Building a Sustainable Data Platform on AWS

Takumi Sakamoto 2016.01.27

Takumi Sakamoto @takus 😍 = ⚽ ✈ 📷

http://bit.ly/1MCOyBX

JAWSDAYS 2015

Mentioned by @jeffbarr

https://twitter.com/jeffbarr/status/649575575787454464

http://www.slideshare.net/smartnews/smart-newss-journey-into-microservices

AWS Case Study

http://aws.amazon.com/solutions/case-studies/smartnews/

Data Platform at SmartNews

What is SmartNews?

• News Discovery App

• Launched in 2012

• 15M+ Downloads in World Wide

https://www.smartnews.com/en/

Our Mission

the world's quality information?

the people who need it?

How?

Machine Learning

URLs Found

Structure Analysis

Semantics Analysis

Importance Estimation

Diversification

Internet

100,000+ /day

1000+ /day

Feedback

DeliverTrending Stories

Data Platform Use Cases

• Product development

• track KPI such as DAU and MAU

• A/B test for new feature, on-boarding, etc...

• ad-hoc analysis

• Provide data to applications

• realtime re-ranking news articles

• CTR prediction of Ads system

• dashboard service for media partners

Data & Its Numbers

• User activities

• ~100 GBs per day (compressed)

• 60+ record types

• User demographics or configurations etc...

• 15M+ records

• Articles metadata

• 100K+ records per day

Sustainable Data Platform?

Sustainable Data Platform

• Provide a reliable and scalable "Lambda Architecture"

• Minimize both operation & running cost

• Be open to uncertain future

Lambda Architecture

http://lambda-architecture.net/

Why Sustainable?

• Do a lot with a few engineers

• no one is a full-time maintainer

• avoid to waste too much time

• Empower brilliant engineers in SmartNews

• everything should be as self-serve as possible

• don't ask for permission, beg for forgiveness

System Design

λ Architecture at SmartNews

Input Batch Serving

Speed

Output

Design Principles

• Decoupled "Computation" and "Storage" layers

• multiple consumers can use the same data

• run consumers on Spot Instances

• prevent serious data lost with minimum effort

• Use the right tool for the job

• leverage AWS managed service as possible

• fill in the missing pieces by Presto & PipelineDB

An Example

Amazon EMRAMI 3.x

Amazon S3

Amazon EMRHive

GeneralUsers

ApplicationEngineer

I wanna upgrade hive

Ad Engineer

I wanna combine news data with

ad data

Amazon EMRAMI 4.x

Amazon EMRSpark

We’re satisfied with current

version

DataScientist

I wanna test my algorithm with the

latest spark

Batch Layer Run multiple EMR clusters for each usages

Kinesis Stream

Sparkon EMR

AWSLambda

DataScientist

I wanna consume streaming data by

Spark

ApplicationEngineer

I wanna add a streaming monitor

by Lambda

Speed Layer Consume the same data for each usages

• AWS managed services • Replicated data into Multiple AZs • High availability

Input Data

Collect Events by Fluentd

• Forwarder (running on each instances)

• store JSON events to S3

• forward events to aggregators

• collect metrics and post them to Datadog

• Aggregator

• input events into Kinesis & PipelineDB

• other reporting tasks (not mentioned today)

Forwards to S3

<source> @type tail format json path /data/log/user_activity.log pos_file /data/log/pos/user_activity.pos tag smartnews.user_activity time_key timestamp </source>

<match smartnews.user_activity> @type copy <store> @type relabel @label @s3 </store> <store> @type forward @label @forward </store> </match>

@include conf.d/s3.conf @include conf.d/forward.conf

<label @s3> <% node[:td_agent][:s3].each do |c| -%> <match <%= c[:tag] %>> @id s3.<%= c[:tag] %> @type s3 ... path fluentd/<%= node[:env] %>/<%= node[:role] %>/<%= c[:tag] %> time_slice_format dt=%Y-%m-%d/hh=%H time_key timestamp include_time_key time_as_epoch reduced_redundancy true format json utc buffer_chunk_limit 2048m </match> <% end -%> </label>

td-agent.conf conf.d/s3.conf

Capture DynamoDB Streams

<source> type dynamodb_streams stream_arn YOUR_DDB_STREAMS_ARN pos_file /path/to/table.pos fetch_interval 1 fetch_size 100 </source>

https://github.com/takus/fluent-plugin-dynamodb-streams

DynamoDB DynamoDBStreams

Amazon S3

AWSLambda

Fluentd

Recommended Practices

• Make configuration simple as possible

• fluentd can cover everything, but shouldn't

• keep stateless

• Use v0.12 or later

• "Filter" : better performance

• "Label": eliminate 'output_tag' configuration

Monitor Fluentd Status

• Monitor traffic volume & retry count by Datadog

• Datadog's fluentd integration

• fluent-plugin-flowcounter

• fluent-plugin-dogstatsd

Archive to Amazon S3

• I have 2 recommended settings

• versioning

• enable to recover from human error

• lifecycle policy

• minify storage cost

Archives to IA or Gracier xx days after the creation date

Keep previous versions xx days Save you in the future!!

Batch Layer

Various ETL Tasks

• Extract

• dump MySQL records by Embulk

• make files on S3 readable to Hive

• Transform

• transform text files into columnar files (RCFile, ORC)

• generate features for machine learning

• aggregate records (by country, by channel)

• Load

• load aggregated metrics into Amazon Aurora

Hive

• Most popular project on Hadoop ecosystem

• famous for its lovely logo :)

• HiveQL and MapReduce

• convert SQL-like query into MR jobs

• Not adopt Tez engine yet

• Amazon EMR doesn't support now

• limited improvement to our queries

How to process JSON?

A. Transform into columnar table periodically

• required converting job

• better performance

B. Use JSON-SerDe for temporary analysis

• easy way for querying raw json text files

• required to "drop table" for change schema

• performance is not good

Transform Tables-- Make S3 files readable by Hive ALTER TABLE raw_activities ADD IF NOT EXISTS PARTITION (dt='${DATE}', hh='${HOUR}');

-- Transform text files into columnar files (Flatten JSON) INSERT OVERWRITE TABLE activities PARTITION (dt='${DATE}', action) SELECT user_id, timestamp, os, country, data, action FROM raw_activities LATERAL VIEW json_tuple( raw_activities.json, 'userId','timestamp','platform','country','action','data' ) a as user_id, timestamp, os, country, action, data WHERE dt = '${DATE}' CLUSTER BY os, country, action, user_id ;

JSON-SerDe

-- Define table with SERDE CREATE TABLE json_table ( country string, languages array<string>, religions map<string,array<int>> ) ROW FORMAT SERDE 'org.openx.data.jsonserde.JsonSerDe' STORED AS TEXTFILE;

-- Result: 10 SELECT religions['catholic'][0] FROM json_table;

cf. hive-ruby-scripting

-- Define your ruby (JRuby) script SET rb.script= require 'json'

def parse (json) j = JSON.load(json) j['profile']['attribute1'] end ;

-- Use the script in HQL SELECT rb_exec('&parse', json) FROM user;

https://github.com/gree/hive-ruby-scripting

Spark

http://www.slideshare.net/smartnews/aws-meetupapache-spark-on-emr

Self-Serve via AWS CLI

# Create EMR clusters that runs Hive & Spark & Ganglia

aws emr create-cluster \

--name "My Cluster" \

--release-label emr-4.2.0 \

--applications Name=Hive Name=Spark Name=GANGLIA \

--ec2-attributes KeyName=myKey \

--instance-type c3.4xlarge \

--instance-count 4 \

--use-default-roles

Minimize expenses

• Use Spot Instances as possible

• typically discount 50-90%

• select instance type with stable price

• C3 families spike often :(

• Dynamic cluster resizing

• x2 capacity during daily batch job

• 1/2 capacity during midnight

Handle Data Dependencies

Typical Anti-Pattern

5 * * * * app hive -f query_1.hql 15 * * * * app hive -f query_2.hql 30 * * * * app hive -f query_3.hql 0 * * * * app hive -f query_4.hql 1 * * * * app hive -f query_5.hql

Workflow Management

• Define dependencies

• task E is executed after finishing task C and task D

• Scheduling

• task A is kicked after 09:00 AM

• throttle concurrent running of the same task

• Monitoring

• notification in failure

• task C must finish before 01:00 PM (SLA)

cf. http://www.slideshare.net/taroleo/workflow-hacks-1-dots-tokyo

Airflow

• A workflow management systems

• define workflow by Python

• built in shiny UI & CLI

• pluggable architecture

http://nerds.airbnb.com/airflow/

Define Tasks

dag = DAG('tutorial', default_args=default_args)

t1 = BashOperator( task_id='print_date', bash_command='date', dag=dag)

t2 = BashOperator( task_id='sleep', bash_command='sleep 5', retries=3, dag=dag)

t3 = BashOperator( task_id='templated', bash_command=""" {% for i in range(5) %} echo "{{ ds }}" echo "{{ macros.ds_add(ds, 7)}}" echo "{{ params.my_param }}" {% endfor %} """, params={'my_param': 'Parameter I passed in'}, dag=dag)

t2.set_upstream(t1) t3.set_upstream(t1)

Task

Dependencies

Python code

DAG

Workflow as Code

Deploy codes automatically after merging into master

Visualize Dependencies

What is done or not?

Alerting to Slack• SLA Violation

• task A should be done till 00:00 PM

• other team's task K has dependency into task A

• Output validation failure

• stop the following tasks if the output is doubtful

Retry from Web UI

Once clear histories, airflow scheduler back fill the histories

Retry from CLI

// Clear some histories from 2016-01-01

airflow clear etl_smartnews \

--task_regex user_ \

--downstream \

--start_date 2016-01-01

// Backfill uncompleted tasks

airflow backfill etl_smartnews \

--start_date 2016-01-01

Check Rendered Query

How Long Each Tasks?

Pluggable Architecture

• Built-in plugins

• operator: bash, hive, preto, mysql

• transfer: hive_to_mysql

• sensor: wait_hive_partition, wait_s3_file

• Written our own plugin

• mysql_partition

Examplesuser_sensor = S3KeySensor(

task_id='wait_user',

bucket_name='smartnews',

bucket_key='user/dt={{ ds }}/dump.csv',

)

etl = HiveOperator(

task_id="task1",

hql="INSERT OVERWRITE INTO...."

)

etl.set_upstream(user_sensor)

import = HiveToMySqlTransfer(

task_id=name,

mysql_preoperator="DELETE FROM %s WHERE date = '{{ ds }}'" % table,

sql="SELECT country, count(*) FROM %s" % table,

mysql_table=table

)

import.set_upstream(etl)

Wait a S3 file creation

After the file is created,

Run ETL Query

After that,

Import into MySQL

Serving Layer

Provides batch views in low-latency and ad-hoc way

Presto

• A distributed SQL query engine

• join multiple data sources (Hive + MySQL)

• support standard ANSI SQL

• designed to handle TBs or PBs scale data

cf. http://www.slideshare.net/frsyuki/presto-hadoop-conference-japan-2014

Presto Architecture

Amazon S3 Kinesis Stream

AmazonRDS

AmazonAurora

PrestoWorker

PrestoWorker

PrestoWorker

PrestoWorker

PrestoWorker

PrestoWorker

PrestoCoordinator

Client

1. Query with Standard SQL

4. Scan data concurrently5. Aggregate data without disk I/O6. Return result to client

2. Generate execution plan3. Dispatch tasks into multiple workers

Amazon EMR(Hive Metastore)

Provides Hive table metadata(S3 access only)

※ https://github.com/qubole/presto-kinesis

※

Why Presto?

• Join multiple data sources

• skip large parts of ETL process

• enable to merge Hive/MySQL/Kinesis/PipelineDB

• Low latency

• ~30s to scan billions records in S3

• Low maintenance cost

• stateless, and easy to integrate with Auto Scaling

Use case: A/B Test-- Suppose that this table exists DESC hive.default.user_activities; user_id bigint action varchar abtest array<map<varchar, bigint>> url varchar

-- Summarize page view per A/B Test identifier -- for comparing two algorithms v1 & v2 SELECT   dt,   t['behaviorId'],   count(*) as pv FROM hive.default.user_activities CROSS JOIN UNNEST(abtest) AS t (t) WHERE dt like '2016-01-%' AND action = 'viewArticle' AND t['definitionId'] = 163 GROUP BY dt, t['behaviorId'] ORDER BY dt ;

2015-12-01 | algorithm_v1 | 40000 2015-12-01 | algorithm_v2 | 62000

Use case: Troubleshoot

-- Store access logs to S3, and query to them -- Summarize access & 95pct response time by SQL SELECT from_unixtime(timestamp), count(*) as access, approx_percentile(reqtime, 0.95) as pct95_reqtime FROM hive.default.access_log WHERE dt = '2015-11-04' AND hh = '13' AND role = 'xxx' GROUP BY timestamp ORDER BY timestamp ;

2015-11-04 22:00:00.000 | 6377 | 0.522 2015-11-04 22:00:01.000 | 3580 | 0.422

Scheduled Auto Scaling

$ aws autoscaling describe-scheduled-actions { "ScheduledUpdateGroupActions": [ { "DesiredCapacity": 2, "AutoScalingGroupName": "presto-worker-prd", "Recurrence": "59 14 * * *", "ScheduledActionName": "scalein-2359-jst" }, { "DesiredCapacity": 20, "AutoScalingGroupName": "presto-worker-prd", "Recurrence": "45 0 * * 1-5", "ScheduledActionName": "scaleout-0945-jst" } ] }

Presto Covers Everything? No!

• Fixed system on Amazon Aurora (or other RDB)

• provides KPI for products & business

• require high availability & low latency

• has no flexibility

• Ad-hoc system on Presto

• provides access to all dataset on data platform

• require high scalability

• has flexibility (join various data sources)

Why Fixed vs Ad-hoc?

• Difficulties on the Ad-hoc only solution

• difficult to prevent heavy queries

• large distinct count exhausts computing resources

• decrease presto maintainability

Output Data

Chartio• Dashboard as A Service

• helps businesses analyze and track their critical data

• one of AWS partners (※)

• Combine multiple data sources at one dashboard • Presto, MySQL, Redshift, BigQuery, Elasticsearch ... • enable to join BigQuery + MySQL internally

• Easy to use for every one • everyone can make their own dashboard • write SQL directly / generate query by drag & drop

※ http://www.aws-partner-directory.com/PartnerDirectory/PartnerDetail?id=8959

Creating dashboard1. Building query (Drag&Drop / SQL)

2. Add step (filter、sort、modify)

3. Select visualize way (table、graph)

Examples

Why Chartio?

• Chartio saves a lot of engineering resources

• before

• maintain in-house dashboard written by rails

• everyone got tired to maintain it

• after

• everyone can build their own dashboard easily

• Chartio's UI is cool

• very important factor for dashboard tool

Missing Pieces of Chartio

• No programable API provides

• need to edit dashboard / chart manually

• No rollback feature

• all changes are recorded, but not rollback to the

previous state

• work around : clone => edit => rename

Speed Layer

Why Speed is Matter?

Today’s News is Wrapping

Tomorrow’s Fish and Chips

↑ Yesterday's News

http://www.personalchefapproach.com/tomorrows-fish-n-chips-wrapper/

How News Behaves?

https://gdsdata.blog.gov.uk/2013/10/22/the-half-life-of-news/

Use cases

• Re-rank news articles by user feedback

• track user's positive/negative signal

• consider gender, age, location, interests

• Realtime article monitoring

• detect high bounce rate (may be broken?)

• make realtime reporting dashboard for A/B test

Realtime Re-Ranking

ref. Stream 処理 (Spark Streaming + Kinesis) と Offline 処理 (Hive) の統合 www.slideshare.net/smartnews/stremspark-streaming-kinesisofflinehive

Amazon CloudSearch

SearchAPI

APIGateway

Kinesis Stream

Amazon S3

Amazon EMR

Amazon S3 Amazon EMR

DynamoDB

RealtimeFeedback

Re-rankArticles

ArticleMetadata

UserInterests

UserBehaviors

Offline Proceesby Hive / Spark

Realtime Monitoring

APIGateway

Stream

Continuous View

Continuous View

Continuous View

Discard raw record soon afterconsumed by Continuous View

Incrementallyupdated in realtime

PipelineDB Chartio

AWSLambda

Slack

Access Continuous Viewby PostgreSQL Client

Record

※1

※1 Use cron on 26 Feb. 2016 Migrate it soon after supporting VPC

PipelineDB

• OSS & enterprise streaming SQL database

• PostgreSQL compatible

• connect to Chartio 😍

• join stream to normal PostgreSQL table

• Support probabilistic data structures

• e.g. HyperLogLog

https://www.pipelinedb.com/ http://developer.smartnews.com/blog/2015/09/09/20150907pipelinedb/

Continuous View

-- Calculate unique users seen per media each day -- Using only a constant amount of space (HyperLogLog) CREATE CONTINUOUS VIEW uniques AS SELECT day(arrival_timestamp), substring(url from '.*://([^/]*)') as hostname, COUNT(DISTINCT user_id::integer) FROM activity_stream GROUP BY day,hostname;

-- How many impressions have we served in the last five minutes? CREATE CONTINUOUS VIEW imps WITH (max_age = '5 minutes') AS SELECT COUNT(*) FROM imps_stream;

-- What are the 90th, 95th, 99th percentiles of request latency? CREATE CONTINUOUS VIEW latency AS SELECT percentile_cont(array[90, 95, 99]) WITHIN GROUP (ORDER BY latency::integer) FROM latency_stream;

Summary

Sustainable Data Platform

• build a reliable and scalable lambda architecture

• minimize operation & running cost

• be open to uncertain future

My Wishlist to AWS

• Support Reduced Redundancy Storage (RRS) on EMR

• Faster EMR Launch

• Set TTL to DynamoDB records

• Auto-scale Kinesis Stream

• Launch Kinesis Analytics in Tokyo region

Thank you!!