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Query idempotence

A query is idempotent if it can be applied multiple times without changing the result of the initial application. For example:

Idempotence matters for nike free runs discount drug
and speculative query executions . The driver will bypass those features if the nike roshe one black 3d anime
flag is set to false , to ensure that the statement does not get executed more than once.

In most cases, you must set that flag manually. The driver does not parse query strings, so it can’t infer it automatically (except for statements coming from the query builder, see below).

Statements start out as non-idempotent by default. You can override the flag on each statement:

The default is also configurable: if you want all statements to start out as idempotent, do this:

Any statement on which you didn’t call setIdempotent gets this default value.

Bound statements inherit the flag from the prepared statement they were created from:

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DSL tries to infer the isIdempotent flag on the statements it generates. The following statements will be marked non-idempotent :

non-idempotent

counter updates:

prepend, append or deletion operations on lists:

queries that insert the result of a function call or a “raw” string in a column (or as an element in a collection column):

This is a conservative approach, since the driver can’t guess whether a function is idempotent, or what a raw string contains. It might yield false negatives, that you’ll have to fix manually.

lightweight transactions (see the next section for a detailed explanation):

If these rules produce a false negative, you can manually override the flag on the built statement:

As explained in the previous section, the query builder considers lightweight transactions as non-idempotent. This might sound counter-intuitive, as these queries can sometimes be safe to execute multiple times. For example, consider the following query:

If we execute it twice, the IF condition will fail the second time, so the second execution will do nothing and v will still have the value 4.

However, the problem appears when we consider multiple clients executing the query with retries:

One important aspect of lightweight transactions is linearizability : given a set of concurrent operations on a column from different clients, there must be a way to reorder them to yield a sequential history that is correct. From our clients’ point of view, there were two operations:

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