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Reproducible distributed
experiments on cloud
vs
Shelan Perera
Ashansa Perera
Kamal Hakimzadeh
“
Reproducing experiments
with
minimal effort
Spark and Flink
▷ Batch Processing vs. Stream Processing
▷ Micro Batching vs. Natural Data Flow
▷ Good fit for scalable deployment in the
cloud
Motivation
▷ Validate Performance claims
▷ Take off deployment overhead
▷ Design reproducible experiments
Karamel =>
“Framework for
reproducible
distributed
experiments”
Benchmark - Batch
Teragen - To generate data
(Hadoop)
Terasort - Benchmarking Algorithm
(Spark, Flink)
To make
Dongwon Kim’s
comparison
reproducible.
http://www.slideshare.net/ssuser6bb12d/a-
comparative-performance-evaluation-of-apache-flink
1 Namenode ⇒ Master
(Low processing )
2 Worker nodes ⇒ Slaves
(High processing )
Our Deployment
EC2
Slave
EC2
Master
Deployment
Hadoop
Name
Node
Spark
Master
Flink Job
Manager
Spark
Worker
Flink Task
Manager
Hadoop
Data Node
Karamel
x 2
Karamel Config
Configuration
Master /
Namenode
2.6
4
16
80
Slave /
Worker
2.5
16
122
1600
CPU (GHz)
No of vCPUs
Memory (GB)
Storage :SSD (GB)
(m3.xlarge) (i2.4xlarge)
Experiment
Hadoop MR : Teragen
HDFS
Spark/Flink : Terasort
200/ 400/ 600 GB
Results
Batch Processing
Application Performance
Flink
1.5 x
Faster
than Spark
▷ Spark : Does not
overlap stages
▷ Flink : Do pipelining
Mainly because...
Collectl- Monitor
● Tool used to collect and
draw results.
● https://github.
com/shelan/collectl-
monitoring
System Performance -CPU (%)
System Performance -Memory (GB)
System Performance -Disk (MB/s)
System Performance -Network (KB/s)
Load Balancing -Workers (CPU %)
Load Balancing -Workers (CPU %)
Outcome
▷ Performance Comparison Results
▷ Karamel experiments to
reproduce the same results with
minimal effort
How
not to reproduce
“our problems”
EC2 claims 800 GB disks, But
Disk File system (DF) does shows
only 30GB.
If you are using I2 or R3 instances you
should create a file system and
partition disks manually.
Large Spark or Flink Batch applications
can fail with not enough disk space
Configure Flink temp directory and
Spark local directory to a partition with
at least enough space to store the total
input.
Reproducing experiments on EC2 may
cost you a lot
Spot instances which allow to reduce
the cost by 10x is also supported by
Karamel
IncompatibleClassChangeError when
running StreamBench built for MR2 on
hadoop2.x
No explicitly defined dependencies for
previous versions, but one of the
dependencies (mahout) had internal
references to hadoop1.x jar
Summary
▷ Introducing reproducible experiments on
cloud
▷ Performance Comparison of Spark and
Flink
▷ Reproducible experiments are available
online (https://github.com/karamel-lab)
Thanks ..!!

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