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Is there some timestamp/counter that can be used to validate that in a read-modify-write cycle, the data in the row did not change between reading and modifying?
In other words, can I read some kind of ID while reading the row, and when I write it back tell Cassandra what that ID was, and the write then fails if the ID changed since then? (Which amounts to saying that some other write took place after I read the data)
Each column in cassandra is a Tuple (or a triplet) that contains a name, value and a timestamp. The timestamp of the column represents the last time it was modified. If you have 100’s of nodes, whichever node has an update with a the most recent timestamp will win. This is how Eventual Consistency is achieved.
zznate has a good presentation: Introduction to Apache Cassandra for Java Developers where this topic is referenced (slide 37)
Accessing timestamp of a Cassandra column
In summary, you don’t need “some kind of ID” when you have the ability to retrieve the timestamp for a given column representing the last time it was modified. However, at scale, with 100’s of nodes, how can you be sure that the node you are connecting to, has the most up to date column? (refer back to the zznate presentation)
Point is, you can’t, without enabling transactions: