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 Apache's ab utility. Below you'll find the full output of a benchmark executed against a Linux box. You need to have a running Redis instance before launching the benchmark.
A typical example would be:. 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. 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 -t option like in the following example:.
It is also possible to specify the command to benchmark directly like in the following example:. 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 k possible keys, I'll use the following command line:.
By default every client the benchmark simulates 50 clients if not otherwise specified with -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 pipeliningso 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:. 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. 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. The 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 hiredisit 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. It does not use pipelining or any parallelism at all one pending query per connection at most, and no multi-threadingif not explicitly enabled via the -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 throughputyou 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. 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.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. If nothing happens, download GitHub Desktop and try again.
If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. Predixy is a high performance and fully featured proxy for redis sentinel and redis cluster. With default predixy. In general, So you will look mass log output, but you can still test it with redis-cli. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.
Sign up. A high performance and fully featured proxy for redis, support redis sentinel and redis cluster. Branch: master. Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again.
Latest commit. Latest commit ff81 Feb 1, Multi-threads support. Supports Redis Cluster. Supports redis block command, eg:blpop, brpop, brpoplpush. Supports scan command, even multi redis instances. Multi-databases support, means redis command select is avaliable. Supports redis transaction, limit in Redis Sentinel single redis group.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.
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Set " emp3 "GetEmployee 3 ; await Redis. Push GetEmployee 1 ; await list. Insert trueGetEmployee 2GetEmployee 3 ; await list. CreateSequence " seq2 " ; await sequeue. CreateHashTable " myhash " ; await table. Del " emp2 " ; await table. Keys. Subscribe ; subscribe. Listen.
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Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I am trying to load all the values in the redis database for which I am using mget r. I have loaded k json dumped numpy arrays into the redis database and running the code shown below to capture the time taken to read the data. I checked the system performance during the runtime and there are no bottlenecks. I expected a faster read performance but can someone confirm if this is the expected behaviour or if I am doing something wrong.
By calling keys you're basically asking Redis to generate a list of all the keys and return them to the client, which is a long blocking operation. Then, by calling r.
I would recommend you switch to use scan and batch the results from Redis. Last, once you move into batches in order to avoid waiting for each batch to return before you ask for the next batch you might want to use pipline. Learn more. Asked 9 months ago. Active 9 months ago. Viewed times. Karthik Karthik 33 4 4 bronze badges. Active Oldest Votes. Guy Korland Guy Korland 6, 11 11 gold badges 42 42 silver badges 84 84 bronze badges.
I used scan and pipeline both. There was not much difference in the the time taken when I used pipeline along with scan. However, when I used only scan the time taken reduced to 12 seconds. I have pasted the code I used for creating and executing the pipelines. Can the 12 seconds be optimised further? Sign up or log in Sign up using Google. Sign up using Facebook.
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Email Required, but never shown. The Overflow Blog. Featured on Meta. Feedback on Q2 Community Roadmap. Technical site integration observational experiment live on Stack Overflow. Question Close Updates: Phase 1. Dark Mode Beta - help us root out low-contrast and un-converted bits. Related This document will help you understand what the problem could be if you are experiencing latency problems with Redis.
In this context latency is the maximum delay between the time a client issues a command and the time the reply to the command is received by the client.
Usually Redis processing time is extremely low, in the sub microsecond range, but there are certain conditions leading to higher latency figures.
The following documentation is very important in order to run Redis in a low latency fashion.Redis database redis cli, get, set String
However I understand that we are busy people, so let's start with a quick checklist. If you fail following these steps, please return here to read the full documentation. If you are experiencing latency problems, you probably know how to measure it in the context of your application, or maybe your latency problem is very evident even macroscopically. However redis-cli can be used to measure the latency of a Redis server in milliseconds, just try:. Since Redis 2. This makes debugging of the problems illustrated in this documentation much simpler, so we suggest enabling latency monitoring ASAP.
Please refer to the Latency monitor documentation. While the latency monitoring sampling and reporting capabilities will make it simpler to understand the source of latency in your Redis system, it is still advised that you read this documentation extensively to better understand the topic of Redis and latency spikes. There is a kind of latency that is inherently part of the environment where you run Redis, that is the latency provided by your operating system kernel and, if you are using virtualization, by the hypervisor you are using.
While this latency can't be removed it is important to study it because it is the baseline, or in other words, you won't be able to achieve a Redis latency that is better than the latency that every process running in your environment will experience because of the kernel or hypervisor implementation or setup.
We call this kind of latency intrinsic latencyand redis-cli starting from Redis version 2. This is an example run under Linux 3.
Note: the argument is the number of seconds the test will be executed. The more time we run the test, the more likely we'll be able to spot latency spikes. Please note that the test is CPU intensive and will likely saturate a single core in your system. Note: redis-cli in this special case needs to run in the server where you run or plan to run Redis, not in the client.
In this special mode redis-cli does no connect to a Redis server at all: it will just try to measure the largest time the kernel does not provide CPU time to run to the redis-cli process itself. In the above example, the intrinsic latency of the system is just 0. Virtualized environments will not show so good numbers, especially with high load or if there are noisy neighbors. The following is a run on a Linode instance running Redis and Apache:.
Here we have an intrinsic latency of 9. However other runs at different times in different virtualization environments with higher load or with noisy neighbors can easily show even worse values.
We were able to measure up to 40 milliseconds in systems otherwise apparently running normally. It actually depends on your network and system hardware. System induced latencies are significantly higher on a virtualized environment than on a physical machine. The consequence is even if Redis processes most commands in sub microsecond range, a client performing many roundtrips to the server will have to pay for these network and system related latencies.
An efficient client will therefore try to limit the number of roundtrips by pipelining several commands together. This is fully supported by the servers and most clients. Starting with Redis 2. On Linux, some people can achieve better latencies by playing with process placement tasksetcgroups, real-time priorities chrtNUMA configuration numactlor by using a low-latency kernel.
Please note vanilla Redis is not really suitable to be bound on a single CPU core. These tasks must never run on the same core as the main event loop. In most situations, these kind of system level optimizations are not needed. Only do them if you require them, and if you are familiar with them.Azure Cache for Redis uses Azure Monitor to provide several options for monitoring your cache instances.
You can view metrics, pin metrics charts to the Startboard, customize the date and time range of monitoring charts, add and remove metrics from the charts, and set alerts when certain conditions are met. These tools enable you to monitor the health of your Azure Cache for Redis instances and help you manage your caching applications.
Metrics for Azure Cache for Redis instances are collected using the Redis INFO command approximately twice per minute and automatically stored for 30 days see Export cache metrics to configure a different retention policy so they can be displayed in the metrics charts and evaluated by alert rules. For more information about the different INFO values used for each cache metric, see Available metrics and reporting intervals. To view cache metrics, browse to your cache instance in the Azure portal.
Azure Cache for Redis provides some built-in charts on the Overview blade and the Redis metrics blade. Each chart can be customized by adding or removing metrics and changing the reporting interval. The Pricing tier displays the cache pricing tier, and can be used to scale the cache to a different pricing tier.
To view Redis metrics and create custom charts using Azure Monitor, click Metrics from the Resource menuand customize your chart using the desired metrics, reporting interval, chart type, and more.
For more information on working with metrics using Azure Monitor, see Overview of metrics in Microsoft Azure. By default, cache metrics in Azure Monitor are stored for 30 days and then deleted. To persist your cache metrics for longer than 30 days, you can designate a storage account and specify a Retention days policy for your cache metrics.
In addition to archiving your cache metrics to storage, you can also stream them to an Event hub or send them to Azure Monitor logs. If you change storage accounts, the data in the previously configured storage account remains available for download, but it is not displayed in the Azure portal. Cache metrics are reported using several reporting intervals, including Past hourTodayPast weekand Custom. The Metric blade for each metrics chart displays the average, minimum, and maximum values for each metric in the chart, and some metrics display a total for the reporting interval.
Each metric includes two versions. One metric measures performance for the entire cache, and for caches that use clusteringa second version of the metric that includes Shard in the name measures performance for a single shard in a cache. For example if a cache has four shards, Cache Hits is the total number of hits for the entire cache, and Cache Hits Shard 3 is just the hits for that shard of the cache. Even when the cache is idle with no connected active client applications, you may see some cache activity, such as connected clients, memory usage, and operations being performed.
This activity is normal during the operation of an Azure Cache for Redis instance. You can configure to receive alerts based on metrics and activity logs. Azure Monitor allows you to configure an alert to do the following when it triggers:. To configure Alert rules for your cache, click Alert rules from the Resource menu.
The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. You can use StringGetAsync, but it may lead you to overloading your server in case of huge amount of keys. You can use the function from below that will fetch all the keys with paging by keys per page.
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Learn more. Asked 3 years, 5 months ago. Active 3 years, 4 months ago. Viewed 2k times. I need a way to reed many keys in one call. Guy Assaf Guy Assaf 4 4 silver badges 20 20 bronze badges. Active Oldest Votes. Give an array of Keys and receive an Array of Redis values.
Buffer StringGetAsync keysPage. ToArraydbIndex ; return redisValues. ToList ; return ids. Buffer 3. WriteLine DateTime.
Delay ; Console. ToList ; kvp. Arkhangelskiy Evgeniy Arkhangelskiy Evgeniy 3 3 silver badges 13 13 bronze badges. Sign up or log in Sign up using Google. Sign up using Facebook. Sign up using Email and Password. Post as a guest Name. Email Required, but never shown.
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