Meet Redis-Monitoring Redis Performance Metrics


In my previous series on Redis I showed basic Redis tutorial,its abilities to work with complex data types, persist and scale out.  In this post idea is to show basic Redis monitoring facilities through Redis CLI. To analyze the Redis metrics you will need to access the actual data.Redis metrics are accessible through the Redis command line interface(redis-cli). So first I will start my MSOpenTech Redis on Windows Server. As I ran into an issue with default memory mapped file being to large for disk space on my laptop , I will feed it on start up configuration file (conf) which has maxmemory parameter cut to 256 MB. Otherwise I would get an error like:

The Windows version of Redis allocates a large memory mapped file for sharing
he heap with the forked process used in persistence operations. This file
ill be created in the current working directory or the directory specified by
he ‘heapdir’ directive in the .conf file. Windows is reporting that there is
insufficient disk space available for this file (Windows error 0x70).

Since I changed maxmemory parameter, but not maxheap parameter which stayed at default 1.5 times maxmemory , my maxheap will be 384 MB (256*1.5).  I do have that much disk space on my laptop for memory mapped file.  As Redis starts I see now familiar screen:


Now I can navigate to CLI.


We will use info command to print important information and metrics on our Redis Server. You can use info command to get information on following:

  • server
  • clients
  • memory
  • persistence
  • stats
  • replication
  • cpu
  • commandstats
  • cluster
  • keyspace

So lets start by getting general information by running info server


With Redis info memory will probably be one of most useful commands.  Here is me running it:


The used_memory metric reports the total number of bytes allocated by Redis. The used_memory_human metric gives the same value in a more readable format.

These metrics reflect the amount of memory that Redis has requested to store your data and the supporting metadata it needs to run. Due to the way memory allocators interact with underlying OS metrics don’t account for memory “lost” due to memory fragmentation and amount of memory reported by this metric may always differ from what is reported by OS.

Memory is critical to Redis performance. If amount of memory used exceeds available memory (used_memory metric>total available memory) the OS will begin swapping and older\unused memory pages will be written to disk to make room for newer\more used memory pages.  Writing or reading from disk is of course much slower that reading\writing to memory and this will have profound effect on Redis performance. By looking at used_memory metric together with OS metrics we can tell if instance is at risk of swapping or swapping has began.

Next we can get some useful statistics by running info stats


The total_commands_processed metric gives the number of commands processed by the Redis server. These commands come from clients connected to Redis Server. Each time Redis completes one of 140 possible commands this metric (total_commands_processed)  is incremented. This can be used to do certain measurement of throughput and queuing discovery , if by repeatedly querying this metric (via automated batch or script for example) you see slowdowns and spikes in total_commands_processed this may indicated queuing.

Note that none of these commands really measure latency to server. I found out that there is a way to measure it in Redis CLI. If you open separate command window, navigate to you Redis directory and run redis-cli.exe –latency –h <server>  -p <port> you can get that metric:


The times will depend on your actual setup, but I have read that on typical 1 Gbits/Sec network it should average well under 200 ms. Anything above probably point to an issue.

Back to stats another important metric is evicted_keys. This is similar to other alike systems such as AppFabric Cache where I profiled similar metric before. The evicted_keys metric gives the number of keys removed by Redis due to hitting the maxmemory limit. Interesting if you don’t set that limit evictions do not occur, but instead you may see something worse such as swapping and running out of memory. This of evictions therefore as protection mechanism here.  Interesting that when encountering memory pressure and electing to evict, Redis doesn’t necessarily evict oldest item. Instead it relies either on LRU (Least Recently Used) or TTL (Time to Live) cache policies.  You have the option to select between the LRU and TTL eviction policies in the config file by setting maxmemory-policy tovolatile-lru or “volatile-ttl respectively. If you are using Redis as in-memory cache server with expiring keys setting up TTL makes more sense, otherwise if you are using it with non-expiring keys you will probably choose LRU.


For more information on info command see –,


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