Cloud Native Data Pipelines (QCon Shanghai & Tokyo 2016)

Cloud Native Data Pipelines

Sid Anand QCon Shanghai & Tokyo 2016

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About Me

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Work [ed | s] @

Committer & PPMC on

Father of 2

Co-Chair for

Apache Airflow

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ライブ中継

Agari

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What We Do!

Agari : What We Do

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Agari : What We Do

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Agari : What We Do

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Agari : What We Do

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Agari : What We Do

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Enterprise Customers

email metadata

apply trust

models

email md + trust score

Agari’s Previous EP Version

Agari : What We Do

Batch

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email metadata

apply trust

modelsemail md + trust score

Agari’s Current EP VersionEnterprise Customers

Agari : What We Do

Near-real time

Quarantine

Data PipelinesBI vs Predictive

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Data Pipelines (BI)

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WebServers

OLTPDB

DataWarehouse

Repor6ngTools

QueryBrowsers

ETL(batch)MySQL,Oracle,Cassandra

Terradata,RedShi;BigQuery

Data Pipelines (Predictive)

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OLTPDBorcache

ETL(batchorstreaming)

MySQL,Oracle,Cassandra,Redis

Spark,Flink,Beam,Storm

WebServers

DataProductsRanking(Search,NewsFeed),RecommenderProducts,FraudDetecGon/PrevenGon

DataSource

Data Products

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BI Predictive

Common Focus of this talk

Data Pipelines

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WebServers

OLTPDB

DataWarehouse

Repor6ngTools

QueryBrowsers

ETL(batch)MySQL,Oracle,Cassandra

Terradata,RedShi;BigQuery

OLTPDBorcache

ETL(batchorstreaming)

MySQL,Oracle,Cassandra,Redis

Spark,Flink,Beam,Storm

WebServers

Ranking(Search,NewsFeed),RecommenderProducts,FraudDetecGon/PrevenGon

DataSource

MotivationCloud Native Data Pipelines

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Big Data Companies like LinkedIn, Facebook, Twitter, & Google build custom, large scale data pipelines that run in their own Data Centers


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Big Data Companies like LinkedIn, Facebook, Twitter, & Google build custom, large scale data pipelines that run in their own Data Centers

Most start-ups run in the public cloud. Can they leverage aspects of the public cloud to build comparable pipelines?


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Cloud Native Techniques

Open Source Technogies

Custom Data Pipeline Stacks seen in Big Data companies

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Design GoalsDesirable Qualities of a Resilient Data Pipeline

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Desirable Qualities of a Resilient Data Pipeline

OperabilityCorrectness

Timeliness Cost

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Timeliness Cost

• Data Integrity (no loss, etc…) • Expected data distributions

• All output within time-bound SLAs

• Fine-grained Monitoring & Alerting of Correctness & Timeliness SLAs

• Quick Recoverability

• Pay-as-you-go

Quickly Recoverable

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• Bugs happen!

• Bugs in Predictive Data Pipelines have a large blast radius

• Optimize for MTTR

Predictive Analytics @ AgariUse Cases

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Use Cases

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Apply trust models (message scoring)

batch + near real time

Build trust models

batch

(Enterprise Protect)

Use-Case : Message Scoring (batch)Batch Pipeline Architecture

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Use-Case : Message Scoring

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enterprise Aenterprise Benterprise C

S3

S3 uploads an Avro file every 15 minutes


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S3

Airflow kicks of a Spark message scoring job

every hour (EMR)


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S3

Spark job writes scored messages and stats to

another S3 bucket

S3


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S3

This triggers SNS/SQS messages events

S3

SNS

SQS


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S3

An Autoscale Group (ASG) of Importers spins up when it detects SQS

messages

S3

SNS

SQS

Importers

ASG

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S3

The importers rapidly ingest scored messages and aggregate statistics into

the DB

S3

SNS

SQS

Importers

ASGDB


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S3

Users receive alerts of untrusted emails & can review them in

the web app

S3

SNS

SQS

Importers

ASGDB


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S3 S3

SNS

SQS

Importers

ASGDB

Airflow manages the entire process


Tackling Cost & TimelinessLeveraging the AWS Cloud

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Tackling Cost

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Between Daily Runs During Daily Runs

When running daily, for 23 hours of a day, we didn’t pay for instances in the ASG or EMR

Tackling Cost

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Between Hourly Runs During Hourly Runs

When running daily, for 23 hours of a day, we didn’t pay for instances in the ASG or EMR

This does not help when runs are hourly since AWS charges at an hourly rate for EC2 instances!

Tackling TimelinessAuto Scaling Group (ASG)

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ASG - Overview

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What is it?

A means to automatically scale out/in clusters to handle variable load/traffic

A means to keep a cluster/service of a fixed size always up

ASG - Data Pipeline

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importer

importer

importer

importer

Importer ASG

scale out / inSQS

DB

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Sent

CPU

ACKd/Recvd

CPU-based auto-scaling is good at scaling in/out to keep the average CPU constant

ASG : CPU-based

ASG : CPU-based

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Sent

CPU

Recv

Premature Scale-in

Premature Scale-in:

• The CPU drops to noise-levels before all messages are consumed

• This causes scale in to occur while the last few messages are still being committed

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Scale-out: When Visible Messages > 0 (a.k.a. when queue depth > 0)

Scale-in: When Invisible Messages = 0 (a.k.a. when the last in-flight message is ACK’d)

This causes the ASG to grow

This causes the ASG to shrink

ASG : Queue-based

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ASG : Queue-based

Shoyu Koto Da!!!!しょうゆうことだ！！

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Timeliness Cost• ASG • EMR Spark

Daily • ASG • EMR Spark Hourly ASG • No Cost Savings

Tackling Operability & CorrectnessLeveraging Tooling

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A simple way to author and manage workflows

Provides visual insight into the state & performance of workflow runs

Integrates with our alerting and monitoring tools

Tackling Operability : Requirements

Apache AirflowWorkflow Automation & Scheduling

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Airflow: Author DAGs in Python! No need to bundle many config files!

Apache Airflow - Authoring DAGs

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Airflow: Visualizing a DAG

Apache Airflow - Authoring DAGs

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Airflow: It’s easy to manage multiple DAGs

Apache Airflow - Managing DAGs

Apache Airflow - Perf. Insights

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Airflow: Gantt chart view reveals the slowest tasks for a run!

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Apache Airflow - Perf. InsightsAirflow: Task Duration chart view show task completion time trends!

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Airflow: …And easy to integrate with Ops tools!Apache Airflow - Alerting

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Apache Airflow - Correctness

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Timeliness Cost

Use-Case : Message Scoring (near-real time)NRT Pipeline Architecture

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Kinesis batch put every second

K


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K

As ASG of scorers is scaled up to one process per core per kinesis shard

Scorers

ASG


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KScorers

ASG

KinesisScorers apply the trust model and send scored messages downstream


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KScorers

ASG

Kinesis

Importers

ASG

As ASG of importers is scaled up to rapidly import messages

DB


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KScorers

ASG

Kinesis

Importers

ASG

Imported messages are also consumed by the

alerter

DB

K

Alerters

ASG


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KScorers

ASG

Kinesis

Importers

ASG


alerter

DB

K

Alerters

ASG

Quarantine Email

InnovationsNRT Pipeline Architecture

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Apache AvroWhat is Avro?

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What is Avro?

Avro is a self-describing serialization format that supports

primitive data types : int, long, boolean, float, string, bytes, etc…

complex data types : records, arrays, unions, maps, enums, etc…

many language bindings : Java, Scala, Python, Ruby, etc…

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What is Avro?

Avro is a self-describing serialization format that supports

primitive data types : int, long, boolean, float, string, bytes, etc…

complex data types : records, arrays, unions, maps, enums, etc…

many language bindings : Java, Scala, Python, Ruby, etc…

The most common format for storing structured Big Data at rest in HDFS, S3, Google Cloud Storage, etc…

Supports Schema Evolution!

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Avro Schema Example

{"namespace": "agari", "type": "record", "name": "User", "fields": [ {"name": "name", "type": "string"}, {"name": "favorite_number", "type": ["int", "null"]}, {"name": "favorite_color", "type": ["string", "null"]} ] }

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complex type (record)

Avro Schema Example

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complex type (record)Schema name : User

Avro Schema Example

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complex type (record)Schema name : User

3 fields in the record: 1 required, 2 optional

Avro Schema Example

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Data

x 1,000,000,000

Avro Schema Data File Example

Schema

Data

0.0001 %

99.999 %

Data

Data

Data

Data

Data

Data

Data

Data

Data

Data

Data

Data

Data

Data

Data

Data

Data

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Binary Data block

Avro Schema Streaming Example

Schema

Data

99 %

1 %

Data

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Binary Data block

Avro Schema Streaming Example

Schema

Data

99 %

1 %

Data

OVERHEAD!!

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Schema Registry

(Lambda)

Innovation 1 : Avro Schema Registry


register_schema

Message Producer (P)

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Schema Registry

(Lambda)


register_schema returns a UUID


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Schema Registry

(Lambda)


Message Producer sends UUID +


Data

Message Consumer (C)

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Schema Registry

(Lambda)



Data


getSchemaById (UUID)

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Schema Registry

(Lambda)



Data


getSchemaById (UUID){"namespace": "agari", "type": "record", "name": "User", "fields": [ {"name": "name", "type": "string"}, {"name": "favorite_number", "type": ["int", "null"]}, {"name": "favorite_color", "type": ["string", "null"]} ] }

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Schema Registry

(Lambda)




getSchemaById (UUID){"namespace": "agari", "type": "record", "name": "User", "fields": [ {"name": "name", "type": "string"}, {"name": "favorite_number", "type": ["int", "null"]}, {"name": "favorite_color", "type": ["string", "null"]} ] }

Message Consumers • download & cache the schema

• then decode the data

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KScorers

ASG

Kinesis

Importers

ASG


alerter

DB

K

Alerters

ASG

SR

SR

SR


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The Architecture is composed of repeated patterns of :

ASG-based compute consumer

Kinesis transport streams (i.e. AWS’ managed “Kafka”)

A Lambda-based Avro Schema Registry

Innovation 2 : Repeatable Units

ComputeiKinesisi

ASGi

SR

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You can chain these repeatable units together to make arbitrary DAGs (Directed Acyclic Graphs)

User Hashicorp’s Terraform to compose your DAG through automation

The example above is a simple Linear DAG with 3 units

Innovation 2 : Repeatable Units

ComputeiKinesisi

ASGi

SR

ComputeiKinesisi

ASGi

SR

ComputeiKinesisi

ASGi

SR

Airflow Job Reactively Scales

Innovation 3 : Reactive-Scaling (WIP)

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KScorers

ASG

Kinesis

Importers

ASGDB

K

Alerters

ASG

SR

SR

SR

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If the ADR is triggered and a model build or code push was recently done to Compute 1, ADR will revert the last code or model push to ASG Compute 1

Innovation 4 : Anomaly-based Rollback (WIP)

ASG

Compute1 Compute2Kinesis

ASG

SR

Anomaly-detector&Reverter

Open Source Plans

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Follow us to be notified when the following is open-sourced

• Avro Schema Registry

• Agari (Kinesis+ASG) scaling tool (Airflow Job)

• Anomaly-detector & Reverter

To be notified, follow @AgariEng & @r39132

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

Questions? (@r39132)

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Engineering

Cloud Native Data Pipelines (QCon Shanghai & Tokyo 2016)