The document discusses the importance of compression, particularly focusing on lossless codecs and their implementation in systems like Hadoop for optimizing data transfer. It emphasizes the necessity of understanding data flows and carefully selecting compression codecs to enhance web delivery and overall system performance. The key takeaway is that effective compression usage is essential in modern data handling.

1. Compression
by Sergei Koren.

2. Compression as we used to know

3. Compression indeed

4. Compression classification
We will focus on lossless codecs

5. There is always trade off

6. There is always trade-off..
● …
● Is there?
It is all about the need and implementation

7. What is difference?

8. Hadoop M/R Data transfer stage

9. Terasoft results
hadoop jar /usr/lib/hadoop-0.20-mapreduce/hadoop-
examples.jar terasort -
Dmapred.compress.map.output=true -
Dmapred.reduce.slowstart.completed.maps=0.95 -
Dmapred.reduce.tasks=100 -
Dmapred.map.output.compression.codec=org.apach
e.hadoop.io.compress.GzipCodec teragen-input
terasort-output

10. Compression for web
Demo: using compression to optimize web delivery

11. Compression for web

12. References
From Salesforce
From Cisco

13. Wrap up
● Compression usage is must in modern systems
● Get to know your data and flows
● Choose codecs carefully. You may no penalty at all
to use them!