Greg Young
Yow Night 15
th Nov 2012
See also:
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Current state:
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Is awful
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Requires large amounts of versioning
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1st level derivative of facts that
have happened
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Look at systems from perspective of no current
state
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Banking, insurance, gambling, etc
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We don’t have current state, we have a series of
facts
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Driving point is from business perspective
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E.g.
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Purchase order
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Line items(n)
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Shipping information
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Models represent our current state
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Document stores are awesome - until you need to
change your schema
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Problem is we want to go and change our previous
representations of data
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E.g. Cart created -> 3 items added ->
shipping information added
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At any time can replay 3 events to get data
model
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Events: append only model
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How do you scale immutable data? Copy it
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Immutable data is awesome
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Once “Cart created” is created it will never
change
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Append-only model, with everything immutable,
what about updates/deletes?
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Update/delete = lost valuable data
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Code with a magic 8-ball to predict what
business is going to want in 2 years?
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Strategic design with DDD
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Don’t apply ES globally
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ES/CQRS is not an architecture
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Small things you apply within a
service/component
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Not losing information is valuable
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2 sets of use cases in different orders that end
up with same ending state?
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Lost info
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Hash collision – non-perfect – lost info coming
into system
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One rule: we don’t lose any data – generating 100Gb
per day
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How do you predict value of data?
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Humans have history of making bad predictions about
future
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Bigger the expert = worse predictive analysis
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Can only say: “I cannot price this option”
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Therefore I should keep it
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When business ask for unexpected data, can say
yes
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Could be something that makes or breaks company –
competitive advantage
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Accounting is not done with a pencil
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If make a mistake, do a reversal
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Partial reversal $10,000 instead of $1,000 =
-$9,000
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Accountants don’t like doing – too complicated
across 8 accounts,
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Do a full reversal instead and then redo
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E.g. Cart created -> 3 items added -> 1 item removed -> shipping
information added
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Same as 2 items added?
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As a series of facts, very different from each
other
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Want to know about how many items removed?
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Most businesses are not just create, read,
update, delete…. Many verbs
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ES gives semantics associated back down to verbs
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Business value comes from fact that we’re not
losing information
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E.g. Large POS, Amazon
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Removed items from cart are more likely to
purchase in the future – still want them can’t afford them
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Old model
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Add RemovedLineItems object or flag & date
on line items
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Query, subquery – time correlation – 3 nested
subqueries
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(Try using a Stream database instead)
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ES model
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Write projection with state inside
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If item found in carts
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Business person can go back into past and see
things at that point in time with a deterministic perception we have today
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Huge win for business
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Useful for predicting future - “Back testing” in finance
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BI reverse engineer CRUD databases into events
(imperfectly)
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Temporal data model
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Smoke testing
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Rerun commands since day 1 every Friday and
compare results from last time
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Won’t protect you from black swans
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Append-only good for hard drives (even SSDs that
burn out rewriting)
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E.g. Secure system
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Gambling
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Chris Harn – edited his bets on hard drive
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How to prevent a super user attack
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E.g. Pick 6 tickets
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CSU/DSU
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Prevent by putting log on “write-once” media –
physically can’t modify data
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Easier to physically secure a machine than to
secure software
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200 partitions within logs
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Every aggregate has its own stream
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Partition
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Rolling snapshot
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20,000 requests per sec if all in memory
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Rents represents functions
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Current state = left fold
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Snapshots = memoisation
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ES = functional way of storing data
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Pattern match functions to events
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ES = FP
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Balance of bank account not a column in db but a
function of account history
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Provable
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Natural fits for ES
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Accounting
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Pubsub
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Don't have to build your own Event Store
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Cassandra - stream per colum
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Scales well
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Medical system
Questions
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How to justify cost of storing everything
because you don’t know what you will need
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Cost of data is low - 5gb for can of coke
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Hard to justify not storing data
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What is it not used for?
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Lots of things
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Things outside of core domain
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Events represent use cases
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Some use cases might not be high value
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E.g claims more valuable than sales
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Only used for competitive advantage – requires analysis
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Pitfalls?
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ES architecture
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Monolithic - systems of systems instead
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Expensive to do analysis
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Does every projection read every event?
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Projection pattern match, function
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Only look at events interested in
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Map reduce
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I asked which databases other than Cassandra were a
good fit for ES?
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Consistency is important
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Need CA for writes, AP for reads
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Hard to find system that can be tuned like that
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Riak but slow, quorum writes
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Event Store has BSD license
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SQL server for small systems
