I know barely anything about redis, except it is in-memory and fast.
But I have a case I consider using it.
I have a system, that may have a huge number of users (500k+ may up to a few million) and I want to do a unique check for email adresses across all users. I consider using redis to maintain a set of all email adresses to do the uniquness check. So I asked my self, is it possible to do something like
if(!set.contains(email)) add email
as an atomic operation and then get a simple result I can handle, just like failure or success.
This code/command should be callable from concurrent code.
If there is a different tool that would fit my needs better, I am open to suggestions.
Use SET datatype for that:
Redis Sets are an unordered collection of Strings. It is possible to add, remove, and test for existence of members in O(1) (constant time regardless of the number of elements contained inside the Set).
Redis Sets have the desirable property of not allowing repeated members. Adding the same element multiple times will result in a set having a single copy of this element. Practically speaking this means that adding a member does not require a check if exists then add operation.
So just use
SADD emails my#email.com
Related
Problem:
I need to efficiently delete keys from my Redis Cache using a wildcard pattern. I don't need atomicity; eventual consistency is acceptable.
Tech stack:
.NET 6 (async all the way through)
StackExchange.Redis 2.6.66
Redis Server 6.2.6
I currently have ~500k keys in Redis.
I'm not able to use RedisJSON for various reasons
Example:
I store the following 3 STRING types with keys:
dailynote:getitemsforuser:region:sw:user:123
dailynote:getitemsforuser:region:fl:user:123
dailynote:getitemsforuser:region:sw:user:456
...
where each STRING stores JSON like so:
> dump dailynote:getitemsforuser:region:fl:user:123
"{\"Name\":\"john\",\"Age\":22}"
The original solution used the KeysAsync method to retrieve the list of keys to delete via a wildcard pattern. Since the Redis Server is 6.x, the SCAN feature is being used by KeysAsync internally by the StackExchange.Redis nuget.
Original implementation used a wildcard pattern dailynote:getitemsforuser:region:*. As one would expect, this solution didn't scale well and we started seeing RedisTimeoutExceptions.
I'm aware of the "avoid this in PROD if you can" and have seen Marc Gravell respond to a couple other questions/issues on SO and StackExchange.Redis GitHub. The only potential alternative I could think of is to use a Redis SET to "track" each RedisKey and then retrieve the list of values from the SET (which are the keys I need to remove). Then delete the SET as well as the returned keys.
Potential Solution?:
Create a Redis SET with a key of dailynote:getitemsforuser with a value which is the key of the form dailynote:getitemsforuser:region:XX...
The SET would look like:
dailynote:getitemsforuser (KEY)
dailynote:getitemsforuser:region:sw:user:123 (VALUE)
dailynote:getitemsforuser:region:fl:user:123 (VALUE)
dailynote:getitemsforuser:region:sw:user:456 (VALUE)
...
I would still have each individual STRING type as well:
dailynote:getitemsforuser:region:sw:user:123
dailynote:getitemsforuser:region:fl:user:123
dailynote:getitemsforuser:region:sw:user:456
...
when it is time to do the "wildcard" remove, I get the members of the dailynote:getitemsforuser SET, then call RemoveAsync passing the members of the set as the RedisKey[]. Then call RemoveAsync with the key of the SET (dailynote:getitemsforuser)
I'm looking for feedback on how viable of a solution this is, alternative ideas, gotchas, and suggestions for improvement. TIA
UPDATE
Added my solution I went with below...
The big problem with both KEYS and SCAN with Redis is that they require a complete scan of the massive hash table that stores every Redis key. Even if you use a pattern, it still needs to check each entry in that hash table to see if it matches.
Assuming you are calling SADD when you are also setting the value in your key—and thus avoiding the call to SCAN—this should work. It is worth noting that calls to SMEMBERS to get all the members of a Set can also cause issues if the Set is big. Redis—being single-threaded—will block while all the members are returned. You can mitigate this by using SSCAN instead. StackExchange.Redis might do this already. I'm not sure.
You might also be able to write a Lua script that reads the Set and UNLINKs all the keys atomically. This would reduce network but could tie Redis up if this takes too long.
I ended up using the solution I suggested above where I use a Redis SET with a known/fixed key to "track" each of the necessary keys.
When a key that needs to be tracked is added, I call StackExchange.Redis.IDatabase.SetAddAsync (SADD) while calling StackExchange.Redis.IDatabase.HashSetAsync (HSET) for adding the "tracked" key (along with its TTL).
When it is time to remove the "tracked" key, I first call StackExchange.Redis.IDatabase.SetScanAsync (SSCAN) (with a page size of 250) iterating on the IAsyncEnumerable and call StackExchange.Redis.IDatabase.KeyDeleteAsync (HDEL) on chunks of the members of the SET. I then call StackExchange.Redis.IDatabase.KeyDeleteAsync on the actual key of the SET itself.
Hope this helps someone else.
I'm facing following problem:
I wan't to keep track of tasks given to users and I want to store this state in Redis.
I can do:
1) create list called "dispatched_tasks" holding many objects (username, task)
2) create many (potentialy thousands) lists called dispatched_tasks:username holding usually few objects (task)
Which approach is better? If I only thought of my comfort, I would choose the second one, as from time to time I will have to search for particular user tasks, and this second approach gives this for free.
But how about Redis? Which approach will be more performant?
Thanks for any help.
Redis supports different kinds of data structures as shown here. There are different approaches you can take:
Scenario 1:
Using a list data type, your list will contain all the task/user combination for your problem. However, accessing and deleting a task runs in O(n) time complexity (it has to traverse the list to get to the element). This can have an impact in performance if your user has a lot of tasks.
Using sets:
Similar to lists, but you can add/delete/check for existence in O(1) and sets elements are unique. So if you add another username/task that already exists, it won't add it.
Scenario 2:
The data types do not change. The only difference is that there will be a lot more keys in redis, which in can increase the memory footprint.
From the FAQ:
What is the maximum number of keys a single Redis instance can hold? and what the max number of elements in a Hash, List, Set, Sorted
Set?
Redis can handle up to 232 keys, and was tested in practice to handle
at least 250 million keys per instance.
Every hash, list, set, and sorted set, can hold 232 elements.
In other words your limit is likely the available memory in your
system.
What's the Redis memory footprint?
To give you a few examples (all obtained using 64-bit instances):
An empty instance uses ~ 3MB of memory. 1 Million small Keys ->
String Value pairs use ~ 85MB of memory. 1 Million Keys -> Hash
value, representing an object with 5 fields, use ~ 160 MB of
memory. To test your use case is trivial using the
redis-benchmark utility to generate random data sets and check with
the INFO memory command the space used.
I can think of two ways of checking existence using redis:
Use the whole database as a 'set', and just SET a key and checking existence by GETing it (or using EXISTS as mentioned in the comment by #Sergio Tulentsev)
Use SADD to add all members to a key and check existence by SISMEMBER
Which one is better? Will it be a problem, compared to the same amount of keys in a single set, if I choose the first method and the number of keys in a database gets larger?
In fact, besides these two methods, you can also use the HASH data structure with HEXISTS command (I'll call this method as the third solution).
All these solutions are fast enough, and it's NOT a problem if you have a large SET, HASH, or keyspace.
So, which one should we use? It depends on lots of things...
Does the key has value?
Keys of both the first and the third solution can have value, while the second solution CANNOT.
So if there's no value for each key, I'd prefer the second solution, i.e. SET solution. Otherwise, you have to use the first or third solution.
Does the value has structure?
If the value is NOT raw string, but a data structure, e.g. LIST, SET. You have to use the first solution, since HASH's value CAN only be raw string.
Do you need to do set operations?
If you need to do intersection, union or diff operations on multiple data sets, you should use the second solution. Redis has built-in commands for these operations, although they might be slow commands.
Memory efficiency consideration
Redis takes more memory-efficient encoding for small SET and HASH. So when you have lots of small data sets, take the second and the third solution can save lots of memory. See this for details.
UPDATE
Do you need to set TTL for these keys?
As #dizzyf points out in the comment, if you need to set TTL for these keys, you have to use the first solution. Because items of HASH and SET DO NOT have expiration property. You can only set TTL for the entire HASH or SET, NOT their elements.
I have a requirement to process multiple records from a queue. But due to some external issues the items may sporadically occur multiple times.
I need to process items only once
What I planned to use is PFADD into redis every record ( as a md5sum) and then see if that returns success. If that shows no increment then the record is a duplicate else process the record.
This seems pretty straightforward , but I am getting too many false positives while using PFADD
Is there a better way to do this ?
Being the probabilistic data structure that it is, Redis' HyperLogLog exhibits 0.81% standard error. You can reduce (but never get rid of) the probability for false positives by using multiple HLLs, each counting a the value of a different hash function on your record.
Also note that if you're using a single HLL there's no real need to hash the record - just PFADD as is.
Alternatively, use a Redis Set to keep all the identifiers/hashes/records and have 100%-accurate membership tests with SISMEMBER. This approach requires more (RAM) resources as you're storing each processed element, but unless your queue is really huge that shouldn't be a problem for a modest Redis instance. To keep memory consumption under control, switch between Sets according to the date and set an expiry on the Set keys (another approach is to use a single Sorted Set and manually remove old items from it by keeping their timestamp in the score).
In general in distributed systems you have to choose between processing items either :
at most once
at least once
Processing something exactly-once would be convenient however this is generally impossible.
That being said there could be acceptable workarounds for your specific use case, and as you suggest storing the items already processed could be an acceptable solution.
Be aware though that PFADD uses HyperLogLog, which is fast and scales but is approximate about the count of the items, so in this case I do not think this is what you want.
However if you are fine with having a small probability of errors, the most appropriate data structure here would be a Bloom filter (as described here for Redis), which can be implemented in a very memory-efficient way.
A simple, efficient, and recommended solution would be to use a simple redis key (for instance a hash) storing a boolean-like value ("0", "1" or "true", "false") for instance with the HSET or SET with the NX option instruction. You could also put it under a namespace if you wish to. It has the added benefit of being able to expire keys also.
It would avoid you to use a set (not the SET command, but rather the SINTER, SUNION commands), which doesn't necessarily work well with Redis cluster if you want to scale to more than one node. SISMEMBER is still fine though (but lacks some features from hashes such as time to live).
If you use a hash, I would also advise you to pick a hash function that has fewer chances of collisions than md5 (a collision means that two different objects end up with the same hash).
An alternative approach to the hash would be to assign an uuid to every item when putting it in the queue (or a squuid if you want to have some time information).
What is the most convenient/fast way to implement a sorted set in redis where the values are objects, not just strings.
Should I just store object id's in the sorted set and then query every one of them individually by its key or is there a way that I can store them directly in the sorted set, i.e. must the value be a string?
It depends on your needs, if you need to share this data with other zsets/structures and want to write the value only once for every change, you can put an id as the zset value and add a hash to store the object. However, it implies making additionnal queries when you read data from the zset (one zrange + n hgetall for n values in the zset), but writing and synchronising the value between many structures is cheap (only updating the hash corresponding to the value).
But if it is "self-contained", with no or few accesses outside the zset, you can serialize to a chosen format (JSON, MESSAGEPACK, KRYO...) your object and then store it as the value of your zset entry. This way, you will have better performance when you read from the zset (only 1 query with O(log(N)+M), it is actually pretty good, probably the best you can get), but maybe you will have to duplicate the value in other zsets / structures if you need to read / write this value outside, which also implies maintaining synchronisation by hand on the value.
Redis has good documentation on performance of each command, so check what queries you would write and calculate the total cost, so that you can make a good comparison of these two options.
Also, don't forget that redis comes with optimistic locking, so if you need pessimistic (because of contention for instance) you will have to do it by hand and/or using lua scripts. If you need a lot of sync, the first option seems better (less performance on read, but still good, less queries and complexity on writes), but if you have values that don't change a lot and memory space is not a problem, the second option will provide better performance on reads (you can duplicate the value in redis, synchronize the values periodically for instance).
Short answer: Yes, everything must be stored as a string
Longer answer: you can serialize your object into any text-based format of your choosing. Most people choose MsgPack or JSON because it is very compact and serializers are available in just about any language.