The document discusses Cassandra's storage internals. It describes how Cassandra writes data to memtables and commit logs in memory before flushing to immutable SSTables on disk. It also explains how compaction merges SSTables to reclaim space and improve performance. For reads, Cassandra uses memtables, bloom filters on SSTables, key caches, and row caches to minimize disk I/O. Counters are implemented by coordinating writes across replicas.

1. Cassandra
storage internals
Nicolas Favre-Felix
Cassandra Europe 2012

2. What this talk covers
• What happens within a Cassandra node
• How Cassandra reads and writes data
• What compaction is and why we need it
• How counters are stored, modified, and read

3. Concepts
• Memtables • On heap, off-heap
• SSTables • Compaction
• Commit Log • Bloom filters
• Key cache • SSTable index
• Row cache • Counters

4. Why is this important?
• Understand what goes on under the hood
• Understand the reasons for these choices
• Diagnose issues
• Tune Cassandra for performance
• Make your data model efficient

5. A word about hard drives

6. A word about hard drives
• Main driver behind Cassandra’s storage choices
• The last moving part
• Fast sequential I/O (150 MB/s)
• Slow random I/O (120-200 IOPS)

7. What SSDs bring
• Fast sequential I/O
• Fast random I/O
• Higher cost
• Limited lifetime
• Performance degradation

8. Disk usage with B-trees
• Important data structure in relational databases
• In-place overwrites (random I/O)
• LogB(N) random accesses for reads and writes

9. Disk usage with Cassandra
• Made for spinning disks
• Sequential writes, much less than 1 I/O per insert
• Several layers of cache
• Random reads, approximately 1 I/O per read
• Generally “write-optimised”

10. Writing
to Cassandra

11. Writing to Cassandra
Let’s add a row with a few columns
Row Key Column Column Column Column

12. The Cassandra write path
In the JVM
New data Memtable
Commit
On disk log

13. The Commit Log
• Each write is added to a log file
• Guarantees durability after a crash
• 1-second window during which data is still in RAM
• Sequential I/O
• A dedicated disk is recommended

14. Memtables
• In-memory Key/Value data structure
• Implemented with ConcurrentSkipListMap
• One per column family
• Very fast inserts
• Columns are merged in memory for the same key
• Flushed at a certain threshold, into an SSTable

15. Dumping a Memtable on disk
In the JVM Full Memtable
Commit
On disk log

16. Dumping a Memtable on disk
In the JVM New Memtable
Commit
On disk log
SSTable

17. The SSTable
• One file, written sequentially
• Columns are in order, grouped by row
• Immutable once written, no updates!

18. SSTables start piling up!
In the JVM Memtable
Commit log SSTable SSTable SSTable
On disk SSTable SSTable SSTable
SSTable SSTable SSTable
SSTable SSTable SSTable

19. SSTables
• Can’t keep all of them forever
• Need to reclaim disk space
• Reads could touch several SSTables
• Scans touch all of them
• In-memory data structures per SSTable

20. Compacting SSTables

21. Compaction
• Merge SSTables of similar size together
• Remove overwrites and deleted data (timestamps)
• Improve range query performance
• Major compaction creates a single SSTable
• I/O intensive operation

22. Recent improvements
• Pluggable compaction
• Different strategies, chosen per column family
• SSTable compression
• More efficient SSTable merges

23. Reading from Cassandra

24. Reading from Cassandra
• Reading all these SSTables would be very inefficient
• We have to read from memory as much as possible
• Otherwise we need to do 2 things efficiently:
• Find the right SSTable to read from
• Find where in that SSTable to read the data

25. First step for reads
• The Memtable!
• Read the most recent data
• Very fast, no need to touch the disk

26. Off-heap (no GC) Row cache
In the JVM Memtable
Commit
On disk log
SSTable

27. Row cache
• Stores a whole row in memory
• Off-heap, not subject to Garbage Collection
• Size is configurable per column family
• Last resort before having to read from disk

28. Finding the right SSTable
In the JVM Memtable
Commit log SSTable SSTable
On disk SSTable SSTable SSTable
SSTable SSTable SSTable SSTable

29. Bloom filter
• Saved with each SSTable
• Answers “contains(Key) :: boolean”
• Saved on disk but kept in memory
• Probabilistic data structure
• Configurable proportion of false positives
• No false negatives

30. Bloom filter
In the JVM Memtable
exists(key)?
Bloom filter Bloom filter Bloom filter
true/false
Commit
On disk log
SSTable SSTable SSTable

31. Reading from an SSTable
• We need to know where in the file our data is saved
• Keys are sorted, why don’t we do a binary search?
• Keys are not all the same size
• Jumping around in a file is very slow
• Log2(N) random I/O, ~20 for 1 million keys

32. Reading from an SSTable
Let’s index key ranges in the SSTable
Key: k-128 Key: k-256 Key: k-384
Position: 12098 Position: 23445 Position: 43678
SSTable

33. SSTable index
• Saved with each SSTable
• Stores key ranges and their offsets: [(Key, Offset)]
• Saved on disk but kept in memory
• Avoids searching for a key by scanning the file
• Configurable key interval (default: 128)

34. SSTable index
In the JVM Memtable
SSTable
Bloom filter
index
Commit
On disk log
SSTable

35. Sometimes not enough
• Storing key ranges is limited
• We can do better by storing the exact offset
• This saves approximately one I/O

36. The key cache
In the JVM Memtable
SSTable
Bloom filter Key cache
index
Commit
On disk log
SSTable

37. Key cache
• Stores the exact location in the SSTable
• Stored in heap
• Avoids having to scan a whole index interval
• Size is configurable per column family

38. 2
Off-heap (no GC) Row cache
1
In the JVM Memtable
3 4 5
SSTable
Bloom filter Key cache
index
6
Commit
On disk log
SSTable

39. 2
Off-heap (no GC) Row cache
1
In the JVM Memtable
3 4 5
SSTable
Bloom filter Key cache
index
6
Commit
On disk log
SSTable

40. 2
Off-heap (no GC) Row cache
1
In the JVM Memtable
3 4 5
SSTable
Bloom filter Key cache
index
6
Commit
On disk log
SSTable

41. 2
Off-heap (no GC) Row cache
1
In the JVM Memtable
3 4 5
SSTable
Bloom filter Key cache
index
6
Commit
On disk log
SSTable

42. 2
Off-heap (no GC) Row cache
1
In the JVM Memtable
3 4 5
SSTable
Bloom filter Key cache
index
6
Commit
On disk log
SSTable

43. 2
Off-heap (no GC) Row cache
1
In the JVM Memtable
3 4 5
SSTable
Bloom filter Key cache
index
6
Commit
On disk log
SSTable

44. 2
Off-heap (no GC) Row cache
1
In the JVM Memtable
3 4 5
SSTable
Bloom filter Key cache
index
6
Commit
On disk log
SSTable

45. Distributed counters

46. Distributed counters
• 64-bit signed integer, replicated in the cluster
• Atomic inc and dec by an arbitrary amount
• Counting with read-inc-write would be inefficient
• Stored differently from regular columns

47. Consider a cluster
with 3 nodes, RF=3

48. Internal counter data
• List of increments received by the local node
• Summaries (Version,Sum) sent by the other nodes
• The total value is the sum of all counts

49. Internal counter data
• List of increments received by the local node
• Summaries (Version,Sum) sent by the other nodes
• The total value is the sum of all counts
Local increments +5 +2 -3
node version: 3
Received from count: 5
version: 5
Received from count: 10

50. Incrementing a counter
• A coordinator node is chosen (blue node)
Local increments +5 +2 -3

51. Incrementing a counter
• A coordinator node is chosen
• Stores its increment locally
Local increments +5 +2 -3 +1

52. Incrementing a counter
• A coordinator node is chosen
• Stores its increment locally
• Reads back the sum of its increments
Local increments +5 +2 -3 +1

53. Incrementing a counter
• A coordinator node is chosen
• Stores its increment locally
• Reads back the sum of its increments
• Forwards a summary to other replicas: (v.4, sum 5)
Local increments +5 +2 -3 +1

54. Incrementing a counter
• A coordinator node is chosen
• Stores its increment locally
• Reads back the sum of its increments
• Forwards a summary to other replicas
• Replicas update their records:
version: 4
Received from count: 5

55. Reading a counter
• Replicas return their counts and versions
• Including what they know about other nodes
• Only the most recent versions are kept

56. Reading a counter
version: 6
count: 12

57. Reading a counter
{
v. 3, count 5
v. 6, count 12
v. 2, count 8
{ v. 3, count 5
v. 5, count 10
v. 4, count 5
version: 6
count: 12

58. Reading a counter
{
v. 3, count 5
v. 6, count 12
v. 2, count 8
{ v. 3, count 5
v. 5, count 10
v. 4, count 5
version: 6
count: 12 Counter value: 5 + 12 + 5 = 22

59. Storage problems

60. Tuning
• Cassandra can’t really use large amounts of RAM
• Garbage Collection pauses stop everything
• Compaction has an impact on performance
• Reading from disk is slow
• These limitations restrict the size of each node

61. Recap
• Fast sequential writes
• ~1 I/O for uncached reads, 0 for cached
• Counter increments read on write, columns don’t
• Know where your time is spent (monitor!)
• Tune accordingly

62. Questions?
http://www.flickr.com/photos/kubina/326628918/sizes/l/in/photostream/
http://www.flickr.com/photos/alwarrete/5651579563/sizes/o/in/photostream/
http://www.flickr.com/photos/pio1976/3330670980/sizes/o/in/photostream/
http://www.flickr.com/photos/lwr/100518736/sizes/l/in/photostream/

63. • In-kernel backend
• No Garbage Collection
• No need to plan heavy compactions
• Low and consistent latency
• Full versioning, snapshots
• No degradation with Big Data