Odnoklassniki uses cassandra for its business data, which doesn't fit into RAM. This data is typically fast growing, frequently accessed by our users and must be always available, because it constitute our primary business as a social network. The way we use cassandra is somewhat unusual - we don't use thrift or netty based native protocol to communicate with cassandra nodes remotely. Instead, we co-locate cassandra nodes in the same JVM with business service logic, exposing not generic data manipulation, but business level interface remotely. This way, we avoid extra network roundtrips within a single business transaction and use internal calls to Cassandra classes to get information faster. Also, this helps us to create many small hacks on Cassandra's internals, making huge gains on efficiency and ease of distributed server development.

1. Being Closer to Cassandra
Oleg Anastasyev
lead platform developer
Odnoklassniki.ru

2. Top 10 of World’s social networks
40M DAU, 80M MAU, 7M peak
~ 300 000 www req/sec,
20 ms render latency
>240 Gbit out
> 5 800 iron servers in 5 DCs
99.9% java
#CASSANDRAEU
* Odnoklassniki means “classmates” in english

3. Cassandra @
* Since 2010
- branched 0.6
- aiming at:
full operation on DC failure, scalability, ease of
operations
* Now
- 23 clusters
- 418 nodes in total
- 240 TB of stored data
- survived several DC failures
#CASSANDRAEU

4. Case #1. The fast
#CASSANDRAEU

5. Like! 103 927
#CASSANDRAEU
You and 103 927

6. Like! widget
* Its everywhere
- Have it on every page, dozen
- On feeds (AKA timeline)
- 3rd party websites elsewhere on internet
* Its on everything
- Pictures and Albums
- Videos
- Posts and comments
- 3rd party shared URLs
#CASSANDRAEU
Like! 103 927

7. Like! widget
* High load
- 1 000 000 reads/sec, 3 000 writes/sec
Like! 103 927
Hard load profile
*
- Read most
- Long tail (40% of reads are random)
- Sensitive to latency variations
- 3TB total dataset (9TB with RF) and growing
- ~ 60 billion likes for ~6bi entities
#CASSANDRAEU

8. Classic solution
SQL table
RefId:long
RefType:byte
UserId:long
Created
9999999999
PICTURE(2)
11111111111
11:00
to render
You and 4256
SELECT TOP 1 WHERE RefId,RefType,UserId=?,?,?
= N >=1
(98% are NONE)
SELECT COUNT (*) WHERE RefId,RefType=?,?
= M>N
(80% are 0)
SELECT TOP N * RefId,RefType=? WHERE IsFriend(?,UserId)
#CASSANDRAEU
= N*140

9. Cassandra solution
LikeByRef (
refType byte,
refId bigint,
userId bigint,
LikeCount (
refType byte,
refId bigint,
likers counter,
PRIMARY KEY ( (RefType,RefId), UserId)
so, to render
PRIMARY KEY ( (RefType,RefId))
You and 4256
SELECT FROM LikeCount WHERE RefId,RefType=?,?
(80% are 0)
SELECT * FROM LikeByRef WHERE RefId,RefType,UserId=?,?,?
(98% are NONE)
#CASSANDRAEU
= N*20%

10. >11 M iops
* Quick workaround ?
LikeByRef (
refType byte,
refId bigint,
userId bigint,
PRIMARY KEY ( (RefType,RefId, UserId) )
SELECT TOP N * RefId,RefType=? WHERE IsFriend(?,UserId)
- Forces Order Pres Partitioner
(random not scales)
- Key range scans
- More network overhead
- Partitions count >10x, Dataset size > x2
#CASSANDRAEU

11. By column bloom filter
* What is does
- Includes pairs of (PartKey, ColumnKey) in
SSTable *-Filter.db
* The good
- Eliminated 98 % of reads
- Less false positives
* The bad
- They become too large
GC Promotion Failures
.. but fixable (CASSANDRA-2466)
#CASSANDRAEU

12. Are we there yet ?
1. COUNT()
application server
> 400
00
2. EXISTS
cassandra
- min 2 roundtrips per render (COUNT+RR)
- THRIFT is slow, esp having lot of connections
- EXISTS() is 200 Gbit/sec (140*8*1Mps*20%)
#CASSANDRAEU

13. Co-locate!
get() : LikeSummary
odnoklassniki-like
Remote Business Intf
Counters Cache
cassandra
Social Graph Cache
- one-nio remoting (faster than java nio)
- topology aware clients
#CASSANDRAEU

14. co-location wins
* Fast TOP N friend likers query
1. Take friends from graph cache
2. Check it with memory bloom filter
3. Read some until N friends found
* Custom caches
- Tuned for application
* Custom data merge logic
- ... so you can detect and resolve conflicts
#CASSANDRAEU

15. Listen for mutations
// Implement it
interface StoreApplyListener {
boolean preapply(String key,
ColumnFamily data);
}
// and register with CFS
store=Table.open(..)
.getColumnFamilyStore(..);
store.setListener(myListener);
* Register it
between commit logs replay and gossip
* RowMutation.apply()
extend original mutation
+ Replica, hints, ReadRepairs
#CASSANDRAEU

16. Like! optimized counters
* Counters cache
- Off heap (sun.misc.Unsafe)
- Compact (30M in 1G RAM)
- Read cached local node only
* Replicated cache state
-
#CASSANDRAEU
cold replica cache problem
making (NOP) mutations
less reads
long tail aware
LikeCount (
refType byte,
refId bigint,
ip inet,
counter int
PRIMARY KEY ( (RefType,RefId), ip)

17. Read latency variations
* CS read behavior
1. Choose 1 node for data and N for digest
2. Wait for data and digest
3. Compare and return (or RR)
* Nodes suddenly slowdown
- SEDA hiccup, commit log rotation, sudden IO
saturation, Network hiccup or partition, page
cache miss
* The bad
- You have spikes.
- You have to wait (and timeout)
#CASSANDRAEU

18. Read Latency leveling
* “Parallel” read handler
1. Ask all replicas for data in parallel
2. Wait for CL responses and return
* The good
- Minimal latency response
- Constant load when DC fails
* The (not so) bad
- “Additional” work and traffic
#CASSANDRAEU

19. More tiny tricks
* On SSD io
- Deadline IO elevator
- 64k -> 4k read request size
* HintLog
- Commit log for hints
- Wait for all hints on startup
* Selective compaction
- Compacts most read CFs more often
#CASSANDRAEU

20. Case #2. The fat
#CASSANDRAEU

21. * Messages in chats
- Last page is accessed on open
- long tail (80%) for rest
- 150 billion, 100 TB in storage
- Read most (120k reads/sec, 8k writes/sec)
#CASSANDRAEU

22. Messages have structure
Message (
chatId, msgId,
MessageCF (
chatId, msgId,
created, type,userIndex,deletedBy,...
text
)
data blob,
PRIMARY KEY ( chatId, msgId )
- All chat’s messages in single partition
- Single blob for message data
to reduce overhead
- The bad
Conflicting modifications can happen
(users, anti-spam, etc..)
#CASSANDRAEU

23. LW conflict resolution
get
get
(version:ts1, data:d1)
(version:ts1, data:d1)
write( ts1, data2 )
delete(version:ts1)
insert(version: ts2=now(), data2)
Messages (
chatId, msgId,
version timestamp,
data blob
PRIMARY KEY ( chatId, msgId, version )
write( ts1, data3 )
delete(version:ts1)
insert(version: ts3=now(), data3)
(ts2, data2)
(ts3, data3)
#CASSANDRAEU
- merged on read

24. Specialized cache
* Again. Because we can
- Off-heap (Unsafe)
- Caches only freshest chat page
- Saves its state to local (AKA system) CF
keys AND values
seq read, much faster startup
- In memory compression
2x more memory almost free
#CASSANDRAEU

25. Disk mgmt
* 4U HDDx24, up to 4TB/node
- Size tiered compaction = 4 TB sstable file
- RAID10 ? LCS ?
* Split CF to 256 pieces
* The good
- Smaller, more frequent memtable flushes
- Same compaction work
in smaller sets
- Can distribute across disks
#CASSANDRAEU

26. Disk Allocation Policies
* Default is
- “Take disk with most free space”
* Some disks have
- Too much read iops
* Generational policy
- Each disk has same # of same gen files
work better for HDD
#CASSANDRAEU

27. Case #3. The ugly
feed my Frankenstein
#CASSANDRAEU

28. * Chats overview
- small dataset (230GB)
- has hot set, short tail (5%)
- list reorders often
- 130k read/s, 21k write/s
#CASSANDRAEU

29. Conflicting updates
* List<Overview> is single blob
.. or you’ll have a lot of tombstones
* Lot of conflicts
updates of single column
* Need conflict detection
* Has merge algoritm
#CASSANDRAEU

30. Vector clocks
* Voldemort
- byte[] key -> byte[] value + VC
- Coordination logic on clients
- Pluggable storage engines
* Plugged
- CS 0.6 SSTables persistance
- Fronted by specialized cache
we love caches
#CASSANDRAEU

31. Performance
* 3 node cluster, RF = 3
- Intel Xeon CPU E5506 2.13GHz
RAM: 48Gb, 1x HDD, 1x SSD
* 8 byte key -> 1 KB byte value
* Results
- 75 k /sec reads, 15 k/ sec writes
#CASSANDRAEU

32. Why cassandra ?
* Reusable distributed DB components
fast persistance, gossip,
Reliable Async Messaging, Fail detectors,
Topology, Seq scans, ...
* Has structure
beyond byte[] key -> byte[] value
* Delivered promises
* Implemented in Java
#CASSANDRAEU

33. THANK YOU
Oleg Anastasyev
oa@odnoklassniki.ru
odnoklassniki.ru/oa
@m0nstermind
github.com/odnoklassniki
shared-memory-cache
java Off-Heap cache using shared
memory
#CASSANDRAEU
one-nio
rmi faster than java nio with fast and
compact automagic java serialization
CASSANDRASUMMITEU