How shit works: Time - Speaker Deck

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How shit works: Time Tomer Gabel, WeWork Prague, 18-19 October 2018

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@tomerg THE STORY OF ADI

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@tomerg Meet Adi.

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@tomerg He has a dream.

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@tomerg Adi is fashionable • Event sourcing is the future, they say • Event sourcing is the shit, he agrees

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@tomerg Adi is fashionable • Event sourcing is the future, they say • Event sourcing is the shit, he agrees • … and designs an event model Created Modified Published Archived Restored

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@tomerg Column Type Key Null site_id binary(16) ✔ ✖ event_time timestamp ✔ ✖ event_type enum(…) ✖ ✖ payload blob ✖ ✖ Adi is fashionable

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@tomerg HEY, KIDS Can you guess what happens next?

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@tomerg Adi is confused • Bad shit happens • Event streams exhibit:

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@tomerg Adi is confused • Bad shit happens • Event streams exhibit: – Out of order events – Conflicting events – Impossible states Created Archived Updated WTF?!

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How shit works: Time Tomer Gabel, WeWork @ GeeCON Prague 2018 Image: Vera Kratochvil (public domain)

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@tomerg Time (noun) “… the system of those sequential relations that any event has to any other, as past, present, or future; indefinite and continuous duration regarded as that in which events succeed one another.” -- dictionary.com

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@tomerg Modeling time • Encoding – Resolution – Epoch “Real” time Epoch Resolution T1 T 2 T3 T4 T 5

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@tomerg Modeling time • Encoding – Resolution – Epoch T3 Instant (or “event”)

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@tomerg Modeling time • Encoding – Resolution – Epoch T3 A B Conflicting events

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@tomerg Modeling time • Encoding – Resolution – Epoch • Bootstrapping Epoch ???

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@tomerg Modeling time • Encoding – Resolution – Epoch • Bootstrapping – Manual Image courtesy of Jim Leonard

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@tomerg Modeling time • Encoding – Resolution – Epoch • Bootstrapping – Manual – Battery-backed Image: pc-restorer.com

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@tomerg Modeling time • Encoding – Resolution – Epoch • Bootstrapping – Manual – Battery-backed – NTP Image: FDA graphic by Michael J. Ermarth (public domain)

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@tomerg Modeling time • Encoding – Resolution – Epoch • Bootstrapping – Manual – Battery-backed – NTP • Updating T = T+1 T1 T 2 W hen?

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@tomerg System Clock (RTC) Modeling time • Encoding – Resolution – Epoch • Bootstrapping – Manual – Battery-backed – NTP • Updating Image: Jeremy Saglimbeni on Vimeo (CC BY-SA 3.0)

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@tomerg RTC isn’t perfect • Alas, clocks drift • Subtle causes – Temperature – Power supply – General relativity – Cosmic radiation – Alien gamma rays Image showing ~220µs clock drift by Luke Bigum

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@tomerg Distributed time Client A Client C Client B Client D RTC RTC RTC RTC Event Stream Event Store RTC event_time = ?

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@tomerg Time source: Application Client A Client C Client B Client D RTC RTC RTC RTC Event Stream Event Store RTC 1 2 3 4

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@tomerg Time source: Application Client A Client C Client B Client D RTC RTC RTC RTC Event Stream Event Store RTC 1 2 3 4 Clocks must be synchronized!

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@tomerg What about NTP? Client A Client C Client B Client D RTC RTC RTC RTC Event Store RTC NTP

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@tomerg What about NTP? Client A Client C Client B Client D RTC RTC RTC RTC Event Store RTC NTP Accurate within ~10ms✝ ✝ Fig. 1, “Characterizing Quality of Time and Topology in a Time Synchronization Network”, Murta et al

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@tomerg Time source: Database Client A Client C Client B Client D RTC RTC RTC RTC Event Stream Event Store RTC Doesn’t scale! Doesn’t work! Such wow!!!

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@tomerg DEALING WITH TIME IS HARD TL;DR

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@tomerg Reprise: Time (noun) “… the system of those sequential relations that any event has to any other, as past, present, or future; indefinite and continuous duration regarded as that in which events succeed one another.” -- dictionary.com

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@tomerg Reprise: Time (noun) “… the system of those sequential relations that any event has to any other, as past, present, or future; indeﬁnite and continuous duration regarded as that in which events succeed one another.” -- dictionary.com

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@tomerg Reframing the problem • When reading events – Do we need wall time? – We don’t care about it – It’s just metadata • We only care about ordering events Created (2017-05-03 10:15) Updated (2017-05-03 10:27) Archived (2017-05-03 10:55)

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@tomerg Causality • Take any two events • What relationship can they have? Created Updated Updated Archived

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@tomerg Causality • Take any two events • What relationship can they have? – Happens before: A→B d B↛A A. Created B. Updated Updated Archived

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@tomerg Causality • Take any two events • What relationship can they have? – Happens before: A→B – Concurrent: A↛B and B↛A Created A. Updated B. Updated Archived

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@tomerg What do we need? • Our versioning scheme should… – Respect causality – Be totally ordered – Not rely on the RTC • We want serializability – ... on an ACID-compliant database!

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@tomerg MAKE ADI GREAT AGAIN! Enough theory. Let’s

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@tomerg Relax • It’s fairly simple in practice • First, the schema Column Type Key Null site_id binary(16) ✔ ✖ event_time timestamp ✔ ✖ event_type enum(…) ✖ ✖ payload blob ✖ ✖

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@tomerg Relax • It’s fairly simple in practice • First, the schema – Don’t key on timestamp Column Type Key Null site_id binary(16) ✔ ✖ event_time timestamp ✖ ✖ event_type enum(…) ✖ ✖ payload blob ✖ ✖

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@tomerg Relax • It’s fairly simple in practice • First, the schema – Don’t key on timestamp – Add explicit version Column Type Key Null site_id binary(16) ✔ ✖ version int ✔ ✖ event_time timestamp ✖ ✖ event_type enum(…) ✖ ✖ payload blob ✖ ✖

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@tomerg Finishing touches • On writes: MySQL Server Client C1-Cn

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@tomerg Finishing touches • On writes: – Read latest version V0 MySQL Server Client C1-Cn V0

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@tomerg Finishing touches • On writes: – Read latest version V0 – Generate events V1...Vn – Write atomically MySQL Server Client C1-Cn V1-Vn

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@tomerg Finishing touches • On writes: – Read latest version V0 – Generate events V1...Vn – Write atomically • Success! – Return new version MySQL Server Client C1-Cn V1-Vn Vn

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@tomerg Finishing touches • On writes: – Read latest version V0 – Generate events V1...Vn – Write atomically • Duplicate primary key? – Optimistic lock failure! – Retry, propagate, resolve MySQL Server Client C1-Cn V1-Vn Error PK violation

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@tomerg GREAT SUCCESS!

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@tomerg KFIR ‘ADI’ BLOCH A round of applause for our special guest: ... and for the terrific camera work I butchered: VAIDAS PILKAUSKAS

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@tomerg QUESTIONS? Thank you for listening tomer@tomergabel.com @tomerg On GitHub: https://github.com/holograph/examples/tree/master/event-sourcing This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Congratulations! You have found the secret level

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@tomerg Lamport timestamps✝ • Provides partial ordering of events • Advantages: – Respects causality – Low overhead – Simple to implement Event Store Host A Host C Host B ✝ “Time, Clocks, and the Ordering of Events in a Distributed System”, Leslie Lamport, 1978

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@tomerg Event Store Host A Host C Host B Lamport timestamps • Each host maintains local logical clock • Start with T=0 T=0 T=0 T=0

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@tomerg Event Store Host A Host C Host B Lamport timestamps • Each host maintains local logical clock • Start with T=0 • On send (i.e. write): – Increment local clock – Attach to message T=0 T=0 T=0 T=1

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@tomerg Lamport timestamps • On receive (i.e. read): – Process event if T < Tin Event Store Host A Host C Host B T=0 Tin =1

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@tomerg Lamport timestamps • On receive (i.e. read): – Process event if T < Tin – Update clock past the latest event: T = max(T, Tin ) + 1 • All done! Event Store Host A Host C Host B T=0 Tin =1 T=2

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@tomerg … well, almost • This is a partial ordering • Causality is dealt with – A→B ⇒ T(A) < T(B) • What about concurrency? – A↛B and B↛A ⇒ ?

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@tomerg Final touches • We want total ordering – Must be stable – Need not correspond to real time Created Updated Updated Archived T=1 T=5 T=2 T=2

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@tomerg Final touches • We want total ordering – Must be stable – Need not correspond to real time • Breaking the tie: – Use any consistent key – Host name, MAC, IP… Created Updated Updated Archived T=1 T=5 T=2 T=2