
Kafka cluster
Anatomy of a topicTopic is a category to which messages are published
Partition is a shard of a topic controlling the level of consumption parallelism
Messages are assigned unique identifiers called osets
1 2 3 4 5 6 7 8 9 10 11 12
1 2 3 4 5 6 7 8 9
1 2 3 4 5 6 7 8 9 10 11 12
Writes
Partition 0
Partition 1
Partition 2
Save the dayKafka is optimized for local writes
Local disk capacity is good for a few
days worth of data
Data needs to be saved (at least) daily
to long term storage - Amazon S3
How soon is eventuallyAmazon S3 is a cloud file system
Eventual consistency model
No guarantees on when uploaded data will become visible to the readers
No monotonicity - data available in the past may magically disappear
Secor design guidelinesObjectives:
Persist Kafka logs to S3
Cause no data loss
Work properly with eventual consistency model
Properties:
Horizontal scalability
Fault tolerance
Customizability
No-S3-reads principleSecor never reads data from S3
Lightweight metadata is stored in strongly
consistent state repository
Strategic choice of file names
s3n://logs//
__
represents software compatibility
version
Inconsistencies introduced by consumer failures
get fixed automatically by file overwrites
Date clusteringData processing tools rely on date-
partitioned directory structure
s3n://logs/event/dt=2014-04-04/
Timestamps extracted from messages on
the fly
Support for pluggable parsers for thrift,
json, etc.
https://github.com/pinterest/secor
Chris Danford (@chrisdanford) Jeremy Stanley (@rouxbot)Web Team
From Development to ProductionThe life of web features
Develop Review Deploy Measure
Develop Review Deploy Measure
Developing - Ideal state able to iterate quickly
easy errors caught automatically
easy-to-understand and powerful abstractions
Fast iterationDeveloping
Build tasks and dependencies modeled as a graph
cumberbatch watches for changes of file contents
orchestrator knows tasks and dependencies
build the minimum tasks to heal damage in the graph
maximize parallelization of tasks
built on Grunt - access to large library of build tasks
Self-healing build graphsprite/*.png
sprite task
sprite.png sprite.scss
Sass task
component/*.scss
components.css
imagemin task Autoprefixer task CSSlint task
Self-healing build graphsprite/*.png
sprite task
sprite.png sprite.scss
Sass task
component/*.scss
components.css
imagemin task Autoprefixer task CSSlint task
Self-healing build graphsprite/*.png
sprite task
sprite.png sprite.scss
Sass task
component/*.scss
components.css
imagemin task Autoprefixer task CSSlint task
Self-healing build graph, example 2sprite/*.png
sprite task
sprite.png sprite.scss
Sass task
component/*.scss
components.css
imagemin task Autoprefixer task CSSlint task
Self-healing build graph, example 2sprite/*.png
sprite task
sprite.png sprite.scss
Sass task
component/*.scss
components.css
imagemin task Autoprefixer task CSSlint task
LintingDeveloping
catches easy bugs
enforces consistent style
pyflakes
pep8
jshint
CSSLint
custom RegEx linting
RegEx-based linter
$(.item).addClass(selected);
BoardPicker.js
this.$(.item).addClass(selected);
Static analysis / type safetyDeveloping
Google Closure Compiler
template static analysis
validation of template inheritance
extract used option variable names
Thrift clients for service calls (Python) and shared constants (JavaScript)
Closure Compiler - type safety
Generated constants from Thrift
AbstractionsDeveloping
component framework
styles are scoped to the component
DOM access is scoped to the components DOM
events up, methods down
scaolding script
live component catalog - discovery of existing components
autoprefixer, spriting - remove boilerplate
component catalog
spriting
.thumbsUpButton {! @include inline-image(sprites/main/thumbUp');!}
Usage
Develop Review Deploy Measure
Reviewing - Ideal state the most-relevant person is reviewing changes
integration-type issues are caught
Code ReviewsReviewing
local test runner script runs Jasmine and Node tests parallelized
PR watcher tool - visibility of relevant PRs
code review process - R+, E+
Parallelized Jasmine tests in PhantomJS
Github Watcher tool
Integration problems are caughtReviewing
PRs trigger a build and tests - 3 minutes
latest.pinterest.com is continuously deployed from head
Selenium integration tests run against every deploy
Pull request builds
Selenium
Develop Review Deploy Measure
Deploying - Ideal state code deploys are invisible to users
frequent and non-disruptive to developers
immediate rollback when theres a problem
User experienceDeploying
stickiness to a version
flip nearly instantaneously between builds
reduce version thrashing
worry less about (style mismatches, JS errors due to data format mismatch with
server)
asset versioning
Serving multiple application versions
new!sessions
old!sticky!sessions
A B
all!sessions
B
1 2
Serving multiple application versions, contd
3 4
new!canary!sessions
all!non-canary!sessions
B C
new!sessions
old!sticky!sessions
B C
Asset versioninglogo.png
renamehash file!contents
logo.59aa9183.png
bundle.css
background-image: logo.png
background-image: logo.59aa9183.png
update references
bundle.css
bundle.907389d8.css
renamehash file!contents
When things go wrongDeploying
experiments dashboard - turn o experiments instantaneously
version rollback is nearly instantaneous
Develop Review Deploy Measure
Monitoring healthA/B Dashboard
A/B dashboard key metrics
Sentry
Stats dashboard
Alarms - Monit, PagerDuty
A/B Dashboard
Sentry
Sentry error emails
Results
Results5 engineers on web team
all teams at Pinterest developing their own web features on our platform
components re-used across teams
2 scheduled deploys a day
anomalies in key metrics surfaced immediately
100s of simultaneous experiments