Block Sampling: Efficient Accurate Online

Aggregation in MapReduce

5th IEEE International Conference on Cloud Computing Technology and Science (CloudCom 2013)

Vasiliki Kalavri, Vaidas Brundza, Vladimir Vlassov{kalavri, vaidas, vladv}@kth.se

3 December 2013, Bristol, UK

Problem and Motivation

Luckily, in many cases results can be useful even before job completion

○ tolerate some inaccuracy○ benefit from faster answers

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Big data processing is usually very time-consuming...

… but many applications require results really fast or can only use results for a limited window of time

MapReduce vs. MapReduce Online

mapper

reducer

Local Disk

Input Record map

function

Output Record

HTTP request

In original MR, a reducer task cannot fetch the output of a map task which hasn't committed its output to disk

mapper

reducer

Input Record map

function

Output Record

TCP- push/pull

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Online Aggregation

● Apply the reduce function to the data seen so far● % input processed to estimate accuracy

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Sampling Challenges

● Data in HDFS○ Disk already access is terribly slow○ Random disk access for sampling is even slower

● Unstructured Data○ Sample based on what?

○ We don’t know the query, we don’t know the

key or the value!

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The Block Sampling Technique

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MapReduce Online vs. Block SamplingAverage Temperature Estimation on Weather Data

Unsorted Sorted

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Takeaway

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● Useful results even before job completion● Disk random access is prohibitively

expensive → efficiently emulate sampling using in-memory shuffling

● Higher sampling rate improves accuracy but also increases communication costs among mapper tasks

Block Sampling: Efficient Accurate Online

Aggregation in MapReduce

5th IEEE International Conference on Cloud Computing Technology and Science (CloudCom 2013)

Vasiliki Kalavri, Vaidas Brundza, Vladimir Vlassov{kalavri, vaidas, vladv}@kth.se

3 December 2013, Bristol, UK

Average Temperature Estimation on Sorted and Unsorted Weather Data

Unsorted Sorted

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How do the block sampling rate and the % of processed input affect accuracy?

Performance - Sampling Rate

Performance - Bias Reduction

snapshot freq = 10%

Experimental Setup

● 8 large-instance OpenStack VMs○ 4 vCPUs, 8 GB memory, 90 GB disk

● Linux Ubuntu 12.04.2 LTS OSm Java 1.7.0 14● up to 17 map tasks and 5 reduce tasks per job, HDFS

block size of 64MB

● weather station data from the National Climatic Data Center ftp server (available years 1901 to 2013)

● the complete Project Gutenberg e-books catalog (30615 e-books in .txt format)

System Configuration Parameters

Bias Reduction

● Access Phase: Store the entire input split in the reader task’s local memory

● Shuffling Phase: Shuffle the records of the block in-place

● Processing Phase: Serve a record to the mapper task from local memory (avoids additional disk I/O)

Future Work

● Integrate statistical estimators○ provide error bounds for users

● Automatically fine-tune sampling parameters based on system configuration

● Explore alternative sampling techniques and wavelet-approximation