As of Redis version 6.2.0, this command is regarded as deprecated.
It can be replaced by
LMOVE with the
LEFT arguments when migrating or writing new code.
RPOPLPUSH source destination
- Available since:
- Time complexity:
- ACL categories:
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
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.
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
(using polling), or
BRPOP if the client is better served by a blocking
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 before it can be processed.
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, pushing timed out items into the queue again if needed.
Pattern: Circular list
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
The above pattern works even if one or both of the following conditions occur:
- 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.
- 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.
One of the following:
One of the following: