Accelerating MapReduce with Distributed Memory Cache

Author: Shubin Zhang, et al.

Institute of Computing Technology, Beijing, China

Reported by: Tzu-Li Tai

National Cheng Kung University, Taiwan

High Performance Parallel and Distributed Systems Lab

2009 IEEE 15th International Conference on Parallel and Distributed Systems

HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU

A. Background and Motivation

B. Goals and Design Decisions

C. System Overview

D. System Details

E. Experimental Results and Analysis

F. Conclusion and Future Works

G. Future Studies for Topic

E. Discussion: Our Chances

HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU

Background and Motivation

Pre-Notes:

- Published in 2009 (1st paper on topic)

- Outdated hardware/software and data size

- Focus on methodology and reasoning of using

distributed cache in Hadoop

- Learn possible tackle points and what to avoid

HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU

Background and Motivation

• Shuffle time becomes the bottleneck

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Inter.

Data

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Inter.

Data

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Inter.

Data

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HDFS pipeline replication

HDFS HDFS HDFS

1 local write

2 remote read

GOAL

HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU

Goals and Design Decisions

• Target clusters are small-scale

- Bandwidth is not scarce

- Node failures are uncommon- Commodity machines

- Heterogeneous

- GB Ethernet

HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU

Goals and Design Decisions

• Stay close to the original

• Retain fault-tolerance (!)

• Local decision-making

HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU

Goals and Design Decisions

• Low-latency, high-throughput access to map

outputs: global storage system

- No central coordinator

- Uniform global namespace- Low-latency, high-throughput data access

- Concurrent access

- Large capacity

- Scalable

⇒ 𝑫𝒊𝒔𝒕𝒓𝒊𝒃𝒖𝒕𝒆𝒅𝑴𝒆𝒎𝒐𝒓𝒚 𝑪𝒂𝒄𝒉𝒆

HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU

System Overview

• Use Memcached: http://memcached.org/

- Open-source distributed memory caching system

- Daemon processes on servers

- Global K-V store

HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU

System Overview

• Map side

! Buffer details

HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU

System Overview

[Extra] from O’Reilly

HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU

System Overview

• Reduce side

HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU

System Overview

[Extra] from O’Reilly

HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU

System Details

1. Memory Cache Capacity

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HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU

System Details

1. Memory Cache Capacity

𝑆𝑖𝑧𝑒𝑚𝑒𝑚𝑐𝑎𝑐ℎ𝑒𝑑 = 𝑚𝑐 × 𝑠 × (𝑟 − 𝑟𝑎)

𝒎𝒄: completed map tasks

𝒔: avg. map output size

𝒓: total no. of reduce tasks

𝒓𝒂: no. of early scheduled reduce tasks

𝑆𝑖𝑧𝑒𝑚𝑒𝑚𝑐𝑎𝑐ℎ𝑒𝑑𝑀𝑖𝑛 = 𝑚 × 𝑠 × (𝑟 − 𝑟𝑎)

𝒎: total no. of map tasks

HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU

System Details

2. Network Traffic Demand

𝑆ℎ𝑢𝑓𝑓𝑙𝑒𝑑 𝐷𝑎𝑡𝑎 = 2 ∗ 𝑆𝑖𝑧𝑒𝑚𝑒𝑚𝑐𝑎𝑐ℎ𝑒𝑑

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HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU

System Details

2. Network Traffic Demand

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- Double amount of data shuffled (!)

- A compression algorithm is used on map outputs to

lessen network traffic

HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU

System Overview

• Map side

! Hashing function?

HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU

System Details

3. Fault Tolerance

• Map task failure

- rerun outputs not yet in memcache/disk

• Reduce task failure (!)

- For inputs that are not yet deleted from memcache, copy and execute

- For inputs that are already deleted from memcache, rerun the map task

• Memcached Server failure (!)

- Reinitialize all related map tasks

• Tasktracker failure

- All currently running map tasks and reduce tasks needs to be reinitialized

- Memcache data is still valid, so reduce tasks can still access them

HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU

System Details

3. Fault Tolerance

Reduce task failure

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HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU

System Details

3. Fault Tolerance

Memcached Server Failure

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HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU

Experimental Results and Analysis

Environment

• Hardware

- Intel Pentium 4, 2.8GHz processor

- 2GB RAM

- 80GB 7200RPM SATA disk

• Software

- RedHat AS4.4, kernel 2.6.9 OS

- Hadoop 0.19.1

- Memcached 1.2.8

- Memcached client for Java 2.5.1

HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU

Experimental Results and Analysis

Hadoop+Memcached Setup

1 node

NameNode +

JobTracker +

Memcached Server (1GB RAM)

1~6 nodes

DataNode + TaskTracker

• 2 map slots + 2 reduce slots per

TaskTracker

• 4MB HDFS file block

• 5 shuffle threads in reduce tasks

HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU

Experimental Results and Analysis

Benchmark Applications

• Wordcount

- 491.4 MB English text

• Spatial Join Algorithm

- 2 data sets from TIGER/Line files

HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU

Experimental Results and Analysis

1. Impact of different node numbers

• No. of reduce tasks: 2*n

• Wordcount improvement: 43.1%

• Spatial Join improvement: 32.9%

HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU

Experimental Results and Analysis

2. Impact on job progress

*Note: Hadoop job progress calculation

- For Map tasks: % of input processed

- For Reduce tasks:

1/3 (copy) + 1/3 (sort) + 1/3 (actual processing)

HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU

Experimental Results and Analysis

2. Impact on job progress - WordCount

!

HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU

Experimental Results and Analysis

2. Impact on job progress – Spatial Join

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HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU

Experimental Results and Analysis

2. Impact on job progress - Extra

33%

66%

sort

copy

reduce

HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU

Conclusion and Future Works

• Enhanced Hadoop to accelerate data

shuffling by using distributed memory

cache (memcached)

• Prototype performs much better than

original Hadoop under moderate load.

• Will modify task scheduling algorithm

(earlier reduce tasks)

HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU

Future Studies for Topic

• Dache: A Data Aware Caching for Big Data

Applications Using The MapReduce Framework,

2013 IEEE INFOCOM

• A Distributed Cache for Hadoop Distributed File

System in Real-Time Cloud Services,

2012 ACM/IEEE GRID

HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU

Discussion: Our Chances

1. Necessity of using Memcached?

HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU

Discussion: Our Chances

1. Necessity of using Memcached?

Properties for map task buffer:

io.sort.mb: buffer size

io.sort.spill.percent: spill-to-disk threshold

Hypothesis:

• Achieve map intermediate output local cache

• Modify reduce shuffle threads + TaskTracker

• RDD for Fault Tolerance?

HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU

Discussion: Our Chances

2. Moving the idea to YARN

HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU

Discussion: Our Chances

2. Moving the idea to YARN

NodeManager

NodeManager NodeManager

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Application

Manager

MAP

Task

REDUCE

Task

“Shuffle and Sort”

NodeManager

Auxiliary Service

HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU

Discussion: Our Chances

2. Moving the idea to YARN

yarn-site.xml

• Entire shuffle and sort phase is implemented as a

pluggable aux. service in YARN

HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU

Discussion: Our Chances

3. Iterative applications

NodeManager

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Application

Manager

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Application

Manager

“Result caching + reuse”

NodeManager

Auxiliary Service

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