Commit 81d45645 authored by antirez's avatar antirez
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ZRANGE WITHSCORES test added

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<b>AppendOnlyFileHowto: Contents</b><br>&nbsp;&nbsp;&nbsp;&nbsp;<a href="#General Information">General Information</a><br>&nbsp;&nbsp;&nbsp;&nbsp;<a href="#Log rewriting">Log rewriting</a><br>&nbsp;&nbsp;&nbsp;&nbsp;<a href="#Wait... but how does this work?">Wait... but how does this work?</a><br>&nbsp;&nbsp;&nbsp;&nbsp;<a href="#How durable is the append only file?">How durable is the append only file?</a>
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<h1 class="wikiname">AppendOnlyFileHowto</h1>
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&iuml;&raquo;&iquest;= Append Only File HOWTO =<h2><a name="General Information">General Information</a></h2>Append only file is an alternative durability option for Redis. What this mean? Let's start with some fact:<br/><br/><ul><li> For default Redis saves snapshots of the dataset on disk, in a binary file called dump.rdb (by default at least). For instance you can configure Redis to save the dataset every 60 seconds if there are at least 100 changes in the dataset, or every 1000 seconds if there is at least a single change in the dataset. This is known as &quot;Snapshotting&quot;.</li><li> Snapshotting is not very durable. If your computer running Redis stops, your power line fails, or you write killall -9 redis-server for a mistake, the latest data written on Redis will get lost. There are applications where this is not a big deal. There are applications where this is not acceptable and Redis <b>was</b> not an option for this applications.</li></ul>
What is the solution? To use append only file as alternative to snapshotting. How it works?<br/><br/><ul><li> It is an 1.1 only feature.</li><li> You have to turn it on editing the configuration file. Just make sure you have &quot;appendonly yes&quot; somewhere.</li><li> Append only files work this way: every time Redis receive a command that changes the dataset (for instance a SET or LPUSH command) it appends this command in the append only file. When you restart Redis it will first <b>re-play</b> the append only file to rebuild the state.</li></ul>
<h2><a name="Log rewriting">Log rewriting</a></h2>As you can guess... the append log file gets bigger and bigger, every time there is a new operation changing the dataset. Even if you set always the same key &quot;mykey&quot; to the values of &quot;1&quot;, &quot;2&quot;, &quot;3&quot;, ... up to 10000000000 in the end you'll have just a single key in the dataset, just a few bytes! but how big will be the append log file? Very very big.<br/><br/>So Redis supports an interesting feature: it is able to rebuild the append log file, in background, without to stop processing client commands. The key is the command <a href="BGREWRITEAOF.html">BGREWRITEAOF</a>. This command basically is able to use the dataset in memory in order to rewrite the shortest sequence of commands able to rebuild the exact dataset that is currently in memory.<br/><br/>So from time to time when the log gets too big, try this command. It's safe as if it fails you will not lost your old log (but you can make a backup copy given that currently 1.1 is still in beta!).<h2><a name="Wait... but how does this work?">Wait... but how does this work?</a></h2>Basically it uses the same fork() copy-on-write trick that snapshotting already uses. This is how the algorithm works:<br/><br/><ul><li> Redis forks, so now we have a child and a parent.</li><li> The child starts writing the new append log file in a temporary file.</li><li> The parent accumulates all the new changes in an in-memory buffer.</li><li> When the child finished to rewrite the file, the parent gets a signal, and append the in-memory buffer at the end of the file generated by the child.</li><li> Profit! Now Redis atomically renames the old file into the new one, and starts appending new data into the new file.</li></ul>
<h2><a name="How durable is the append only file?">How durable is the append only file?</a></h2>Check redis.conf, you can configure how many times Redis will fsync() data on disk. There are three options:<br/><br/><ul><li> Fsync() every time a new command is appended to the append log file. Very very slow, very safe.</li><li> Fsync() one time every second. Fast enough, and you can lose 1 second of data if there is a disaster.</li><li> Never fsync(), just put your data in the hands of the Operating System. The faster and unsafer method.</li></ul>
Warning: by default Redis will fsync() after <b>every command</b>! This is because the Redis authors want to ship a default configuration that is the safest pick. But the best compromise for most datasets is to fsync() one time every second.
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<b>AuthCommand: Contents</b><br>&nbsp;&nbsp;<a href="#AUTH _password_">AUTH _password_</a><br>&nbsp;&nbsp;&nbsp;&nbsp;<a href="#Return value">Return value</a>
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<h1 class="wikiname">AuthCommand</h1>
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&iuml;&raquo;&iquest;#sidebar <a href="ConnectionHandlingSidebar.html">ConnectionHandlingSidebar</a><h1><a name="AUTH _password_">AUTH _password_</a></h1><blockquote>Request for authentication in a password protected Redis server.A Redis server can be instructed to require a password before to allow clientsto issue commands. This is done using the <i>requirepass</i> directive in theRedis configuration file.</blockquote>
<blockquote>If the password given by the client is correct the server replies withan OK status code reply and starts accepting commands from the client.Otherwise an error is returned and the clients needs to try a new password.Note that for the high performance nature of Redis it is possible to trya lot of passwords in parallel in very short time, so make sure to generatea strong and very long password so that this attack is infeasible.</blockquote>
<h2><a name="Return value">Return value</a></h2><a href="ReplyTypes.html">Status code reply</a>
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<b>Benchmarks: Contents</b><br>&nbsp;&nbsp;<a href="#How Fast is Redis?">How Fast is Redis?</a><br>&nbsp;&nbsp;<a href="#Latency percentiles">Latency percentiles</a>
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<h1><a name="How Fast is Redis?">How Fast is Redis?</a></h1>Redis includes the <code name="code" class="python">redis-benchmark</code> utility that simulates <a href="SETs.html">SETs</a>/GETs done by N clients at the same time sending M total queries (it is similar to the Apache's <code name="code" class="python">ab</code> utility). Below you'll find the full output of the benchmark executed against a Linux box.<br/><br/><ul><li> The test was done with 50 simultaneous clients performing 100000 requests.</li><li> The value SET and GET is a 256 bytes string.</li><li> The Linux box is running <b>Linux 2.6</b>, it's <b>Xeon X3320 2.5Ghz</b>.</li><li> Text executed using the loopback interface (127.0.0.1).</li></ul>
Results: <b>about 110000 <a href="SETs.html">SETs</a> per second, about 81000 GETs per second.</b><h1><a name="Latency percentiles">Latency percentiles</a></h1><pre class="codeblock python" name="code">
./redis-benchmark -n 100000
====== SET ======
100007 requests completed in 0.88 seconds
50 parallel clients
3 bytes payload
keep alive: 1
58.50% &lt;= 0 milliseconds
99.17% &lt;= 1 milliseconds
99.58% &lt;= 2 milliseconds
99.85% &lt;= 3 milliseconds
99.90% &lt;= 6 milliseconds
100.00% &lt;= 9 milliseconds
114293.71 requests per second
====== GET ======
100000 requests completed in 1.23 seconds
50 parallel clients
3 bytes payload
keep alive: 1
43.12% &lt;= 0 milliseconds
96.82% &lt;= 1 milliseconds
98.62% &lt;= 2 milliseconds
100.00% &lt;= 3 milliseconds
81234.77 requests per second
====== INCR ======
100018 requests completed in 1.46 seconds
50 parallel clients
3 bytes payload
keep alive: 1
32.32% &lt;= 0 milliseconds
96.67% &lt;= 1 milliseconds
99.14% &lt;= 2 milliseconds
99.83% &lt;= 3 milliseconds
99.88% &lt;= 4 milliseconds
99.89% &lt;= 5 milliseconds
99.96% &lt;= 9 milliseconds
100.00% &lt;= 18 milliseconds
68458.59 requests per second
====== LPUSH ======
100004 requests completed in 1.14 seconds
50 parallel clients
3 bytes payload
keep alive: 1
62.27% &lt;= 0 milliseconds
99.74% &lt;= 1 milliseconds
99.85% &lt;= 2 milliseconds
99.86% &lt;= 3 milliseconds
99.89% &lt;= 5 milliseconds
99.93% &lt;= 7 milliseconds
99.96% &lt;= 9 milliseconds
100.00% &lt;= 22 milliseconds
100.00% &lt;= 208 milliseconds
88109.25 requests per second
====== LPOP ======
100001 requests completed in 1.39 seconds
50 parallel clients
3 bytes payload
keep alive: 1
54.83% &lt;= 0 milliseconds
97.34% &lt;= 1 milliseconds
99.95% &lt;= 2 milliseconds
99.96% &lt;= 3 milliseconds
99.96% &lt;= 4 milliseconds
100.00% &lt;= 9 milliseconds
100.00% &lt;= 208 milliseconds
71994.96 requests per second
</pre>Notes: changing the payload from 256 to 1024 or 4096 bytes does not change the numbers significantly (but reply packets are glued together up to 1024 bytes so GETs may be slower with big payloads). The same for the number of clients, from 50 to 256 clients I got the same numbers. With only 10 clients it starts to get a bit slower.<br/><br/>You can expect different results from different boxes. For example a low profile box like <b>Intel core duo T5500 clocked at 1.66Ghz running Linux 2.6</b> will output the following:
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./redis-benchmark -q -n 100000
SET: 53684.38 requests per second
GET: 45497.73 requests per second
INCR: 39370.47 requests per second
LPUSH: 34803.41 requests per second
LPOP: 37367.20 requests per second
</pre>Another one using a 64 bit box, a Xeon L5420 clocked at 2.5 Ghz:<br/><br/><pre class="codeblock python python python" name="code">
./redis-benchmark -q -n 100000
PING: 111731.84 requests per second
SET: 108114.59 requests per second
GET: 98717.67 requests per second
INCR: 95241.91 requests per second
LPUSH: 104712.05 requests per second
LPOP: 93722.59 requests per second
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<b>BgrewriteaofCommand: Contents</b><br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href="#BGREWRITEAOF">BGREWRITEAOF</a><br>&nbsp;&nbsp;&nbsp;&nbsp;<a href="#Return value">Return value</a>
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&iuml;&raquo;&iquest;#sidebar <a href="ControlCommandsSidebar.html">ControlCommandsSidebar</a><h3><a name="BGREWRITEAOF">BGREWRITEAOF</a></h3>
<blockquote>Please for detailed information about the Redis Append Only File check<a href="AppendOnlyFileHowto.html">the Append Only File Howto</a>.</blockquote>
<blockquote>BGREWRITEAOF rewrites the Append Only File in background when it gets toobig. The Redis Append Only File is a Journal, so every operation modifyingthe dataset is logged in the Append Only File (and replayed at startup).This means that the Append Only File always grows. In order to rebuildits content the BGREWRITEAOF creates a new version of the append only filestarting directly form the dataset in memory in order to guarantee thegeneration of the minimal number of commands needed to rebuild the database.</blockquote>
<blockquote>The <a href="AppendOnlyFileHowto.html">Append Only File Howto</a> contains further details.</blockquote>
<h2><a name="Return value">Return value</a></h2><a href="ReplyTypes.html">Status code reply</a>
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<b>BgsaveCommand: Contents</b><br>&nbsp;&nbsp;<a href="#BGSAVE">BGSAVE</a><br>&nbsp;&nbsp;&nbsp;&nbsp;<a href="#Return value">Return value</a>
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&iuml;&raquo;&iquest;#sidebar <a href="ControlCommandsSidebar.html">ControlCommandsSidebar</a><h1><a name="BGSAVE">BGSAVE</a></h1>
<blockquote>Save the DB in background. The OK code is immediately returned.Redis forks, the parent continues to server the clients, the childsaves the DB on disk then exit. A client my be able to check if theoperation succeeded using the <a href="LastsaveCommand.html">LASTSAVE</a> command.</blockquote>
<h2><a name="Return value">Return value</a></h2><a href="ReplyTypes.html">Status code reply</a>
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<b>CommandReference: Contents</b><br>&nbsp;&nbsp;&nbsp;&nbsp;<a href="#Connection handling">Connection handling</a><br>&nbsp;&nbsp;&nbsp;&nbsp;<a href="#Commands operating on all the kind of values">Commands operating on all the kind of values</a><br>&nbsp;&nbsp;&nbsp;&nbsp;<a href="#Commands operating on string values">Commands operating on string values</a><br>&nbsp;&nbsp;&nbsp;&nbsp;<a href="#Commands operating on lists">Commands operating on lists</a><br>&nbsp;&nbsp;&nbsp;&nbsp;<a href="#Commands operating on sets">Commands operating on sets</a><br>&nbsp;&nbsp;&nbsp;&nbsp;<a href="#Commands operating on sorted sets (zsets, Redis version &gt;">Commands operating on sorted sets (zsets, Redis version &gt;</a><br>&nbsp;&nbsp;&nbsp;&nbsp;<a href="#Sorting">Sorting</a><br>&nbsp;&nbsp;&nbsp;&nbsp;<a href="#Persistence control commands">Persistence control commands</a><br>&nbsp;&nbsp;&nbsp;&nbsp;<a href="#Remote server control commands">Remote server control commands</a>
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<h1 class="wikiname">CommandReference</h1>
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&iuml;&raquo;&iquest;= Redis Command Reference =<br/><br/>Every command name links to a specific wiki page describing the behavior of the command.<h2><a name="Connection handling">Connection handling</a></h2><ul><li> <a href="QuitCommand.html">QUIT</a> <code name="code" class="python">close the connection</code></li><li> <a href="AuthCommand.html">AUTH</a> <code name="code" class="python">simple password authentication if enabled</code></li></ul>
<h2><a name="Commands operating on all the kind of values">Commands operating on all the kind of values</a></h2><ul><li> <a href="ExistsCommand.html">EXISTS</a> <i>key</i> <code name="code" class="python">test if a key exists</code></li><li> <a href="DelCommand.html">DEL</a> <i>key</i> <code name="code" class="python">delete a key</code></li><li> <a href="TypeCommand.html">TYPE</a> <i>key</i> <code name="code" class="python">return the type of the value stored at key</code></li><li> <a href="KeysCommand.html">KEYS</a> <i>pattern</i> <code name="code" class="python">return all the keys matching a given pattern</code></li><li> <a href="RandomkeyCommand.html">RANDOMKEY</a> <code name="code" class="python">return a random key from the key space</code></li><li> <a href="RenameCommand.html">RENAME</a> <i>oldname</i> <i>newname</i> <code name="code" class="python">rename the old key in the new one, destroing the newname key if it already exists</code></li><li> <a href="RenamenxCommand.html">RENAMENX</a> <i>oldname</i> <i>newname</i> <code name="code" class="python">rename the old key in the new one, if the newname key does not already exist</code></li><li> <a href="DbsizeCommand.html">DBSIZE</a> <code name="code" class="python">return the number of keys in the current db</code></li><li> <a href="ExpireCommand.html">EXPIRE</a> <code name="code" class="python">set a time to live in seconds on a key</code></li><li> <a href="TtlCommand.html">TTL</a> <code name="code" class="python">get the time to live in seconds of a key</code></li><li> <a href="SelectCommand.html">SELECT</a> <i>index</i> <code name="code" class="python">Select the DB having the specified index</code></li><li> <a href="MoveCommand.html">MOVE</a> <i>key</i> <i>dbindex</i> <code name="code" class="python">Move the key from the currently selected DB to the DB having as index dbindex</code></li><li> <a href="FlushdbCommand.html">FLUSHDB</a> <code name="code" class="python">Remove all the keys of the currently selected DB</code></li><li> <a href="FlushallCommand.html">FLUSHALL</a> <code name="code" class="python">Remove all the keys from all the databases</code></li></ul>
<h2><a name="Commands operating on string values">Commands operating on string values</a></h2><ul><li> <a href="SetCommand.html">SET</a> <i>key</i> <i>value</i> <code name="code" class="python">set a key to a string value</code></li><li> <a href="GetCommand.html">GET</a> <i>key</i> <code name="code" class="python">return the string value of the key</code></li><li> <a href="GetsetCommand.html">GETSET</a> <i>key</i> <i>value</i> <code name="code" class="python">set a key to a string returning the old value of the key</code></li><li> <a href="MgetCommand.html">MGET</a> <i>key1</i> <i>key2</i> ... <i>keyN</i> <code name="code" class="python">multi-get, return the strings values of the keys</code></li><li> <a href="SetnxCommand.html">SETNX</a> <i>key</i> <i>value</i> <code name="code" class="python">set a key to a string value if the key does not exist</code></li><li> <a href="MsetCommand.html">MSET</a> <i>key1</i> <i>value1</i> <i>key2</i> <i>value2</i> ... <i>keyN</i> <i>valueN</i> <code name="code" class="python">set a multiple keys to multiple values in a single atomic operation</code></li><li> <a href="MsetCommand.html">MSETNX</a> <i>key1</i> <i>value1</i> <i>key2</i> <i>value2</i> ... <i>keyN</i> <i>valueN</i> <code name="code" class="python">set a multiple keys to multiple values in a single atomic operation if none of the keys already exist</code></li><li> <a href="IncrCommand.html">INCR</a> <i>key</i> <code name="code" class="python">increment the integer value of key</code></li><li> <a href="IncrCommand.html">INCRBY</a> <i>key</i> <i>integer</i><code name="code" class="python"> increment the integer value of key by integer</code></li><li> <a href="IncrCommand.html">DECR</a> <i>key</i> <code name="code" class="python">decrement the integer value of key</code></li><li> <a href="IncrCommand.html">DECRBY</a> <i>key</i> <i>integer</i> <code name="code" class="python">decrement the integer value of key by integer</code></li></ul>
<h2><a name="Commands operating on lists">Commands operating on lists</a></h2><ul><li> <a href="RpushCommand.html">RPUSH</a> <i>key</i> <i>value</i> <code name="code" class="python">Append an element to the tail of the List value at key</code></li><li> <a href="RpushCommand.html">LPUSH</a> <i>key</i> <i>value</i> <code name="code" class="python">Append an element to the head of the List value at key</code></li><li> <a href="LlenCommand.html">LLEN</a> <i>key</i> <code name="code" class="python">Return the length of the List value at key</code></li><li> <a href="LrangeCommand.html">LRANGE</a> <i>key</i> <i>start</i> <i>end</i> <code name="code" class="python">Return a range of elements from the List at key</code></li><li> <a href="LtrimCommand.html">LTRIM</a> <i>key</i> <i>start</i> <i>end</i> <code name="code" class="python">Trim the list at key to the specified range of elements</code></li><li> <a href="LindexCommand.html">LINDEX</a> <i>key</i> <i>index</i> <code name="code" class="python">Return the element at index position from the List at key</code></li><li> <a href="LsetCommand.html">LSET</a> <i>key</i> <i>index</i> <i>value</i> <code name="code" class="python">Set a new value as the element at index position of the List at key</code></li><li> <a href="LremCommand.html">LREM</a> <i>key</i> <i>count</i> <i>value</i> <code name="code" class="python">Remove the first-N, last-N, or all the elements matching value from the List at key</code></li><li> <a href="LpopCommand.html">LPOP</a> <i>key</i> <code name="code" class="python">Return and remove (atomically) the first element of the List at key</code></li><li> <a href="LpopCommand.html">RPOP</a> <i>key</i> <code name="code" class="python">Return and remove (atomically) the last element of the List at key</code></li><li> <a href="RpoplpushCommand.html">RPOPLPUSH</a> <i>srckey</i> <i>dstkey</i> <code name="code" class="python">Return and remove (atomically) the last element of the source List stored at _srckey_ and push the same element to the destination List stored at _dstkey_</code></li></ul>
<h2><a name="Commands operating on sets">Commands operating on sets</a></h2><ul><li> <a href="SaddCommand.html">SADD</a> <i>key</i> <i>member</i> <code name="code" class="python">Add the specified member to the Set value at key</code></li><li> <a href="SremCommand.html">SREM</a> <i>key</i> <i>member</i> <code name="code" class="python">Remove the specified member from the Set value at key</code></li><li> <a href="SpopCommand.html">SPOP</a> <i>key</i> <code name="code" class="python">Remove and return (pop) a random element from the Set value at key</code></li><li> <a href="SmoveCommand.html">SMOVE</a> <i>srckey</i> <i>dstkey</i> <i>member</i> <code name="code" class="python">Move the specified member from one Set to another atomically</code></li><li> <a href="ScardCommand.html">SCARD</a> <i>key</i> <code name="code" class="python">Return the number of elements (the cardinality) of the Set at key</code></li><li> <a href="SismemberCommand.html">SISMEMBER</a> <i>key</i> <i>member</i> <code name="code" class="python">Test if the specified value is a member of the Set at key</code></li><li> <a href="SinterCommand.html">SINTER</a> <i>key1</i> <i>key2</i> ... <i>keyN</i> <code name="code" class="python">Return the intersection between the Sets stored at key1, key2, ..., keyN</code></li><li> <a href="SinterstoreCommand.html">SINTERSTORE</a> <i>dstkey</i> <i>key1</i> <i>key2</i> ... <i>keyN</i> <code name="code" class="python">Compute the intersection between the Sets stored at key1, key2, ..., keyN, and store the resulting Set at dstkey</code></li><li> <a href="SunionCommand.html">SUNION</a> <i>key1</i> <i>key2</i> ... <i>keyN</i> <code name="code" class="python">Return the union between the Sets stored at key1, key2, ..., keyN</code></li><li> <a href="SunionstoreCommand.html">SUNIONSTORE</a> <i>dstkey</i> <i>key1</i> <i>key2</i> ... <i>keyN</i> <code name="code" class="python">Compute the union between the Sets stored at key1, key2, ..., keyN, and store the resulting Set at dstkey</code></li><li> <a href="SdiffCommand.html">SDIFF</a> <i>key1</i> <i>key2</i> ... <i>keyN</i> <code name="code" class="python">Return the difference between the Set stored at key1 and all the Sets key2, ..., keyN</code></li><li> <a href="SdiffstoreCommand.html">SDIFFSTORE</a> <i>dstkey</i> <i>key1</i> <i>key2</i> ... <i>keyN</i> <code name="code" class="python">Compute the difference between the Set key1 and all the Sets key2, ..., keyN, and store the resulting Set at dstkey</code></li><li> <a href="SmembersCommand.html">SMEMBERS</a> <i>key</i> <code name="code" class="python">Return all the members of the Set value at key</code></li><li> <a href="SrandmemberCommand.html">SRANDMEMBER</a> <i>key</i> <code name="code" class="python">Return a random member of the Set value at key</code></li></ul>
<h2><a name="Commands operating on sorted sets (zsets, Redis version &gt;">Commands operating on sorted sets (zsets, Redis version &gt;</a></h2> 1.1) ==<br/><br/><ul><li> <a href="ZaddCommand.html">ZADD</a> <i>key</i> <i>score</i> <i>member</i> <code name="code" class="python">Add the specified member to the Sorted Set value at key or update the score if it already exist</code></li><li> <a href="ZremCommand.html">ZREM</a> <i>key</i> <i>member</i> <code name="code" class="python">Remove the specified member from the Sorted Set value at key</code></li><li> <a href="ZincrbyCommand.html">ZINCRBY</a> <i>key</i> <i>increment</i> <i>member</i> <code name="code" class="python">If the member already exists increment its score by _increment_, otherwise add the member setting _increment_ as score</code></li><li> <a href="ZrangeCommand.html">ZRANGE</a> <i>key</i> <i>start</i> <i>end</i> <code name="code" class="python">Return a range of elements from the sorted set at key</code></li><li> <a href="ZrangeCommand.html">ZREVRANGE</a> <i>key</i> <i>start</i> <i>end</i> <code name="code" class="python">Return a range of elements from the sorted set at key, exactly like ZRANGE, but the sorted set is ordered in traversed in reverse order, from the greatest to the smallest score</code></li><li> <a href="ZrangebyscoreCommand.html">ZRANGEBYSCORE</a> <i>key</i> <i>min</i> <i>max</i> <code name="code" class="python">Return all the elements with score &gt;= min and score &lt;= max (a range query) from the sorted set</code></li><li> <a href="ZcardCommand.html">ZCARD</a> <i>key</i> <code name="code" class="python">Return the cardinality (number of elements) of the sorted set at key</code></li><li> <a href="ZscoreCommand.html">ZSCORE</a> <i>key</i> <i>element</i> <code name="code" class="python">Return the score associated with the specified element of the sorted set at key</code></li><li> <a href="ZremrangebyscoreCommand.html">ZREMRANGEBYSCORE</a> <i>key</i> <i>min</i> <i>max</i> <code name="code" class="python">Remove all the elements with score &gt;= min and score &lt;= max from the sorted set</code></li></ul>
<h2><a name="Sorting">Sorting</a></h2><ul><li> <a href="SortCommand.html">SORT</a> <i>key</i> BY <i>pattern</i> LIMIT <i>start</i> <i>end</i> GET <i>pattern</i> ASC|DESC ALPHA <code name="code" class="python">Sort a Set or a List accordingly to the specified parameters</code></li></ul>
<h2><a name="Persistence control commands">Persistence control commands</a></h2><ul><li> <a href="SaveCommand.html">SAVE</a> <code name="code" class="python">Synchronously save the DB on disk</code></li><li> <a href="BgsaveCommand.html">BGSAVE</a> <code name="code" class="python">Asynchronously save the DB on disk</code></li><li> <a href="LastsaveCommand.html">LASTSAVE</a> <code name="code" class="python">Return the UNIX time stamp of the last successfully saving of the dataset on disk</code></li><li> <a href="ShutdownCommand.html">SHUTDOWN</a> <code name="code" class="python">Synchronously save the DB on disk, then shutdown the server</code></li><li> <a href="BgrewriteaofCommand.html">BGREWRITEAOF</a> <code name="code" class="python">Rewrite the append only file in background when it gets too big</code></li></ul>
<h2><a name="Remote server control commands">Remote server control commands</a></h2><ul><li> <a href="InfoCommand.html">INFO</a> <code name="code" class="python">Provide information and statistics about the server</code></li><li> <a href="MonitorCommand.html">MONITOR</a> <code name="code" class="python">Dump all the received requests in real time</code></li><li> <a href="SlaveofCommand.html">SLAVEOF</a> <code name="code" class="python">Change the replication settings</code></li></ul>
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<b>Comparisons: Contents</b><br>&nbsp;&nbsp;&nbsp;&nbsp;<a href="#Memcached">Memcached</a><br>&nbsp;&nbsp;&nbsp;&nbsp;<a href="#Tokyo Cabinet / Toyo Tyrant">Tokyo Cabinet / Toyo Tyrant</a>
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&iuml;&raquo;&iquest;if your are asking yourself how is Redis different fom other key-value stores here you will find it compared to some of the most popular contendors (all great software) in this category. <h2><a name="Memcached">Memcached</a></h2><ul><li> Memcached is not persistent, it just holds everything in memory without saving since its main goal is to be used as a cache, while Redis is <a href="Persistence.html">persistent</a>.</li></ul>
<ul><li> Like memcached Redis uses a key-value model, but while keys can just be strings, values in Redis can be <a href="Lists.html">Lists</a>, <a href="Sets.html">Sets</a> or <a href="SortedSets.html">SortedSets</a> and complex operations like intersections, set/get n-th element of lists, pop/push of elements, can be performed against sets and lists.</li></ul>
<h2><a name="Tokyo Cabinet / Toyo Tyrant">Tokyo Cabinet / Toyo Tyrant</a></h2>Redis and Tokyo Cabinet can be used for the same applications, but actually they are <i>very different</i> beasts. If you read Twitter messages of people involved in scalable things both products are reported to work well, but surely there are times where one or the other can be the best choice.<br/><br/><ul><li> Tokyo Cabinet writes synchronously on disk, Redis takes the whole dataset on memory and writes on disk asynchronously. Tokyo Cabinet is safer and probably a better idea if your dataset is going to be bigger than RAM, but Redis is faster (note that Redis supports master-slave replication that is trivial to setup, so you are safe anyway if you want a setup where data can't be lost even after a disaster). </li></ul>
<ul><li> Redis supports higher level operations and data structures. Tokyo Cabinet supports a kind of database that is able to organize data into rows with named fields (in a way very similar to Berkeley DB) but can't do things like server side List and Set operations Redis is able to do: pushing or popping from Lists in an atomic way, in O(1) time complexity, server side Set intersections, Sorting of schema free data in complex ways (By the way TC supports sorting in the table-based database format). Redis on the other hand does not support the abstraction of tables with fields, the idea is that you can build this stuff in software easily if you really need a table-alike approach. </li></ul>
<ul><li> Tokyo Cabinet does not implement a networking layer. You have to use a networking layer called Tokyo Tyrant that interfaces to Tokyo Cabinet so you can talk to Tokyo Cabinet in a client-server fashion. In Redis the networking support is built-in inside the server, and is basically the only interface between the external world and the dataset. </li></ul>
<ul><li> Redis is reported to be much faster, especially if you plan to access Tokyo Cabinet via Tokyo Tyrant. Here I can only say that with Redis you can expect 100,000 operations/seconds with a normal Linux box and 50 concurrent clients. You should test Redis, Tokyo, and the other alternatives with your specific work load to get a feeling about performances for your application. </li></ul>
<ul><li> Redis is not an on-disk DB engine like Tokyo: the latter can be used as a fast DB engine in your C project without the networking overhead just linking to the library. Still in many scalable applications you need multiple servers talking with multiple clients, so the client-server model is almost always needed, this is why in Redis this is built-in. </li></ul>
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The <code name="code" class="python">redis.conf</code> file included in the source code distribution is a starting point, you should be able to modify it in order do adapt it to your needs without troubles reading the comments inside the file.<br/><br/>In order to start Redis using a configuration file just pass the file name as the sole argument when starting the server:<br/><br/><pre class="codeblock python" name="code">
$ ./redis-server redis.conf
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&iuml;&raquo;&iquest;== Connection handling ==<br/><br/><ul><li> <a href="QuitCommand.html">QUIT</a></li><li> <a href="AuthCommand.html">AUTH</a></li></ul>
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&iuml;&raquo;&iquest;== Control Commands ==<br/><br/><ul><li> <a href="SaveCommand.html">SAVE</a></li><li> <a href="BgsaveCommand.html">BGSAVE</a></li><li> <a href="BgrewriteaofCommand.html">BGREWRITEAOF</a></li><li> <a href="LastsaveCommand.html">LASTSAVE</a></li><li> <a href="ShutdownCommand.html">SHUTDOWN</a></li><li> <a href="InfoCommand.html">INFO</a></li><li> <a href="MonitorCommand.html">MONITOR</a></li><li> <a href="SlaveofCommand.html">SLAVEOF</a></li></ul>
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<h1><a name="Credits">Credits</a></h1><ul><li> The Redis server was designed and written by <a href="http://invece.org" target="_blank">Salvatore Sanfilippo (aka antirez)</a></li><li> <a href="http://brainspl.at/" target="_blank">Ezra Zygmuntowicz (aka ezmobius)</a> - Ruby client lib initial version and hacking</li><li> <a href="http://qix.it" target="_blank">Ludovico Magnocavallo (aka ludo)</a> - Python clinet lib</li><li> <a href="http://www.adroll.com/" target="_blank">Valentino Volonghi of Adroll</a> - Erlang client lib</li><li> <b>brettbender</b> - found and fixed a bug in sds.c that caused the server to crash at least on 64 bit systems, and anyway to be buggy since we used the same vararg thing against vsprintf without to call va_start and va_end every time.</li><li> <a href="http://www.rot13.org/~dpavlin" target="_blank">Dobrica Pavlinusic</a> - Perl client lib</li><li> Brian Hammond - AUTH command implementation, C++ client lib</li><li> <a href="http://www.clorophilla.net/" target="_blank">Daniele Alessandri</a> - Lua client lib</li><li> Corey Stup - C99 cleanups</li><li> Taylor Weibley - Ruby client improvements</li><li> Bob Potter - Rearrange redisObject struct to reduce memory usage in 64bit environments</li><li> Luca Guidi and Brian McKinney - Ruby client improvements</li><li> Aman Gupta - SDIFF / SDIFFSTORE, other Set operations improvements, ability to disable clients timeout.</li><li> Diego Rosario Brogna - Code and ideas about dumping backtrace on sigsegv and similar error conditions.</li></ul>
p.s. sorry to take this file in sync is hard in this early days. Please drop me an email if I forgot to add your name here!
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<b>DbsizeCommand: Contents</b><br>&nbsp;&nbsp;<a href="#DBSIZE">DBSIZE</a><br>&nbsp;&nbsp;&nbsp;&nbsp;<a href="#Return value">Return value</a>
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&iuml;&raquo;&iquest;#sidebar <a href="GenericCommandsSidebar.html">GenericCommandsSidebar</a><h1><a name="DBSIZE">DBSIZE</a></h1><blockquote>Return the number of keys in the currently selected database.</blockquote>
<h2><a name="Return value">Return value</a></h2><a href="ReplyTypes.html">Integer reply</a>
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<b>DelCommand: Contents</b><br>&nbsp;&nbsp;<a href="#DEL _key1_ _key2_ ... _keyN_">DEL _key1_ _key2_ ... _keyN_</a><br>&nbsp;&nbsp;&nbsp;&nbsp;<a href="#Return value">Return value</a>
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&iuml;&raquo;&iquest;#sidebar <a href="GenericCommandsSidebar.html">GenericCommandsSidebar</a><h1><a name="DEL _key1_ _key2_ ... _keyN_">DEL _key1_ _key2_ ... _keyN_</a></h1>
<i>Time complexity: O(1)</i><blockquote>Remove the specified keys. If a given key does not existno operation is performed for this key. The commnad returns the number ofkeys removed.</blockquote>
<h2><a name="Return value">Return value</a></h2><a href="ReplyTypes.html">Integer reply</a>, specifically:<br/><br/><pre class="codeblock python" name="code">
an integer greater than 0 if one or more keys were removed
0 if none of the specified key existed
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Use random keys instead of incremental keys in order to avoid a single-key that gets incremented by many servers. This can can't be distributed among servers.
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&iuml;&raquo;&iquest;#sidebar <a href="GenericCommandsSidebar.html">GenericCommandsSidebar</a><h1><a name="EXISTS _key_">EXISTS _key_</a></h1>
<i>Time complexity: O(1)</i><blockquote>Test if the specified key exists. The command returns&quot;0&quot; if the key exists, otherwise &quot;1&quot; is returned.Note that even keys set with an empty string as value willreturn &quot;1&quot;.</blockquote>
<h2><a name="Return value">Return value</a></h2><a href="ReplyTypes.html">Integer reply</a>, specifically:<br/><br/><pre class="codeblock python" name="code">
1 if the key exists.
0 if the key does not exist.
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<b>ExpireCommand: Contents</b><br>&nbsp;&nbsp;<a href="#EXPIRE _key_ _seconds_">EXPIRE _key_ _seconds_</a><br>&nbsp;&nbsp;<a href="#EXPIREAT _key_ _unixtime_ (Redis &gt;">EXPIREAT _key_ _unixtime_ (Redis &gt;</a><br>&nbsp;&nbsp;&nbsp;&nbsp;<a href="#How the expire is removed from a key">How the expire is removed from a key</a><br>&nbsp;&nbsp;&nbsp;&nbsp;<a href="#Restrictions with write operations against volatile keys">Restrictions with write operations against volatile keys</a><br>&nbsp;&nbsp;&nbsp;&nbsp;<a href="#Setting the timeout again on already volatile keys">Setting the timeout again on already volatile keys</a><br>&nbsp;&nbsp;&nbsp;&nbsp;<a href="#Enhanced Lazy Expiration algorithm">Enhanced Lazy Expiration algorithm</a><br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href="#Version 1.0">Version 1.0</a><br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href="#Version 1.1">Version 1.1</a><br>&nbsp;&nbsp;&nbsp;&nbsp;<a href="#Return value">Return value</a>
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<h1 class="wikiname">ExpireCommand</h1>
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&iuml;&raquo;&iquest;#sidebar <a href="GenericCommandsSidebar.html">GenericCommandsSidebar</a><h1><a name="EXPIRE _key_ _seconds_">EXPIRE _key_ _seconds_</a></h1>
<h1><a name="EXPIREAT _key_ _unixtime_ (Redis &gt;">EXPIREAT _key_ _unixtime_ (Redis &gt;</a></h1> 1.1)=
<i>Time complexity: O(1)</i><blockquote>Set a timeout on the specified key. After the timeout the key will beautomatically delete by the server. A key with an associated timeout issaid to be <i>volatile</i> in Redis terminology.</blockquote>
<blockquote>Voltile keys are stored on disk like the other keys, the timeout is persistenttoo like all the other aspects of the dataset. Saving a dataset containingthe dataset and stopping the server does not stop the flow of time as Redisregisters on disk when the key will no longer be available as Unix time, andnot the remaining seconds.</blockquote>
<blockquote>EXPIREAT works exctly like EXPIRE but instead to get the number of secondsrepresenting the Time To Live of the key as a second argument (that is arelative way of specifing the TTL), it takes an absolute one in the form ofa UNIX timestamp (Number of seconds elapsed since 1 Gen 1970).</blockquote>
<blockquote>EXPIREAT was introduced in order to implement [Persistence append only saving mode] so that EXPIRE commands are automatically translated into EXPIREAT commands for the append only file. Of course EXPIREAT can alsoused by programmers that need a way to simply specify that a given key should expire at a given time in the future.</blockquote>
<h2><a name="How the expire is removed from a key">How the expire is removed from a key</a></h2><blockquote>When the key is set to a new value using the SET command, the INCR commandor any other command that modify the value stored at key the timeout isremoved from the key and the key becomes non volatile.</blockquote>
<h2><a name="Restrictions with write operations against volatile keys">Restrictions with write operations against volatile keys</a></h2><blockquote>Write operations like LPUSH, LSET and every other command that has theeffect of modifying the value stored at a volatile key have a special semantic:basically a volatile key is destroyed when it is target of a write operation.See for example the following usage pattern:</blockquote>
<pre class="codeblock python" name="code">
% ./redis-cli lpush mylist foobar /Users/antirez/hack/redis
OK
% ./redis-cli lpush mylist hello /Users/antirez/hack/redis
OK
% ./redis-cli expire mylist 10000 /Users/antirez/hack/redis
1
% ./redis-cli lpush mylist newelement
OK
% ./redis-cli lrange mylist 0 -1 /Users/antirez/hack/redis
1. newelement
</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>
<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>
<h2><a name="Return value">Return value</a></h2><a href="ReplyTypes.html">Integer reply</a>, specifically:<br/><br/><pre class="codeblock python python" name="code">
1: the timeout was set.
0: the timeout was not set since the key already has an associated timeout, or the key does not exist.
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<b>FAQ: Contents</b><br>&nbsp;&nbsp;<a href="#Isn't this key-value thing just hype?">Isn't this key-value thing just hype?</a><br>&nbsp;&nbsp;<a href="#Can I backup a Redis DB while the server is working?">Can I backup a Redis DB while the server is working?</a><br>&nbsp;&nbsp;<a href="#What's the Redis memory footprint?">What's the Redis memory footprint?</a><br>&nbsp;&nbsp;<a href="#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><br>&nbsp;&nbsp;<a href="#Why Redis takes the whole dataset in RAM?">Why Redis takes the whole dataset in RAM?</a><br>&nbsp;&nbsp;<a href="#If my dataset is too big for RAM and I don't want to use consistent hashing or other ways to distribute the dataset across different nodes, what I can do to use Redis anyway?">If my dataset is too big for RAM and I don't want to use consistent hashing or other ways to distribute the dataset across different nodes, what I can do to use Redis anyway?</a><br>&nbsp;&nbsp;<a href="#Do you plan to implement Virtual Memory in Redis? Why don't just let the Operating System handle it for you?">Do you plan to implement Virtual Memory in Redis? Why don't just let the Operating System handle it for you?</a><br>&nbsp;&nbsp;<a href="#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><br>&nbsp;&nbsp;<a href="#What happens if Redis runs out of memory?">What happens if Redis runs out of memory?</a><br>&nbsp;&nbsp;<a href="#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><br>&nbsp;&nbsp;<a href="#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><br>&nbsp;&nbsp;<a href="#Are Redis on disk snapshots atomic?">Are Redis on disk snapshots atomic?</a><br>&nbsp;&nbsp;<a href="#Redis is single threaded, how can I exploit multiple CPU / cores?">Redis is single threaded, how can I exploit multiple CPU / cores?</a><br>&nbsp;&nbsp;<a href="#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><br>&nbsp;&nbsp;<a href="#What is the maximum number of keys a single Redis instance can hold? and what the max number of elements in a List, Set, Ordered Set?">What is the maximum number of keys a single Redis instance can hold? and what the max number of elements in a List, Set, Ordered Set?</a><br>&nbsp;&nbsp;<a href="#What Redis means actually?">What Redis means actually?</a><br>&nbsp;&nbsp;<a href="#Why did you started the Redis project?">Why did you started the Redis project?</a>
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<h1 class="wikiname">FAQ</h1>
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&iuml;&raquo;&iquest;= Why I need Redis if there is already memcachedb, Tokyo Cabinet, ...? =<br/><br/>Memcachedb is basically memcached done persistent. Redis is a different evolution
path in the key-value DBs, the idea is that the main advantages of key-value DBs
are retained even without a so severe loss of comfort of plain key-value DBs.
So Redis offers more features:<br/><br/><ul><li> Keys can store different data types, not just strings. Notably Lists and Sets. For example if you want to use Redis as a log storage system for different computers every computer can just <code name="code" class="python">RPUSH data to the computer_ID key</code>. Don't want to save more than 1000 log lines per computer? Just issue a <code name="code" class="python">LTRIM computer_ID 0 999</code> command to trim the list after every push.</li></ul>
<ul><li> Another example is about Sets. Imagine to build a social news site like <a href="http://reddit.com" target="_blank">Reddit</a>. Every time a user upvote a given news you can just add to the news_ID_upmods key holding a value of type SET the id of the user that did the upmodding. Sets can also be used to index things. Every key can be a tag holding a SET with the IDs of all the objects associated to this tag. Using Redis set intersection you obtain the list of IDs having all this tags at the same time.</li></ul>
<ul><li> We wrote a <a href="http://retwis.antirez.com" target="_blank">simple Twitter Clone</a> using just Redis as database. Download the source code from the download section and imagine to write it with a plain key-value DB without support for lists and sets... it's <b>much</b> harder.</li></ul>
<ul><li> Multiple DBs. Using the SELECT command the client can select different datasets. This is useful because Redis provides a MOVE atomic primitive that moves a key form a DB to another one, if the target DB already contains such a key it returns an error: this basically means a way to perform locking in distributed processing.</li></ul>
<ul><li> <b>So what is Redis really about?</b> The User interface with the programmer. Redis aims to export to the programmer the right tools to model a wide range of problems. <b>Sets, Lists with O(1) push operation, lrange and ltrim, server-side fast intersection between sets, are primitives that allow to model complex problems with a key value database</b>.</li></ul>
<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 &quot;Hello World&quot; 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>Short answer: If you are using a Redis client that supports consistent hashing you can distribute the dataset across different nodes. For instance the Ruby clients supports this feature. There are plans to develop redis-cluster that basically is a dummy Redis server that is only used in order to distribute the requests among N different nodes using consistent hashing.<h1><a name="Why Redis takes the whole dataset in RAM?">Why Redis takes the whole dataset in RAM?</a></h1>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.<br/><br/>Amazon EC2 now provides instances with 32 or 64 GB of RAM.<h1><a name="If my dataset is too big for RAM and I don't want to use consistent hashing or other ways to distribute the dataset across different nodes, what I can do to use Redis anyway?">If my dataset is too big for RAM and I don't want to use consistent hashing or other ways to distribute the dataset across different nodes, what I can do to use Redis anyway?</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.<br/><br/>Update: it could be interesting to test how Redis performs with datasets larger than memory if the OS swap partition is in one of this very fast Intel SSD disks.<h1><a name="Do you plan to implement Virtual Memory in Redis? Why don't just let the Operating System handle it for you?">Do you plan to implement Virtual Memory in Redis? Why don't just let the Operating System handle it for you?</a></h1>Yes, in order to support datasets bigger than RAM there is the plan to
implement transparent Virtual Memory in Redis, that is, the ability to
transfer large values associated to keys rarely used on Disk, and
reload them transparently in memory when this values are requested in
some way.<br/><br/>So you may ask why don't let the operating system VM do the work for
us. There are two main reasons: in Redis even a large value stored at
a given key, for instance a 1 million elements list, is not allocated
in a contiguous piece of memory. It's actually <b>very</b> fragmented since
Redis uses quite aggressive object sharing and allocated Redis Objects
structures reuse.<br/><br/>So you can imagine the memory layout composed of 4096 bytes pages that
actually contain different parts of different large values. Not only,
but a lot of values that are large enough for us to swap out to disk,
like a 1024k value, is just one quarter the size of a memory page, and
likely in the same page there are other values that are not rarely
used. So this value wil never be swapped out by the operating system.
This is the first reason for implementing application-level virtual
memory in Redis.<br/><br/>There is another one, as important as the first. A complex object in
memory like a list or a set is something <b>10 times bigger</b> than the
same object serialized on disk. Probably you already noticed how Redis
snapshots on disk are damn smaller compared to the memory usage of
Redis for the same objects. This happens because when data is in
memory is full of pointers, reference counters and other metadata. Add
to this malloc fragmentation and need to return word-aligned chunks of
memory and you have a clear picture of what happens. So this means to
have 10 times the I/O between memory and disk than otherwise needed.<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/>The 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/>You can also use the &quot;maxmemory&quot; option in the config file to put a limit to the memory Redis can use. If this limit is reached Redis will start to reply with an error to write commands (but will continue to accept read-only commands).<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 &gt; /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="Are Redis on disk snapshots atomic?">Are Redis on disk snapshots atomic?</a></h1>Yes, redis background saving process is always fork(2)ed when the server is outside of the execution of a command, so every command reported to be atomic in RAM is also atomic from the point of view of the disk snapshot.<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 &quot;foo&quot;, what's the right server where to put &quot;foo&quot; in order to distribute keys evenly among different servers? Just perform the <i>crc</i> = CRC32(&quot;foo&quot;), 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 &quot;foo&quot; 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 &quot;foo{bared}&quot; the key hashing code will simply perform the CRC32 of &quot;bared&quot;. 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? and what the max number of elements in a List, Set, Ordered Set?">What is the maximum number of keys a single Redis instance can hold? and what the max number of elements in a List, Set, Ordered Set?</a></h1>In theory Redis can handle up to 2<sup>32 keys, and was tested in practice to handle at least 150 million of keys per instance. We are working in order to experiment with larger values.<br/><br/>Every list, set, and ordered set, can hold 2</sup>32 elements.<br/><br/>Actually Redis internals are ready to allow up to 2<sup>64 elements but the current disk dump format don't support this, and there is a lot time to fix this issues in the future as currently even with 128 GB of RAM it's impossible to reach 2</sup>32 elements.<h1><a name="What Redis means actually?">What Redis means actually?</a></h1>Redis means two things:
<ul><li> it's a joke on the word Redistribute (instead to use just a Relational DB redistribute your workload among Redis servers)</li><li> it means REmote DIctionary Server</li></ul>
<h1><a name="Why did you started the Redis project?">Why did you started the Redis project?</a></h1>In order to scale <a href="http://lloogg.com" target="_blank">LLOOGG</a>. But after I got the basic server working I liked the idea to share the work with other guys, and Redis was turned into an open source project.
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<b>Features: Contents</b><br>&nbsp;&nbsp;<a href="#Features (DRAFT)">Features (DRAFT)</a><br>&nbsp;&nbsp;&nbsp;&nbsp;<a href="#Speed">Speed</a><br>&nbsp;&nbsp;&nbsp;&nbsp;<a href="#Persistence">Persistence</a><br>&nbsp;&nbsp;&nbsp;&nbsp;<a href="#Support for Data Structures">Support for Data Structures</a><br>&nbsp;&nbsp;&nbsp;&nbsp;<a href="#Atomic Operations">Atomic Operations</a><br>&nbsp;&nbsp;&nbsp;&nbsp;<a href="#Variety of Supported Languages">Variety of Supported Languages</a><br>&nbsp;&nbsp;&nbsp;&nbsp;<a href="#Master/Slave Replication">Master/Slave Replication</a><br>&nbsp;&nbsp;&nbsp;&nbsp;<a href="#Sharding">Sharding</a><br>&nbsp;&nbsp;&nbsp;&nbsp;<a href="#Hot Backups">Hot Backups</a><br>&nbsp;&nbsp;&nbsp;&nbsp;<a href="#Simple to Install, Setup and Manage">Simple to Install, Setup and Manage</a><br>&nbsp;&nbsp;&nbsp;&nbsp;<a href="#Portable">Portable</a><br>&nbsp;&nbsp;&nbsp;&nbsp;<a href="#Liberal Licensing">Liberal Licensing</a><br>&nbsp;&nbsp;&nbsp;&nbsp;<a href="#What's next?">What's next?</a>
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<h1><a name="Features (DRAFT)">Features (DRAFT)</a></h1>Checking Redis for the first time? Here your will find the most important features, and pointers to a lot more information.<h2><a name="Speed">Speed</a></h2>Redis is written in ANSI C, and loads the whole dataset in memory, so it is wicked <i><b>fast</b>!</i> Up to 110,000 <a href="SETs.html">SETs</a>/second, 81,000 GETs/second can be achieved in an entry level Linux box. Read more about Redis <a href="Speed.html">Speed</a>.<br/><br/>Also Redis supports <a href="Pipelining.html">Pipelining</a> of commands and <a href="MultiBulkCommands.html">getting and setting m&Atilde;&ordm;ltiple values in a single command</a> to speed up communication with the client libraries.<h2><a name="Persistence">Persistence</a></h2>While all the data lives in memory, changes are <i>asynchronously</i> saved on disk using flexible policies based on elapsed time and/or number of updates since last save. <br/><br/>If you can't afford losing some data, starting on version 1.1 (currently in beta but you can download it from the Git repository) Redis supports an append-only file persistence mode. Check more on <a href="Persistence.html">Persistence</a>, or read the <a href="AppendOnlyFileHowto.html">AppendOnlyFileHowto</a> for more information.<h2><a name="Support for Data Structures">Support for Data Structures</a></h2>Values in Redis can be <a href="Strings.html">Strings</a> as in a conventional key-value store, but also <a href="Lists.html">Lists</a>, <a href="Sets.html">Sets</a>, and <a href="SortedSets.html">SortedSets</a> (to be support in <a href="RoadMap.html">version 1.1</a>). This data types allow pushing/poping elements, or adding/removing them, also perform server side union, intersection, difference between sets, and so forth depending on the types. Redis supports different kind of sorting abilities for <a href="Sets.html">Sets</a> and <a href="Lists.html">Lists</a>.<br/><br/>You can think in Redis as a <b>Data Structures Server</b>, that allows you to model non trivial problems. Read <a href="DataTypes.html">Data Types</a> to learn more about the way Redis handle <a href="Strings.html">Strings</a>, and the <a href="Commands.html">Commands</a> supported by <a href="Lists.html">Lists</a>, <a href="Sets.html">Sets</a> and <a href="SortedSets.html">SortedSets</a><h2><a name="Atomic Operations">Atomic Operations</a></h2>Redis operations working on the different Data Types are <b>atomic</b>, so setting or increasing a key, adding and removing elements from a set, increasing a counter will all be accomplished safely.<h2><a name="Variety of Supported Languages">Variety of Supported Languages</a></h2>Ruby, Python, Twisted Python, PHP, Erlang, Tcl, Perl, Lua, Java, Scala, Clojure, choose your poison. Check the list of <a href="SupportedLanguages.html">Supported Languages</a> for all the details.<br/><br/>If your favorite language is not supported yet, you can write your own client library, as the <a href="ProtocolSpecification.html">Protocol</a> is pretty simple.<h2><a name="Master/Slave Replication">Master/Slave Replication</a></h2>Redis supports a very simple and fast Master/Slave replication. Is so simple it takes only one line in the <a href="Configuration.html">configuration file</a> to set it up, and 21 seconds for a Slave to complete the initial sync of 10 MM key set in a Amazon EC2 instance.<br/><br/>Read more about Master/Slave <a href="Replication.html">Replication</a>. <h2><a name="Sharding">Sharding</a></h2>Distributing the dataset across multiple Redis instances is easy in Redis, as in any other key-value store. And this depends basically on the <a href="Supported.html">Languages</a> client libraries being able to do so. <br/><br/>Read more about <a href="Sharding.html">Sharding</a> if you want to know more abour distributing data and workload in Redis.<h2><a name="Hot Backups">Hot Backups</a></h2>TODO<h2><a name="Simple to Install, Setup and Manage">Simple to Install, Setup and Manage</a></h2>Installing Redis requires little more than downloading it, uncompressing it and running make. Management is near zero, so you can start using Redis in a matter of minutes.<br/><br/>Go on and read about Redis <a href="Installation.html">installation</a>, its <a href="Setup.html">Setup</a> and <a href="Management.html">Management</a>.<h2><a name="Portable">Portable</a></h2>Redis is written in ANSI C and works in most POSIX systems like Linux, BSD, Mac OS X, Solaris, and so on. Redis is reported to compile and work under WIN32 if compiled with Cygwin, but there is no official support for Windows currently.<h2><a name="Liberal Licensing">Liberal Licensing</a></h2>Redis is free software released under the very liberal BSD license.<h2><a name="What's next?">What's next?</a></h2>Want to get started with Redis? Try the <a href="QuickStart.html">Quick Start</a> you will be up and running in just a matter of minutes. <br/><br/>Check the <a href="CodeSamples.html">Code Samples</a> and find how you can use Redis with your favorite programming language.<br/><br/><a href="Comparisons.html">Compare</a> Redis with other key-value stores, like Tokyo Cabinet or Memcached.
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<b>FlushallCommand: Contents</b><br>&nbsp;&nbsp;<a href="#FLUSHALL">FLUSHALL</a><br>&nbsp;&nbsp;&nbsp;&nbsp;<a href="#Return value">Return value</a>
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&iuml;&raquo;&iquest;#sidebar <a href="GenericCommandsSidebar.html">GenericCommandsSidebar</a><h1><a name="FLUSHALL">FLUSHALL</a></h1>
<blockquote>Delete all the keys of all the existing databases, not just the currently selected one. This command never fails.</blockquote>
<h2><a name="Return value">Return value</a></h2><a href="ReplyTypes.html">Status code reply</a>
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<b>FlushdbCommand: Contents</b><br>&nbsp;&nbsp;<a href="#FLUSHDB">FLUSHDB</a><br>&nbsp;&nbsp;&nbsp;&nbsp;<a href="#Return value">Return value</a>
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&iuml;&raquo;&iquest;#sidebar <a href="GenericCommandsSidebar.html">GenericCommandsSidebar</a><h1><a name="FLUSHDB">FLUSHDB</a></h1>
<blockquote>Delete all the keys of the currently selected DB. This command never fails.</blockquote>
<h2><a name="Return value">Return value</a></h2><a href="ReplyTypes.html">Status code reply</a>
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