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© 2013 A. Haeberlen NETS 212: Scalable and Cloud Computing 1 University of Pennsylvania Storage at Facebook December 3, 2013
© 2013 A. Haeberlen Announcements Second midterm on Tuesday next week Comparable to first midterm (open-book, closed-Google,...) Covers all material up to, and including, December 5 lecture Practice questions at the end of Thursday's lecture Project: Any questions? You should have a first running prototype by the end of this week Don't try to finish this at the last minute! Please test early and often! If you're 'stuck' on something or would like feedback on something, please see me (or one of the TAs) during office hours!! 2 University of Pennsylvania
© 2013 A. Haeberlen Reminder: Facebook award The team with the best PennBook application will receive an award (sponsored by ) Criteria: Architecture/design, supported features, stability, performance, security, scalability, deployment Winning teams get Facebook backpacks and/or T-shirts Winners will be announced on the course web page 3 University of Pennsylvania
© 2013 A. Haeberlen Menu for today Two recent research papers from Facebook They're not as secretive as some other companies Lots of papers with reasonable details about their systems: https://www.facebook.com/publications Paper #1: A scalable storage system + cache for the social graph (improves on Memcache) True graph data model - not a KV store like Memcache Impressive performance: 1B reads/sec, 1M writes/sec! Paper #2: A special storage system for their images (where caching doesn't help as much) Reason: "Long tail" of requests that can't easily be cached 4 University of Pennsylvania
© 2013 A. Haeberlen What you should look out for Not so much the exact details of how it works Highly technical, and somewhat problem-specific Rather, what principles they used! How did they make it scale? Why did they make the design decisions the way they did? What kinds of problems did they face? Etc. Also, I am hoping to give you a sense of how things "really work" today (as far as we know) 5 University of Pennsylvania
© 2013 A. Haeberlen TAO: Facebook's Distributed Data Store for the Social Graph Nathan Bronson, Zach Amsden, George Cabrera, Prasad Chakka, Peter Dimov, Hui Ding, Jack Ferris, Anthony Giardullo, Sachin Kulkarni, Harry Li, Mark Marchukov, Dimitri Petrov, Lovro Puzar, Yee Jiun Song, Venkat Venkataramani USENIX Annual Technical Conference University of Pennsylvania
© 2013 A. Haeberlen Motivation From David's guest lecture: Social graph stored in mySQL databases Memcache used as a (scalable) lookaside cache This is great - but can we do even better? Some challenges with this design: Inefficient edge lists: Key-value cache is not a good fit for the edge lists in a graph; need to always fetch entire list Distributed control logic: Cache control logic is run on clients that don't communicate with each other More failure modes; difficult to avoid "thundering herds" ( leases) Expensive read-after-write consistency: In original design, writes always have to go to the 'master' Can we write to caches directly, without inter-regional communication? 7 University of Pennsylvania
© 2013 A. Haeberlen Goals for TAO Provide a data store with a graph abstraction (vertexes and edges), not keys+values Optimize heavily for reads Remember what David said? More than 2 orders of magnitude more reads than writes! Explicitly favor efficiency and availability over consistency Slightly stale data is often okay (for Facebook) Communication between data centers in different regions is expensive 8 University of Pennsylvania
© 2013 A. Haeberlen Thinking about related objects We can represent related objects as a labeled, directed graph Entities are typically represented as nodes; relationships are typically edges Nodes all have IDs, and possibly other properties Edges typically have values, possibly IDs and other properties 9 fan-of friend-of fan-of Alice Sunita Jose Mikhail Magna Carta Facebook Images by Jojo Mendoza, Creative Commons licensed Remember this slide?
© 2013 A. Haeberlen TAO's data model Facebook's data model is exactly like that! Focuses on people, actions, and relationships These are represented as vertexes and edges in a graph Example: Alice visits a landmark with Bob Alice 'checks in' with her mobile phone Alice 'tags' Bob to indicate that he is with her Cathy added a comment David 'liked' the comment 10 University of Pennsylvania vertexes and edges in the graph
© 2013 A. Haeberlen TAO's data model and API TAO "objects" (vertexes) 64-bit integer ID (id) Object type (otype) Data, in the form of key-value pairs Object API: allocate, retrieve, update, delete TAO "associations" (edges) Source object ID (id1) Association type (atype) Destination object ID (id2) 32-bit timestamp Data, in the form of key-value pairs Association API: add, delete, change type Associations are unidirectional But edges often come in pairs (each edge type has an 'inverse type' for the reverse edge) 11 University of Pennsylvania
© 2013 A. Haeberlen Example: Encoding in TAO 12 University of Pennsylvania Data (KV pairs) Inverse edge types
© 2013 A. Haeberlen Association queries in TAO TAO is not a general graph database Has a few specific (Facebook-relevant) queries 'baked into it' Common query: Given object and association type, return an association list (all the outgoing edges of that type) Example: Find all the comments for a given checkin Optimized based on knowledge of Facebook's workload Example: Most queries focus on the newest items (posts, etc.) There is creation-time locality can optimize for that! Queries on association lists: assoc_get(id1, atype, id2set, t_low, t_high) assoc_count(id1, atype) assoc_range(id1, atype, pos, limit) "cursor" assoc_time_range(id1, atype, high, low, limit) 13 University of Pennsylvania
© 2013 A. Haeberlen TAO's storage layer Objects and associations are stored in mySQL But what about scalability? Facebook's graph is far too large for any single mySQL DB!! Solution: Data is divided into logical shards Each object ID contains a shard ID Associations are stored in the shard of their source object Shards are small enough to fit into a single mySQL instance! A common trick for achieving scalability What is the 'price to pay' for sharding? 14 University of Pennsylvania
© 2013 A. Haeberlen Caching in TAO (1/2) Problem: Hitting mySQL is very expensive But most of the requests are read requests anyway! Let's try to serve these from a cache TAO's cache is organized into tiers A tier consists of multiple cache servers (number can vary) Sharding is used again here each server in a tier is responsible for a certain subset of the objects+associations Together, the servers in a tier can serve any request! Clients directly talk to the appropriate cache server Avoids bottlenecks! In-memory cache for objects, associations, and association counts (!) 15 University of Pennsylvania
© 2013 A. Haeberlen Caching in TAO (2/2) How does the cache work? New entries filled on demand When cache is full, least recently used (LRU) object is evicted Cache is "smart": If it knows that an object had zero associ- ations of some type, it knows how to answer a range query Could this have been done in Memcached? If so, how? If not, why not? What about write requests? Need to go to the database (write-through) But what if we're writing a bidirectonal edge? This may be stored in a different shard need to contact that shard! What if a failure happens while we're writing such an edge? You might think that there are transactions and atomicity but in fact, they simply leave the 'hanging edges' in place (why?) Asynchronous repair job takes care of them eventually 16 University of Pennsylvania
© 2013 A. Haeberlen Leaders and followers How many machines should be in a tier? Too many is problematic: More prone to hot spots, etc. Solution: Add another level of hierarchy Each shard can have multiple cache tiers: one leader, and multiple followers The leader talks directly to the mySQL database Followers talk to the leader Clients can only interact with followers Leader can protect the database from 'thundering herds' 17 University of Pennsylvania
© 2013 A. Haeberlen Leaders/followers and consistency What happens now when a client writes? Follower sends write to the leader, who forwards to the DB Does this ensure consistency? Need to tell the other followers about it! Write to an object Leader tells followers to invalidate any cached copies they might have of that object Write to an association Don't want to invalidate. Why? Followers might have to throw away long association lists! Solution: Leader sends a 'refill message' to followers If follower had cached that association, it asks the leader for an update What kind of consistency does this provide? 18 University of Pennsylvania No!
© 2013 A. Haeberlen Scaling geographically Facebook is a global service. Does this work? No - laws of physics are in the way! Long propagation delays, e.g., between Asia and U.S. What tricks do we know that could help with this? 19 University of Pennsylvania
© 2013 A. Haeberlen Scaling geographically Idea: Divide data centers into regions; have one full replica of the data in each region What could be a problem with this approach? Again, consistency! Solution: One region has the 'master' database; other regions forward their writes to the master Database replication makes sure that the 'slave' databases eventually learn of all writes; plus invalidation messages, just like with the leaders and followers 20 University of Pennsylvania
© 2013 A. Haeberlen Handling failures What if the master database fails? Can promote another region's database to be the master But what about writes that were in progress during switch? What would be the 'database answer' to this? TAO's approach: Why is (or isn't) this okay in general / for Facebook? 21 University of Pennsylvania
© 2013 A. Haeberlen Consistency in more detail What is the overall level of consistency? During normal operation: Eventual consistency (why?) Refills and invalidations are delivered 'eventually' (typical delay is less than one second) Within a tier: Read-after-write (why?) When faults occur, consistency can degrade In some situations, clients can even observe values 'go back in time'! How bad is this (for Facebook specifically / in general)? Is eventual consistency always 'good enough'? No - there are a few operations on Facebook that need stronger consistency (which ones?) TAO reads can be marked 'critical' ; such reads are handled directly by the master. 22 University of Pennsylvania
© 2013 A. Haeberlen Fault handling in more detail General principle: Best-effort recovery Preserve availability and performance, not consistency! Database failures: Choose a new master Might happen during maintenance, after crashes, repl. lag Leader failures: Replacement leader Route around the faulty leader if possible (e.g., go to DB) Refill/invalidation failures: Queue messages If leader fails permanently, need to invalidate cache for the entire shard Follower failures: Failover to other followers The other followers jointly assume responsibility for handling the failed follower's requests 23 University of Pennsylvania
© 2013 A. Haeberlen Production deployment at Facebook Impressive performance Handles 1 billion reads/sec and 1 million writes/sec! Reads dominate massively Only 0.2% of requests involve a write Most edge queries have zero results 45% of assoc_count calls return 0... but there is a heavy tail: 1% return >500,000! (why?) Cache hit rate is very high Overall, 96.4%! 24 University of Pennsylvania
© 2013 A. Haeberlen Summary The data model really does matter! KV pairs are nice and generic, but you sometimes can get better performance by telling the storage system more about the kind of data you are storing in it ( optimizations!) Several useful scaling techniques "Sharding" of databases and cache tiers (not invented at Facebook, but put to great use) Primary-backup replication to scale geographically Interesting perspective on consistency On the one hand, quite a bit of complexity & hard work to do well in the common case (truly "best effort") But also, a willingness to accept eventual consistency (or worse!) during failures, or when the cost would be high 25 University of Pennsylvania
© 2013 A. Haeberlen Finding a needle in Haystack: Facebook's photo storage Doug Beaver, Sanjeev Kumar, Harry C. Li, Jason Sobel, Peter Vajgel OSDI University of Pennsylvania
© 2013 A. Haeberlen Motivation Facebook stores a huge number of images In 2010, over 260 billion (~20PB of data) One billion (~60TB) new uploads each week How to serve requests for these images? Typical approach: Use a CDN (and Facebook does do that) 27 University of Pennsylvania
© 2013 A. Haeberlen The problem When would the CDN approach work well? If most requests were for a small # of images But is this the case for Facebook photos? Problem: "Long tail" of requests for old images! CDN can help, but can't serve everything! Facebook's system still needs to handle a lot of requests 28 University of Pennsylvania "Long tail"
© 2013 A. Haeberlen Facebook pre-2010 (1/2) Images were kept on NAS devices NAS = network-attached storage File system can be mounted on servers via NFS CDN can request images from the servers 29 University of Pennsylvania
© 2013 A. Haeberlen Facebook pre-2010 (2/2) Problem: Accesses to metadata OS needs to translate file name to inode number, read inode from disk, etc., before it can read the file itself Often more than 10 disk I/Os - and these are really slow! (Disk head needs to be moved, wait for rotating media,...) Hmmm... what happens once they have SSDs? Result: Disk I/Os for metadata were limiting their read throughput Could you have guessed that this was going to be the bottleneck? 30 University of Pennsylvania Directories, inodes, block maps,...
© 2013 A. Haeberlen What could be done? They considered various ways to fix this including kernel extensions etc. But in the end they decided to build a special-purpose storage system for images Goal: Massively reduce size of metadata When is this a good idea (compared to using an existing storage system)? Pros and cons - in general?... and for Facebook specifically? 31 University of Pennsylvania
© 2013 A. Haeberlen Haystack overview Three components: Directory Cache Store Physical volumes Several of these make up a logical volume Replication - why? When the user visits a page: Web server constructs a URL for each image volume, photo) 32 University of Pennsylvania
© 2013 A. Haeberlen Reading an image Read path: CDN tries to find the image first If not found, strips CDN part off and contacts the Cache If not found there either, Cache strips off the cache part and contacts the specified Store machine Store machine finds the volume, and the photo within the volume All the necessary information comes from the URL! 33 University of Pennsylvania volume, photo)
© 2013 A. Haeberlen Uploading an image User uploads image to a web server Web server requests a write-enabled volume Volumes become read-only when they are full, or for operational reasons Web server assigns unique ID and sends image to each of the physical volumes that belong to the chosen logical volume 34 University of Pennsylvania
© 2013 A. Haeberlen Haystack: The Directory Directory serves four functions: Maps logical to physical volumes Balances writes across logical volumes, and reads across physical volumes Determines whether request should be handed by the CDN or by the Cache Identifies the read-only volumes How does it do this? Information stored as usual: replicated database, Memcache to reduce latency 35 University of Pennsylvania
© 2013 A. Haeberlen Haystack: The Cache Organized as a distributed hashtable Remember the Pastry lecture? Caches images ONLY if: 1) the request didn't come from the CDN, and 2) the request came from a write-enabled volume Why?!? Post-CDN caching not very effective (hence #1) Photos tend to be most heavily accessed soon after they uploaded (hence #2)... and file systems tend to perform best when they're either reading or writing (but not both at the same time!) 36 University of Pennsylvania
© 2013 A. Haeberlen Haystack: The Store (1/2) Volumes are simply very large files (~100GB) Few of them needed In-memory data structures small Structure of each file: A header, followed by a number of 'needles' (images) Cookies included to prevent guessing attacks Writes simply append to the file; deletes simply set a flag 37 University of Pennsylvania
© 2013 A. Haeberlen Haystack: The Store (2/2) Store machines have an in-memory index Maps photo IDs to offsets in the large files What to do when the machine is rebooted? Option #1: Rebuild from reading the files front-to-back Is this a good idea? Option #2: Periodically write the index to disk What if the index on disk is stale? File remembers where the last needle was appended Server can start reading from there Might still have missed some deletions - but the server can 'lazily' update that when someone requests the deleted img 38 University of Pennsylvania
© 2013 A. Haeberlen Recovery from failures Lots of failures to worry about Faulty hard disks, defective controllers, bad motherboards... Pitchfork service scans for faulty machines Periodically tests connection to each machine Tries to read some data, etc. If any of this fails, logical (!) volumes are marked read-only Admins need to look into, and fix, the underlying cause Bulk sync service can restore the full state... by copying it from another replica Rarely needed 39 University of Pennsylvania
© 2013 A. Haeberlen How well does it work? How much metadata does it use? Only about 12 bytes per image (in memory) Comparison: XFS inode alone is 536 bytes! More performance data in the paper Cache hit rates: Approx. 80% 40 University of Pennsylvania
© 2013 A. Haeberlen Summary Different perspective from TAO's Presence of "long tail" caching won't help as much Interesting (and unexpected) bottleneck To get really good scalability, you need to understand your system at all levels! In theory, constants don't matter - but in practice, they do! Shrinking the metadata made a big difference to them, even though it is 'just' a 'constant factor' Don't (exclusively) think about systems in terms of big-O notations! 41 University of Pennsylvania
© 2013 A. Haeberlen Stay tuned Next time you will learn about: Special topics 42 University of Pennsylvania
© 2013 A. Haeberlen, Z. Ives NETS 212: Scalable and Cloud Computing 1 University of Pennsylvania Case studies September 24, 2013.
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