Architecting a Next Generation Data Platform – Strata Singapore 2017

Hadoop Application
Architectures:
Architecting a Next
Generation Data Platform
Strata Data Conference, Singapore 2017
Ted Malaska | @tedmalaska
Jonathan Seidman | @jseidman

Questions?
tiny.cloudera.com/sgquestions
Logistics
▪ Break at 3:00 – 3:30 PM
▪ Questions at the end of each section
▪ Slides at tiny.cloudera.com/app-arch-singapore-2017
▪ Code at https://github.com/hadooparchitecturebook/Taxi360

Questions?
About the book
▪ @hadooparchbook
▪ hadooparchitecturebook.com
▪ github.com/hadooparchitecturebook
▪ slideshare.com/hadooparchbook

Questions?
About the presenters
▪ BNet Group Architect at Blizzard
▪ Cloudera Principal Solution Architect
▪ Architect at FINRA
▪ Contributor to:
▪ Apache Spark,
▪ Hadoop,
▪ Hive,
▪ Sqoop,
▪ Yarn,
▪ Flume,
▪ Etc.
Ted Malaska

Questions?
About the presenters
▪ Software Engineer at Cloudera
▪ Contributor to Apache Sqoop
▪ Previously Technical Lead on the big data team at
Orbitz
▪ Co-founder of the Chicago Hadoop User Group and
Chicago Big Data
Jonathan Seidman

Case Study Overview
Internet of Things and Entity 360

Questions?
Customer 360

Questions?
Connected Cars

Questions?
Entity (Taxi) 360 View
Geo-location/
Traffic Data
Customer Data
Maintenance
Data
Other Data
Sources
Streaming
Vehicle Data

Questions?
In the old days
Data
Warehouse
Extract Transform
(SQL)
Load Data Mart

Questions?
At the time of our Book
SERVERS MARTS EDWS DOCUMENTS STORAGE SEARCH ARCHIVE
ERP, CRM, RDBMS, MACHINES FILES, IMAGES, VIDEOS, LOGS, CLICKSTREAMS EXTERNAL DATA SOURCES

Questions?
Today...
Data Sources Streaming Pipes
Schema Validation
Enrichment
Stream Processing
Routing
StorageProducers
Transport
Replication
Long Term SQL
Speed Layer SQL
Time Series
Reverse Indexed
Stream
Access
SQL
Machine Learning
Request Response
Batch Processing
Code
Agents
Log Aggregators

Questions?
Enabling a Range of New Use Cases…
Fraud Detection Market
Transactions
Internet of
Things
Network Security

Questions?
Challenges
And so many more

Questions?
Challenges – Architectural Considerations
▪ Reliable and scalable ingress of multiple data types and sources:
- High volume event data? Batch data?
▪ Reliable and scalable storage to support multiple workloads and access patterns
- Historical data? Real-time search? Analytics?
▪ Processing engines (for background processing):
- Stream processing? Batch processing?
▪ Data Modeling
- Modeling data for real-time random access? Analytic access? Batch access?
- Deployment Considerations
- On-premise, cloud?

Case Study
Requirements
Overview

Questions?
Requirements
▪ Allow users (technical and non-technical) to analyze and visualize data…

Questions?
Requirements
▪ Provide analysts with query capabilities via a standard interface…

Questions?
Requirements
▪ Provide developers the ability to perform batch processing on historical data…

Questions?
Requirements
▪ To support all this, we need:
- Reliable ingestion of streaming and batch data.
- Ability to perform transformations on streaming data in flight.
- Ability to perform sophisticated processing of historical data.
- Reliable and scalable storage to support modeling and processing of multiple data
formats.

High level architecture
Walkthrough

Questions?
High level architecture
Schema Validation
Enrichment
Stream Processing
Routing
StorageProducers
Transport
Replication
Long Term SQL
Speed Layer SQL
Time Series
Reverse Indexed
Stream
Access
SQL
Machine Learning
Request Response
Batch Processing
Code
Agents
Log Aggregators

Data Sources
And Data Acquisition
Considerations

Questions?
Key to Customer 360 Success
Your project is only as good as the quality and variety of data sources
Geo-location/
Traffic Data
Customer DataMaintenance
Data
Other Data
Sources
Streaming
Vehicle Data
Files
CSV? XML?
JSON?
Twitter?
Mainframe?
Database Salesforce?
MQTT

Questions?
High Level Architecture
Schema Validation
Enrichment
Stream Processing
Routing
StorageProducers
Transport
Replication
Long Term SQL
Speed Layer SQL
Time Series
Reverse Indexed
Stream
Access
SQL
Machine Learning
Request Response
Batch Processing
Code
Agents
Log Aggregators

Questions?
Producers – Push and Pull
▪ Code
- Agents
- Log Aggregation

Questions?
▪ Code
- Directly to pipeline
- To aggregation layer
- REST
- Kafka Producer

Questions?
▪ Agents
- Snap
- Prometheus
- Telegraf

Questions?
▪ Log Aggregations
- Syslog
- Logger Implementation

Questions?
Kafka Clients
Apache Kafka Clients Ecosystem Clients

Questions?
REST Proxy
Talking to Non-native Kafka Apps and Outside the Firewall
REST Proxy
Non-Java Applications
Native Kafka Java Applications
REST / HTTP
Simplifies administrative
actions
Simplifies message creation
and consumption
Provides a RESTful
interface to a Kafka
cluster

Questions?
Kafka Connect
Streaming Data Capture
JDBC
Logs
MQTT
RDBMS
Key/Value
HDFS
Kafka Connect API
Kafka
Sources Sinks
Fault tolerant
Manage hundreds of data
sources and sinks
Preserves data schema
Part of Apache Kafka
project
Includes simple
transformations

Questions?
Ecosystem of Connectors
Databases
This image cannot currently be displayed.
Datastore/File Store
Analytics Applications / Other

Questions?
Why Schema Validation?
▪ Schema declaration
- Regulation (GDPR)
- Consistency
▪ Common formats
▪ Additional Information
- Routing
- Filtering
- Transformations

Questions?
How to Implement Schema Validation
▪ Kafka Schema Registry
▪ Custom implementation
▪ Other solutions

Questions?
Schema Registry
Elastic
Cassandra
HDFS
Example Consumers
Serializer
Source 1
Serializer
Source 2
!
Kafka Topic!
Schema Registry
Define the expected fields for each Kafka topic
Automatically handle schema changes (e.g. new fields)Prevent backwards incompatible changes
Get different data sources to talk the same language

Questions?
Goals for our Transport Layer
▪ Buffering
▪ Partitioning
▪ Speed
▪ Replayability
▪ High throughput
▪ Reliable

Questions?
▪ To meet these goals we want some kind of publish-subscribe queue:
- Kafka
- Kinesis
- RabbitMQ
- Azure Queues
- Azure Service Bus
- Google Pub/Sub
- etc…

Questions?
▪ In general you have two decisions:
- Cloud service or your own managed component
- Guarantee Levels

Questions?
Buffering Data
▪ What do we mean by “buffering” and why do we need it?
event,event,event,event,event,event…
This is bad!
▪ Network partitions happen
▪ Producers and
Consumers work at
different rates
▪ Reliable storage is hard
Stream processing is hard
Lets do one at a time

Questions?
Buffering Data – Message Brokers
Publisher
Publisher
Publisher
Message
Queue
Subscriber
Subscriber
Subscriber

Questions?
What is Kafka?
▪ It’s like a message queue, right?
- Actually, it’s a “distributed commit log”
- Or “streaming data platform”
0 1 2 3 4 5 6 7 8
Data
Source
Data
Consumer
A
Data
Consumer
B

Questions?
Topics and Partitions
▪ Messages are organized into topics, and each topic is split into partitions.
- Each partition is an immutable, time-sequenced log of messages on disk.
- Note that time ordering is guaranteed within, but not across, partitions.
0 1 2 3 4 5 6 7 8
0 1 2 3 4 5 6 7 8
0 1 2 3 4 5 6 7 8
Partition 0
Partition 1
Partition 2
Data
Source
Topic

Questions?
Consumers
In Our Architecture
Taxi Trip Data
Producer
Kafka
taxi-trip-input
Topic
Stream
Processing
(Analytic)
Stream
Processing
(Lookup)
Stream
Processing
(Search)
Stream
Processing
(Long Term)

Questions?
Input Events
CMT,2009-01-05 08:31:55,2009-01-05 8:37:50,1,0.90000000000000002,-73.977936999999997,
40.745919000000001,,,-
73.983609000000001,40.755051000000002,Credit,5.2999999999999998,0,,0.79000000000000004,0,6.0
899999999999999
vendor_name,Trip_Pickup_DateTime,Trip_Dropoff_DateTime,Passenger_Count,Trip_Distance,
Start_Lon,Start_Lat,Rate_Code,store_and_forward,End_Lon,End_Lat,Payment_Type,Fare_Amt,
surcharge,mta_tax,Tip_Amt,Tolls_Amt,Total_Amt

Questions?
Kafka Considerations – Reliability
▪ But remember there are tradeoffs…

Questions?
Kafka Considerations – Reliability
▪ Different reliability levels for topics:
Taxi Trip Data
Kafka
taxi-trip-input
Twitter customer-sentiment
100% – dups
are ok
(“At least
once”)
<=100%
(“At most
once”)
News Flash:
Kafka’s Exactly Once
Producer is on the way

Questions?
Kafka Reliability – Replication
ProducerBroker
Partition1
Partition2
Partition3
Leader

Questions?
ProducerBroker
Partition1
Partition2
Partition3

Questions?
ProducerBroker
Partition1
Partition2
Partition3
Broker
Partition1
Partition2
Partition3
Leader

Questions?
Kafka Reliability– Replication
ProducerBroker
Partition1
Partition2
Partition3
Broker
Partition1
Partition2
Partition3
Leader
Leader

Questions?
ProducerBroker
Partition1
Partition2
Partition3
Broker
Partition1
Partition2
Partition3
Broker
Partition1
Partition2
Partition3
Leader

Questions?
View from the Consumer
Kafka Broker Cluster
Partition A
Partition B
Partition C
Consumer Group A
Consumer Group B
Consumer
Consumer
Consumer
Consumer
A
B
A
B
A
B

Questions?
▪ So how does this relate to our application?
kafka-topics --zookeeper ZKHOST:ZKPORT –partition 2 --replication-factor 3
--create --topic taxi-trip-input
kafka-topics --zookeeper ZKHOST:ZKPORT –partition 2 --replication-factor 1
--create –topic customer-sentiment

Questions?
Kafka Reliability – Producers
Taxi Trip Data
Kafka
taxi_trip_input
Partition 1
Partition 2
Partition 3
Topic B
Partition 1
Partition 2
Partition 3
Message
failure?
Producer
Resend
message
acks=all

Questions?
Kafka Reliability – Producers
▪ What about duplicates?
Taxi Trip Data
Kafka
taxi_trip_input
Partition 1
Partition 2
Partition 3
Topic B
Partition 1
Partition 2
Partition 3
Producer
ID Message
1000 2009-01-04 03:02:00,1,2.629,...
1001 2009-01-04 03:38:00,3,4.549…
1001 2009-01-04 03:38:00,3,4.549…

Questions?
Kafka Scaling – Partitions
Producer
Kafka
taxi-trip-input
Partition 1
Partition 2
Partition 3
Consumer Group
Consumer
Consumer
Consumer

Questions?
Kafka Scaling – Partitions
Producer
Kafka
taxi-trip-input
Partition 1
Partition 2
Partition 3
Consumer Group
Consumer
Consumer
Consumer
Partition 4
Partition 5
Consumer
Consumer
Higher
throughput
Higher
throughput
More
resources
(memory)
More
resources
(file handles)
Producer

Questions?
How many partitions?
▪ Adding partitions late in the game is painful
▪ Basic formula:
total desired throughput / throughput of slowest consumer or producer
▪ Or ~25GB disk space
▪ Not too many because:
- Each partition takes broker heap memory and file handles
- Each partition slows down node shutdown / recovery
- 1000 – 4000 partitions per broker max
- Producers will produce smaller batches – lower throughput

Questions?
Kafka Scaling – Producers
Producer
Kafka
taxi-trip-input
Partition 1
Partition 2
Partition 3
Consumer Group
Consumer
Consumer
Consumer
Partition 4
Partition 5
Consumer
Consumer
Producer

Questions?
Guarding Against Message Loss
▪ Producer – What happens if the producer loses connection to Kafka and the buffer
overflows?
- You get an exception. You can choose to… block? Write to file?
▪ Source – What happens if events are lost before getting sent to producer?
- Once again use some kind of buffer to provide sufficient retention of data.

Stream Processing
Considerations

Questions?
Streaming agenda
▪ What do we mean by streaming?
▪ Streaming use-cases
▪ Streaming semantics
▪ Which streaming engine to choose?
▪ Streaming in our use-case

Questions?
What do we mean by streaming?
Constant low
milliseconds & under
Low milliseconds to
seconds, delay in
case of failures
10s of seconds or
more, re-run in case
of failures
Real-time Near real-time Batch

Questions?
But, there’s no free lunch
Constant low
milliseconds & under
Low milliseconds to
seconds, delay in
case of failures
10s of seconds or
more, re-run in case
of failures
Real-time Near real-time Batch
“Difficult” architectures, lower
latency
“Easier” architectures, higher
latency

Questions?
What are Streaming Use Cases
- Atomic Enrichment
- Notifications
- Joining
- Partitioning
- Windowing
- Ingestion
- Aggregation & Counting
- Incremental Machine Learning
- Progressive Analytics

Questions?
Atomic Enrichment
- Isolated Transforms
- Transform with context
- FlatMap & Filtering & Routing

Questions?
Atomic Enrichment
- Isolated Transforms
Normal Queue
Basic Transform
Incoming Events
Transform Logic
Transformed Events
Queue

Questions?
Atomic Enrichment
- Transform with context
Normal Queue
Context Transform
Incoming Events
Transform Logic
Transformed Events
Queue
Cached Context
Cached Source

Questions?
Atomic Enrichment
- Flap Map & Filtering
Normal Queue
FlapMap Transform
Incoming Events
Transform Logic
Transformed Events
Queue
Cached Context
Transformed Events
Queue
Cached Source

Questions?
Notifications
- Making a decision on a event
- Send out a notification to an action taker

Questions?
Notifications
Normal Queue
Processor
Incoming Events
Transform Logic
Notification Queue
Cached Context
Rules & Logic
Recording Decisions
Queue
Cached Store
Storage
Action Taker
Research & Machine
Learning

Questions?
Joining
- Joining big to small table
- Broadcast join
- Joining two large tables
- Getting latest on same key from different feeds
- Windowing

Questions?
Windowing
- Being able to process a set of events between a start and ending time period
- A sliding window

Questions?
Windowing
- Real Data Example
- ???

Questions?
Windowing
- Processing Time
- Time the event reaches your system
- Event Time
- Time the event was created

Questions?
Windowing
- Window Length
- Slide Interval
- Watermarks

Questions?
Ingestion
- Taking values from a stream into a data store
- File System
- Object Store
- NoSQL
- NoSQL Time Series
- Document Store
- Lucene based System
- Spanner System

Questions?
Ingestion
- File Systems & Object Store
- Normally you want larger files
- Normally you want high compression
- Normally you want deduping
- Window of deduping
- 100% deduping in this case is difficult
- Think about sequence numbering from source

Questions?
Ingestion
- NoSQL, Lucene, and Spanner Systems
- If you use the key deduping is given for free
- Different levels of latency and through put
- NoSQL
- Document
- Spanner
- Lucene
- Partitioning on the way in

Questions?
Ingestion
- NoSQL Time Series
- Dumb inserts
- You may need Aggregation across metrics
- To increase throughput you may want to buffer writes
- Context
- Row is a key time and data points There
- Row1 Time1, DataPoint1
- Row1 Time2, DataPoint2
- Is slower then
- Row1 Time1, DataPoint1, Time2, DataPoint2

Questions?
Aggregation & Counting
- Counting value with respect to an entity ID
- Counting totals and with respect to window times

Questions?
- This is were we talk about Lamdba
- There are many definitions
- Common but not correct: Jobs that involve both Batch & Streaming
- Correct: Perfect count is not possible with streaming so we use a combination of streaming and
batch to show the right value

Questions?
- Why is streaming not perfect
Incrementing Speed Layer NoSQL
Get Event to Process
Increment
Acknowledge Event
Get Event Again
Increment
Acknowledge Event
Value: 10
Value: 12
Value: 12
Value: 12
Value: 14
Value: 14
+2
+2

Questions?
- Why is streaming not perfect (more)
- Duping in general

Questions?
- What is Lamdba
Time Window of Events
Streaming Results
So over time lots of errors

Questions?
- What is Lamdba
Time Window of Events
Streaming Results
Batch Corrections Batch Corrections Batch Corrections Batch Corrections
Less room for error.
So in theory more correct

Questions?
- How is lamdba Resolved with out batch?
- Failure problem
- Deduping problem

Questions?
- Failure Problem Solved by Adding internal State
- <Spark Streaming Example>

Questions?
- Failure Problem Solved by Adding internal State
Micro Batch Layer
Get Batch Y
Count Batch Y
Update StateByKey
Foreach Value Put
Acknowledge Batch
Reset State to Start of X Batch
Get Batch Y
Count Batch Y Value
Update StateByKey
Foreach Value Put
Acknowledge Batch
NoSQL
Value: 12
Value: 12
Value: 12
Value: 14
Value: 14
Value: 14
Value: 14
Value: 14
Value: 14
Value: 14
Value: 14
Micro Batch Layer
Result as Batch X
Value = 10
Result as Batch X
Value = 12
Micro Batch Layer
Result as Batch X
Value = 10
Result as Batch X
Value = 12
Micro Batch Layer
Result as Batch X
Value = 10
Result as Batch X
Value = 12
Put(14)
Put(14)

Questions?
- Deduping Problem Solved by
- Some deduping can happen with in a batch
- But this is not good enough
- Source Sequence Numbers
- With Order, Partitioning, and Internal State

Questions?
- Deduping with Sequence Numbers
Source Sequence Value
A 1 10
B 1 100
A 2 10
B 2 100
A 3 10
B 2 100
B 3 100
Seq of A Value of A Seq of B Value of B
1 10 - -
1 10 1 100
2 20 1 100
2 20 2 200
3 30 2 200
3 30 2 200
3 30 3 300

Questions?
- Closing Thoughts on Lamdba

Questions?
Incremental Machine Learning
- Clustering
- Recommendations
- Neural Networks

Questions?
- Clustering
- ???

Questions?
- Recommendations
- ???

Questions?
- Neural Networks
- ???

Questions?
Progressive Analytics
- Running SQL on a stream
- The idea of streams and tables being one

Questions?
- Table and Streaming
- Tables are a set of columns and rows
- They represent the current state of things
- Stream is a set of change longs
- The combined total of the changed logs equals a table

Questions?
A 1 10
B 1 100
A 2 10
B 2 100
A 3 10
B 2 100
B 3 100
A 2 20
B 1 100

Questions?
A 1 10
B 1 100
A 2 10
B 2 100
A 3 10
B 2 100
B 3 100
A 3 30
B 2 200

Questions?
Delivery Types
▪ At most once
- Not good for many cases
- Only where performance/SLA is more important than accuracy
▪ Exactly once
- Expensive to achieve but desirable
▪ At least once
- Easiest to achieve

Questions?
Semantics of our architecture
Source System 1
Destination
systemSource System 2
Source System 3
Ingest Extract Streaming
engine
Push
Message broker

Questions?
Classification of storage systems
▪ File based
- S3
- HDFS
▪ NoSQL
- HBase
- Cassandra
- MongoDB
- Duid.io
▪ Document based
- ES & Solr
▪ NoSQL-SQL
- Kudu, CockRoachdb

Questions?
Classification of storage systems
▪ File based
- S3
- HDFS
▪ NoSQL
- HBase
- Cassandra
- MongoDB
- Duid.io
▪ Document based
- ES & Solr
▪ NoSQL-SQL
- Kudu, CockRoachdb
De-duplication at file level
Semantics at key/record level

Which streaming
engine to choose?

Questions?
Requirements
▪ Fault-tolerant and distributed
▪ Effectively once semantics
▪ Handle processing time vs. event time
▪ Allow stateful transformations

Questions?
Spark Streaming
▪ Micro batch based architecture
▪ Allows stateful transformations
▪ Feature rich
- Windowing
- Sessionization
- ML
- SQL (Structured Streaming)

Questions?
DStream
DStream
DStream
Single Pass
Source Receiver RDD
Source Receiver RDD
RDD
Filter Count Print
Source Receiver RDD
RDD
RDD
Single Pass
Filter Count Print
First
Batch
Second
Batch

Questions?
DStream
DStream
DStream
Single Pass
Source Receiver RDD
Source Receiver RDD
RDD
Filter Count
Print
Source Receiver
RDD
partitions
RDD
Parition
RDD
Single Pass
Filter Count
Pre-first
Batch
First
Batch
Second
Batch
Stateful
RDD 1
Print
Stateful
RDD 2
Stateful
RDD 1

Questions?
Spark Streaming - Gaps
▪ Not as low of a latency
- Efforts towards reducing latency e.g. RISElab’s Drizzle
▪ Global consistent execution state
- Stop overall execution of distributed computation
- Eagerly persist records in transit meaning larger snapshots

Questions?
Flink
▪ True “streaming” system, but not as feature rich as Spark
▪ Much better event time handling
▪ Good built-in backpressure support
▪ Allows stateful transformations
▪ Lower Latency
- No Micro Batching
- Asynchronous Barrier Snapshotting (ABS)

Questions?
Flink - ABS
Operator
Buffer

Questions?
Operator
Buffer
Operator
Buffer
Flink - ABS
Barrier 1A Hit
Barrier 1B
Still Behind

Questions?
Operator
Buffer
Flink - ABS
Both Barriers
Hit
Operator
Buffer
Barrier 1A Hit
Barrier 1B
Still Behind

Questions?
Operator
Buffer
Flink - ABS Both Barriers
Hit
Operator
Buffer Barrier is
combined and
can move on
Buffer can be
flushed out

Questions?
Storm
▪ Old school
▪ Didn’t manage state – had to use Trident
▪ No good support for batch processing

Questions?
Samza
▪ Good integration with Kafka
▪ Doesn’t support batch
▪ Forked by Kafka Streams

Questions?
Flume
▪ Well integrated with the Hadoop ecosystem
▪ Allowed interceptors (for simple transformations)
▪ Supports buffering
- Memory
- File
- Kafka
▪ But no real fault-tolerance
▪ No state management

Questions?
Kafka Streams
▪ Good integration with Kafka
▪ Light-weight library (not a framework)
▪ No micro-batching, uses Kafka as internal messaging layer
▪ Maintains local state per node (in RocksDB, or in memory hash map)
▪ Handles late events
▪ Stream-to-stream joins

Questions?
Topic
Partition 1
Partition 2
Task 1 Re-partition topic
Partition 1
Partition 2
Task 3
Task 2
Task 4
Kafka Streams architecture

Questions?
Apache Beam
▪ Abstraction on top of Streaming Engines
▪ Best support for Google Dataflow

Questions?
Others
▪ Apache Apex
▪ Heron

Questions?
Structured Landing Zones
- Long Term Storage
- Local - Remote
- Styles
- Relational
- Nested
- Speed Layer
- Time Series
- Reverse Index
- Graph

Questions?
Basic of GFS => HDFS
- NameNode
- Metadata of all the files/blocks
- Which data node they are assigned too
- Replication management
- Data Nodes
- Metadata for each block location on disk

Questions?
Client
NameNode
DataNode A
DataNode B
DataNode C
A
B
C
Write Path
A. Ask Name Node for Location to Write
B. Write to DataNode with NN Instructions
C. DataNode does replication
D. Confirms file is persisted to client

Questions?
- File are immutable
- File can be of any type
- Files are block up into Blocks (128MB -> 1GB)
- Metadata cost is at the Block not the data size
- File may be splittable or may not be when reading

Questions?
Object Store (Like and not like HDFS)
- Like a HDFS
- Contains files
- Break up large files
- Not like a HDFS
- Not really a file system and is more Key value like a NoSQL
- Doesn’t have any metadata limit problem
- Traversing Folder directories is more work
- There is no rename, only copy and delete
- Eventually consist issues with listing files
- (seen with things like MR and Spark)
- Can be mostly addressed with EMRFS

Questions?
Object Store (Many Ideas Continue)
- Because it is like a File System
- Almost everything you learned from HDFS stores true
- Hive works the same
- Files and file formats are the same

Questions?
Object Store (Thinking Remote)
- Unlike HDFS the storage is always remote
- Not on the same nodes as the execution
- Which allow you to save money in the cloud
- Execution nodes are expensive vs storage only
- Network will be used to Read and Write
- In fact you are normally throttled well before the network limit of your node
- You will want the highest rates of compression possible
- To save money on storage
- To read and write faster

Questions?
Compression Styles and Entropy
Columns
Rows

Questions?
Compression Styles and Entropy
Block
Block
Block
Column
Column
Column
Column
Column
Column
Row
Group
Row
Group

Questions?
Compression Codecs
- Snappy: 2x-3x : Fast Read, Fast Write
- Lzo : 2x-3x : Fast Read, Fast Write
- Gzip : ~8x: ~Fast Read, Normal Write
- Default : ~8x: ~Fast Read, Normal Write
- BZip2 : ~10x ~Fast Read, Slow Write
- Others ..
- Always be skeptical
- All data compresses differently
- Use your own data

Questions?
Introducing the Hive Metastore
- Hive Metastore
- Adds a table like metadata layer over a file system, block store, NoSql, or other
- Allows for SQL access
- Allows for greater security options
- Allows for external metadata
- Allows for partitioning

Questions?
Typical Hive Table
- ParentFolder
- TableFolder
- Date=20171212
- DataFiles
- DataFiles
- Date=20171211
- DataFiles
- DataFiles

Questions?
Access Patterns
- Partitioning
- Filter push down
- Indexing should be considered poor
- Ideal for large scans

Questions?
Thinking about Object/Tables
1. Lets start off easy
1. Use Case: We are a Netflix type company and we have a log of users and movies watched
that looks something like this:
User ID Age Account Start
Date
Category Of User Movie Watched Movie Category Start Time Events List
Bob 42 12/12/2012 Basic Die Hard Action 5/4/2016 12:00 Play 0, pause at
15, FF at 40 to 55,
E at 90
Kat 31 12/12/2012 Platum Beauty and the
Beast
Family 5/4/2016 12:00 Play 0, pause at
15, FF at 40 to 55,
E at 90

Questions?
Thinking about Object/Tables
1. To make this into objects we need to do some separation
User
User_id
Age
St_dt
Category
Movie
Movie_id
Title
Category
Watch_session
Watch_id
St_dt
En_dt
User_id
Movie_id
Watch_Events
Watch_id
St_dt
Type
Duration
Category_Typ
Category_id
Stream_rt
Is_feature_enabled
1 *
*
1
1
*
1*

Questions?
Query Considerations
- Data is normally big so
- Partition respectively to access patterns
- Join with care
- Consider sampling or local testing before experimenting
- Data is files
- Latency to accessibility it high – seconds, minutes or more.

Questions?
Look for big tables
User
User_id
Age
St_dt
Category
Movie
Movie_id
Title
Category
Watch_session
Watch_id
St_dt
En_dt
User_id
Movie_id
Watch_Events
Watch_id
St_dt
Type
Duration
Category_Typ
Category_id
Stream_rt
Is_feature_enabled
1 *
*
1
1
*
1*

Questions?
Mutation Patterns
- File is written once and can not be mutated
- Fine for append or snapshot use cases
- Mutation will require a compaction

Questions?
Compaction Recap
Key Time Value
A 1 101
B 1 101
C 1 101
D 1 101
E 1 101
F 1 101
G 1 101
Key Time Value
A 2 102
D 2 102
F 2 102
F 3 103
H 3 103
Key Time Value
A 2 102
B 1 101
C 1 101
D 2 102
E 1 101
F 3 103
G 1 101
H 3 103

Questions?
View Strategies
Hive Relational Model
Hive Nested Model
Models
Hive Normal Views
Hive Materialized Table
Views
Use in the cases where the view requires
a join that is done through a shuffle
Use only for tables that filter
records/columns or use for marking fields

Questions?
Nested
▪ Less Space than Denormalization
▪ Still have tables but the cost of joins is all but gone
▪ Also great for cartesian joins
- N x M vs N + M
▪ Not really supported yet with Kudu or HBase with SQL

Questions?
Nested Example
CREATE TABLE fact_contacts (id BIGINT, name STRING, address
STRING) STORED AS PARQUET;
CREATE TABLE dim_phones
(
contact_id BIGINT
, category STRING
, international_code STRING
, area_code STRING
, exchange STRING
, extension STRING
, mobile BOOLEAN
, carrier STRING
, current BOOLEAN
, service_start_date TIMESTAMP
, service_end_date TIMESTAMP
)

Questions?
Nested Example
CREATE TABLE contacts_detailed_phones
(
id BIGINT, name STRING, address STRING
, phone ARRAY < STRUCT <
category: STRING
, international_code: STRING
, area_code: STRING
, exchange: STRING
, extension: STRING
, mobile: BOOLEAN
, carrier: STRING
, current: BOOLEAN
, service_start_date: TIMESTAMP
, service_end_date: TIMESTAMP
>>
) STORED AS PARQUET;
https://www.cloudera.com/documentation/enterprise/latest/topics/impala_complex_types.html

Questions?
De-normalized vs Nested
- Nested Pros
- Co-location
- Faster to group by
- Faster to window
- Joins are free
- Less data
- Better compression
- Tables and Columns can be read with out penalty from one not read
- Great for limiting the effort are Cartesian Joins
- Nested Cons
- Size limitation of parent row
- Adding child requires the re-write the the whole parent record

Questions?
Options for appending Nested
- It is all about the parent record
- We can add more then one Partition key for the parent
- In our use case
- User & watch month or day

Questions?
Storage and In Memory
- Also don’t limit the idea of nested to just tables
- In Spark they can be used as in memory constructs to
- conserve on networking
- In memory cost

Questions?
Nested Writing Example in Spark
{
"id": "0001",
"type": "donut",
"name": "Cake",
"ppu": 0.55,
"batters":
{
"batter":
[
{ "id": "1001", "type": "Regular" },
{ "id": "1002", "type": "Chocolate" },
{ "id": "1003", "type": "Blueberry" },
{ "id": "1004", "type": "Devil's Food" }
]
},
"topping":
[
{ "id": "5001", "type": "None" },
{ "id": "5002", "type": "Glazed" },
{ "id": "5005", "type": "Sugar" },
{ "id": "5007", "type": "Powdered Sugar" },
{ "id": "5006", "type": "Chocolate with Sprinkles" }
]

Questions?
Nested Writing Example in Spark
val jsonDF = hiveContext.read.json(jsonRDD)
jsonDF.write.parquet("./parquet")
hiveContext.createExternalTable("jsonNestedTable", "./parquet")

Questions?
Time Series Options
▪ HBase
▪ Cassandra
▪ InfluxDB
▪ Data Dog
▪ Many More

Questions?
Entity Centric Time Series
▪ Partition by Entity ID
▪ Order by Time
▪ Allows for free windowing
▪ Allows for fetching of single time window of single entity at web scale

Questions?
HBase Entity Time Series
Cust-A, 10
Cust-A, 20
Cust-A, 40
Cust-C, 10
Cust-C, 20
Cust-C, 30
Cust-C, 40
Cust-B, 10
Cust-B, 20
Cust-B, 30
Cust-B, 40
Cust-F, 20
Cust-F, 30
Cust-F, 40
Cust-D, 10
Cust-D, 20
Cust-D, 40
Cust-G, 10
Cust-G, 20
Cust-G, 30
Cust-G, 40

Questions?
Cust-A, 10
Cust-A, 20
Cust-A, 40
Cust-C, 10
Cust-C, 20
Cust-C, 30
Cust-C, 40
Cust-B, 10
Cust-B, 20
Cust-B, 30
Cust-B, 40
Cust-F, 20
Cust-F, 30
Cust-F, 40
Cust-D, 10
Cust-D, 20
Cust-D, 40
Cust-G, 10
Cust-G, 20
Cust-G, 30
Cust-G, 40
Rest Call Short Scan

Questions?
Cust-A, 10
Cust-A, 20
Cust-A, 40
Cust-C, 10
Cust-C, 20
Cust-C, 30
Cust-C, 40
Cust-B, 10
Cust-B, 20
Cust-B, 30
Cust-B, 40
Cust-F, 20
Cust-F, 30
Cust-F, 40
Cust-D, 10
Cust-D, 20
Cust-D, 40
Cust-G, 10
Cust-G, 20
Cust-G, 30
Cust-G, 40
Mapper Mapper Mapper

Questions?
Cust-A, 10
Cust-A, 20
Cust-A, 40
Cust-C, 10
Cust-C, 20
Cust-C, 30
Cust-C, 40
Cust-B, 10
Cust-B, 20
Cust-B, 30
Cust-B, 40
Cust-F, 20
Cust-F, 30
Cust-F, 40
Cust-D, 10
Cust-D, 20
Cust-D, 40
Cust-G, 10
Cust-G, 20
Cust-G, 30
Cust-G, 40
Mapper
Mapper Mapper

Questions?
What is meant by Bucketing and Sorting
- Partitioning on a Key
- Then sorting on that key + another field(s)
- Example
- User_id + Watch Event Time

Questions?
Example of Bucketed Sorted
Cust-A, 10
Cust-A, 20
Cust-A, 40
Cust-B, 10
Cust-B, 20
Cust-B, 30
Cust-B, 40Cust-C, 10
Cust-C, 20
Cust-C, 30
Cust-C, 40
Cust-F, 10
Cust-F, 20
Cust-F, 40
Cust-D, 10
Cust-D, 20
Cust-D, 40
Cust-G, 10
Cust-G, 20
Cust-G, 30
Cust-G, 40

Questions?
Good for Appending Nested
Cust-A, 10
Cust-A, 20
Cust-A, 40
Cust-B, 10
Cust-B, 20
Cust-B, 30
Cust-C, 20
Cust-C, 30
Cust-C, 40
Cust-F, 10
Cust-F, 20
Cust-F, 40
Cust-D, 10
Cust-D, 20
Cust-D, 40
Cust-G, 10
Cust-G, 20
Cust-G, 30
Cust-G, 40
Cust-A, 50
Cust-A, 60
Cust-B, 50
Cust-B, 60
Cust-C, 50
Cust-D, 50
Cust-G, 50
Existing DataNew Data

Questions?
Cust-A, 10
Cust-A, 20
Cust-A, 40
Cust-B, 10
Cust-B, 20
Cust-B, 30
Cust-C, 20
Cust-C, 30
Cust-C, 40
Cust-F, 10
Cust-F, 20
Cust-F, 40
Cust-D, 10
Cust-D, 20
Cust-D, 40
Cust-G, 10
Cust-G, 20
Cust-G, 30
Cust-G, 40
Cust-A, 50
Cust-A, 60
Cust-B, 50
Cust-B, 60
Cust-C, 50
Cust-D, 50
Cust-G, 50
Shuffle Join

Questions?
Cust-A, 10
Cust-A, 20
Cust-A, 40
Cust-B, 10
Cust-B, 20
Cust-B, 30
Cust-C, 20
Cust-C, 30
Cust-C, 40
Cust-F, 10
Cust-F, 20
Cust-F, 40
Cust-D, 10
Cust-D, 20
Cust-D, 40
Cust-G, 10
Cust-G, 20
Cust-G, 30
Cust-G, 40
Cust-B, 50
Cust-B, 60
Cust-A, 50
Cust-A, 60
Cust-C, 50
Cust-D, 50
Cust-G, 50
Merge Join

Questions?
Cust-A, 10
Cust-A, 20
Cust-A, 40
Cust-C, 10
Cust-C, 20
Cust-C, 30
Cust-C, 40
Cust-A, 50
Cust-A, 60
Cust-C, 50
Merge Join
Cust-A, 10
Cust-A, 20
Cust-A, 40
Cust-C, 10
Cust-C, 20
Cust-C, 30
Cust-C, 40
Cust-A, 50
Cust-A, 60
Cust-C, 50
Order Retained

Questions?
What is a NoSQL
- It’s not NO SQL
- It’s not a Database
- Think of it more like a
- HashMap
- Log
- Bucketed and Ordered

Questions?
Hash Map
- There is a Key and a Value
- It is really fast to grab a key/value
- It is really fast to add a key/value
- Iteration is also possible
Key Value
A 1
B 1
C 1
D 1
E 1
F 1
G 1
Client

Questions?
Log with Compactions
- When new records come in they don’t rewrite the old
- They compact in
Key Time Value
A 1 101
B 1 101
C 1 101
D 1 101
E 1 101
F 1 101
G 1 101
Key Time Value
A 2 102
D 2 102
F 2 102
F 3 103
H 3 103
Key Time Value
A 2 102
B 1 101
C 1 101
D 2 102
E 1 101
F 3 103
G 1 101
H 3 103

Questions?
HDFS
Log with Compactions
- Write Path
- Get Local for Record (Cached)
- First to WAL
- Then to Memstore
- Sorting & batching
- Flush to New Hfile
- Later Hfiles will be compacted
Client
Master
RegionServer
Memstore
HFiles New HFiles
HFiles
WAL

Questions?
HDFS
Ordered
- All Records Columns are ordered
- Ordering allows for simpler indexing
- Ordering allows for simpler compactions
- We will also use this ordering
- Windowing
- Time series
- Local scanning
Client
Master
RegionServer
Memstore
HFiles New HFiles
HFiles

Questions?
Bucketing or Partitions
- HBase
- Out of the Box:
- Range
- Desired:
- Salt
- Cassandra
- Out of the Box:
- HashMod
- Bucketed HashMod

Questions?
So what about SQL
- Well SQL could totally work
- CQL for cassandra
- Hive and SparkSQL on HBase
- Why is it not the best idea
- Built more for point look ups
- Scans are not as fast as parquet
- However the mutability may be more important than speed
- Partitioning is not simple
- It must be put into the key

Questions?
Let’s talk about CAP for a Minute
- Strong Consistency
- HBase & Kudu
- Variable Consistency
- Cassandra

Questions?
HBase Model
Client
Master
Region Server 1
Region Server 2
- Region Server owns range splits
- Region Server 1 fails
- Master needs to figure that out
- Master needs to assign new Region Server to own splits
- Region Server 2 has to get organized
- Region Server 2 is read to server reads and writes

Questions?
Cassandra Model
Client
Replica Node
(Has Replica)
Replica Node
(Has Replica)
Replica Node
(Has Replica)
Replica Node
(Random Node)
Client
Replica Node
(Has Replica)
Replica Node
(Has Replica)
Replica Node
(Has Replica)
Replica Node
(Random Node)

Questions?
Cassandra Model
Client
Replica Node
(Has Replica)
Replica Node
(Has Replica)
Replica Node
(Has Replica)
Client

Questions?
Cassandra Model (Common Models)
Client
Replica Node
(Has Replica)
Replica Node
(Has Replica)
Replica Node
(Has Replica)
Client
Client
Replica Node
(Has Replica)
Replica Node
(Has Replica)
Replica Node
(Has Replica)
Client
Client
Replica Node
(Has Replica)
Replica Node
(Has Replica)
Replica Node
(Has Replica)
Client
3 Write - 1 Read
1 Write - 3 Read
1 Write - 1 Read

Questions?
What do they share (Head Types)
- Master and a HA
- Leader Election
Master Hot Standby
Client
Leader Elected Follower Follower
Client
ZooKeeper
Elections

Questions?
What do they share (Headless)
Headless Headless Headless
Client
Headless Headless Headless
Client

Questions?
What do they share (CAP theorem)
Consistency

Questions?
Consistency
Eventual Consistency

Questions?
Consistency
Eventual Consistence
- Cheating the CAP Theorem
- Cassandra is a good model
- Where they expand the definition of failure
with variable consistence
- CAP still holds but …

Questions?
Lucene Indexing (Features)
- We don’t have enough time in this whole class
- Ordering logic
- NGrams
- Weights
- Text Indexing
- Translations
- Facets *

Questions?
Lucene Indexing (Facets)
- Facets are a side effect of out wonderful indexes
- It allows us to counts all the document that below to given indexes to produce
- Grouped Counts
- Charts and Graphs (kibana or Banana)
- People will also call this access pattern cubing a dataset

Questions?
Lucene Indexing (Kibana & Banana)

Questions?
Lucene Indexing (Facets Example)
- Time Series Example
Document
ID
Hour of Day User State Event
1 12 4201 MD click
2 12 4202 VA click
3 12 4203 VA click
4 1 4201 MD click
5 1 4202 VA view
6 2 4204 CA click
7 2 4205 VA view
8 2 4201 MD click

Questions?
Hour of
Day
12 1 2 3
1 4 5
2 6 7 8 9
Document
ID
Hour of
Day
User State Event
1 12 4201 MD click
2 12 4202 VA click
3 12 4203 VA click
4 1 4201 MD click
5 1 4202 VA view
6 2 4204 CA click
7 2 4205 VA view
8 2 4201 MD click
9 2 4204 CA click
User
4201 1 4 8
4202 2 5
4203 3
4204 6 9
4205 7
State
MD 1 4 8
VA 2 3 5 7
CA 6 9
Event
click 1 2 3 4 6 8 9
view 5 7

Questions?
- Events per hour
- Simple array count
Hour of
Day
12 1 2 3
1 4 5
2 6 7 8 9

Questions?
- Events per hour by State
- Simple array count
State
MD 1 4 8
VA 2 3 5 7
CA 6 9
Hour of
Day
12 1 2 3
1 4 5
2 6 7 8 9

Questions?
- Note the bucketing and ordered pattern
Hour of
Day 2
State
MD
State
VA
State CA
6 1 2 6
7 4 3 9
8 8 5
9 7State
MD 1 4 8
VA 2 3 5 7
CA 6 9
Hour of
Day
12 1 2 3
1 4 5
2 6 7 8 9

Questions?
Hour of
Day 2
State
MD
State
VA
State CA
6 1 2 6
7 4 3 9
8 8 5
9 7
Hour of
Day 2
State
MD
State
VA
State CA
6 1 2 6
7 4 3 9
8 8 5
9 7
+1 CA

Questions?
Hour of
Day 2
State
MD
State
VA
State CA
6 1 2 6
7 4 3 9
8 8 5
9 7
+1
VA
Hour of
Day 2
State
MD
State
VA
State CA
6 1 2 6
7 4 3 9
8 8 5
9 7
+1 MD
Hour of
Day 2
State
MD
State
VA
State CA
6 1 2 6
7 4 3 9
8 8 5
9 7
+1 CA

Questions?
Partitioning
- SolR and Elastic Search partition the document o land on all nodes
- This means
- You have the power of the cluster when querying
- This mean you are accessing the cluster when querying

Questions?
Writing Latency
- Lucene Indexing is more expensive then NoSQL work
- Think of it as micro batching
- Larger batches ~= better throughput
- Compaction is also invalid
- Deletes impact storage and performance until they are compacted

Questions?
Storage Cost
- TTL is your friend
- Think of Lucene based systems as great if
- You dataset is manageable in size
- You have a good TTL strategy
- You have a boat load of money

Questions?
Thinking in terms of Graphs
- Nodes and Edges
Node:1
…
Node:3
…
Node:2
…
Node:0
…
Friend
Child
Father
CoachWife

Questions?
Thinking in terms of Graphs
- Use cases
- Querying
- Cassandra with Sparkle
- Neo4j
- Batch operations
- Giraph
- GraphX
- GraphLab

Questions?
BSP Bulk Synchronous Parallel
- Process every Node Atomically
- Node gets all messages sent to it
- Nodes can mutate them selves and their edges
- Nodes can send messages to other nodes
- But nothing is received yet
- BSP waits until all the Node processing is done
- Then send messages to the right partition
- Repeat

Honorable Mentions
Kudu
CockroachDB
Druid.io

Questions?
Why Honorable Mentions

Questions?
Kudu
1. Replace the Region Servers with Tablet Servers
2. Replace block format HFile files with a parquet like TFiles
3. Replace the byte array focused HBase API with one that is more JDBC friendly
4. Tight integration with Spark SQL and Impala for SQL
5. Completely rewrite the compaction process to make for perfectly sized files with our having major
compactions but always manageable micro compactions.

Questions?
Kudu
Master
Tablet Server
SSD
Tablet Server
SSD
Tablet Server
SSD
Tablet Server
SSD
Metadata
Splits

Questions?
CoackroachDB
1. Think about Kudu but
1. Nested tables
2. Transitions

Questions?
CockroachDB
SQL
Structured Data API
Distributed Monolithic KV Store
Tablet Server
Store 1
SSD
RocksD
B
Store 2
SSD
RocksD
B
Tablet Server
Store 3
SSD
RocksD
B
Store 4
SSD
RocksD
B
Tablet Server
Store 5
SSD
RocksD
B
Store 6
SSD
RocksD
B
Tablet Server
Store 7
SSD
RocksD
B
Store 8
SSD
RocksD
B

Questions?
Druid.IO
1. Not a general SQL type store
2. More focused on metric
1. Where larger aggregations may be required
2. Many variables for same metric tag combinations

Questions?
Druid.IO
Client
Broker Cluster
Broker
Node
Broker
Node
Broker
Node
Real Time Cluster
Real Time
Node
Real Time
Node
Real Time
Node
History Cluster
History
Node
History
Node
History
Node
Pluggable
Storage
Batch Ingestion
Streaming
Ingestion
ZookKeeper
Metadata Storage
Query Planning and
Response Preparation
Try to optimize read
path based on time
request of the query
Async large batching
Main Ingestion Path
Short TTL for hot
memory cache
House keeping services

Questions?
Why have batch processing?
▪ When you need a larger context
- Say, to train a model
▪ Complex periodic job that does something
- Convert data to a nested structure for reduced number of shuffles
▪ For example,
- Kudu -> HDFS Nested is batch processing
- KMeans calculation, etc.

Questions?
Types of Processing
▪ ETL
▪ Analytics
▪ Graph Analytics
▪ Machine Learning
▪ Deep Learning

Questions?
Batch processing options
▪ Spark (+ MLlib)
▪ MapReduce (+ Mahout)
▪ Flink (+ Flink ML)
▪ Distributed query engines

Questions?
Spark
▪ Pretty popular
▪ Much faster than MapReduce
▪ Thriving community

Questions?
MapReduce
▪ Sloooooow

Questions?
Flink
▪ Pretty popular
▪ Batch is a special case of Streaming
▪ Developing community

Questions?
Just SQL
▪ Apache Spark SQL
▪ Hive
▪ MR, Spark, Tez
▪ Impala
▪ Presto

Questions?
Distributed Machine Learning
▪ Apache Spark MlLib
▪ Apache Spark Custom
▪ H2o
▪ Data Robot
▪ Google Tensorflow
▪ Microsoft Congnitive

Questions?
GPUs vs CPUs
▪ GPU are much better for floating point operations
▪ Consider Gradient Descent
▪ Encourages hardware to be separated from storage

Data Access
Interactive Access

Questions?
Types of data access
▪ REST server/APIs for querying entities and aggregates
▪ UI for displaying search facets
▪ SQL engine

Questions?
REST Servers
import org.mortbay.jetty.Server
import org.mortbay.jetty.servlet.{Context, ServletHolder}
…
val server = new Server(port)
val sh = new ServletHolder(classOf[ServletContainer])
sh.setInitParameter("com.sun.jersey.config.property.resourceConfigClass",
"com.sun.jersey.api.core.PackagesResourceConfig")
sh.setInitParameter("com.sun.jersey.config.property.packages",
"com.hadooparchitecturebook.taxi360.server.hbase")
sh.setInitParameter("com.sun.jersey.api.json.POJOMappingFeature", "true”)
val context = new Context(server, "/", Context.SESSIONS)
context.addServlet(sh, "/*”)
server.start()
server.join()

Questions?
UI requirements
Something that can
▪ Represent search results really well
▪ Integrates with Apache Solr, query engines, etc.

Questions?
UI options
▪ Hue
▪ Banana
▪ Kibana

Questions?
Hue In Our Use Case
▪ Because it’s included
▪ Please look at the others

Questions?
SQL engine criteria
▪ Low latency SQL access
▪ Allows for high concurrency
▪ JDBC/ODBC integration
▪ Capable of large scale aggregation
▪ Optionally integrates with Multiple Storage Systems for real-time updates to SQL
tables

Questions?
Apache Hive
▪ Good JDBC integration
▪ Not really low latency, even when using Tez
▪ Doesn’t integrate with Kudu
▪ Can run with MapReduce, Spark, or Tez

Questions?
Presto
▪ Low latency SQL engine from Facebook
▪ Provides JDBC/ODBC access
▪ Is only in-memory, large aggregations can lead to OOM errors

Questions?
Apache Impala
▪ Low latency SQL access
▪ Excellent concurrency support
▪ Integrates with Kudu for real-time SQL

Questions?
Apache Drill
▪ Similar in architecture to Impala

Questions?
Spark SQL
▪ Builds on top of Spark
▪ JDBC/ODBC access only via Spark Thrift Server
- Doesn’t scale well with larger number of concurrent users
- Doesn’t fully provide secure access.

Questions?
Other Sessions
▪ Top Five Mistakes When Writing Streaming Applications – Wednesday, 11:15

Thank you!
@hadooparchbook
tiny.cloudera.com/app-arch-newyork
Mark Grover | @mark_grover
Gwen Shapira | @gwenshap
Jonathan Seidman | @jseidman

Architecting a Next Generation Data Platform – Strata Singapore 2017

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