flex cookbook

memcache@facebook

Marc Kwiatkowskimemcache tech leadQCon 2010

Monday, April 12, 2010

How big is facebook?


!"" million active users


400M Active

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400M Active

Objects▪ More than 60 million status updates posted each day

▪ 694/s

▪ More than 3 billion photos uploaded to the site each month

▪ 23/s

▪ More than 5 billion pieces of content (web links, news stories, blog posts, notes, photo albums, etc.) shared each week

▪ 8K/s

▪ Average user has 130 friends on the site

▪ 50 Billion friend graph edges

▪ Average user clicks the Like button on 9 pieces of content each month


- Infrastructure▪ Thousands of servers in several data centers in two regions

▪ Web servers

▪ DB servers

▪ Memcache Servers

▪ Other services


The scale of memcache @ facebook▪ Memcache Ops/s

▪ over 400M gets/sec

▪ over 28M sets/sec

▪ over 2T cached items

▪ over 200Tbytes

▪ Network IO

▪ peak rx 530Mpkts/s 60GB/s

▪ peak tx 500Mpkts/s 120GB/s


A typical memcache server’s P.O.V.▪ Network I/O

▪ rx 90Kpkts/s 9.7MB/s

▪ tx 94Kpkts/s 19MB/s

▪ Memcache OPS

▪ 80K gets/s

▪ 2K sets/s

▪ 200M items


All rates are 1 day moving averages

Evolution of facebook’s architecture



•When Mark Zuckerberg and his roommates started Facebook in a Harvard dorm in 2004, the put everyone on one server

•Then as Facebook grew, they could scale like a traditional site by just adding servers

•Even as the site grew beyond Harvard to Stanford, Columbia and thousands of other campuses, each was a separated network that could be served on an isolated set of servers

•But as people connected more between schools connected, the model changed--and the big change came when Facebook opened to everyone in Sept. 2006

•[For globe]: That led to people being connected everywhere around the world--not just on a single college campus.

•[For globe]: This visualization shows accepted friend requests animating from requesting friend to accepting friend

Scaling Facebook: Interconnected data

Bob


•On Facebook, the data required to serve your home page or any other page s incredibly interconnected

•Your data can’t sit on one server or cluster of servers because almost every piece of content on Facebook requires information about your network of friends

•And the average user has 130 friends

•As we scale, we have to be able to quickly pull data across all of our servers, wherever it’s stored.


Bob Brian







Bob BrianFelicia






Memcache Rules of the Game▪ GET object from memcache

▪ on miss, query database and SET object to memcache

▪ Update database row and DELETE object in memcache

▪ No derived objects in memcache

▪ Every memcache object maps to persisted data in database


Scaling memcache


Phatty Phatty Multiget


Phatty Phatty Multiget (notes)▪ PHP runtime is single threaded and synchronous

▪ To get good performance for data-parallel operations like retrieving info for all friends, it’s necessary to dispatch memcache get requests in parallel

▪ Initially we just used polling I/O in PHP.

▪ Later we switched to true asynchronous I/O in a PHP C extension

▪ In both case the result was reduced latency through parallelism.


Pools and Threads

PHP Client


PHP Client

sp:12345

sp:12346 sp:12347 cs:12345cs:12346 cs:12347


Different objects have different sizes and access patterns. We began creating memcache pools to segregate different kinds of objects for better cache efficiency and memory utilization.

PHP Client

sp:12345 sp:12346 sp:12347 cs:12345 cs:12346 cs:12347



PHP Client



Pools and Threads (notes)▪ Privacy objects are small but have poor hit rates

▪ User-profiles are large but have good hit rates

▪ We achieve better overall caching by segregating different classes of objects into different pools of memcache servers

▪ Memcache was originally a classic single-threaded unix daemon

▪ This meant we needed to run 4 instances with 1/4 the RAM on each memcache server

▪ 4X the number of connections to each both

▪ 4X the meta-data overhead

▪ We needed a multi-threaded service


Connections and Congestion▪ [animation]


Connections and Congestion (notes)▪ As we added web-servers the connections to each memcache box

grew.

▪ Each webserver ran 50-100 PHP processes

▪ Each memcache box has 100K+ TCP connections

▪ UDP could reduce the number of connections

▪ As we added users and features, the number of keys per-multiget increased

▪ Popular people and groups

▪ Platform and FBML

▪ We began to see incast congestion on our ToR switches.


Serialization and Compression▪ We noticed our short profiles weren’t so short

▪ 1K PHP serialized object

▪ fb-serialization

▪ based on thrift wire format

▪ 3X faster

▪ 30% smaller

▪ gzcompress serialized strings


Multiple Datacenters

SC Memcache

SC Web

SC MySQL



SF Web

SF Memcache

SC Memcache

SC Web

SC MySQL



SF Web

SF Memcache

SC Memcache

SC Web

SC MySQL

Memcache ProxyMemcache Proxy


▪Multiple Datacenters (notes)▪ In the early days we had two data-centers

▪ The one we were about to turn off

▪ The one we were about to turn on

▪ Eventually we outgrew a single data-center

▪ Still only one master database tier

▪ Rules of the game require that after an update we need to broadcast deletes to all tiers

▪ The mcproxy era begins


Multiple Regions

SC Memcache

SC Web

SC MySQL

East Coast

VA MySQL

VA Web

VA Memcache

Memcache Proxy

West Coast


Multiple Regions

SF Web

SF Memcache

SC Memcache

SC Web

SC MySQL


East Coast

VA MySQL

VA Web

VA Memcache

Memcache Proxy

West Coast


Multiple Regions

SF Web

SF Memcache

SC Memcache

SC Web

SC MySQL


MySql replication

East Coast

VA MySQL

VA Web

VA Memcache

Memcache Proxy

West Coast


▪ Multiple Regions (notes)

▪ Latency to east coast and European users was/is terrible.

▪ So we deployed a slave DB tier in Ashburn VA

▪ Slave DB tracks syncs with master via MySQL binlog

▪ This introduces a race condition

▪ mcproxy to the rescue again

▪ Add memcache delete pramga to MySQL update and insert ops

▪ Added thread to slave mysqld to dispatch deletes in east coast via mcpro


Replicated Keys

PHP Client PHP ClientPHP Client

Memcache MemcacheMemcache


key

Replicated Keys




key

Replicated Keys

key key key


Memcache MemcacheMemcacheMemcache


key

Replicated Keys

key key key




key

Replicated Keys


MemcacheMemcache Memcache


key

Replicated Keys

key#" key#& key#'


MemcacheMemcache Memcache


Replicated Keys (notes)▪ Viral groups and applications cause hot keys

▪ More gets than a single memcache server can process

▪ (Remember the rules of the game!)

▪ That means more queries than a single DB server can process

▪ That means that group or application is effectively down

▪ Creating key aliases allows us to add server capacity.

▪ Hot keys are published to all web-servers

▪ Each web-server picks an alias for gets

▪ get key:xxx => get key:xxx#N

▪ Each web-server deletes all aliases


Memcache Rules of the Game▪ New Rule

▪ If a key is hot, pick an alias and fetch that for reads

▪ Delete all aliases on updates


Mirrored Pools

General pool with wide fanout

Shard & Shard #

Specialized Replica !

Shard & Shard #

Shard & Shard # Shard ' Shard n

Specialized Replica "

...


Mirrored Pools (notes)▪ As our memcache tier grows the ratio of keys/packet decreases

▪ 100 keys/1 server = 1 packet

▪ 100 keys/100 server = 100 packets

▪ More network traffic

▪ More memcache server kernel interrupts per request

▪ Confirmed Info - critical account meta-data

▪ Have you confirmed your account?

▪ Are you a minor?

▪ Pulled from large user-profile objects

▪ Since we just need a few bytes of data for many users


Hot Misses▪ [animation]


Hot Misses (notes)▪ Remember the rules of the game

▪ update and delete

▪ miss, query, and set

▪ When the object is very, very popular, that query rate can kill a database server

▪ We need flow control!


Memcache Rules of the Game▪ For hot keys, on miss grab a mutex before issuing db query

▪ memcache-add a per-object mutex

▪ key:xxx => key:xxx#mutex

▪ If add succeeds do the query

▪ If add fails (because mutex already exists) back-off and try again

▪ After set delete mutex


Hot Deletes▪ [hot groups graphics]


Hot Deletes (notes) ▪ We’re not out of the woods yet

▪ Cache mutex doesn’t work for frequently updated objects

▪ like membership lists and walls for viral groups and applications.

▪ Each process that acquires a mutex finds that the object has been deleted again

▪ ...and again

▪ ...and again


Rules of the Game: Caching Intent▪ Each memcache server is in the perfect position to detect and

mitigate contention

▪ Record misses

▪ Record deletes

▪ Serve stale data

▪ Serve lease-ids

▪ Don’t allow updates without a valid lease id


Next Steps


Shaping Memcache Traffic▪ mcproxy as router

▪ admission control

▪ tunneling inter-datacenter traffic


Cache Hierarchies▪ Warming up Cold Clusters

▪ Proxies for Cacheless Clusters


Big Low Latency Clusters▪ Bigger Clusters are Better

▪ Low Latency is Better

▪ L2.5

▪ UDP

▪ Proxy Facebook Architecture


Worse IS better▪ Richard Gabriel’s famous essay contrasted

▪ ITS and Unix

▪ LISP and C

▪ MIT and New Jersey


http://www.jwz.org/doc/worse-is-better.html

Why Memcache Works▪ Uniform, low latency with partial results is a better user

experience

▪ memcache provides a few robust primitives

▪ key-to-server mapping

▪ parallel I/O

▪ flow-control

▪ traffic shaping

▪ that allow ad hoc solutions to a wide range of scaling issues


We started with simple, obvious improvements.As we grew we deployed less obvious improvements...But they’ve remained pretty simple

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