The document discusses functional architectural patterns in data processing, highlighting the benefits of immutability, event sourcing, and the lambda architecture. It contrasts functional and object-oriented programming approaches, focuses on data pipeline design and orchestration, and examines anti-patterns in API and database management. Key concepts include the importance of maintaining consistency and the challenges of managing large datasets effectively.

1. Functional architectural
patterns
Lars Albertsson
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2. Who’s talking?
Swedish Institute of Comp. Sc. (test tools)
Sun Microsystems (very large machines)
Google (Hangouts, productivity)
Recorded Future (NLP startup)
Cinnober Financial Tech. (trading systems)
Spotify (data processing & modelling)
Schibsted (data processing & modelling)
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3. Why functional?
Verbs
... has made ... expanding ...
... flourishes ... merged ... has been unable to escape lingering .. built ...
... are ... placed ... say ... are ... to explode ...
.. are considering ... to reopen … to recall ...
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4. Or object-oriented?
Nouns, pronouns
... bankruptcy ... government bailout ... automaker Chrysler ... comeback ... sales ... Jeep sport utility vehicles.
... Chrysler ... part ... Fiat Chrysler Automobiles, it ... concerns ... the safety ... Jeeps ...
... Jeeps ... gas tanks ... regulators ... safety advocates ... rear-end crash.
... regulators ... an investigation ... those Jeeps ... Fiat Chrysler’s agreement ... models.
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5. Functional benefits? My version.
Matches a few problems
Data processing
Matches a few computer properties
Consistency through immutability
Deterministic - replay for resilience
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6. Local vs distributed properties
Local
Hardware provides
strong consistency
Faults -> death
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Distributed
Eventual consistency
Faults must be
survived

7. Architectural functional patterns
Personal anti-pattern experiences
Strive to look for
Immutability
Reexecution
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8. MapReduce
Discovered pattern, not invention
Well known, enough said
Succeeded by Spark RDD paradigm
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9. Data flows
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Users
Page
views
Sales
Sales
reports
Views with
demographics
Sales with
demographics
Conversion
analytics
Conversion
analytics
Views with
demographics
Dataset artifacts, typically files with date
parameter.
Raw Derived

10. Anti-pattern - isolated batch jobs
Get data (more on that later)
Cron an ETL batch job (function)
Output solidifies. Mostly.
Steps in isolation - often different teams
What to do on ETL code changes?
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Sales with
demographics
Views with
demographics

11. Pattern: data pipeline
End-to-end sequences/DAG of jobs
Not only exist, but treated end-to-end
Input is raw, original data
Separate raw data from generated
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Users
Page
views
Sales with
demographics
Conversio
n analytics
Conversion
analytics
Views with
demographics

12. Lambda architecture, part 1
Save all collected data without preprocessing
But timestamp on generation, register,
arrival
Rerun everything downstream on code change
Human fault tolerance
In conflict with privacy management?
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13. Pipeline workflow orchestration
Ideally: Good old make + cluster + IDE + xUnit
Test end-to-end
Rebuild on upstream changes (but not all)
State of practice: Luigi, Pinball, Azkaban
Don’t take you all the way :-(
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14. Lambda architecture, part 2
Parallel batch and real-time pipelines
Batch more accurate, overrides
Real-time for window of recent data
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15. Obtaining data
Log things. Conceptually stable, but collection
is challenging at scale.
Have legacy code and master data in
databases? Let us have a look.
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16. Database dimensioned for online traffic
Hadoop = herd of elephants
Load spike
Height = #mapper nodes
Area = #users
Anti-pattern: direct dump
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API

17. Direct dumps in the trenches
Company successful - #users increasing
More Sqoop mappers - higher DB load
Daily dump jobs went to 25h
Devops firewalled off Hadoop to recover
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18. Anti-pattern: dump through API
SOA/microservice culture
DB protected by throttling
API not used to elephants
Query area is still large
Herd of elephants through gate - 1-2 weeks
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API

19. Anti-pattern: slave dump
Protect live service by mirroring to a dump
slave
No online service risk, good!
Why anti-pattern?
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20. All dumps are non-deterministic
HDFS down? Dump later.
State is gone - dump not accurate
Slave replication down?
Dump not accurate
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21. Anti-pattern: deterministic mirror
Replay commit log until full day/hour
Discovered through archaeology :-)
Not scalable, point of failure
Hourly dump took 45 minutes, increasing...
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22. (Anti-)pattern: better dumping
Netflix Aegisthus
Snapshot Cassandra (fast, atomic,
reliable)
Transfer SSTables to HDFS
Replicate compaction in MapReduce
Other DBs? Depends on atomic snapshot.
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23. All dumps are anti-patterns?
Typical use: Join activity events with user info
Event time != dump time
Aggregation discards information
Which users enabled X, tried, and disabled?
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24. Pattern: Event source
All facts are events. Immutable, timestamped
Event stream is source of truth
No explicit “current state”
The functional data architecture?
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25. Event source incarnated: unified log
Pour events into pub/sub bus, with long history.
Kafka de-facto standard.
Tap from bus to HDFS/S3 in time buckets.
Camus/Secor
Stream processing pipelines to dest topics
Replay on code changes
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26. Unified log, practical considerations
Long history necessary
Must have time to fix stream process bugs
Use 3+ months and use stream as temp
DB
Unified log also useful for meta and control
Tweak Kafka for low latency
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27. Event source + views
View = snapshot of aggregated state @ time
For ETL, choice of hourly/daily aggregates or
exact views
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Logs
View View

28. Event source + database
Business logic may demand “current state”
Event stream is truth, keep DB in sync
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29. Event source, synced database
A. Service interface generates
events and DB transactions
B. Generate stream from DB
commit log.
Postgres, MySQL -> Kafka
C.Build DB with stream
processing
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APIAPIAPI

30. Deployment & orchestration
System = many machines
Desired system state = code + config
Actual state = Orchestrator(current, desired)
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31. Anti-pattern: stateful orchestration
Orchestrator = Puppet|Chef|Ansible {
current.changeSomeProperties(desired)
return current
// current.otherProperties unchanged
}
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32. Stateful orchestration in the trench
Desired = { case roleA: install(x,y)
case roleB: install(z) }
Current = x installed on roleB. Old x. Zombie
woke up when B load decreased.
Puppet+apt = No simple way to remove
undesired state
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33. Pattern: artifacts from source
Orchestrator = Docker|Packer {
delete current
return Image(desired)
}
No state leak from existing state. Sort of.
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34. Deterministic, predictable?
Image building leaky on purpose
E.g. “apt-get update && apt-get install”
Imports external state
Ephemeral databases preserve state
Ability to rebuild from unified log is
valuable
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35. Jay Kreps, Confluent: Unified log
Martin Kleppman: Unified log, Bottled Water
Nathan Marz: Lambda
Sander Mak @ Jfokus: Event sourcing
Datomic
Questions?
More?
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