Breaking Point: Building Scalable, Resilient APIs

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@markhibberd Breaking Point Building Scalable, Resilient APIs

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“A common mistake that people make when trying to design something completely foolproof was to underestimate the ingenuity of complete fools.” Douglas Adams -! Mostly Harmless (1992)

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How Did We Get Here

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THE API

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THE API

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THE API

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THE API

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THE API

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THE API

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THE API

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THE API

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THE API G

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THE API G

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THE API G

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THE API G

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THE API G

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THE API G

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THE API G

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THE API G $ $ $ $

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THE API G $ $ $ $

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THE API G $ $ $ $

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failure is inevitable

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How Systems Fail

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“You live and learn. At any rate, you live.” Douglas Adams -! Mostly Harmless (1992)

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The Crash one

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Systems Never Fail Cleanly

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Cascading Failures two

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OCTOBER 27, 2012 ! Bug triggers Cascading Failures Causes Major Amazon Outage ! https://aws.amazon.com/message/680342/

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At 10:00AM PDT Monday, a small number of Amazon Elastic Block Store (EBS) volumes in one of our five Availability Zones in the US-East Region began seeing degraded performance, and in some cases, became “stuck”

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Can Be Triggered By As Little As A Performance Issue

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Don’t Listen To Programmers, Performance Matters

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(well, at least asymptotics matter)

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A Failure is Indistinguishable from a Slow Response

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Chain Reactions three

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12.5%

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14.3%

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17%

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17% 100k req/s 12.5k >>> 14.3k >>> 17k req/s

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17% 100k req/s 12.5k >>> 14.3k >>> 17k req/s 4.5% extra traffic means a 36% load increase on each server

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25% 100k req/s 12.5% extra traffic means a 300% load increase on each server 12.5k >>> 14.3k >>> 17k >>> 50k req/s

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Capacity Skew four

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JUNE 11, 2010 ! A Perfect Storm.....of Whales Heavy Loads Causes Series of Twitter Outages During World Cup http://engineering.twitter.com/2010/06/perfect-stormof-whales.html

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Since Saturday, Twitter has experienced several incidences of poor site performance and a high number of errors due to one of our internal sub-networks being over-capacity.

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Self Denial of Service five

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Clients Server

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Clients Server

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Clients Server

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Clients Server

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Clients Server

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a very painful experience ! The Quiet Time

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critical licensing service, 100 million + active users a day, millions of $$$. ! A couple of “simple” services. Thick clients, non- updatable, load-balanced on client.

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server client

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/call server client on-demand

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/call server client on-demand

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/call server client /check on-demand periodically

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/call server client /check on-demand periodically

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/call server client /check on-demand periodically /check2 /check2z /v3check

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/call server client /check on-demand periodically /check2 /check2z /v3check

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/call server /check /check2 /check2z /v3check

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System Collusion six

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NOVEMBER 14, 2014 ! Link Imbalance Mystery Issue Causes Metastable Failure State ! https://code.facebook.com/posts/1499322996995183/solving-the-mystery-of-link-imbalance-a-metastable- failure-state-at-scale/

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Bonded Link Should Evenly Utilise Each Network Pipe

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multiple causes for imbalance

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systems couldn’t correct themselves

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individually each component was behaving correctly

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Temporary Latency To Db Caused Skew Connection Pool Started Favouring Overloaded Link

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failure is not clean

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Incident Reports Are For You and Your Customers

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A Good Incident Report, Helps Others Learn From Your Mistake, And Ensures You Really Understand What Went Wrong

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1. Summary & Impact 2. Timeline 3. Root Cause 4. Resolution and Recovery 5. Corrective and Preventative Measures 5 Steps To A Good Incident Report https://sysadmincasts.com/episodes/20-how-to-write-an-incident-report-postmortem

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How To Control Failure

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“Anything that happens, happens. ! Anything that, in happening, causes something else to happen, causes something else to happen. ! Anything that, in happening, causes itself to happen again, happens again. ! It doesn’t necessarily do it in chronological order, though.” Douglas Adams -! Mostly Harmless (1992)

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Timeouts one

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Other Systems Will Always Be Your Most Vulnerable Failure Modes

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Never Make a Network Call, or Go to Disk, Without a Timeout

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Pay Particular Attention to Reusable & Pooled Resources

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Backoff Requests That Time Out

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Heartbeats two

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If You Know Something Is Failing, Fail Fast

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{ “name”: 123, “version”: “mth”, “stats”: {…}, “status”: “ok” } /status

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Circuit Breakers three

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{ “id”: 123, “username”: “mth”, “profile”: { “bio”: “…” “image”: “…” }, “friends”: [ 191, 1 ] }

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{ “id”: 123, “username”: “mth”, “profile”: { “bio”: “…” “image”: “…” }, “friends”: [ 191, 1 ] }

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{ “id”: 123, “username”: “mth”, “profile”: { “bio”: “…” “image”: “…” } }

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Requires Co-Ordination to Manage Degradation Of Service

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Partitioning four

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traffic Spike

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traffic Spike

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traffic Spike

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traffic Spike

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traffic Spike

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survives another day

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Partitioning Can Also Be Performed Within Services Via Limited Thread & Resource Pools

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multiple types of request /shout /download

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multiple types of request /shout /download

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multiple types of request /shout /download fast

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multiple types of request /shout /download fast slow

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multiple types of request /shout /download fast really really slow

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multiple types of request /shout /download

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multiple types of request /shout /download

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multiple types of request /shout /download

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Backpressure five

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Fail One Request, Instead of Failing All Requests

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Measure And Signal Slow Requests

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Upstream Drops Requests To Allow For Recovery

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Taper Limits

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Taper Limits

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Taper Limits 200k Db Requests

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Taper Limits 200k Db Requests 50k Server Requests

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Taper Limits 200k Db Requests 50k Server Requests 45k Proxy Requests

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failure can be mitigated

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How To Prevent Failure

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“A beach house isn't just real estate. It's a state of mind.” Douglas Adams -! Mostly Harmless (1992)

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one Testing

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the testing you probably don’t want to do is the testing you need to do most

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Speed Scalability Stability

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tools help charles proxy ipfw / pf netem monitoring tools simian army ab / siege

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two Measure Everything

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every result computed should have traceability back to the code & data

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gather metadata for everything that touches a request

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services: { auth: {…} ! ! ! }

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services: { auth: {…}, profile: {…}, recommend: {…} ! }

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services: { auth: {…}, profile: {…}, recommend: {…}, friends: {…} }

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services: { auth: {…}, profile: {…}, recommend: {…}, friends: {…} } { version: {…}, stats: {…}, source: {…} }

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statistics work, measurements over time will find errors

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! deviation: … percentiles: 90: 95: histogram: 20x: 121 30x: 12 40x: 13 50x: 121311313

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statistics work, we can use them to automate corrective actions

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three Production In Development

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production quality data automation of environments lots of testing

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production quality data automation of environments lots of testing Rather Old Hat

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four Development in Production

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yes, really. i want to ship your worst, un-tried, experimental code to production

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@ambiata we deal with ingesting and processing lots of data 100s TB / per day / per customer scientific experiment and measurement is key experiments affect users directly researchers / non-specialist engineers produce code

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query /chord

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query /chord {id: ab123} datastore ;chord

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query /chord {id: ab123} datastore ;chord report ;result

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query /chord {id: ab123} datastore ;chord report ;result /chord/ab123 client

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split environments

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query /chord {id: ab123}

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query /chord {id: ab123} production:live

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/chord {id: ab123} production:live proxy query

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/chord {id: ab123} production:exp proxy query

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/chord {id: ab123} production:* proxy query query

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implemented through machine level acls experiment live control

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implemented through machine level acls experiment live control write read

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implemented through machine level acls experiment live control

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implemented through machine level acls experiment live control write read

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implemented through machine level acls experiment live control write read

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checkpoints

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query /chord {id: ab123} datastore ;chord report ;result /chord/ab123 client x x

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query /chord {id: ab123} datastore ;chord report ;result /chord/ab123 client x x

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query /chord {id: ab123} datastore ;chord report ;result /chord/ab123 client x x

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query /chord {id: ab123} datastore ;chord report ;result x x behaviour change through in production testing

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query /chord {id: ab123} datastore ;chord report ;result /chord/ab123 client x x

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deep implementation, intra- and inter- process crosschecks

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tandem deployments

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/chord {id: ab123} production:* proxy query query x x

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/chord {id: ab123} production:* proxy query query x x

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/chord {id: ab123} production:* proxy query query x x

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staged deployments

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/chord {id: ab123} production:* proxy query query x x

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/chord {id: ab123} production:* proxy query x

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/chord {id: ab123} production:* proxy query x

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failure is inevitable

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failure is not clean

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failure can be mitigated

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Unmodified. CC BY 2.0 (https://creativecommons.org/licenses/by/2.0/)! https://www.flickr.com/photos/timothymorgan/75288582/! https://www.flickr.com/photos/timothymorgan/75288583/! https://www.flickr.com/photos/timothymorgan/75294154/! https://www.flickr.com/photos/timothymorgan/75593155/! https://www.flickr.com/photos/timothymorgan/75593155/