Welcome to Hadoop2Land!

25.03.2014
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Your Travel Guide
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Your Travel Destination
Welcome Office
YARN Land
HDFS 2 Land
YARN App Land Enterprise Land
Goodbye Photo

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Stop #1: Welcome Office
Welcome Office

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…there was MapReduce
In the beginning of Hadoop
• It could handle data sizes way beyond those
of its competitors
• It was resilient in the face of failure
• It made it easy for users to bring their code
and algorithms to the data

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…but it was too low level
Hadoop 1 (2007)

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…but it was too rigid
Hadoop 1 (2007)

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HDFS
…but it was Batch
HDFS HDFS
Single App
Batch
Single App
Batch
Single App
Batch
Single App
Batch
Single App
Batch
Hadoop 1 (2007)

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…but it had limitations
Hadoop 1 (2007)
• Scalability
– Maximum cluster size ~ 4,500 nodes
– Maximum concurrent tasks – 40,000
– Coarse synchronization in JobTracker
• Availability
– Failure kills all queued and running jobs
• Hard partition of resources into map & reduce slots
– Low resource utilization
• Lacks support for alternate paradigms and services

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YARN to the rescue!
Hadoop 2 (2013)

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Stop #2: YARN Land
Welcome Office
YARN Land

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A brief history of Hadoop 2
• Originally conceived & architected by the
team at Yahoo!
– Arun Murthy created the original JIRA in 2008 and now is
the Hadoop 2 release manager
• The community has been working on
Hadoop 2 for over 4 years
• Hadoop 2 based architecture running at
scale at Yahoo!
– Deployed on 35,000+ nodes for 6+ months

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Hadoop 1
HDFS
Redundant, reliable
storage
Hadoop 2: Next-gen platform
MapReduce
Cluster resource mgmt.
+ data processing
Hadoop 2
HDFS 2
Redundant, reliable storage
MapReduce
Data processing
Single use system
Batch Apps
Multi-purpose platform
Batch, Interactive, Streaming, …
YARN
Cluster resource management
Others
Data processing

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Taking Hadoop beyond batch
Applications run natively in Hadoop
HDFS 2
Batch
MapReduce
Store all data in one place
Interact with data in multiple ways
YARN
Interactive
Tez
Online
HOYA
Streaming
Storm, …
Graph
Giraph
In-Memory
Spark
Other
Search, …

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YARN: Design Goals
• Build a new abstraction layer by splitting up
the two major functions of the JobTracker
• Cluster resource management
• Application life-cycle management
• Allow other processing paradigms
• Flexible API for implementing YARN apps
• MapReduce becomes YARN app
• Lots of different YARN apps

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Hadoop: BigDataOS™
Storage:
File System
Traditional Operating
System
Execution / Scheduling:
Processes / Kernel
Storage:
HDFS
Hadoop
Execution / Scheduling:
YARN

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YARN: Architectural Overview
Split up the two major functions of the JobTracker
Cluster resource management & Application life-cycle management
ResourceManager
NodeManager NodeManager NodeManager NodeManager
Scheduler
AM 1
Container 1.2
Container 1.1
AM 2
Container 2.1
Container 2.2
Container 2.3

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YARN: Multi-tenancy I
ResourceManager
Scheduler
MapReduce 1
map 1.2
map 1.1
MapReduce 2
map 2.1
map 2.2
reduce 2.1
reduce 1.1 Tez map 2.3
reduce 2.2
vertex 1
vertex 2
vertex 3
vertex 4
HOYA
HBase Master
Region server 1
Region server 2
Region server 3 Storm
nimbus 1
nimbus 2
Different types of applications on the same cluster

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YARN: Multi-tenancy II
ResourceManager
Scheduler
MapReduce 1
map 1.2
map 1.1
MapReduce 2
map 2.1
map 2.2
reduce 2.1
reduce 2.2
vertex 1
vertex 2
vertex 3
vertex 4
HOYA
HBase Master
Region server 1
Region server 2
nimbus 1
nimbus 2
DWHUser
Ad-
Hoc
root
30% 60% 10%
Dev Prod
20% 80%
Dev Prod
Dev1 Dev2
25% 75%
60% 40%
Different users and
organizations on the
same cluster

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YARN: Your own app
ResourceManager
NodeManager NodeManager
NodeManager
Scheduler
Container
Container
MyApp
YARNClient
App specific
API
myAM
AMRMClient
NMClient
Application
Client
Protocol
Application
Master
Protocol
Container Management Protocol
DistributedShell is the
new WordCount!

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Stop #3: HDFS 2 Land
Welcome Office
YARN Land
HDFS 2 Land

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HDFS 2: In a nutshell
• Removes tight coupling of Block
Storage and Namespace
• Adds (built-in) High Availability
• Better Scalability & Isolation
• Increased performance
Details: https://issues.apache.org/jira/browse/HDFS-1052

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HDFS 2: Federation
NameNodes do not talk to each other
NameNodes manages
only slice of namespace
DataNodes can store
blocks managed by
any NameNode
NameNode 1
Namespace 1
Namespace
State
Block
Map
NameNode 2
Namespace 2
Block Pools
Pool 1 Pool 2
Block Storage as
generic storage service
Data Nodes
b3 b1
b2 b4
b2 b1
b5
b3 b2
b5 b4
JBOD JBOD JBOD
Namespace
State
Block
Map
Horizontally scale IO and storage

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HDFS 2: Architecture
Active NameNode Standby NameNode
DataNodeDataNodeDataNode DataNode DataNode
Maintains Block
Map and Edits File Simultaneously
reads and applies
the edits
Report to both NameNodes
Block
Map
Edits
File
Block
Map
Edits
File
NFS Shared state on NFS
OR
Quorum based storage
Journal
Node
Journal
Node
Journal
Node
Take orders
only from the
Active
or

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ZKFailover
Controller
ZKFailover
Controller
HDFS 2: High Availability
Active NameNode Standby NameNode
DataNodeDataNodeDataNode DataNode DataNode
Block
Map
Edits
File
Block
Map
Edits
File
ZooKeeper
Node
ZooKeeper
Node
ZooKeeper
Node
Send Heartbeats & Block Reports
Shared State
Monitors health
of NN, OS, HW
Heartbeat Heartbeat
Holds special
lock znode
Journal
Node
Journal
Node
Journal
Node

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HDFS 2: Write-Pipeline
• Earlier versions of HDFS
• Files were immutable
• Write-once-read-many model
• New features in HDFS 2
• Files can be reopened for append
• New primitives: hflush and hsync
• Replace data node on failure
• Read consistency
DataNode
1
DataNode
2
DataNode
3
DataNode
4
Writer
Add new node to
the pipeline
Reader
Data Data Data
Can read from any node and
then failover to any other node

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HDFS 2: Snapshots
• Admin can create point in time snapshots of HDFS
• Of the entire file system
• Of a specific data-set (sub-tree directory of file system)
• Restore state of entire file system or data-set to a
snapshot (like Apple Time Machine)
• Protect against user errors
• Snapshot diffs identify changes made to data set
• Keep track of how raw or derived/analytical data changes
over time

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HDFS 2: NFS Gateway
• Supports NFS v3 (NFS v4 is work in progress)
• Supports all HDFS commands
• List files
• Copy, move files
• Create and delete directories
• Ingest for large scale analytical workloads
• Load immutable files as source for analytical processing
• No random writes
• Stream files into HDFS
• Log ingest by applications writing directly to HDFS client
mount

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HDFS 2: Performance
• Many improvements
• New AppendableWrite-Pipeline
• Read path improvements for fewer memory copies
• Short-circuit local reads for 2-3x faster random
reads
• I/O improvements using posix_fadvise()
• libhdfs improvements for zero copy reads
• Significant improvements: I/O 2.5 - 5x faster

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Stop #4: YARN App Land
Welcome Office
YARN Land
HDFS 2 Land
YARN App Land

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YARN Apps: Overview
• MapReduce 2 Batch
• Tez DAG Processing
• Storm Stream Processing
• Samza Stream Processing
• Apache S4 Stream Processing
• Spark In-Memory
• HOYA HBase on YARN
• Apache Giraph Graph Processing
• Apache Hama Bulk Synchronous Parallel
• Elastic Search Scalable Search

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MapReduce 2: In a nutshell
• MapReduce is now a YARN app
• No more map and reduce slots, it’s containers now
• No more JobTracker, it’s YarnAppmaster library now
• Multiple versions of MapReduce
• The older mapred APIs work without modification or recompilation
• The newer mapreduce APIs may need to be recompiled
• Still has one master server component: the Job History Server
• The Job History Server stores the execution of jobs
• Used to audit prior execution of jobs
• Will also be used by YARN framework to store charge backs at that level
• Better cluster utilization
• Increased scalability & availability

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MapReduce 2: Shuffle
• Faster Shuffle
• Better embedded server: Netty
• Encrypted Shuffle
• Secure the shuffle phase as data moves across the cluster
• Requires 2 way HTTPS, certificates on both sides
• Causes significant CPU overhead, reserve 1 core for this work
• Certificates stored on each node (provision with the cluster), refreshed every
10 secs
• Pluggable Shuffle Sort
• Shuffle is the first phase in MapReduce that is guaranteed to not be data-
local
• Pluggable Shuffle/Sort allows application developers or hardware
developers to intercept the network-heavy workload and optimize it
• Typical implementations have hardware components like fast networks and
software components like sorting algorithms
• API will change with future versions of Hadoop

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MapReduce 2: Performance
• Key Optimizations
• No hard segmentation of resource into map and reduce slots
• YARN scheduler is more efficient
• MR2 framework has become more efficient than MR1: shuffle
phase in MRv2 is more performant with the usage of Netty.
• 40.000+ nodes running YARN across over 365 PB of data.
• About 400.000 jobs per day for about 10 million hours of
compute time.
• Estimated 60% – 150% improvement on node usage per day
• Got rid of a whole 10,000 node datacenter because of their
increased utilization.

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Apache Tez: In a nutshell
• Distributed execution framework that works on
computations represented as dataflow graphs
• Tez is Hindi for “speed”
• Naturally maps to execution plans
produced by query optimizers
• Highly customizable to meet a
broad spectrum of use cases and to
enable dynamic performance
optimizations at runtime
• Built on top of YARN

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Apache Tez: Architecture
• Task with pluggable Input, Processor & Output
Task
HDFS
Input
Map
Processor
Sorted
Output
„Classical“ Map
Task
Shuffle
Input
Reduce
Processor
HDFS
Output
„Classical“ Reduce YARN ApplicationMaster to run
DAG of Tez Tasks

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Apache Tez: Tez Service
• MapReduce Query Startup is expensive:
– Job launch & task-launch latencies are fatal for
short queries (in order of 5s to 30s)
• Solution:
– Tez Service (= Preallocated Application Master)
• Removes job-launch overhead (Application Master)
• Removes task-launch overhead (Pre-warmed Containers)
– Hive (or Pig)
• Submit query plan to Tez Service
– Native Hadoop service, not ad-hoc

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Hadoop 1
HDFS
Apache Tez: The new primitive
MapReduce
Cluster resource mgmt. + data
processing
Hadoop 2
MapReduce as Base Apache Tez as Base
Pig Hive Other
HDFS
YARN
Tez
Execution Engine
MR Pig Hive Real
time
Storm
O
t
h
e
r

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Apache Tez: Performance
SELECT a.state, COUNT(*),
AVERAGE(c.price)
FROM a
JOIN b ON (a.id = b.id)
JOIN c ON (a.itemId = c.itemId)
GROUP BY a.state
Existing Hive
Parse Query 0.5s
Create Plan 0.5s
Launch Map-
Reduce
20s
Process Map-
Reduce
10s
Total 31s
Hive/Tez
Parse Query 0.5s
Create Plan 0.5s
Launch Map-
Reduce
20s
Process Map-
Reduce
2s
Total 23s
Tez & Hive Service
Parse Query 0.5s
Create Plan 0.5s
Submit to Tez
Service
0.5s
Process Map-Reduce 2s
Total 3.5s
* No exact numbers, for illustration only

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Stinger Initiative: In a nutshell

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Stinger: Overall Performance
* Real numbers, but handle with care!

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Storm: In a nutshell
• Stream-processing
• Real-time processing
• Developed as standalone application
• https://github.com/nathanmarz/storm
• Ported on YARN
• https://github.com/yahoo/storm-yarn

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Storm: Conceptual view
Spout
Spout
Spout
Source of streams
Bolt
Bolt
Bolt
Bolt
Bolt
Tuple
Bolt
• Consumer of streams
• Processing of tuples
• Possibly emits new
tuplesStream
Unbound sequence of
tuples
Tuple
Tuple
Tuple
List of name-value pairs
Topology
Network of spouts & bolts as the nodes and
streams as the edges

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Spark: In a nutshell
• High-speed in-memory analytics over
Hadoop and Hive
• Separate MapReduce-like engine
– Speedup of up to 100x
– On-disk queries 5-10x faster
• Spark is now a top-level Apache project
– http://spark.apache.org
• Compatible with Hadoop‘s Storage API
• Spark can be run on top of YARN
– http://spark.apache.org/docs/0.9.0/running-on-yarn.html

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Spark: RDD
• Key idea: Resilient Distributed Datasets
(RDDs)
• Read-only partitioned collection of
records
• Optionally cached in memory across cluster
• Manipulated through parallel operators
• Support only coarse-grained operations
• Map
• Reduce
• Group-by transformations
• Automatically recomputed on failure
RDD
A11
A12
A13

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Spark: Data Sharing

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HOYA: In a nutshell
• Create on-demand HBase clusters
• Small HBase cluster in large YARN cluster
• Dynamic HBase clusters
• Self-healing HBase Cluster
• Elastic HBase clusters
• Transient/intermittent clusters for workflows
• Configure custom configurations & versions
• Better isolation
• More efficient utilization/sharing of cluster

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HOYA: Creation of AppMaster
ResourceManager
NodeManager
Scheduler
Container
Container
HOYA Client
YARNClient
HOYA
specific API
HOYA
Application
Master
Container
Container
Container
Container

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HOYA: Deployment of HBase
ResourceManager
NodeManager
Scheduler
Container
Container
HOYA Client
YARNClient
HOYA
specific API
HOYA
Application
Master
Container
Container
Container
Container
HBase Master
Region Server
Region Server

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HOYA: Bind via ZooKeeper
ResourceManager
NodeManager
Scheduler
Container
Container
HOYA Client
YARNClient
HOYA
specific API
HOYA
Application
Master
Container
Container
Container
Container
HBase Master
Region Server
Region Server
HBase
Client
ZooKeeper

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Giraph: In a nutshell
• Giraph is a framework for processing semi-
structured graph data on a massive scale
• Giraph is loosely based upon Google's
Pregel
• Both systems are inspired by the Bulk
Synchronous Parallel model
• Giraph performs iterative calculations
on top of an existing Hadoop cluster
• Uses Single Map-only Job
• Apache top level project since 2012
– http://giraph.apache.org

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Stop #5: Enterprise Land
Welcome Office
YARN Land
HDFS 2 Land

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Falcon: In a nutshell
• A framework for managing data processing
in Hadoop Clusters
• Falcon runs as a standalone server as part of
the Hadoop cluster
• Key Features:
• Data Replication Handling
• Data Lifecycle Management
• Process Coordination & Scheduling
• Declarative Data Process Programming
• Apache Incubation Status
• http://falcon.incubator.apache.org

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Falcon: One-stop Shop
Data Management Needs Tool Orchestration
Data Processing
Replication
Retention
Scheduling
Reprocessing
Multi Cluster Mgmt.
Oozie
Sqoop
Distcp
Flume
MapReduce
Pig & Hive

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Falcon: Weblog Use Case
• Weblogs saved hourly to primary cluster
• HDFS location is /weblogs/{date}
• Desired Data Policy:
• Replicate weblogs to secondary cluster
• Evict weblogs from primary cluster after 2 days
• Evict weblogs from secondary cluster after 1
week

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Falcon: Weblog Use Case
<feed description=“" name=”feed-weblogs” xmlns="uri:falcon:feed:0.1">
<frequency>hours(1)</frequency>
<clusters>
<cluster name=”cluster-primary" type="source">
<validity start="2012-01-01T00:00Z" end="2099-12-31T00:00Z"/>
<retention limit="days(2)" action="delete"/>
</cluster>
<cluster name=”cluster-secondary" type="target">
<validity start="2012-01-01T00:00Z" end="2099-12-31T00:00Z"/>
<retention limit=”days(7)" action="delete"/>
</cluster>
</clusters>
<locations>
<location type="data” path="/weblogs/${YEAR}-${MONTH}-${DAY}-
${HOUR}"/>
</locations>
<ACL owner=”hdfs" group="users" permission="0755"/>
<schema location="/none" provider="none"/>
</feed>

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Knox: In a nutshell
• System that provides a single point of
authentication and access for Apache
Hadoop services in a cluster.
• The gateway runs as a server (or cluster of
servers) that provide centralized access to
one or more Hadoop clusters.
• The goal is to simplify Hadoop security for
both users and operators
• Apache Incubation Status
• http://knox.incubator.apache.org

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Knox: Architecture
ResourceManager
Scheduler
MapReduce 1
map 1.2
map 1.1
MapReduce
2
map 2.1
map 2.2
reduce 2.1
reduce 2.2
vertex 1
vertex 2
vertex 3
vertex 4
HOYA
HBase Master
Region server 1
Region server 2
nimbus 1
nimbus 2
ResourceManager
Scheduler
MapReduce 1
map 1.2
map 1.1
MapReduce
2
map 2.1
map 2.2
reduce 2.1
reduce 2.2
vertex 1
vertex 2
vertex 3
vertex 4
HOYA
HBase Master
Region server 1
Region server 2
nimbus 1
nimbus 2
Kerberos
SSO
Provider
Identity Providers
Secure Hadoop Cluster 1
Secure Hadoop Cluster 2
Gateway Cluster
#1 #3#2
Ambari / Hue
Server
Browser
Ambari
Client
REST
Client
JDBC
Client
DMZ
Firewall
Firewall

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Final Stop: Goodbye Photo
Welcome Office
YARN Land
HDFS2 Land
Goodbye Photo

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Hadoop 2: Summary
1. Scale
2. New programming models &
services
3. Beyond Java
4. Improved cluster utilization
5. Enterprise Readiness

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One more thing…
Let’s get started with
Hadoop 2!

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Hortonworks Sandbox 2.0
http://hortonworks.com/products/hortonworks-sandbox

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Trainings
Developer Training
• 19. - 22.05.2014, Düsseldorf
• 30.06 - 03.07.2014, München
• 04. - 07.08.2014, Frankfurt
Admin Training
• 26. - 28.05.2014, Düsseldorf
• 07. - 09.07.2014, München
• 11. - 13.08.2014, Frankfurt
Details:
https://www.codecentric.de/schulungen-und-workshops/

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Thanks for traveling with me

Welcome to Hadoop2Land!

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Welcome to Hadoop2Land!