Finding a needle in Haystack Facebook’s Photo Storage Shakthi Bachala

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

Scenario : April 2009 October 2011 Total 15 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 Rate 220 million photos / week 25 terabytes of data 1 billion photos / week 60 terabytes of data Serving Rate 550,000 images / sec 1 million images / sec

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

Previous Approach : Typical design for Photo Sharing

Previous Approach : NFS based design for Photo Sharing at facebook

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

Long Tail Issue

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 2.5-3.0

Current Approach – Haystack Architecture

Current Approach- Haystack Components The main components of Haystack architecture are: Haystack Directory Haystack Cache Haystack Store

Current Approach- Haystack Directory The main goals of directory are: Map logical volumes to physical volumes 3 Physical volumes( 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 http://<CDN>/<Cache>/<Node>/<Logical volume id, Image id> 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

Current Approach- Haystack Cache 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

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 Facebook’s 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

Current Approach- Haystack Cache Hit Rate

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

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

Current Approach: Haystack Store File

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

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

Current Approach: Haystack Index File

Haystack Based Design - Photo Upload

Haystack Based Design - Photo Download

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

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

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

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

Evaluation -Data

Evaluation – Production Workload

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

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

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

References http://www.usenix.org/event/osdi10/tech/ http://www.cs.uiuc.edu/class/sp11/cs525/sched.htm