Review of Wormhole: Reliable Pub-Sub for Geo-replicated Internet Services
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Review of
Wormhole: Reliable Pub-Sub
to Support Geo-replicated
Internet Services
Presented by Michael Rausch
Summary
Paper presented Wormhole, Facebook’s reliable, high-throughput,
low-latency, geo-replicated pub-sub system
2
Pros and Cons
Pros
Pub-Sub system is failure-tolerant
Can be deployed on existing data
stores (MySQL, HDFS, RocksDB)
Manage tradeoff between IO
bandwidth and latency with caravans
Transparency: Pub uses standard
data format regardless of data store
Utilized at Facebook
o
35 GBytes/sec to 200 GBytes/sec
o
99.5% of updates < 100 ms
o
Used for 3 years
3
Cons
What is the research
contribution?
Not Open Source
No confidence intervals in
evaluation
At-least-once vs exactly-once
Subscriber cannot handle >
600k updates/sec ?
Discussion
Would it be better to compress all the updates that an application
missed and deliver all at once?
Could UDP be substituted for TCP?
If the local data store fails, then the remote publisher pushes
updates to the local subscriber. Compare this choice with the local
publisher receiving data from the remote data store?
What in the design limits this approach to Facebook?
4
Summary and discussion on Wormhole, a reliable pub-sub system for geo-replicated internet services presented by Michael Rausch. The paper outlines pros and cons, research contributions, deployment options, system limitations, and potential improvements such as compressing missed updates and using UDP. Comparison between local and remote update handling methods is explored, with a focus on practical implications for Facebook's usage.
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Presentation Transcript
Review of Wormhole: Reliable Pub-Sub to Support Geo-replicated Internet Services Presented by Michael Rausch
Summary Paper presented Wormhole, Facebook s reliable, high-throughput, low-latency, geo-replicated pub-sub system 2
Pros and Cons Pros Cons Pub-Sub system is failure-tolerant What is the research contribution? Can be deployed on existing data stores (MySQL, HDFS, RocksDB) Not Open Source Manage tradeoff between IO bandwidth and latency with caravans No confidence intervals in evaluation Transparency: Pub uses standard data format regardless of data store At-least-once vs exactly-once Subscriber cannot handle > 600k updates/sec ? Utilized at Facebook o35 GBytes/sec to 200 GBytes/sec o99.5% of updates < 100 ms oUsed for 3 years 3
Discussion Would it be better to compress all the updates that an application missed and deliver all at once? Could UDP be substituted for TCP? If the local data store fails, then the remote publisher pushes updates to the local subscriber. Compare this choice with the local publisher receiving data from the remote data store? What in the design limits this approach to Facebook? 4
