Big Data: Hadoop and Memcached - HPC Advisory Council...Hadoop Distributed File System (HDFS)...

Big Data: Hadoop and Memcached

Dhabaleswar K. (DK) Panda

The Ohio State University

E-mail: [email protected]

http://www.cse.ohio-state.edu/~panda

Talk at HPC Advisory Council Stanford Conference and Exascale

Workshop (Feb 2014)

by




Introduction to Big Data Applications and Analytics

• Big Data has become the one of the most

important elements of business analytics

• Provides groundbreaking opportunities

for enterprise information management

and decision making

• The amount of data is exploding;

companies are capturing and digitizing

more information than ever

• The rate of information growth appears

to be exceeding Moore’s Law

2 HPC Advisory Council Stanford Conference, Feb '14

• Commonly accepted 3V’s of Big Data • Volume, Velocity, Variety Michael Stonebraker: Big Data Means at Least Three Different Things, http://www.nist.gov/itl/ssd/is/upload/NIST-stonebraker.pdf

• 5V’s of Big Data – 3V + Value, Veracity

http://www.nist.gov/itl/ssd/is/upload/NIST-stonebraker.pdf



• Scientific Data Management, Analysis, and Visualization

• Applications examples

– Climate modeling

– Combustion

– Fusion

– Astrophysics

– Bioinformatics

• Data Intensive Tasks

– Runs large-scale simulations on supercomputers

– Dump data on parallel storage systems

– Collect experimental / observational data

– Move experimental / observational data to analysis sites

– Visual analytics – help understand data visually

HPC Advisory Council Stanford Conference, Feb '14 3

Not Only in Internet Services - Big Data in Scientific Domains

• Hadoop: http://hadoop.apache.org

– The most popular framework for Big Data Analytics

– HDFS, MapReduce, HBase, RPC, Hive, Pig, ZooKeeper, Mahout, etc.

• Spark: http://spark-project.org

– Provides primitives for in-memory cluster computing; Jobs can load data into memory and

query it repeatedly

• Shark: http://shark.cs.berkeley.edu

– A fully Apache Hive-compatible data warehousing system

• Storm: http://storm-project.net

– A distributed real-time computation system for real-time analytics, online machine learning,

continuous computation, etc.

• S4: http://incubator.apache.org/s4

– A distributed system for processing continuous unbounded streams of data

• Web 2.0: RDBMS + Memcached (http://memcached.org)

– Memcached: A high-performance, distributed memory object caching systems


Typical Solutions or Architectures for Big Data Applications and Analytics

http://hadoop.apache.org

http://hadoop.apache.org

http://spark-project.org



http://shark.cs.berkeley.edu

http://shark.cs.berkeley.edu

http://storm-project.net



http://incubator.apache.org/s4

http://incubator.apache.org/s4

http://memcached.org

http://memcached.org

• Focuses on large data and data analysis

• Hadoop (e.g. HDFS, MapReduce, HBase, RPC) environment

is gaining a lot of momentum

• http://wiki.apache.org/hadoop/PoweredBy


Who Are Using Hadoop?

http://wiki.apache.org/hadoop/PoweredBy

http://wiki.apache.org/hadoop/PoweredBy

• Scale: 42,000 nodes, 365PB HDFS, 10M daily slot hours • http://developer.yahoo.com/blogs/hadoop/apache-hbase-yahoo-multi-tenancy-helm-again-171710422.html

• A new Jim Gray’ Sort Record: 1.42 TB/min over 2,100 nodes • Hadoop 0.23.7, an early branch of the Hadoop 2.X

• http://developer.yahoo.com/blogs/ydn/hadoop-yahoo-sets-gray-sort-record-yellow-elephant-092704088.html


Hadoop at Yahoo!

http://developer.yahoo.com/blogs/ydn/hadoop-yahoo-more-ever-54421.html

• Overview of Hadoop and Memcached

• Trends in Networking Technologies and Protocols

• Challenges in Accelerating Hadoop and Memcached

• Designs and Case Studies – Hadoop

• HDFS

• MapReduce

• HBase

• RPC

• Combination of components

– Memcached

• Open Challenges

• Conclusion and Q&A

Presentation Outline


Big Data Processing with Hadoop

• The open-source implementation of MapReduce programming model for Big Data Analytics

• Major components

– HDFS

– MapReduce

• Underlying Hadoop Distributed File System (HDFS) used by both MapReduce and HBase

• Model scales but high amount of communication during intermediate phases can be further optimized

8

HDFS

MapReduce

Hadoop Framework

User Applications

HBase

Hadoop Common (RPC)

HPC Advisory Council Stanford Conference, Feb '14

Hadoop MapReduce Architecture & Operations

Disk Operations

• Map and Reduce Tasks carry out the total job execution

• Communication in shuffle phase uses HTTP over Java Sockets


Hadoop Distributed File System (HDFS)

• Primary storage of Hadoop;

highly reliable and fault-tolerant

• Adopted by many reputed

organizations

– eg: Facebook, Yahoo!

• NameNode: stores the file system namespace

• DataNode: stores data blocks

• Developed in Java for platform-

independence and portability

• Uses sockets for communication!

(HDFS Architecture)

10

RPC

RPC

RPC

RPC


Client


System-Level Interaction Between Clients and Data Nodes in HDFS

High

Performance

Networks

(HDD/SSD)

(HDD/SSD)

(HDD/SSD)

...

...

(HDFS Data Nodes)(HDFS Clients)

...

...


HBase

• Apache Hadoop Database

(http://hbase.apache.org/)

• Semi-structured database,

which is highly scalable

• Integral part of many

datacenter applications

– eg:- Facebook Social Inbox

• Developed in Java for platform-

independence and portability

• Uses sockets for

communication!

ZooKeeper

HBase

Client

HMaster

HRegion

Servers

HDFS

(HBase Architecture)


System-Level Interaction Among HBase Clients, Region Servers, and Data Nodes

(HBase Clients) (HRegion Servers) (Data Nodes)

(HDD/SSD)

(HDD/SSD)

(HDD/SSD)

...

......

... ...

...

High

Performance

Networks

High

Performance

Networks


Memcached Architecture

• Integral part of Web 2.0 architecture

• Distributed Caching Layer

– Allows to aggregate spare memory from multiple nodes

– General purpose

• Typically used to cache database queries, results of API calls

• Scalable model, but typical usage very network intensive

!"#$%"$#&

Web Frontend Servers (Memcached Clients)

(Memcached Servers)

(Database Servers)

High

Performance

Networks

High

Performance

Networks

Main

memoryCPUs

SSD HDD

High Performance Networks

... ...

...

Main

memoryCPUs

SSD HDD

Main

memoryCPUs

SSD HDD

Main

memoryCPUs

SSD HDD

Main

memoryCPUs

SSD HDD





• HDFS

• MapReduce

• HBase

• RPC


– Memcached

• Open Challenges





Trends for Commodity Computing Clusters in the Top 500 List (http://www.top500.org)

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HPC and Advanced Interconnects

• High-Performance Computing (HPC) has adopted advanced interconnects and protocols

– InfiniBand

– 10 Gigabit Ethernet/iWARP

– RDMA over Converged Enhanced Ethernet (RoCE)

• Very Good Performance

– Low latency (few micro seconds)

– High Bandwidth (100 Gb/s with dual FDR InfiniBand)

– Low CPU overhead (5-10%)

• OpenFabrics software stack with IB, iWARP and RoCE interfaces are driving HPC systems

• Many such systems in Top500 list


18

Trends of Networking Technologies in TOP500 Systems

Percentage share of InfiniBand is steadily increasing

Interconnect Family – Systems Share


• Message Passing Interface (MPI)

– Most commonly used programming model for HPC applications

• Parallel File Systems

• Storage Systems

• Almost 13 years of Research and Development since

InfiniBand was introduced in October 2000

• Other Programming Models are emerging to take

advantage of High-Performance Networks

– UPC

– OpenSHMEM

– Hybrid MPI+PGAS (OpenSHMEM and UPC)

19

Use of High-Performance Networks for Scientific Computing



One-way Latency: MPI over IB with MVAPICH2

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1.66

1.56

1.64

1.82

0.99 1.09

1.12

DDR, QDR - 2.4 GHz Quad-core (Westmere) Intel PCI Gen2 with IB switch FDR - 2.6 GHz Octa-core (SandyBridge) Intel PCI Gen3 with IB switch

ConnectIB-Dual FDR - 2.6 GHz Octa-core (SandyBridge) Intel PCI Gen3 with IB switch ConnectIB-Dual FDR - 2.8 GHz Deca-core (IvyBridge) Intel PCI Gen3 with IB switch

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Large Message Latency


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Bandwidth: MPI over IB with MVAPICH2

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ConnectIB-Dual FDR - 2.6 GHz Octa-core (SandyBridge) Intel PCI Gen3 with IB switch ConnectIB-Dual FDR - 2.8 GHz Deca-core (IvyBridge) Intel PCI Gen3 with IB switch





• HDFS

• MapReduce

• HBase

• RPC


– Memcached

• Open Challenges




Can High-Performance Interconnects Benefit Hadoop?

• Most of the current Hadoop environments use Ethernet Infrastructure with Sockets

• Concerns for performance and scalability

• Usage of High-Performance Networks is beginning to draw interest from many companies

• What are the challenges?

• Where do the bottlenecks lie?

• Can these bottlenecks be alleviated with new designs (similar to the designs adopted for MPI)?

• Can HPC Clusters with High-Performance networks be used for Big Data applications using Hadoop?


Designing Communication and I/O Libraries for Big Data Systems: Challenges


Big Data Middleware (HDFS, MapReduce, HBase and Memcached)

Networking Technologies

(InfiniBand, 1/10/40GigE and Intelligent NICs)

Storage Technologies

(HDD and SSD)

Programming Models (Sockets)

Applications

Commodity Computing System Architectures

(Multi- and Many-core architectures and accelerators)

Other Protocols?

Communication and I/O Library

Point-to-Point Communication

QoS

Threaded Models and Synchronization

Fault-Tolerance I/O and File Systems

Virtualization

Benchmarks


Common Protocols using Open Fabrics

Application/ Middleware

Verbs Sockets

Application /Middleware Interface

SDP

RDMA

SDP

InfiniBand Adapter

InfiniBand Switch

RDMA

IB Verbs

InfiniBand Adapter

InfiniBand Switch

User

space

RDMA

RoCE

RoCE Adapter

User

space

Ethernet Switch

TCP/IP

Ethernet Driver

Kernel Space

Protocol

InfiniBand Adapter

InfiniBand Switch

IPoIB

Ethernet Adapter

Ethernet Switch

Adapter

Switch

1/10/40 GigE

iWARP

Ethernet Switch

iWARP

iWARP Adapter

User

space IPoIB

TCP/IP

Ethernet Adapter

Ethernet Switch

10/40 GigE-TOE

Hardware Offload

RSockets

InfiniBand Adapter

InfiniBand Switch

User

space

RSockets

Can Hadoop be Accelerated with High-Performance Networks and Protocols?

26

Enhanced Designs

Application

Accelerated Sockets

10 GigE or InfiniBand

Verbs / Hardware Offload

Current Design

Application

Sockets

1/10 GigE Network

• Sockets not designed for high-performance

– Stream semantics often mismatch for upper layers (Hadoop and HBase)

– Zero-copy not available for non-blocking sockets

Our Approach

Application

OSU Design

10 GigE or InfiniBand

Verbs Interface






• HDFS

• MapReduce

• HBase

• RPC


– Memcached

• Open Challenges




• High-Performance Design of Hadoop over RDMA-enabled Interconnects

– High performance design with native InfiniBand and RoCE support at the verbs-

level for HDFS, MapReduce, and RPC components

– Easily configurable for native InfiniBand, RoCE and the traditional sockets-

based support (Ethernet and InfiniBand with IPoIB)

• Current release: 0.9.8

– Based on Apache Hadoop 1.2.1

– Compliant with Apache Hadoop 1.2.1 APIs and applications

– Tested with

• Mellanox InfiniBand adapters (DDR, QDR and FDR)

• RoCE

• Various multi-core platforms

• Different file systems with disks and SSDs

– http://hadoop-rdma.cse.ohio-state.edu

RDMA for Apache Hadoop Project


http://hadoop-rdma.cse.ohio-state.edu/







Design Overview of HDFS with RDMA

• JNI Layer bridges Java based HDFS with communication library written in native code

• Only the communication part of HDFS Write is modified; No change in HDFS architecture

29

HDFS

Verbs

RDMA Capable Networks (IB, 10GE/ iWARP, RoCE ..)

Applications

1/10 GigE, IPoIB Network

Java Socket Interface

Java Native Interface (JNI)

Write Others

OSU Design

Enables high performance RDMA communication, while supporting traditional socket interface

• Design Features

– RDMA-based HDFS write

– RDMA-based HDFS replication

– InfiniBand/RoCE support


Communication Time in HDFS

• Cluster with HDD DataNodes

– 30% improvement in communication time over IPoIB (QDR)

– 56% improvement in communication time over 10GigE

• Similar improvements are obtained for SSD DataNodes

Reduced by 30%

N. S. Islam, M. W. Rahman, J. Jose, R. Rajachandrasekar, H. Wang, H. Subramoni, C. Murthy and D. K. Panda ,

High Performance RDMA-Based Design of HDFS over InfiniBand , Supercomputing (SC), Nov 2012

30

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Co

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10GigE IPoIB (QDR) OSU-IB (QDR)


Evaluations using TestDFSIO

• Cluster with 8 HDD DataNodes, single disk per node

– 24% improvement over IPoIB (QDR) for 20GB file size

• Cluster with 4 SSD DataNodes, single SSD per node


Cluster with 8 HDD Nodes, TestDFSIO with 8 maps Cluster with 4 SSD Nodes, TestDFSIO with 4 maps

Increased by 24%

Increased by 61%

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Evaluations using TestDFSIO

• Cluster with 4 DataNodes, 1 HDD per node


• Cluster with 4 DataNodes, 2 HDD per node


• 2 HDD vs 1 HDD

– 76% improvement for OSU-IB (QDR)

– 66% improvement for IPoIB (QDR)

Increased by 31%

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OSU-IB -1HDD (QDR) OSU-IB -2HDD (QDR)


33

Evaluations using Enhanced DFSIO of Intel HiBench on SDSC-Gordon

• Cluster with 32 DataNodes, single SSD per node

– 47% improvement in throughput over IPoIB (QDR) for 128GB file size

– 16% improvement in latency over IPoIB (QDR) for 128GB file size


Increased by 47% Reduced by 16%

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Evaluations using Enhanced DFSIO of Intel HiBench on TACC-Stampede

• Cluster with 64 DataNodes, single HDD per node

– 64% improvement in throughput over IPoIB (FDR) for 256GB file size

– 37% improvement in latency over IPoIB (FDR) for 256GB file size

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• Trends in High Performance Networking and Protocols



• HDFS

• MapReduce

• HBase

• RPC


– Memcached

• Open Challenges




Design Overview of MapReduce with RDMA

• Enables high performance RDMA communication, while supporting traditional socket interface

• JNI Layer bridges Java based MapReduce with communication library written in native code

• InfiniBand/RoCE support

MapReduce

Verbs

RDMA Capable Networks

(IB, 10GE/ iWARP, RoCE ..)

OSU Design

Applications




Job

Tracker

Task

Tracker

Map

Reduce

Design features for RDMA

Prefetch/Caching of MOF

In-Memory Merge

Overlap of Merge & Reduce


Evaluations using Sort

8 HDD DataNodes

• With 8 HDD DataNodes for 40GB sort

– 41% improvement over IPoIB (QDR)

– 39% improvement over Hadoop-A-IB

(QDR)

37

M. W. Rahman, N. S. Islam, X. Lu, J. Jose, H. Subramon, H. Wang, and D. K. Panda, High-Performance RDMA-

based Design of Hadoop MapReduce over InfiniBand, HPDIC Workshop, held in conjunction with IPDPS, May

2013.


8 SSD DataNodes

• With 8 SSD DataNodes for 40GB sort


– 44% improvement over Hadoop-A-IB

(QDR)

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Hadoop-A-IB (QDR) OSU-IB (QDR)

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Evaluations using TeraSort

• 100 GB TeraSort with 8 DataNodes with 2 HDD per node


– 41% improvement over Hadoop-A-IB (QDR)


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• For 240GB Sort in 64 nodes


with HDD used for HDFS

39

Performance Evaluation on Larger Clusters


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Data Size: 60 GB Data Size: 120 GB Data Size: 240 GB

Cluster Size: 16 Cluster Size: 32 Cluster Size: 64

Job

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Sort in OSU Cluster

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Data Size: 80 GB Data Size: 160 GBData Size: 320 GB

Cluster Size: 16 Cluster Size: 32 Cluster Size: 64

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IPoIB (FDR) Hadoop-A-IB (FDR) OSU-IB (FDR)

TeraSort in TACC Stampede

• For 320GB TeraSort in 64 nodes

– 38% improvement over IPoIB

(FDR) with HDD used for HDFS

• 50% improvement in Self Join over IPoIB (QDR) for 80 GB data size

• 49% improvement in Sequence Count over IPoIB (QDR) for 30 GB data size

Evaluations using PUMA Workload


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• HDFS

• MapReduce

• HBase

• RPC


– Memcached

• Open Challenges





Motivation – Detailed Analysis on HBase Put/Get

• HBase 1KB Put – Communication Time – 8.9 us – A factor of 6X improvement over 10GE for communication time

• HBase 1KB Get – Communication Time – 8.9 us – A factor of 6X improvement over 10GE for communication time M. W. Rahman, J. Huang, J. Jose, X. Ouyang, H. Wang, N. Islam, H. Subramoni, Chet Murthy and D. K. Panda,

Understanding the Communication Characteristics in HBase: What are the Fundamental Bottlenecks?, ISPASS’12

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Communication

Communication Preparation

Server Processing

Server Serialization

Client Processing

Client Serialization


HBase Single-Server-Multi-Client Results

• HBase Get latency

– 4 clients: 104.5 us; 16 clients: 296.1 us

• HBase Get throughput

– 4 clients: 37.01 Kops/sec; 16 clients: 53.4 Kops/sec

• 27% improvement in throughput for 16 clients over 10GE

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J. Huang, X. Ouyang, J. Jose, M. W. Rahman, H. Wang, M. Luo, H. Subramoni, Chet Murthy, and D. K. Panda, High-Performance Design of HBase with RDMA over InfiniBand, IPDPS’12

OSU-IB (DDR)

IPoIB (DDR)


HBase – YCSB Read-Write Workload

• HBase Get latency (Yahoo! Cloud Service Benchmark)

– 64 clients: 2.0 ms; 128 Clients: 3.5 ms

– 42% improvement over IPoIB for 128 clients

• HBase Get latency

– 64 clients: 1.9 ms; 128 Clients: 3.5 ms

– 40% improvement over IPoIB for 128 clients

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Read Latency Write Latency

OSU-IB (QDR) IPoIB (QDR)





• HDFS

• MapReduce

• HBase

• RPC


– Memcached

• Open Challenges




Design Overview of Hadoop RPC with RDMA

Hadoop RPC

Verbs

RDMA Capable Networks (IB, 10GE/ iWARP, RoCE ..)

Applications




Our Design

Default

OSU Design

Enables high performance RDMA communication, while supporting traditional socket interface

46

• Design Features

– JVM-bypassed buffer management

– On-demand connection setup

– RDMA or send/recv based adaptive communication

– InfiniBand/RoCE support


Hadoop RPC over IB - Gain in Latency and Throughput

• Hadoop RPC over IB PingPong Latency

– 1 byte: 39 us; 4 KB: 52 us

– 42%-49% and 46%-50% improvements compared with the performance on 10 GigE

and IPoIB (32Gbps), respectively

• Hadoop RPC over IB Throughput

– 512 bytes & 48 clients: 135.22 Kops/sec

– 82% and 64% improvements compared with the peak performance on 10 GigE and

IPoIB (32Gbps), respectively

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IPoIB(QDR)

OSU-IB(QDR)

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• HDFS

• MapReduce

• HBase

• RPC


– Memcached

• Open Challenges




• For 5GB,

– 53.4% improvement over IPoIB, 126.9% improvement over 10GigE with HDD used for HDFS

– 57.8% improvement over IPoIB, 138.1% improvement over 10GigE with SSD used for HDFS

• For 20GB,

– 10.7% improvement over IPoIB, 46.8% improvement over 10GigE with HDD used for HDFS

– 73.3% improvement over IPoIB, 141.3% improvement over 10GigE with SSD used for HDFS

49

Performance Benefits - TestDFSIO

Cluster with 4 HDD Nodes, TestDFSIO with total 4 maps Cluster with 4 SSD Nodes, TestDFSIO with total 4 maps

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• For 80GB Sort in a cluster size of 8,

– 40.7% improvement over IPoIB, 32.2% improvement over 10GigE with HDD

used for HDFS

– 43.6% improvement over IPoIB, 42.9% improvement over 10GigE with SSD

used for HDFS

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Performance Benefits - Sort

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• For 100GB TeraSort in a cluster size of 8,

– 45% improvement over IPoIB, 39% improvement over 10GigE with HDD used

for HDFS

– 46% improvement over IPoIB, 40% improvement over 10GigE with SSD used for

HDFS

51

Performance Benefits - TeraSort

Cluster with 8 HDD Nodes, TeraSort with <8 maps, 8reduces>/host Cluster with 8 SSD Nodes, TeraSort with <8 maps, 8reduces>/host

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Cluster: 16 Cluster: 32 Cluster: 48 Cluster: 64

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Performance Benefits – RandomWriter & Sort in SDSC-Gordon

• RandomWriter in a cluster of single SSD per

node

– 16% improvement over IPoIB (QDR) for

50GB in a cluster of 16

– 20% improvement over IPoIB (QDR) for

300GB in a cluster of 64

Reduced by 20% Reduced by 36%


• Sort in a cluster of single SSD per node


for 50GB in a cluster of 16


for 300GB in a cluster of 64





• HDFS

• MapReduce

• HBase

• RPC


– Memcached

• Open Challenges




Memcached-RDMA Design

• Server and client perform a negotiation protocol

– Master thread assigns clients to appropriate worker thread

• Once a client is assigned a verbs worker thread, it can communicate directly and is “bound” to that thread

• All other Memcached data structures are shared among RDMA and Sockets worker threads

Sockets Client

RDMA Client

Master Thread

Sockets Worker Thread

Verbs Worker Thread

Sockets Worker Thread

Verbs Worker Thread

Shared

Data

Memory Slabs Items

…

1

1

2

2


55

• Memcached Get latency

– 4 bytes RC/UD – DDR: 6.82/7.55 us; QDR: 4.28/4.86 us

– 2K bytes RC/UD – DDR: 12.31/12.78 us; QDR: 8.19/8.46 us

• Almost factor of four improvement over 10GE (TOE) for 2K bytes on

the DDR cluster

Intel Clovertown Cluster (IB: DDR) Intel Westmere Cluster (IB: QDR)

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Tim

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IPoIB (QDR)

10GigE (QDR)

OSU-RC-IB (QDR)

OSU-UD-IB (QDR)

Memcached Get Latency (Small Message)


Tim

e%(us)%

Message%Size%

Tim

e%(us)%

Message%Size%

Tim

e%(us)%

Message%Size%

Time%(us)%

Message%Size%

Tim

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Message%Size%

Tim

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Message%Size%

Tim

e%(us)%

Message%Size%

Time%(us)%

Message%Size%

IPoIB (DDR)

10GigE (DDR)

OSU-RC-IB (DDR)

OSU-UD-IB (DDR)


Memcached Get TPS (4byte)

• Memcached Get transactions per second for 4 bytes

– On IB QDR 1.4M/s (RC), 1.3 M/s (UD) for 8 clients

• Significant improvement with native IB QDR compared to SDP and IPoIB

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Tho

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No. of Clients

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No. of Clients

Tim

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Tim

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Message%Size%

Time%(us)%

Message%Size%

IPoIB (QDR)

OSU-RC-IB (QDR)

OSU-UD-IB (QDR)

1

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64

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25

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2

1K

2K

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Tim

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OSU-IB (FDR)

IPoIB (FDR)

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• Memcached Get latency

– 4 bytes OSU-IB: 2.84 us; IPoIB: 75.53 us

– 2K bytes OSU-IB: 4.49 us; IPoIB: 123.42 us

• Memcached Throughput (4bytes)

– 4080 clients OSU-IB: 556 Kops/sec, IPoIB: 233 Kops/s

– Nearly 2X improvement in throughput

Memcached GET Latency Memcached Throughput

Memcached Performance (FDR Interconnect)

Experiments on TACC Stampede (Intel SandyBridge Cluster, IB: FDR)

Latency Reduced by nearly 20X

2X



Application Level Evaluation – Olio Benchmark

• Olio Benchmark

– RC – 1.6 sec, UD – 1.9 sec, Hybrid – 1.7 sec for 1024 clients

• 4X times better than IPoIB for 8 clients

• Hybrid design achieves comparable performance to that of pure RC design

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ms)

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ms)

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IPoIB (QDR)

OSU-RC-IB (QDR)

OSU-UD-IB (QDR)

OSU-Hybrid-IB (QDR)

Time%(ms)%

No.%of%Clients%


Application Level Evaluation – Real Application Workloads

• Real Application Workload – RC – 302 ms, UD – 318 ms, Hybrid – 314 ms for 1024 clients

• 12X times better than IPoIB for 8 clients • Hybrid design achieves comparable performance to that of pure RC design

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J. Jose, H. Subramoni, M. Luo, M. Zhang, J. Huang, W. Rahman, N. Islam, X. Ouyang, H. Wang, S. Sur and D. K.

Panda, Memcached Design on High Performance RDMA Capable Interconnects, ICPP’11

J. Jose, H. Subramoni, K. Kandalla, W. Rahman, H. Wang, S. Narravula, and D. K. Panda, Scalable Memcached

design for InfiniBand Clusters using Hybrid Transport, CCGrid’12

IPoIB (QDR)

OSU-RC-IB (QDR)

OSU-UD-IB (QDR)

OSU-Hybrid-IB (QDR)

Time%(ms)%

No.%of%Clients%





• HDFS

• MapReduce

• HBase

• RPC


– Memcached

• Open Challenges








(HDD and SSD)


Applications



Other Protocols?



QoS



Virtualization

Benchmarks

RDMA Protocol

Designing Communication and I/O Libraries for Big Data Systems: Solved a Few Initial Challenges


Upper level

Changes?

61

• Multi-threading and Synchronization

– Multi-threaded model exploration

– Fine-grained synchronization and lock-free design

– Unified helper threads for different components

– Multi-endpoint design to support multi-threading communications

• QoS and Virtualization

– Network virtualization and locality-aware communication for Big

Data middleware

– Hardware-level virtualization support for End-to-End QoS

– I/O scheduling and storage virtualization

– Live migration


More Challenges

• Support of Accelerators

– Efficient designs for Big Data middleware to take advantage of

NVIDA GPGPUs and Intel MICs

– Offload computation-intensive workload to accelerators

– Explore maximum overlapping between communication and

offloaded computation

• Fault Tolerance Enhancements

– Novel data replication schemes for high-efficiency fault tolerance

– Exploration of light-weight fault tolerance mechanisms for Big Data

processing

• Support of Parallel File Systems

– Optimize Big Data middleware over parallel file systems (e.g.

Lustre) on modern HPC clusters


More Challenges (Cont’d)


– 44% improvement over IPoIB (FDR)

64

Performance Improvement Potential of MapReduce over Lustre on HPC Clusters – An Initial Study


Sort in TACC Stampede Sort in SDSC Gordon



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IPoIB (FDR)

OSU-IB (FDR)

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IPoIB (QDR)

OSU-IB (QDR)

• Can more optimizations be achieved by leveraging more features of Lustre?

• The current benchmarks provide some performance

behavior

• However, do not provide any information to the

designer/developer on:

– What is happening at the lower-layer?

– Where the benefits are coming from?

– Which design is leading to benefits or bottlenecks?

– Which component in the design needs to be changed and what will

be its impact?

– Can performance gain/loss at the lower-layer be correlated to the

performance gain/loss observed at the upper layer?


Are the Current Benchmarks Sufficient for Big Data?





(HDD and SSD)


Applications



Other Protocols?



QoS



Virtualization

Benchmarks

RDMA Protocols

Challenges in Benchmarking of RDMA-based Designs

66

Current

Benchmarks

No Benchmarks

Correlation?


OSU MPI Micro-Benchmarks (OMB) Suite

• A comprehensive suite of benchmarks to – Compare performance of different MPI libraries on various networks and systems

– Validate low-level functionalities

– Provide insights to the underlying MPI-level designs

• Started with basic send-recv (MPI-1) micro-benchmarks for latency, bandwidth and bi-directional bandwidth

• Extended later to – MPI-2 one-sided

– Collectives

– GPU-aware data movement

– OpenSHMEM (point-to-point and collectives)

– UPC

• Has become an industry standard

• Extensively used for design/development of MPI libraries, performance comparison of MPI libraries and even in procurement of large-scale systems

• Available from http://mvapich.cse.ohio-state.edu/benchmarks

• Available in an integrated manner with MVAPICH2 stack


http://mvapich.cse.ohio-state.edu/benchmarks







(HDD and SSD)


Applications



Other Protocols?



QoS



Virtualization

Benchmarks

RDMA Protocols

Iterative Process – Requires Deeper Investigation and Design for Benchmarking Next Generation Big Data Systems and Applications


Applications-

Level

Benchmarks

Micro-

Benchmarks

• Upcoming RDMA for Apache Hadoop Releases will support

– HDFS Parallel Replication

– Advanced MapReduce

– HBase

– Hadoop 2.0.x

• Memcached-RDMA

• Exploration of other components (Threading models, QoS,

Virtualization, Accelerators, etc.)

• Advanced designs with upper-level changes and optimizations

• Comprehensive set of Benchmarks

• More details at http://hadoop-rdma.cse.ohio-state.edu


Future Plans of OSU Big Data Project

69

• Presented an overview of Big Data, Hadoop and Memcached

• Provided an overview of Networking Technologies

• Discussed challenges in accelerating Hadoop

• Presented initial designs to take advantage of InfiniBand/RDMA

for HDFS, MapReduce, RPC, HBase and Memcached

• Results are promising

• Many other open issues need to be solved

• Will enable Big Data community to take advantage of modern

HPC technologies to carry out their analytics in a fast and scalable

manner


Concluding Remarks

70


Personnel Acknowledgments

Current Students

– S. Chakraborthy (Ph.D.)

– N. Islam (Ph.D.)

– J. Jose (Ph.D.)

– M. Li (Ph.D.)

– S. Potluri (Ph.D.)

– R. Rajachandrasekhar (Ph.D.)

Past Students

– P. Balaji (Ph.D.)

– D. Buntinas (Ph.D.)

– S. Bhagvat (M.S.)

– L. Chai (Ph.D.)

– B. Chandrasekharan (M.S.)

– N. Dandapanthula (M.S.)

– V. Dhanraj (M.S.)

– T. Gangadharappa (M.S.)

– K. Gopalakrishnan (M.S.)

– G. Santhanaraman (Ph.D.)

– A. Singh (Ph.D.)

– J. Sridhar (M.S.)

– S. Sur (Ph.D.)

– H. Subramoni (Ph.D.)

– K. Vaidyanathan (Ph.D.)

– A. Vishnu (Ph.D.)

– J. Wu (Ph.D.)

– W. Yu (Ph.D.)

71

Past Research Scientist – S. Sur

Current Post-Docs

– X. Lu

– K. Hamidouche

Current Programmers

– M. Arnold

– J. Perkins

Past Post-Docs – H. Wang

– X. Besseron

– H.-W. Jin

– M. Luo

– W. Huang (Ph.D.)

– W. Jiang (M.S.)

– S. Kini (M.S.)

– M. Koop (Ph.D.)

– R. Kumar (M.S.)

– S. Krishnamoorthy (M.S.)

– K. Kandalla (Ph.D.)

– P. Lai (M.S.)

– J. Liu (Ph.D.)

– M. Luo (Ph.D.)

– A. Mamidala (Ph.D.)

– G. Marsh (M.S.)

– V. Meshram (M.S.)

– S. Naravula (Ph.D.)

– R. Noronha (Ph.D.)

– X. Ouyang (Ph.D.)

– S. Pai (M.S.)

– M. Rahman (Ph.D.)

– D. Shankar (Ph.D.)

– R. Shir (Ph.D.)

– A. Venkatesh (Ph.D.)

– J. Zhang (Ph.D.)

– E. Mancini

– S. Marcarelli

– J. Vienne

Current Senior Research Associate

– H. Subramoni

Past Programmers

– D. Bureddy


Pointers

http://nowlab.cse.ohio-state.edu

[email protected]


72

http://mvapich.cse.ohio-state.edu

http://hadoop-rdma.cse.ohio-state.edu

http://nowlab.cse.ohio-state.edu/




mailto:[email protected]






http://mvapich.cse.ohio-state.edu/









Big Data: Hadoop and Memcached - HPC Advisory Council...Hadoop Distributed File System (HDFS)...

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Transcript of Big Data: Hadoop and Memcached - HPC Advisory Council...Hadoop Distributed File System (HDFS)...