I have a db with about 350,000 keys. Currently my code just loops through all keys and gets its value from the db.

However this takes almost 2 minutes to do, which seems really slow, redis-benchmark gave 100k reqs/3s.

I've looked at pipelining but I need each value returned so that I end up with a dict of key, value pairs.

At the moment I'm thinking of using threading in my code if possible to speed this up, is this the best way to handle this usecase?

Here's the code I have so far.

import redis, timeit
start_time = timeit.default_timer()
count = redis.Redis(host='127.0.0.1', port=6379, db=9)
keys = count.keys()data = {}for key in keys:value = count.get(key)if value:data[key.decode('utf-8')] = int(value.decode('utf-8'))elapsed = timeit.default_timer() - start_timeprint('Time to read {} records: '.format(len(keys)), elapsed)

First, the fastest way is doing all of this inside EVAL.

Next, recommended approach to iterate all keys is SCAN. It would not iterate faster than KEYS, but will allow Redis to process some other actions in between, so it will help with overall application behavior.

The script will be something like local data={} local i=1 local mykeys=redis.call(\"KEYS\",\"*\") for k=1,#mykeys do local tmpkey=mykeys[k] data[i]={tmpkey,redis.call(\"GET\",tmpkey)} i=i+1 end return data, but it will fail if you have keys inaccessible with GET (like sets, lists). You need to add error handling to it. If you need sorting, you can do it either in LUA directly, or later on the client side. The second will be slower, but would not let other users of redis instance wait.

Sample output:

127.0.0.1:6370> eval "local data={} local i=1 local mykeys=redis.call(\"KEYS\",\"*\") for k=1,#mykeys do local tmpkey=mykeys[k] data[i]={tmpkey,redis.call(\"GET\",tmpkey)} i=i+1 end return data" 0
1) 1) "a"2) "aval"
2) 1) "b"2) "bval"
3) 1) "c"2) "cval"
4) 1) "d"2) "dval"
5) 1) "e"2) "eval"
6) 1) "f"2) "fval"
7) 1) "g"2) "gval"
8) 1) "h"2) "hval"