*How fast is Redis?
Redis includes the
redis-benchmark utility that simulates running commands done
by N clients at the same time sending M total queries (it is similar to the
ab utility). Below you'll find the full output of a benchmark executed
against a Linux box.
The following options are supported:
Usage: redis-benchmark [-h <host>] [-p <port>] [-c <clients>] [-n <requests]> [-k <boolean>] -h <hostname> Server hostname (default 127.0.0.1) -p <port> Server port (default 6379) -s <socket> Server socket (overrides host and port) -a <password> Password for Redis Auth -c <clients> Number of parallel connections (default 50) -n <requests> Total number of requests (default 100000) -d <size> Data size of SET/GET value in bytes (default 2) --dbnum <db> SELECT the specified db number (default 0) -k <boolean> 1=keep alive 0=reconnect (default 1) -r <keyspacelen> Use random keys for SET/GET/INCR, random values for SADD Using this option the benchmark will expand the string __rand_int__ inside an argument with a 12 digits number in the specified range from 0 to keyspacelen-1. The substitution changes every time a command is executed. Default tests use this to hit random keys in the specified range. -P <numreq> Pipeline <numreq> requests. Default 1 (no pipeline). -q Quiet. Just show query/sec values --csv Output in CSV format -l Loop. Run the tests forever -t <tests> Only run the comma separated list of tests. The test names are the same as the ones produced as output. -I Idle mode. Just open N idle connections and wait.
You need to have a running Redis instance before launching the benchmark. A typical example would be:
redis-benchmark -q -n 100000
Using this tool is quite easy, and you can also write your own benchmark, but as with any benchmarking activity, there are some pitfalls to avoid.
*Running only a subset of the tests
You don't need to run all the default tests every time you execute redis-benchmark.
The simplest thing to select only a subset of tests is to use the
like in the following example:
$ redis-benchmark -t set,lpush -n 100000 -q SET: 74239.05 requests per second LPUSH: 79239.30 requests per second
In the above example we asked to just run test the SET and LPUSH commands,
in quiet mode (see the
It is also possible to specify the command to benchmark directly like in the following example:
$ redis-benchmark -n 100000 -q script load "redis.call('set','foo','bar')" script load redis.call('set','foo','bar'): 69881.20 requests per second
*Selecting the size of the key space
By default the benchmark runs against a single key. In Redis the difference between such a synthetic benchmark and a real one is not huge since it is an in-memory system, however it is possible to stress cache misses and in general to simulate a more real-world work load by using a large key space.
This is obtained by using the
-r switch. For instance if I want to run
one million SET operations, using a random key for every operation out of
100k possible keys, I'll use the following command line:
$ redis-cli flushall OK $ redis-benchmark -t set -r 100000 -n 1000000 ====== SET ====== 1000000 requests completed in 13.86 seconds 50 parallel clients 3 bytes payload keep alive: 1 99.76% `<=` 1 milliseconds 99.98% `<=` 2 milliseconds 100.00% `<=` 3 milliseconds 100.00% `<=` 3 milliseconds 72144.87 requests per second $ redis-cli dbsize (integer) 99993
By default every client (the benchmark simulates 50 clients if not otherwise
-c) sends the next command only when the reply of the previous
command is received, this means that the server will likely need a read call
in order to read each command from every client. Also RTT is paid as well.
Redis supports pipelining, so it is possible to send multiple commands at once, a feature often exploited by real world applications. Redis pipelining is able to dramatically improve the number of operations per second a server is able do deliver.
This is an example of running the benchmark in a MacBook Air 11" using a pipelining of 16 commands:
$ redis-benchmark -n 1000000 -t set,get -P 16 -q SET: 403063.28 requests per second GET: 508388.41 requests per second
Using pipelining results in a significant increase in performance.
*Pitfalls and misconceptions
The first point is obvious: the golden rule of a useful benchmark is to only compare apples and apples. Different versions of Redis can be compared on the same workload for instance. Or the same version of Redis, but with different options. If you plan to compare Redis to something else, then it is important to evaluate the functional and technical differences, and take them in account.
- Redis is a server: all commands involve network or IPC round trips. It is meaningless to compare it to embedded data stores such as SQLite, Berkeley DB, Tokyo/Kyoto Cabinet, etc ... because the cost of most operations is primarily in network/protocol management.
- Redis commands return an acknowledgment for all usual commands. Some other data stores do not. Comparing Redis to stores involving one-way queries is only mildly useful.
- Naively iterating on synchronous Redis commands does not benchmark Redis itself, but rather measure your network (or IPC) latency and the client library intrinsic latency. To really test Redis, you need multiple connections (like redis-benchmark) and/or to use pipelining to aggregate several commands and/or multiple threads or processes.
- Redis is an in-memory data store with some optional persistence options. If you plan to compare it to transactional servers (MySQL, PostgreSQL, etc ...), then you should consider activating AOF and decide on a suitable fsync policy.
- Redis is, mostly, a single-threaded server from the POV of commands execution (actually modern versions of Redis use threads for different things). It is not designed to benefit from multiple CPU cores. People are supposed to launch several Redis instances to scale out on several cores if needed. It is not really fair to compare one single Redis instance to a multi-threaded data store.
A common misconception is that redis-benchmark is designed to make Redis performances look stellar, the throughput achieved by redis-benchmark being somewhat artificial, and not achievable by a real application. This is actually not true.
redis-benchmark program is a quick and useful way to get some figures and
evaluate the performance of a Redis instance on a given hardware. However,
by default, it does not represent the maximum throughput a Redis instance can
sustain. Actually, by using pipelining and a fast client (hiredis), it is fairly
easy to write a program generating more throughput than redis-benchmark. The
default behavior of redis-benchmark is to achieve throughput by exploiting
concurrency only (i.e. it creates several connections to the server).
It does not use pipelining or any parallelism at all (one pending query per
connection at most, and no multi-threading), if not explicitly enabled via
-P parameter. So in some way using
redis-benchmark and, triggering, for
example, a BGSAVE operation in the background at the same time, will provide
the user with numbers more near to the worst case than to the best case.
To run a benchmark using pipelining mode (and achieve higher throughput), you need to explicitly use the -P option. Please note that it is still a realistic behavior since a lot of Redis based applications actively use pipelining to improve performance. However you should use a pipeline size that is more or less the average pipeline length you'll be able to use in your application in order to get realistic numbers.
Finally, the benchmark should apply the same operations, and work in the same way with the multiple data stores you want to compare. It is absolutely pointless to compare the result of redis-benchmark to the result of another benchmark program and extrapolate.
For instance, Redis and memcached in single-threaded mode can be compared on GET/SET operations. Both are in-memory data stores, working mostly in the same way at the protocol level. Provided their respective benchmark application is aggregating queries in the same way (pipelining) and use a similar number of connections, the comparison is actually meaningful.
This perfect example is illustrated by the dialog between Redis (antirez) and memcached (dormando) developers.
You can see that in the end, the difference between the two solutions is not so staggering, once all technical aspects are considered. Please note both Redis and memcached have been optimized further after these benchmarks.
Finally, when very efficient servers are benchmarked (and stores like Redis or memcached definitely fall in this category), it may be difficult to saturate the server. Sometimes, the performance bottleneck is on client side, and not server-side. In that case, the client (i.e. the benchmark program itself) must be fixed, or perhaps scaled out, in order to reach the maximum throughput.
*Factors impacting Redis performance
There are multiple factors having direct consequences on Redis performance. We mention them here, since they can alter the result of any benchmarks. Please note however, that a typical Redis instance running on a low end, untuned box usually provides good enough performance for most applications.
- Network bandwidth and latency usually have a direct impact on the performance. It is a good practice to use the ping program to quickly check the latency between the client and server hosts is normal before launching the benchmark. Regarding the bandwidth, it is generally useful to estimate the throughput in Gbit/s and compare it to the theoretical bandwidth of the network. For instance a benchmark setting 4 KB strings in Redis at 100000 q/s, would actually consume 3.2 Gbit/s of bandwidth and probably fit within a 10 Gbit/s link, but not a 1 Gbit/s one. In many real world scenarios, Redis throughput is limited by the network well before being limited by the CPU. To consolidate several high-throughput Redis instances on a single server, it worth considering putting a 10 Gbit/s NIC or multiple 1 Gbit/s NICs with TCP/IP bonding.
- CPU is another very important factor. Being single-threaded, Redis favors fast CPUs with large caches and not many cores. At this game, Intel CPUs are currently the winners. It is not uncommon to get only half the performance on an AMD Opteron CPU compared to similar Nehalem EP/Westmere EP/Sandy Bridge Intel CPUs with Redis. When client and server run on the same box, the CPU is the limiting factor with redis-benchmark.
- Speed of RAM and memory bandwidth seem less critical for global performance especially for small objects. For large objects (>10 KB), it may become noticeable though. Usually, it is not really cost-effective to buy expensive fast memory modules to optimize Redis.
- Redis runs slower on a VM compared to running without virtualization using
the same hardware. If you have the chance to run Redis on a physical machine
this is preferred. However this does not mean that Redis is slow in
virtualized environments, the delivered performances are still very good
and most of the serious performance issues you may incur in virtualized
environments are due to over-provisioning, non-local disks with high latency,
or old hypervisor software that have slow
- When the server and client benchmark programs run on the same box, both the TCP/IP loopback and unix domain sockets can be used. Depending on the platform, unix domain sockets can achieve around 50% more throughput than the TCP/IP loopback (on Linux for instance). The default behavior of redis-benchmark is to use the TCP/IP loopback.
- The performance benefit of unix domain sockets compared to TCP/IP loopback tends to decrease when pipelining is heavily used (i.e. long pipelines).
- When an ethernet network is used to access Redis, aggregating commands using pipelining is especially efficient when the size of the data is kept under the ethernet packet size (about 1500 bytes). Actually, processing 10 bytes, 100 bytes, or 1000 bytes queries almost result in the same throughput. See the graph below.
- On multi CPU sockets servers, Redis performance becomes dependent on the NUMA configuration and process location. The most visible effect is that redis-benchmark results seem non-deterministic because client and server processes are distributed randomly on the cores. To get deterministic results, it is required to use process placement tools (on Linux: taskset or numactl). The most efficient combination is always to put the client and server on two different cores of the same CPU to benefit from the L3 cache. Here are some results of 4 KB SET benchmark for 3 server CPUs (AMD Istanbul, Intel Nehalem EX, and Intel Westmere) with different relative placements. Please note this benchmark is not meant to compare CPU models between themselves (CPUs exact model and frequency are therefore not disclosed).
- With high-end configurations, the number of client connections is also an important factor. Being based on epoll/kqueue, the Redis event loop is quite scalable. Redis has already been benchmarked at more than 60000 connections, and was still able to sustain 50000 q/s in these conditions. As a rule of thumb, an instance with 30000 connections can only process half the throughput achievable with 100 connections. Here is an example showing the throughput of a Redis instance per number of connections:
- With high-end configurations, it is possible to achieve higher throughput by tuning the NIC(s) configuration and associated interruptions. Best throughput is achieved by setting an affinity between Rx/Tx NIC queues and CPU cores, and activating RPS (Receive Packet Steering) support. More information in this thread. Jumbo frames may also provide a performance boost when large objects are used.
- Depending on the platform, Redis can be compiled against different memory
allocators (libc malloc, jemalloc, tcmalloc), which may have different behaviors
in term of raw speed, internal and external fragmentation.
If you did not compile Redis yourself, you can use the INFO command to check
mem_allocatorfield. Please note most benchmarks do not run long enough to generate significant external fragmentation (contrary to production Redis instances).
*Other things to consider
One important goal of any benchmark is to get reproducible results, so they can be compared to the results of other tests.
- A good practice is to try to run tests on isolated hardware as much as possible. If it is not possible, then the system must be monitored to check the benchmark is not impacted by some external activity.
- Some configurations (desktops and laptops for sure, some servers as well) have a variable CPU core frequency mechanism. The policy controlling this mechanism can be set at the OS level. Some CPU models are more aggressive than others at adapting the frequency of the CPU cores to the workload. To get reproducible results, it is better to set the highest possible fixed frequency for all the CPU cores involved in the benchmark.
- An important point is to size the system accordingly to the benchmark.
The system must have enough RAM and must not swap. On Linux, do not forget
to set the
overcommit_memoryparameter correctly. Please note 32 and 64 bit Redis instances do not have the same memory footprint.
- If you plan to use RDB or AOF for your benchmark, please check there is no other I/O activity in the system. Avoid putting RDB or AOF files on NAS or NFS shares, or on any other devices impacting your network bandwidth and/or latency (for instance, EBS on Amazon EC2).
- Set Redis logging level (loglevel parameter) to warning or notice. Avoid putting the generated log file on a remote filesystem.
- Avoid using monitoring tools which can alter the result of the benchmark. For instance using INFO at regular interval to gather statistics is probably fine, but MONITOR will impact the measured performance significantly.
*Benchmark results on different virtualized and bare-metal servers.
WARNING: Note that most of the following benchmarks are a few years old and are obtained using old hardware compared to today's standards. This page should be updated, but in many cases you can expect twice the numbers you are seeing here using state of hard hardware. Moreover Redis 4.0 is faster than 2.6 in many workloads.
- The test was done with 50 simultaneous clients performing 2 million requests.
- Redis 2.6.14 is used for all the tests.
- Test was executed using the loopback interface.
- Test was executed using a key space of 1 million keys.
- Test was executed with and without pipelining (16 commands pipeline).
Intel(R) Xeon(R) CPU E5520 @ 2.27GHz (with pipelining)
$ ./redis-benchmark -r 1000000 -n 2000000 -t get,set,lpush,lpop -P 16 -q SET: 552028.75 requests per second GET: 707463.75 requests per second LPUSH: 767459.75 requests per second LPOP: 770119.38 requests per second
Intel(R) Xeon(R) CPU E5520 @ 2.27GHz (without pipelining)
$ ./redis-benchmark -r 1000000 -n 2000000 -t get,set,lpush,lpop -q SET: 122556.53 requests per second GET: 123601.76 requests per second LPUSH: 136752.14 requests per second LPOP: 132424.03 requests per second
Linode 2048 instance (with pipelining)
$ ./redis-benchmark -r 1000000 -n 2000000 -t get,set,lpush,lpop -q -P 16 SET: 195503.42 requests per second GET: 250187.64 requests per second LPUSH: 230547.55 requests per second LPOP: 250815.16 requests per second
Linode 2048 instance (without pipelining)
$ ./redis-benchmark -r 1000000 -n 2000000 -t get,set,lpush,lpop -q SET: 35001.75 requests per second GET: 37481.26 requests per second LPUSH: 36968.58 requests per second LPOP: 35186.49 requests per second
*More detailed tests without pipelining
$ redis-benchmark -n 100000 ====== SET ====== 100007 requests completed in 0.88 seconds 50 parallel clients 3 bytes payload keep alive: 1 58.50% <= 0 milliseconds 99.17% <= 1 milliseconds 99.58% <= 2 milliseconds 99.85% <= 3 milliseconds 99.90% <= 6 milliseconds 100.00% <= 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% <= 0 milliseconds 96.82% <= 1 milliseconds 98.62% <= 2 milliseconds 100.00% <= 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% <= 0 milliseconds 96.67% <= 1 milliseconds 99.14% <= 2 milliseconds 99.83% <= 3 milliseconds 99.88% <= 4 milliseconds 99.89% <= 5 milliseconds 99.96% <= 9 milliseconds 100.00% <= 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% <= 0 milliseconds 99.74% <= 1 milliseconds 99.85% <= 2 milliseconds 99.86% <= 3 milliseconds 99.89% <= 5 milliseconds 99.93% <= 7 milliseconds 99.96% <= 9 milliseconds 100.00% <= 22 milliseconds 100.00% <= 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% <= 0 milliseconds 97.34% <= 1 milliseconds 99.95% <= 2 milliseconds 99.96% <= 3 milliseconds 99.96% <= 4 milliseconds 100.00% <= 9 milliseconds 100.00% <= 208 milliseconds 71994.96 requests per second
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.
You can expect different results from different boxes. For example a low profile box like Intel core duo T5500 clocked at 1.66 GHz running Linux 2.6 will output the following:
$ ./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
Another one using a 64-bit box, a Xeon L5420 clocked at 2.5 GHz:
$ ./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
*Other Redis benchmarking tools
There are several third-party tools that can be used for benchmarking Redis. Refer to each tool's documentation for more information about its goals and capabilities.
- memtier_benchmark from Redis Labs is a NoSQL Redis and Memcache traffic generation and benchmarking tool.
- rpc-perf from Twitter is a tool for benchmarking RPC services that supports Redis and Memcache.
- YCSB from Yahoo @Yahoo is a benchmarking framework with clients to many databases, including Redis.
*Example of redis-benchmark results with optimized high-end server hardware
- Redis version 2.4.2
- Default number of connections, payload size = 256
- The Linux box is running SLES10 SP3 18.104.22.168-0.54.5-smp, CPU is 2 x Intel X5670 @ 2.93 GHz.
- Test executed while running Redis server and benchmark client on the same CPU, but different cores.
Using a unix domain socket:
$ numactl -C 6 ./redis-benchmark -q -n 100000 -s /tmp/redis.sock -d 256 PING (inline): 200803.22 requests per second PING: 200803.22 requests per second MSET (10 keys): 78064.01 requests per second SET: 198412.69 requests per second GET: 198019.80 requests per second INCR: 200400.80 requests per second LPUSH: 200000.00 requests per second LPOP: 198019.80 requests per second SADD: 203665.98 requests per second SPOP: 200803.22 requests per second LPUSH (again, in order to bench LRANGE): 200000.00 requests per second LRANGE (first 100 elements): 42123.00 requests per second LRANGE (first 300 elements): 15015.02 requests per second LRANGE (first 450 elements): 10159.50 requests per second LRANGE (first 600 elements): 7548.31 requests per second
Using the TCP loopback:
$ numactl -C 6 ./redis-benchmark -q -n 100000 -d 256 PING (inline): 145137.88 requests per second PING: 144717.80 requests per second MSET (10 keys): 65487.89 requests per second SET: 142653.36 requests per second GET: 142450.14 requests per second INCR: 143061.52 requests per second LPUSH: 144092.22 requests per second LPOP: 142247.52 requests per second SADD: 144717.80 requests per second SPOP: 143678.17 requests per second LPUSH (again, in order to bench LRANGE): 143061.52 requests per second LRANGE (first 100 elements): 29577.05 requests per second LRANGE (first 300 elements): 10431.88 requests per second LRANGE (first 450 elements): 7010.66 requests per second LRANGE (first 600 elements): 5296.61 requests per second