Scala - ExperiencesLeon Maia@leonmaia

CPH Scala Meetup3.11.16

About me

● HP, Hotel Urbano, Globo.com and now Tradeshift.● Scala for the last 4 years.● Interests: Distributed Systems, highly available and fault tolerant systems.

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Agenda

● Globo?● Challenges - Goals.● Geofencing - how we solved it?● Finagle - Filters.● Scalaz - Either.● The Extractor Pattern.● Readers.● Rio 2016 Olympics.● Metrics, Metrics, Everywhere.● DEMO.

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Globo?

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$4.83 billion in media revenue

Our stack

● Scala.● Finagle.● Twitter Server.● Guava Cache.● Zipkin.

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Challenges - Goals

● Be able to handle absurd spikes on the API during the beginning of the events.

● Make it super simple to adjust settings live.● ZERO DOWNTIME.

○ HA + FT!

● High-throughput and low-latency!● Non-disruptive background loading.● Geofencing.

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Geofencing

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The geofencing problem

● Geofences extensively used for geo-based transmissions.● MySQL Geometry functions.● 3 decimal places - 110m.● Not able to cache.

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Numbers..?

● The response time was < 5 ms at 95th percentile, and < 50 ms at the 99th percentile.

● Was able to handle 64000 req/s with 7 machines running at 40% CPU usage.

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Using Either

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Using Either - Part 2

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Filters

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Clients

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The extractor pattern

● Decompose various kinds of data structures using pattern matching.● Constructor: creates an object from a given list of parameters.● Extractor: extracts the parameters from which an object passed to it was

created.

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Besides all that, it must be simple.

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What we achieved

● Good maintainability.● Absurdly low response times.● Robust error handling and recovery.● Smart caching.● Able to handle huge traffic spikes.

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Rio 2016 Olympics.

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Audience during the game BRA x SWE.

“We used around 5.5 TB of memory with 1056 CPUs across two PoPs located in the southeast of the country.

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DEMO

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Thanks!

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