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Akka & Data Science:Making real-time predictionsBrian Gawalt2nd International Conference on Predictive APIs and AppsAugust 7, 2015

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[A]Sometimes, data scientists need to worry about throughput.

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[B]One way to increase throughput is with concurrency.

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[C]The Actor Model is an easy way to build a concurrent system.

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[D]Scala+Akka provides an easy-to-use Actor Model context.

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[A + B + C + D ⇒ E]Data scientists should check out Scala+Akka.

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Consider:● building a model, ● vs. using a model

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Lots of ways to practice building a model

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The Classic Process

1. Load your data set’s raw materials

2. Produce feature vectors:

o Training,

o Validation,

o Testing

3. Build the model with training and validation vectors

4 U th d l t t/ t9

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The Classic Process: One-time Testing

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Load train/valid./test materials

Make train/valid./test feature vectors

Train Model

Make test predictions

Build

Use

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The Classic Process: Repeated Testing

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Load train/valid. materials

Make train/valid. feature vectors

Train Model

Load test/new materials

Make test/new feature vectors

Make test/new predictions

(saved model)

(repeat every K minutes)

Build

Use

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Sometimes my tasks work like that, too!

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But this talk is about the other kind of tasks.

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[A]Sometimes, data scientists need to worry about throughput.

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Example:Freelancer availability on

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Hiring Freelancers on Upwork

1. Post a job

2. Search for freelancers

3. Find someone you like

4. Ask them to interview

o Request Accepted!

o or rejected/ignored...16

THE TASK:

Look at recent freelancer behavior, and predict, at time Step 2, who’s likely to accept an invite at time Step 4

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Building this model is business as usual:

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Building Availability Model

1. Load raw materials:

o Examples of accepts/rejects

o Histories of freelancer site activity

Job applications sent or received

Hours worked

Click logs

Profile updates

2. Produce feature vectors: 18

Greenplum

Amazon S3

Internal Service

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Using Availability Model

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Load train/valid. materials

Make train/valid. feature vectors

Train Model

Load test/new materials

Make test/new feature vectors

Make test/new predictions

(saved model)

(repeat every 60 minutes)

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Using Availability Model

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Load test/new materials

Make test/new feature vectors

Make test/new predictions

(saved model)

(repeat every 60 minutes)

Load job app data(4 min.)

Load click log data(30 min.)

Load work hours data(5 min.)

Load profile data(20 ms/profile)

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Using Availability Model

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Load job app data(4 min.)

Load click log data(30 min.)

Load work hours data(5 min.)

Load profile data(20 ms/profile)

● Left with under 21 minutes to collect profile data○ Rate limit: 20 ms/profile○ At most, 63K profiles per

hour● Six Million freelancers who

need avail. predictions: expect ~90 hours between re-scoring any individual

● Still need to spend time actually building vectors and exporting scores!

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[B]One way to increase throughput is with concurrency.

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Expensive Option:Major infrastructure overhaul

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… but that takes a lot of time, attention, and cooperation…

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Simpler Option:The Actor Model

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[C]The Actor Model is an easy way to build a concurrent system.

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● Imagine a mailbox with a brain● Computation only begins when/if a

message arrives● Keeps its thoughts private:

○ No other actor can actively read this actor’s state

○ Other actors will have to wait to hear a message from this actor

An Actor

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● Lots of Actors, and each has:○ Private message queue○ Private state, shared only sending more

messages● Execution context:

○ Manages threading of each Actor’s computation

○ Handles asynch. message routing○ Can send prescheduled messages

● Each received message’s computation is fully completedbefore Actor moves on to next message in queue

The Actor Model of Concurrency

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The Actor Model of Concurrency

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Execution Context

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Parallelizing predictions

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Refresh work hours

Vectorizer:● Keep copies of raw data● Emit vector for each new

profile received

Refresh job apps

Refresh click log Fetch 10 profiles

Apply model; export

prediction

raw data

raw data

Schedule: Fetch once per hour Schedule: Fetch once per hour

Schedule: Fetch once per hour Schedule: Fetch every 300ms

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Serial processing

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Refresh job apps

Make feature vectors

Export predictions

(repeat every 60 minutes)

Refresh work hours

Refresh click log

Fetch ~50K profiles

...

55 min

5 min

4 min

5 min

30 min

55 - 4 - 5 - 30 = 16 min

...

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Serial processing

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Refresh job apps

Make feature vectors

Export predictions

(repeat every 60 minutes)

Refresh work hours

Refresh click log

Fetch ~50K profiles

...

55 min

5 min

4 min

5 min

30 min

55 - 4 - 5 - 30 = 16 min

... Throughput:48K users/hr

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Parallel Processing with Actors

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Refresh job apps

...

Refresh click log

Refresh work hrs.

Rx data

Fetch pro.

Export

Rx data

Fetch pro.

Fetch pro.

Fetch pro.

Fetch pro.= msg. sent= msg. rx’d

1/hr.

1/hr.

1/hr. 3/sec. (as rx’ed)

Store

Store

Vectorize

Vectorize

Store

1/hr.

Thr. 1 Thr. 2 Thr. 3 Thr. 4

Vectorize

Fetch pro.

Fetch pro.(msg. processing time not to scale)

Rx data

Vectorize

...

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Parallel Processing with Actors

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Refresh job apps

...

Refresh click log

Refresh work hrs.

Rx data

Fetch pro.

Export

Rx data

Fetch pro.

Fetch pro.

Fetch pro.

Fetch pro.= msg. sent= msg. rx’d

1/hr.

1/hr.

1/hr. 3/sec. (as rx’ed)

Store

Store

Vectorize

Vectorize

Store

1/hr.

Thr. 1 Thr. 2 Thr. 3 Thr. 4

Vectorize

Fetch pro.

Fetch pro.

Throughput:180K users/hr

Rx data

Vectorize

...

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[D]Scala+Akka provides an easy-to-use Actor Model context.

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Message passing, scheduling, & computation behavior defined in 445 lines.

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Scala+Akka Actors

● Create Scala class, mix in Actor trait

● Implement the required partial function: receive: PartialFunction[Any, Unit]

● Define family of message objects this actor’s planning to handle

● Define behavior for each message case in receive

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Scala+Akka Actors

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Mixin same code used for export in non-Actor version

Private, mutable state: stored scores

Private, mutable state: time of last export

If receiving new scores: store them!

If storing lots of scores, or if it’s been awhile: upload what’s stored, then erase them

If told to shut down, stop accepting new scores

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Scala+Akka Pros

● Easy to get productive in the Scala language

● SBT dependency management makes it easy to move to any box with a JRE

● No global interpreter lock!

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Scala+Akka Cons

● Moderate Scala learning curve

● Object representation on the JVM has pretty lousy memory efficiency

● Not a lot of great options for building models in Scala (compared to R, Python, Julia)

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[A]Sometimes, data scientists need to worry about throughput.

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[B]One way to increase throughput is with concurrency.

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[C]The Actor Model is an easy way to build a concurrent system.

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[D]Scala+Akka provides an easy-to-use Actor Model context.

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[A + B + C + D ⇒ Z]Data scientists should check out Scala+Akka

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Thanks!Questions?

bgawalt@{upwork, gmail}.comtwitter.com/bgawalt