37 <h1><a name=
"Isn't this key-value thing just hype?">Isn't this key-value thing just hype?
</a></h1>I imagine key-value DBs, in the short term future, to be used like you use memory in a program, with lists, hashes, and so on. With Redis it's like this, but this special kind of memory containing your data structures is shared, atomic, persistent.
<br/><br/>When we write code it is obvious, when we take data in memory, to use the most sensible data structure for the work, right? Incredibly when data is put inside a relational DB this is no longer true, and we create an absurd data model even if our need is to put data and get this data back in the same order we put it inside (an ORDER BY is required when the data should be already sorted. Strange, dont' you think?).
<br/><br/>Key-value DBs bring this back at home, to create sensible data models and use the right data structures for the problem we are trying to solve.
<h1><a name=
"Can I backup a Redis DB while the server is working?">Can I backup a Redis DB while the server is working?
</a></h1>Yes you can. When Redis saves the DB it actually creates a temp file, then rename(
2) that temp file name to the destination file name. So even while the server is working it is safe to save the database file just with the
<i>cp
</i> unix command. Note that you can use master-slave replication in order to have redundancy of data, but if all you need is backups, cp or scp will do the work pretty well.
<h1><a name=
"What's the Redis memory footprint?">What's the Redis memory footprint?
</a></h1>Worst case scenario:
1 Million keys with the key being the natural numbers from
0 to
999999 and the string
"Hello World
" as value use
100MB on my Intel macbook (
32bit). Note that the same data stored linearly in an unique string takes something like
16MB, this is the norm because with small keys and values there is a lot of overhead. Memcached will perform similarly.
<br/><br/>With large keys/values the ratio is much better of course.
<br/><br/>64 bit systems will use much more memory than
32 bit systems to store the same keys, especially if the keys and values are small, this is because pointers takes
8 bytes in
64 bit systems. But of course the advantage is that you can have a lot of memory in
64 bit systems, so to run large Redis servers a
64 bit system is more or less required.
<h1><a name=
"I like Redis high level operations and features, but I don't like it takes everything in memory and I can't have a dataset larger the memory. Plans to change this?">I like Redis high level operations and features, but I don't like it takes everything in memory and I can't have a dataset larger the memory. Plans to change this?
</a></h1>The whole key-value hype started for a reason: performances. Redis takes the whole dataset in memory and writes asynchronously on disk in order to be very fast, you have the best of both worlds: hyper-speed and persistence of data, but the price to pay is exactly this, that the dataset must fit on your computers RAM.
<br/><br/>If the data is larger then memory, and this data is stored on disk, what happens is that the bottleneck of the disk I/O speed will start to ruin the performances. Maybe not in benchmarks, but once you have real load from multiple clients with distributed key accesses the data must come from disk, and the disk is damn slow. Not only, but Redis supports higher level data structures than the plain values. To implement this things on disk is even slower.
<br/><br/>Redis will always continue to hold the whole dataset in memory because this days scalability requires to use RAM as storage media, and RAM is getting cheaper and cheaper. Today it is common for an entry level server to have
16 GB of RAM! And in the
64-bit era there are no longer limits to the amount of RAM you can have in theory.
<h1><a name=
"Ok but I absolutely need to have a DB larger than memory, still I need the Redis features">Ok but I absolutely need to have a DB larger than memory, still I need the Redis features
</a></h1>You may try to load a dataset larger than your memory in Redis and see what happens, basically if you are using a modern Operating System, and you have a lot of data in the DB that is rarely accessed, the OS's virtual memory implementation will try to swap rarely used pages of memory on the disk, to only recall this pages when they are needed. If you have many large values rarely used this will work. If your DB is big because you have tons of little values accessed at random without a specific pattern this will not work (at low level a page is usually
4096 bytes, and you can have different keys/values stored at a single page. The OS can't swap this page on disk if there are even few keys used frequently).
<br/><br/>Another possible solution is to use both MySQL and Redis at the same time, basically take the state on Redis, and all the things that get accessed very frequently: user auth tokens, Redis Lists with chronologically ordered IDs of the last N-comments, N-posts, and so on. Then use MySQL as a simple storage engine for larger data, that is just create a table with an auto-incrementing ID as primary key and a large BLOB field as data field. Access MySQL data only by primary key (the ID). The application will run the high traffic queries against Redis but when there is to take the big data will ask MySQL for specific resources IDs.
<h1><a name=
"I have an empty Redis server but INFO and logs are reporting megabytes of memory in use!">I have an empty Redis server but INFO and logs are reporting megabytes of memory in use!
</a></h1>This may happen and it's prefectly ok. Redis objects are small C structures allocated and freed a lot of times. This costs a lot of CPU so instead of being freed, released objects are taken into a free list and reused when needed. This memory is taken exactly by this free objects ready to be reused.
<h1><a name=
"What happens if Redis runs out of memory?">What happens if Redis runs out of memory?
</a></h1>With modern operating systems malloc() returning NULL is not common, usually the server will start swapping and Redis performances will be disastrous so you'll know it's time to use more Redis servers or get more RAM.
<br/><br/>However it is planned to add a configuration directive to tell Redis to stop accepting queries but instead to SAVE the latest data and quit if it is using more than a given amount of memory. Also the new INFO command (work in progress in this days) will report the amount of memory Redis is using so you can write scripts that monitor your Redis servers checking for critical conditions.
<br/><br/>Update: redis SVN is able to know how much memory it is using and report it via the
<a href=
"InfoCommand.html">INFO
</a> command.
<h1><a name=
"How much time it takes to load a big database at server startup?">How much time it takes to load a big database at server startup?
</a></h1>Just an example on normal hardware: It takes about
45 seconds to restore a
2 GB database on a fairly standard system, no RAID. This can give you some kind of feeling about the order of magnitude of the time needed to load data when you restart the server.
<h1><a name=
"Background saving is failing with a fork() error under Linux even if I've a lot of free RAM!">Background saving is failing with a fork() error under Linux even if I've a lot of free RAM!
</a></h1>Short answer:
<code name=
"code" class=
"python">echo
1 > /proc/sys/vm/overcommit_memory
</code> :)
<br/><br/>And now the long one:
<br/><br/>Redis background saving schema relies on the copy-on-write semantic of fork in modern operating systems: Redis forks (creates a child process) that is an exact copy of the parent. The child process dumps the DB on disk and finally exits. In theory the child should use as much memory as the parent being a copy, but actually thanks to the copy-on-write semantic implemented by most modern operating systems the parent and child process will
<i>share
</i> the common memory pages. A page will be duplicated only when it changes in the child or in the parent. Since in theory all the pages may change while the child process is saving, Linux can't tell in advance how much memory the child will take, so if the
<code name=
"code" class=
"python">overcommit_memory
</code> setting is set to zero fork will fail unless there is as much free RAM as required to really duplicate all the parent memory pages, with the result that if you have a Redis dataset of
3 GB and just
2 GB of free memory it will fail.
<br/><br/>Setting
<code name=
"code" class=
"python">overcommit_memory
</code> to
1 says Linux to relax and perform the fork in a more optimistic allocation fashion, and this is indeed what you want for Redis.
<h1><a name=
"Redis is single threaded, how can I exploit multiple CPU / cores?">Redis is single threaded, how can I exploit multiple CPU / cores?
</a></h1>Simply start multiple instances of Redis in different ports in the same box and threat them as different servers! Given that Redis is a distributed database anyway in order to scale you need to think in terms of multiple computational units. At some point a single box may not be enough anyway.
<br/><br/>In general key-value databases are very scalable because of the property that different keys can stay on different servers independently.
<br/><br/>In Redis there are client libraries such Redis-rb (the Ruby client) that are able to handle multiple servers automatically using
<i>consistent hashing
</i>. We are going to implement consistent hashing in all the other major client libraries. If you use a different language you can implement it yourself otherwise just hash the key before to SET / GET it from a given server. For example imagine to have N Redis servers, server-
0, server-
1, ..., server-N. You want to store the key
"foo
", what's the right server where to put
"foo
" in order to distribute keys evenly among different servers? Just perform the
<i>crc
</i> = CRC32(
"foo
"), then
<i>servernum
</i> =
<i>crc
</i> % N (the rest of the division for N). This will give a number between
0 and N-
1 for every key. Connect to this server and store the key. The same for gets.
<br/><br/>This is a basic way of performing key partitioning, consistent hashing is much better and this is why after Redis
1.0 will be released we'll try to implement this in every widely used client library starting from Python and PHP (Ruby already implements this support).
<h1><a name=
"I'm using some form of key hashing for partitioning, but what about SORT BY?">I'm using some form of key hashing for partitioning, but what about SORT BY?
</a></h1>With
<a href=
"SortCommand.html">SORT
</a> BY you need that all the
<i>weight keys
</i> are in the same Redis instance of the list/set you are trying to sort. In order to make this possible we developed a concept called
<i>key tags
</i>. A key tag is a special pattern inside a key that, if preset, is the only part of the key hashed in order to select the server for this key. For example in order to hash the key
"foo
" I simply perform the CRC32 checksum of the whole string, but if this key has a pattern in the form of the characters {...} I only hash this substring. So for example for the key
"foo{bared}
" the key hashing code will simply perform the CRC32 of
"bared
". This way using key tags you can ensure that related keys will be stored on the same Redis instance just using the same key tag for all this keys. Redis-rb already implements key tags.
<h1><a name=
"What is the maximum number of keys a single Redis instance can hold?">What is the maximum number of keys a single Redis instance can hold?
</a></h1>The latest versions of Redis in the Git repository are able to handle at least
150 million of keys per instance. We are working in order to experiment with larger values.
<h1><a name=
"What Redis means actually?">What Redis means actually?
</a></h1>Redis means two things:
projects / redis.git / blob