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Finding a needle in Haystack Facebooks Photo Storage Shakthi Bachala.

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Presentation on theme: "Finding a needle in Haystack Facebooks Photo Storage Shakthi Bachala."— Presentation transcript:

1 Finding a needle in Haystack Facebooks Photo Storage Shakthi Bachala

2 Outline Scenario Goal Problem Previous Approach Current Approach Evaluation Advantages Critic Conclusion

3 Scenario : April 2009October 2011 Total15 billion Photos 4*15 billion images= 60 billion images 1.5 petabytes of data 65 billion Photos 4*65 billion images = 260 billion images 20 petabytes of data Upload Rate220 million photos / week 25 terabytes of data 1 billion photos / week 60 terabytes of data Serving Rate550,000 images / sec1 million images / sec

4 Goal High throughput and low latency Fault-tolerant Cost-effective Simple

5 Previous Approach : Typical design for Photo Sharing

6 Previous Approach : NFS based design for Photo Sharing at facebook

7 Previous Approach – NFS based design Traditional file system architecture performs poorly under Facebook's kind of workload NFS - based Design: CDN effectively serves the hottest photos (profile pictures and recently updated photos), but facebook also generates a lot of requests for less popular images (long tail images). These are not handled by CDN Normal website had 99% CDN hit rate but facebook had around 80% CDN hit rate

8 Long Tail Issue 8

9 Previous Approach cont.. Problems with that approach were: Wastage of storage capacity due to metadata – Large metadata per file – Each image stored as a file Large number of disk operations for reads – Because of large directories (large directories containing thousands of files) – Change of the directory structures and changing from large directories to small directories has brought down the iops approximately from 10 to

10 Current Approach – Haystack Architecture

11 Current Approach- Haystack Components The main components of Haystack architecture are: 1.Haystack Directory 2.Haystack Cache 3.Haystack Store

12 Current Approach- Haystack Directory The main goals of directory are: Map logical volumes to physical volumes – 3 Physicalvolumes( on 3 nodes) per one logical volume Load balance – Writes across logical volumes – Reads across physical volumes (any of the 3 stores) Caching strategy: Whether the photo request should be handled by the CDN or by the cache – URL generation / / / The directory would Identify the logical volumes that are read only either because of operational reason or because those volumes have reached their storage capacity

13 Current Approach- Haystack Cache Approach: – The Cache receives HTTP requests for photos from browser or CDNs – It is a distributed hash table with photo id as the key to locate the cached data – If the photo id is missing in cache, the cache fetches the data from photo server and replies it to the browser or CDN depending on the request

14 Current Approach- Haystack Cache Caches a photo if it satisfies the following two conditions: The request directly come from a user and instead of CDN – Facebooks experience with the NFS-based design showed post- CDN caching is ineffective as it is unlikely that a request misses in the CDN would hit in our internal cache The photos is fetched by the write enabled store – Photos are most heavily accessed soon after they are uploaded – File systems generally work better when doing either writes or reads but not both

15 Current Approach- Haystack Cache Hit Rate

16 Current Approach : Haystack Store Replaces the storage and photo server layer in NFS based Design with this structure:

17 Current Approach : Haystack Store Storage : – 12x 1TB SATA, RAID6 Filesytem: – Single approx. 10 TB xfs filesystem. Haystack: – Log structured, append only object store containing needles as object abstractions – 100 haystacks per node each 100GB in size

18 Current Approach: Haystack Store File

19 Current Approach: Operations in Haystack Photo Read – Look up offset /size of the image in the incore index – Read Data (approx. 1 iop) Photo Write – Asynchronously append images one by one to the haystack file – Next haystack file when becomes full – Asynchronously append index records to the index file – Flush index file if too many dirty index records – Update incore index

20 Current Approach: Operations in Haystack Photo Delete – Lookup offset of the image in the incore index – Mark the image needle flag as DELETED – Update incore index Index File: – Provides minimum metadata to locate the needle in the Haystack store – Subset of Header metadata

21 Current Approach: Haystack Index File

22 Haystack Based Design - Photo Upload

23 Haystack Based Design - Photo Download

24 Current Approach: Operations in Haystack Filesystem: – Haystack uses XFS, an extent based file system It has two main advantages: – The block maps for several contiguous large files can be small enough to be stored in the main memory – XFS provides efficient file pre allocation, mitigating fragmentation and reigning in how large block maps can grow

25 Current Approach: Haystack Optimization Compaction: – Infrequent online operation – Create a copy of haystack skipping duplicates and deleted photos – The patterns of deletes to photo views, young photos are a lot more likely to be deleted – Last year about 25% of the photos got deleted

26 Current Approach: Haystack Optimization Saving More Memory: – With the following two techniques store machines reduced their main memory footprints by 20% – Eliminate the need for an in-memory representation of flags by setting the offset to be 0 for deleted photos. – Store machine do not keep track of cookie values in main memory and instead check the supplied cookie after reading from the disk

27 Current Approach: Haystack Optimization Batch Uploads: – Disks perform better with large sequential writes instead of small random writes, so facebook uses batch uploads whenever possible – Many users upload entire albums to facebook instead of each picture which gives an opportunity to batch the uploads

28 Evaluation -Data

29 Evaluation – Production Workload

30 Advantages Simple design Decrease number of disk operations by reducing the average metadata per photo This system is robust enough to handle a very large amount of data Fault Tolerant

31 Critic I thought this approach is very facebook specific. Any other?

32 Conclusion Built a simple but robust data storage mechanism for facebook photo storage to accommodate long tail of photo requests which was not possible by previous approaches

33 References 1. 2. hed.htm hed.htm

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