Distributed Counters in Cassandra (Cassandra Summit 2010)

Distributed Counters
in Cassandra

Friday, August 13, 2010

I: Goal
II: Design
III: Implementation

Distributed Counters in Cassandra


I: Goal



Goal

Low Latency,
Highly Available
Counters



II: Design



I: Traditional Counter Design
II: Abstract Strategy
III: Distributed Counter Design



Design

I: Traditional Counter Design



Traditional Counter Design
Atomic Counters

1. single machine
2. one order of execution
3. strongly consistent



Problems

1. SPOF / single master
2. high latency
3. manually sharded



Question

What constraints can we relax?



Design

II: Abstract Strategy



Abstract Strategy
Constraints to Relax

1. one order of execution
2. strong consistency



Abstract Strategy
Relax: One Order of Execution

commutative operation:
- operations must be re-orderable



Abstract Strategy
Relax: Strong Consistency

partitioned work:
- each op must occur once
- unique partition identiﬁer
idempotent repair:
- recognize ops from other partitions



Design

III: Distributed Counter Design



Distributed Counter Design
Requirements

1. commutative operation
2. partitioned work
3. idempotent repair



Commutative Operation

addition:
- commutative operation
- sum ops performed by all replicas
-a + b = b + a



Partitioned Work

each op assigned to a replica:
- every replica sums all of its ops



Idempotent Repair

save counts from remote replicas:
- keep highest count seen
prevent multiple execution:
- do not transfer the target replica’s count



III: Implementation



I: Data Structure
II: Single Node
III: Eventual Consistency



I: Data Structure



Data Structure
Requirements

local counts:
- incrementally update
remote counts:
- independently track partitions



Data Structure
Context Format

list of (replica id, count) tuples:
[(replica A, count), (replica B, count), ...]



Data Structure
Context Mutations

local write:
sum local count and write delta
note: memtable



Data Structure
Context Mutations

remote repair:
for each replica,
keep highest count seen
(local or from repair)



II: Single Node



Single Node
Write Path

client
1. construct column
- value: delta (big-endian long)
- clock: empty
2. thrift: insert / batch_mutate



Single Node
Write Path

coordinator
1. choose partition
- choose target replica
- requirement: ConsistencyLevel.ONE
2. construct clock
- context format: [(target replica id, count delta)]



Single Node
Write Path

target replica
insert:
1. memtable does not contain column
2. insert column into memtable



Single Node
Write Path
target replica
update:
1. memtable contains column
2. retrieve existing column
3. create new column
- context: sum local count w/ delta from write
4. replace column in ConcurrentSkipListMap
5. if failed to replace column, go to step 2.



Single Node
Write Path
Interesting Note:
MTs are serialized to SSTs, as-is
- each SST encapsulates the updates
when it was an MT
- local count total must be aggregated
across the MT and all SSTs



Single Node
Read Path
target replica
read:
1. construct collating iterator over:
- frozen snapshot of MT
- all relevant SSTs
2. resolve column
- local counts: sum
- remote counts: keep max
3. construct value
- sum local and remote counts (big-endian long)



Single Node
Compaction

replica
compaction:
1. construct collating iterator over all SSTs
2. resolve every column in the CF
- local counts: sum
- remote counts: keep max
3. write out resolved CF



III: Eventual Consistency



Eventual Consistency
Read Repair

coordinator / replica
read repair:
1. calculate resolved (superset) CF
- resolve every column (local: sum, remote: max)
2. return resolved CF to client



Read Repair

coordinator / replica
read repair:
1. calculate repair CF for each replica
- calculate diff CF between resolved and received
- modify columns to remove target replica’s counts
2. send repair CF to each replica



Anti-Entropy Service

sending replica
AES:
1. follow normal AES code path
- calculate repair SST based on shared ranges
- send repair SST



Anti-Entropy Service

receiving replica
AES:
1. post-process streamed SST
- re-build streamed SST
- note: strip out local replica’s counts
2. remove temporary descriptor
3. add to SSTableTracker



Questions?



More Information
Issues:
#580: Vector Clocks
#1072: Distributed Counters

Related Work:
Helland and Campbell, Building on Quicksand, CIDR (2009),
Sections 5 & 6.

My email address:
kakugawa@gmail.com



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