Comparison between two distributed file systems: HDFS and Tachyon.

1. HDFS v TachyonKevin Wong

2. 110x faster 2x faster

3. AMPLab’s Machines
r3.8xlarge
244GB 32 cores

4. My Machines
m1.large
7.5GB 2 cores

5. Word Count on 4-Node ClusterInputSize(GB)
1
3
5
10
30
50
100
300
500
Throughput (GB/sec)
0 0.008 0.015 0.023 0.03
HDFS Tachyon
Broke!
Broke!

6. Word Count on 10-Node ClusterInputSize(GB)
1
3
5
10
30
50
100
300
Throughput (GB/sec)
0 0.01 0.02 0.03 0.04
HDFS Tachyon

7. Word Count on 20-Node ClusterInputSize(GB)
1
3
5
10
30
50
100
300
Throughput (GB/sec)
0 0.015 0.03 0.045 0.06
HDFS Tachyon

8. Word Count on 25-Node ClusterInputSize(GB)
1
3
5
10
30
50
100
300
Throughput (GB/sec)
0 0.018 0.035 0.053 0.07
HDFS Tachyon

9. SQL Join on 25-Node ClusterInputSize(GB)
1
3
5
10
30
50
100
300
Throughput (GB/sec)
0 0.125 0.25 0.375 0.5
HDFS Tachyon

10. Challenges
• Could not use MapReduce, so had to use Spark
• Scala and python
• Files don’t fit in memory

11. Read and Write
70 seconds
Tachyon HDFS
54 seconds

12. Configurations
--executor-memory 3G
--executor-memory 4G
--executor-memory 5G

13. 5x r3.8xlarge
244GB
Tachyon: 200GB
Spark: 20GB

14. Read and Write
22 seconds
Tachyon HDFS
2354 seconds
107x faster

15. No change
• Word Count
• SQL Join
• Reads

16. For moderate machines, don’t use Tachyon.
Use Tachyon on machines with large memory running write-heavy jobs.