Distributed Counter

http://rockthecode.io/blog/highly-available-counters-using-cassandra/

This is the simplest counter to implement, especially for people coming from the RDBMS world. Acquire a lock on the counter, increment the counter and release the lock. In distributed systems, lockscan be implemented using an external coordinator, like Zookeeper.

 1 lock.acquire();
 2 try {
 3    counter.increment(5);
 4 } finally {
 5    lock.release();
 6 } 

1. Locking involves coordination and coordination is expensive
2. If the counter is a hotspot, there will be high lock contention
3. In distributed systems, messages may be redelivered. The solution will not handle duplicate messages and is not idempotent

LINEARIZABLE COUNTERS

This involves implementing a CAS (compare-and-swap) operation on a counter. This can be achieved using Zookeeper or Paxos based consensus in Cassandra.

 1 int retries = 5;
 2 while(retries > 0) {
 3     int currentValue = counter.getValue();
 4     if (!counter.compareAndSwap(currentValue, currentValue + 5)) {
 5         try {
 6             Thread.sleep(500); // You want to ideally use an exponential backoff algorithm here
 7         } catch (InterruptedException e) {}
 8         retries--;
 9     } else {
10         break;
11     }
12 }  

1. High contention
2. Not very scalable
3. Non-idempotent
4. May exhaust retries if the counter is a hotspot

CASSANDRA COUNTERS USING CRDTS

In this example, we will see how we can implement a CRDT based counter using Cassandra. We will show how we can model a counter using a CmRDT (operation-based) and a CvRDT (state-based). We’ll then model these using CQL.