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1 CREATE TABLE COUNTER ( 2 type text, 3 actor text, 4 version bigint, 5 increment int, 6 PRIMARY KEY(type, actor, version)) Let’s say we have to keep count of how many shares of IBM are currently being traded in the market. CVRDT: 1 CREATE TABLE COUNTER ( 2 type text, 3 actor text, 4 version int, 5 increment int, 6 PRIMARY KEY(type, actor, version)) The above events will be captured as follows: 1 INSERT INTO COUNTER(type, actor, version, increment) VALUES('IBM', 'P1', 1, 1000); 2 INSERT INTO COUNTER(type, actor, version, increment) VALUES('IBM', 'P2', 1, 500); 3 INSERT INTO COUNTER(type, actor, version, increment) VALUES('IBM', 'P1', 2, 1500); Notice the difference in the last INSERT statement.

Since this is a state-based CvRDT, the merge function will take the most recent value for each actor and calculate the sum of all the increments. 1 SELECT increment, version FROM COUNTER WHERE type = 'IBM' In this case, it will be 1500(for P1) + 500(for P2) = 2000. We can further optimize this using Cassandra’s last-write-wins policy if we use version as timestamp. The new table model will be: 1 CREATE TABLE COUNTER ( 2 type text, 3 actor text, 4 increment int, 5 PRIMARY KEY(type, actor)) And the new insert statements updated as: 1 INSERT INTO COUNTER(type, actor, increment) VALUES('IBM', 'P1', 1000) USING TIMESTAMP 1; 2 INSERT INTO COUNTER(type, actor, increment) VALUES('IBM', 'P2', 500) USING TIMESTAMP 1; 3 UPDATE COUNTER SET increment = 1500 WHERE type = 'IBM' AND actor = 'P1' USING TIMESTAMP 2; As noted above, the merge function for a CvRDT will take the most recent value for each actor and calculate the sum of all the increments. As noted above, the merge function for a CvRDT will take the most recent value for each actor and calculate the sum of all the increments. 1 SELECT increment FROM COUNTER WHERE type = 'IBM' In this case, we just used Cassandra’s LWW policy to manage the most recent value for the actor.