Atomically returns and removes the last element (tail) of the list stored at
source, and pushes the element at the first element (head) of the list stored
For example: consider
source holding the list
holding the list
Executing RPOPLPUSH results in
source does not exist, the value
nil is returned and no operation is
destination are the same, the operation is equivalent to
removing the last element from the list and pushing it as first element of the
list, so it can be considered as a list rotation command.
Bulk reply: the element being popped and pushed.
(integer) 1redis> RPUSH mylist "two"
(integer) 2redis> RPUSH mylist "three"
(integer) 3redis> RPOPLPUSH mylist myotherlist
"three"redis> LRANGE mylist 0 -1
1) "one" 2) "two"redis> LRANGE myotherlist 0 -1
Pattern: Reliable queue
Redis is often used as a messaging server to implement processing of background jobs or other kinds of messaging tasks. A simple form of queue is often obtained pushing values into a list in the producer side, and waiting for this values in the consumer side using RPOP (using polling), or BRPOP if the client is better served by a blocking operation.
However in this context the obtained queue is not reliable as messages can be lost, for example in the case there is a network problem or if the consumer crashes just after the message is received but it is still to process.
RPOPLPUSH (or BRPOPLPUSH for the blocking variant) offers a way to avoid this problem: the consumer fetches the message and at the same time pushes it into a processing list. It will use the LREM command in order to remove the message from the processing list once the message has been processed.
An additional client may monitor the processing list for items that remain there for too much time, and will push those timed out items into the queue again if needed.
Pattern: Circular list
Using RPOPLPUSH with the same source and destination key, a client can visit all the elements of an N-elements list, one after the other, in O(N) without transferring the full list from the server to the client using a single LRANGE operation.
The above pattern works even if the following two conditions: * There are multiple clients rotating the list: they'll fetch different elements, until all the elements of the list are visited, and the process restarts. * Even if other clients are actively pushing new items at the end of the list.
The above makes it very simple to implement a system where a set of items must be processed by N workers continuously as fast as possible. An example is a monitoring system that must check that a set of web sites are reachable, with the smallest delay possible, using a number of parallel workers.
Note that this implementation of workers is trivially scalable and reliable, because even if a message is lost the item is still in the queue and will be processed at the next iteration.