Varnish Summit 2016

2. Massive
Storage Engine
2.0Evenmassiver!

3. Scaling issues with file and
malloc backends
● Built for web sites with gigabytes of
content
● Memory based backend is limited in size
● Allocation is unreliable under high
pressure
● File based has performance and
fragmentation issues

4. The problem with mmaps
1. Varnish has a cache miss
2. CPU writes to a page not in physical
memory
3. CPU creates a Page Fault
4. OS reads underlying page
5. Page gets overwritten (dumb)

5. Feature set
● Built for up to 100+ terabytes of storage
on each node.
● Fragmentation proof allocation algorithm
● Higher cache hit rates due to LRU
replaced with LFU
● Optionally persistent datastore

6. Architecture
● Threads lock allocated memory for
reliable allocations
● Multiple active segments for reduced lock
contention - round robin access
● “Hole expansion” to eliminate
fragmentation
● External persistence storage for high
performance

7. LRU Eviction
Least valuable object Most valuable object

8. Kill zone
The LFU approach to evictionMostvaluable
Leastvaluable

9. Free space
Kill candidates
Hole expansion

10. Adding persistence
● Feature requested much inVarnish Cache
1.0
● Feature request disappeared
● Came back with CDN andVOD
workloads

11. Persistence implementation:
“The Book”
● Mirror metadata structures to disk
● Kept in memory if persistence is disabled
● 2.5 copies on disk
● Journal is checksummed
● ~No blocking operations in critical path
(deletions can block)
● Delivery is unchanged

12. File layout
Store
Book (for persistence)
Segment
Header

13. Book layout
● A/B indices + working copy
● Journal
● Statistics (# of objects, free space)
● Ban journal (per book, not per segment)

14. Using persistence
● Storage initiated by mkfs.mse
● Book and Store can be on separate volumes
● Both need to be sized according to use -
store for size and book for # of objects
● Book size is 1-2% of store
● Bans and purges are atomic/consistent - the
rest isn’t

15. Results and characteristics
● Deployed on two public CDNs and a
number of private CDNs
● Performance is stable over time
● Handles larger files in the dataset well
● Persistence ads little overhead
● Crash recovery: ~reading the book
● Cache misses during book reading

16. Applications
● Video distribution
● CDN Workload
● Large caches (image banks, etc)

17. Thank you!Thank you!

18. Thank you!