An introduction to Apache Airflow

1. Introducing Apache
Airflow (Incubating)
Sid Anand (@r39132)
Data Day Seattle 2016
1

2. About Me
2
Work [ed | s] @
Maintainer on
Reports to
Co-Chair for

3. Apache Airflow
3
What is it?

4. 4
Apache Airflow : What is it?
Airflow is a platform to programmatically author,
schedule and monitor workflows (a.k.a. DAGs)

5. 5
Apache Airflow : What is it?
Airflow is a platform to programmatically author,
schedule and monitor workflows (a.k.a. DAGs)
It ships with
• DAG Scheduler
• Web application (UI)
• Powerful CLI

6. 6
Apache Airflow : What is it?

7. Airflow - Authoring DAGs
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Airflow: Visualizing a DAG

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Airflow: Author DAGs in Python! No need to bundle many XML files!
Airflow - Authoring DAGs

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Airflow: The Tree View offers a view of DAG Runs over time!
Airflow - Authoring DAGs

10. Airflow - Performance Insights
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Airflow: Gantt charts reveal the slowest tasks for a run!

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Airflow: …And we can easily see performance trends over time
Airflow - Performance Insights

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Apache Airflow : What is it?
When would you use a Workflow Scheduler like
Airflow?
• ETL Pipelines
• Machine Learning Pipelines
• Predictive Data Pipelines
• Fraud Detection, Scoring/Ranking, Classification,
Recommender System, etc…
• General Job Scheduling (e.g. Cron)
• DB Back-ups, Scheduled code/config deployment

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Apache Airflow : What is it?
What should a Workflow Scheduler do well?
• Schedule a graph of dependencies
• where Workflow = A DAG of Tasks
• Handle task failures
• Report / Alert on failures
• Monitor performance of tasks over time
• Enforce SLAs
• E.g. Alerting if time or correctness SLAs are not met
• Scale

14. 14
Apache Airflow : What is it?
What Does Apache Airflow Add?
• Configuration-as-code
• Usability - Stunning UI / UX
• Centralized configuration
• Resource Pooling
• Extensibility

15. Apache Airflow
15
Incubating

16. 16
Apache Airflow : Incubating
Timeline
• Airflow was created @ Airbnb in 2015 by Maxime
Beauchemin
• Max launched it @ Hadoop Summit in Summer 2015
• On 3/31/2016, Airflow —> Apache Incubator
Today
• 166+ Contributors
• 300+ Users
• 40+ companies officially using it!
• 9 Committers/Maintainers <— We’re growing here

17. Agari
17
What We Do!

18. Agari : What We Do
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19. 19
Agari : What We Do

20. 20
Agari : What We Do

21. 21
Agari : What We Do

22. 22
Agari : What We Do

23. 23
Enterprise
Customers
email
metadata
apply
trust
models
email md
+ trust
score
Agari’s Current Product
Agari : What We Do

24. 24
email
metadata
apply
trust
models
email md
+ trust
score
Agari’s Future ProductEnterprise
Customers
Agari : What We Do

25. Apache Airflow @ Agari
How Do We Use It?
25

26. Classes of Orchestration
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apply trust
models
(message
scoring)
build trust
models
cron++
(general
job
scheduler)
New Product
(Enterprise Protect)
Operational
Automation
BI / ETL
N / A

27. Classes of Orchestration
27
apply trust
models
(message
scoring)
build trust
models
cron++
(general
job
scheduler)
New Product
(Enterprise Protect)
Operational
Automation
BI / ETL
N / A
This Talk

28. Use-Case : Message
Scoring
Batch Pipeline Architecture
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29. Use-Case : Message Scoring
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enterprise A
enterprise B
enterprise C
S3
S3 uploads every 15
minutes

30. Use-Case : Message Scoring
30
enterprise A
enterprise B
enterprise C
S3
Airflow kicks of a Spark
message scoring job
every hour

31. Use-Case : Message Scoring
31
enterprise A
enterprise B
enterprise C
S3
Spark job writes scored
messages and stats to
another S3 bucket
S3

32. Use-Case : Message Scoring
32
enterprise A
enterprise B
enterprise C
S3
This triggers SNS/SQS
messages events
S3
SNS
SQS

33. Use-Case : Message Scoring
33
enterprise A
enterprise B
enterprise C
S3
An Autoscale Group
(ASG) of Importers spins
up when it detects SQS
messages
S3
SNS
SQS
Importers
ASG

34. 34
enterprise A
enterprise B
enterprise C
S3
The importers rapidly ingest scored
messages and aggregate statistics into
the DB
S3
SNS
SQS
Importers
ASG
DB
Use-Case : Message Scoring

35. 35
enterprise A
enterprise B
enterprise C
S3
Users receive alerts of
untrusted emails &
can review them in
the web app
S3
SNS
SQS
Importers
ASG
DB
Use-Case : Message Scoring

36. 36
enterprise A
enterprise B
enterprise C
S3 S3
SNS
SQS
Importers
ASG
DB
Airflow manages the entire process
Use-Case : Message Scoring

37. Use-Case : Message
Scoring
Airflow DAG
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38. 38
Airflow DAG

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5 minute wait for S3
eventual consistency
Airflow DAG

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1 hour a day, we
also build new
models
Airflow DAG

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build
models
Airflow DAG

42. 42
dummy needed for
branch operator
Airflow DAG

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• trigger_rule:
one_success
Airflow DAG

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Prep Spark
Run
Airflow DAG

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• Run Spark
• Verify a record is
written to the DB
• Wait for the SQS
queue to empty
Airflow DAG

46. 46
• Compute
discrepancies
• Send email report
• Update monitoring
graphs
• Raise SLA
(correctness) alerts
Airflow DAG

47. SLAs & Insights
Airflow
47

48. 48
Desirable Qualities of a Resilient
Data Pipeline
OperabilityCorrectness
Timeliness Scalable/Available
• Data Integrity (no loss, etc…)
• Expected data distributions
• All output within time-bound SLAs
(e.g. 1 hour)
• Fine-grained Monitoring &
Alerting of Correctness &
Timeliness SLAs
• Quick Recoverability
• ASGs, SQS, SNS, S3

49. 49
Desirable Qualities of a Resilient
Data Pipeline
OperabilityCorrectness
Timeliness
• Data Integrity (no loss, etc…)
• Expected data distributions
• All output within time-bound SLAs
(e.g. 1 hour)
• Fine-grained Monitoring &
Alerting of Correctness &
Timeliness SLAs
• Quick Recoverability
SLA
SLA
• ASGs, SQS, SNS, S3
Scalable/Available

50. 50
Correctness : Email Reporting
orgs

51. 51
Correctness : Email Reporting
For each org, we check for duplicate or missing data
as a count & percentage
orgs

52. 52
Correctness : Email Reporting
These are the 3 stages of the pipeline. We can detect where a
discrepancy is coming from - often related to a code push!
orgs

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Correctness : Monitoring

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Airflow: …And easy to integrate with Ops tools!
Correctness & Timeliness : Alerting

55. 55
Airflow: …And easy to integrate with Ops tools!
Correctness & Timeliness : Alerting
Timeliness SLA
miss

56. 56
Airflow: …And easy to integrate with Ops tools!
Correctness & Timeliness : Alerting
Timeliness SLA
miss
dag = DAG(DAG_NAME,
schedule_interval='@hourly',
default_args=default_args,
sla_miss_callback=sla_alert_func)

57. 57
Airflow: …And easy to integrate with Ops tools!
Correctness & Timeliness : Alerting
Timeliness SLA
miss
Correctness
SLA miss

58. 58
Airflow: …And easy to integrate with Ops tools!
Correctness & Timeliness : Alerting
Timeliness &
Correctness SLA
misses sent to
PagerDuty/VictorOps

59. Use-Case : Model Building v2
For Both Batch & Near Realtime Scoring
Pipelines
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60. 60
Airflow DAG

61. 61
Model Building DAG
Launch an
EMR cluster

62. 62
Run model
building as
EMR steps
Model Building DAG

63. 63
Validate
models
Send email
notification if
tests fail
Model Building DAG

64. 64
Terminate
EMR cluster
Model Building DAG

65. Apache Airflow Next Steps
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Areas for Improvement

66. 66
Apache Airflow Next Steps
Improvement Areas
• Security
• API (though we do have a CLI)
• Deployment / Versioning
• Execution Scale Out
• On-demand Execution

67. Acknowledgments
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• Vidur Apparao
• Stephen Cattaneo
• Jon Chase
• Andrew Flury
• William Forrester
• Chris Haag
• Mike Jones
• Scot Kennedy
• Thede Loder
• Paul Lorence
• Kevin Mandich
• Gabriel Ortiz
• Jacob Rideout
• Josh Yang
• Julian Mehnle
None of this work would be possible without the
contributions of the strong team below

68. Questions? (@r39132)
68