-</pre><blockquote>What happened here is that lpush against the key with a timeout set deletedthe key before to perform the operation. There is so a simple rule, writeoperations against volatile keys will destroy the key before to perform theoperation. Why Redis uses this behavior? In order to retain an importantproperty: a server that receives a given number of commands in the samesequence will end with the same dataset in memory. Without the delete-on-writesemantic what happens is that the state of the server depends on the timeof the commands to. This is not a desirable property in a distributed databasethat supports replication.</blockquote>
-<h2><a name="Setting the timeout again on already volatile keys">Setting the timeout again on already volatile keys</a></h2><blockquote>Trying to call EXPIRE against a key that already has an associated timeoutwill not change the timeout of the key, but will just return 0. If insteadthe key does not have a timeout associated the timeout will be set and EXPIREwill return 1.</blockquote>
+</pre><blockquote>What happened here is that LPUSH against the key with a timeout set deletedthe key before to perform the operation. There is so a simple rule, writeoperations against volatile keys will destroy the key before to perform theoperation. Why Redis uses this behavior? In order to retain an importantproperty: a server that receives a given number of commands in the samesequence will end with the same dataset in memory. Without the delete-on-writesemantic what happens is that the state of the server depends on the timethe commands were issued. This is not a desirable property in a distributed databasethat supports replication.</blockquote>
+<h2><a name="Restrictions for write operations with volatile keys as sources">Restrictions for write operations with volatile keys as sources</a></h2>Even when the volatile key is not modified as part of a write operation, if it is
+read in a composite write operation (such as SINTERSTORE) it will be cleared at the
+start of the operation. This is done to avoid concurrency issues in replication.
+Imagine a key that is about to expire and the composite operation is run against it.
+On a slave node, this key might already be expired, which leaves you with a
+desync in your dataset.<h2><a name="Setting the timeout again on already volatile keys">Setting the timeout again on already volatile keys</a></h2><blockquote>Trying to call EXPIRE against a key that already has an associated timeoutwill not change the timeout of the key, but will just return 0. If insteadthe key does not have a timeout associated the timeout will be set and EXPIREwill return 1.</blockquote>
+<h2><a name="Enhanced Lazy Expiration algorithm">Enhanced Lazy Expiration algorithm</a></h2><blockquote>Redis does not constantly monitor keys that are going to be expired.Keys are expired simply when some client tries to access a key, andthe key is found to be timed out.</blockquote>
+<blockquote>Of course this is not enough as there are expired keys that will neverbe accessed again. This keys should be expired anyway, so once everysecond Redis test a few keys at random among keys with an expire set.All the keys that are already expired are deleted from the keyspace. </blockquote>
+<h3><a name="Version 1.0">Version 1.0</a></h3><blockquote>Each time a fixed number of keys where tested (100 by default). So ifyou had a client setting keys with a very short expire faster than 100for second the memory continued to grow. When you stopped to insertnew keys the memory started to be freed, 100 keys every second in thebest conditions. Under a peak Redis continues to use more and more RAMeven if most keys are expired in each sweep.</blockquote>
+<h3><a name="Version 1.1">Version 1.1</a></h3><blockquote>Each time Redis:</blockquote>
+<ol><li> Tests 100 random keys from expired keys set.</li><li> Deletes all the keys found expired.</li><li> If more than 25 keys were expired, it start again from 1.</li></ol>
+<blockquote>This is a trivial probabilistic algorithm, basically the assumption isthat our sample is representative of the whole key space,and we continue to expire until the percentage of keys that are likelyto be expired is under 25%</blockquote>
+<blockquote>This means that at any given moment the maximum amount of keys alreadyexpired that are using memory is at max equal to max setting operations per second divided by 4.</blockquote>