I have used Redis before in many projects, usually as a temporary cache, or to share small pieces of data between different subsystems. you get the idea. But now, I'm building a service that mainly revolves around the concept of a key-value store and needs super-fast data retrieval, so I'm using Redis. And because of this, all my data would be on Redis (it's not just a temporary cache or something of that sort). So I'm wondering, is this OK or should I take backups to another more "stable" database like MongoDB or MySQL?
Note that I'm not talking about the usual periodic backups. I would do that regardless of what database I end up using.
Related
I have a site where you can list your car for sale. There is a list and a map with filtering on car types and other car specifications. My idea was to cache cars table and use that to filter on when user is searching for a car on the website. Currently, especially when zooming in/out on the map, each time user does that, http request is made and it's querying the database, and that can be slow and heavy on the server.
As an experiment with 1 000 items, I have cached map data (trimmed data with only basic info) and it's working fine. I was thinking of doing a basically copy of cars table instead with all needed joins added in Memory Cache and use that instead of querying the DB every request for both list and the map. I would have Cron Job every 5 minutes (as data can change, but it doesn't have to be immediate) to update Memory Cache with latest cars data from DB.
What would be the cons of using this approach in long term and for using it for example storing 100 000 records? Beside server needing more RAM, would there be any concerns about scalability or usability of this approach? Would it be better to use Redis instead?
I do have in place now "search as you type" service, but I don't really need that functionality as filtering is pretty exact, I have added it more as a caching server but I think I would be better off just using Memory Cache until a real need for that kind of service is required.
Thank you
Since memory isn’t infinite, we need to limit the number of items stored in the In-Memory cache.
MemoryCache VS Redis
MemoryCache
MemoryCache is embedded in the process , hence can only be used as a plain key-value store from that process.
Redis
Redis is a remote data structure server. It is certainly slower than just storing the data in local memory.
I conclude that MemoryCache is running in the web server of the current application, and it is limited by the performance of the web server. Of course, it will be very fast under the same configuration. I think the disadvantage is that the stored data cannot be shared with other applications.
If redis is used, reading data directly from memory is not as fast as memorycache, but it has high reliability and high scalability.
Related Post:
1. How to update redis after updating database?
2. how to keep caching up to date
3. How can MySQL update data in real time in redis cache?
I have a fairly small relational database currently setup (SQLite, changing to PostgreSQL) that has some relatively simple many-to-one and many-to-many relations. The app uses Websockets to give real-time updates to any clients so I want to make any operations as quick as possible. I was planning on using redis to cache parts of the data in memory as required (parts that will be read/written frequently) so that queries will be faster. I know with the database currently so small, performance gains aren't going to be noticeable but I want it to be scalable.
There seems to be a lot of material/information suggesting using redis as a cache is a good idea, but I'm struggling to find much information about when it is suitable to write updates to the SQL database and how is best to do it.
For example, should I write updates to the redis-store, then send updated data out to clients and then write the same update to the SQL database all in the same request (in that order)? (i.e. more frequent smaller writes)
Or should I simply just write updates to the redis-store and send the updated data out to clients. Then, periodically (every minute?) read back from the redis-store and subsequently save it in the SQL database? (i.e. less frequent but larger writes)
Or is there some other best practice for keeping a redis store and SQL database consistent?
Would my first example perform poorly due to the larger number of writes to disk and the CPU being more active or would this be negligible?
Users of our platform will have large amounts of stored data on our system. Through an application, once connected, that data will be transferred to them and no longer need to remain on our servers. There could potentially be hundreds or thousands of users connected at any given time, performing their downloads.
Here's the proposed architecture:
User management, configuration, and data download statistics will be maintained in a SQL Server database, while using either Redis or DynamoDB for the large data sets.
The reason for choosing either Redis or DynamoDB is based on cost - cheaper than running another SQL Server instance, and performance. The data format will be similar to a datamart - flat table with no joins.
Initially the queries would be simple - get all data for user X between a date range, and optionally delete.
Since we may want to add free text searching for certain fields of that data using elasticsearch may be a better option to use from the get-go.
I want this to be auto-scaling but not sure which database would be best to use for this scenario.
Here's some great discussion on Database + Search tier from AWS ReInvent:
https://youtu.be/K7o5OlRLtvU?t=1574
I would not take Elastic-search alone because it does not provide auto-scaling for writing capacity. In fact, it's not trivial to augment the number of shard of an index. Secondly it can only handle the JSON format, which could be an issue for you.
Redis could be a good idea because it is really fast, everything is done in RAM, and it provides keys with a limited time-to-live which could be interesting for you. Unfortunately, if your data size exceeds the capacity in RAM of your amazon instance you will have to shard your Redis database. And Redis does not support it, you will have to deal it on your application code. Moreover, as far as I know Redis does not handle complex queries. You will also need to save your data in a Redis data structure which could be an issue for you
DynamoDB handles auto-scaling really well but on the other hand it is a key/value database so it does not allow you to make queries like "get all data for user X between a date range". DynamoDB also allows you to save your data in any format.
The solution will be to use either DynamoDB or either Redis depending of the size of your datas, and to use ElasticSearch in order to index your key with only the meta-data (user and dates). Like that your index will be small, and if you lost the ability to index because of ElasticSearch get too buzy, you keep the ability to save user's datas.
I have been using redis a lot lately, and really am loving it. I am mostly familiar with persistence (rdb and aof). I do have one concern. I would like to be able to selectively "archive" some of my data to disk (or cheaper storage) once it is no longer important. I don't really want to delete it because it might be valuable at some point.
All of my keys are named id_<id>_<someattribute>. So when I am done with id 4, I want to "archive" all all keys that match id_4_*. I can view them quite easily in with the command line, but I can't do anything with them, persay. I have quite a bit of data (very large bitmaps) associated with this data set, and frankly I can't afford the space once the id is no longer relevant or important.
If this were mysql, I would have my different tables and would very easily just dump it to a .sql file and then drop the table. The actual .sql file isn't directly useful to me, but I could reimport the data if/when I need it. Or maybe I have to mysql database and I want to move one table to another database. Are there redis corollaries to these processes? Is there someway to make an rdb or aof file that is a subset of the data?
Any help or input on this matter would be appreciated! Thanks!
#Hoseong Hwang recently asked what I did, so I'm posting what I ended up doing.
It was really quite simple, actually. I was benefited by the fact that my key space is segmented out by different users. All of my keys were of the structure user_<USERID>_<OTHERVALUES>. My archival needs were on a user basis, some user's data was no longer needed to be kept in redis.
So, I started up another instance of redis-server, on another port locally (6380?) or another machine, it makes no difference. Then, I wrote a short script that basically just called KEYS user_<USERID>_* (I understand the blocking nature of KEYS, my key space is so small it didn't matter, you can use SCAN if that is an issue for you.) Then, for each key, I MIGRATED them to that new redis-server instance. After they were all done. I did a SAVE to ensure that the rdb file for that instance was up to date. And now I have that rdb, which is just the content that I wanted to archive. I then terminated that temporary redis-server and the memory was reclaimed.
Now, keep that rdb file somewhere for cheap, safe keeping. And if you ever needed it again, doing the reverse of my process above to get those keys back into your main redis-server would be fairly straightforward.
Instead of trying to extract data from a live Redis instance for archiving purpose, my suggestion would be to extract the data from a dump file.
Run a bgsave command to generate a dump, and then use redis-rdb-tools to extract the keys you are interested in - you can easily get the result as a json file.
See https://github.com/sripathikrishnan/redis-rdb-tools
You can keep the json data in flat files, or try to store them into a relational database or a document store if you need them to be indexed for retrieval purpose.
A few suggestions for you...
I would like to be able to selectively "archive" some of my data to
disk (or cheaper storage) once it is no longer important. I don't
really want to delete it because it might be valuable at some point.
If such data is that valuable, use a traditional database for storage. Despite redis supporting snap-shotting to disk and AOF logs, you should view it as mostly volatile storage. The primary use case for redis is reducing latency, not persistence of valuable data.
So when I am done with id 4, I want to "archive" all all keys that
match id_4_*
What constitutes done? You need to ask yourself this question; does it mean after 1 day the data can fall out of redis? If so, just use TTL and expiration to let redis remove the object from memory. If you need it again, fall back to the database and pull the object back into redis. That first client will take the hit of pulling from the db, but subsequent requests will be cached. If done means something not associated with a specific duration, then you'll have to remove items from redis manually to conserve memory space.
If this were mysql, I would have my different tables and would very
easily just dump it to a .sql file and then drop the table. The actual
.sql file isn't directly useful to me, but I could reimport the data
if/when I need it.
We do the same at my firm. Important data is imported into redis from rdbms executed as on-demand job. We don't drop tables, we just selectively import data from the database into redis; nothing wrong with that.
Is there someway to make an rdb or aof file that is a subset of the
data?
I don't believe there is a way to do selective archiving; it's either all or none.
IMO, spend more time playing with redis. I highly recommend leveraging out-of-box features instead of reinventing and/or over-engineering solutions to suit your needs.
Hope that helps!...
I'm creating a mobile app and it requires a API service backend to get/put information for each user. I'll be developing the web service on ServiceStack, but was wondering about the storage. I love the idea of a fast in-memory caching system like Redis, but I have a few questions:
I created a sample schema of what my data store should look like. Does this seems like it's a good case for using Redis as opposed to a MySQL DB or something like that?
schema http://www.miles3.com/uploads/redis.png
How difficult is the setup for persisting the Redis store to disk or is it kind of built-in when you do writes to the store? (I'm a newbie on this NoSQL stuff)
I currently have my setup on AWS using a Linux micro instance (because it's free for a year). I know many factors go into this answer, but in general will this be enough for my web service and Redis? Since Redis is in-memory will that be enough? I guess if my mobile app skyrockets (hey, we can dream right?) then I'll start hitting the ceiling of the instance.
What to think about when desigining a NoSQL Redis application
1) To develop correctly in Redis you should be thinking more about how you would structure the relationships in your C# program i.e. with the C# collection classes rather than a Relational Model meant for an RDBMS. The better mindset would be to think more about data storage like a Document database rather than RDBMS tables. Essentially everything gets blobbed in Redis via a key (index) so you just need to work out what your primary entities are (i.e. aggregate roots)
which would get kept in its own 'key namespace' or whether it's non-primary entity, i.e. simply metadata which should just get persisted with its parent entity.
Examples of Redis as a primary Data Store
Here is a good article that walks through creating a simple blogging application using Redis:
http://www.servicestack.net/docs/redis-client/designing-nosql-database
You can also look at the source code of RedisStackOverflow for another real world example using Redis.
Basically you would need to store and fetch the items of each type separately.
var redisUsers = redis.As<User>();
var user = redisUsers.GetById(1);
var userIsWatching = redisUsers.GetRelatedEntities<Watching>(user.Id);
The way you store relationship between entities is making use of Redis's Sets, e.g: you can store the Users/Watchers relationship conceptually with:
SET["ids:User>Watcher:{UserId}"] = [{watcherId1},{watcherId2},...]
Redis is schema-less and idempotent
Storing ids into redis sets is idempotent i.e. you can add watcherId1 to the same set multiple times and it will only ever have one occurrence of it. This is nice because it means you don't ever need to check the existence of the relationship and can freely keep adding related ids like they've never existed.
Related: writing or reading to a Redis collection (e.g. List) that does not exist is the same as writing to an empty collection, i.e. A list gets created on-the-fly when you add an item to a list whilst accessing a non-existent list will simply return 0 results. This is a friction-free and productivity win since you don't have to define your schemas up front in order to use them. Although should you need to Redis provides the EXISTS operation to determine whether a key exists or a TYPE operation so you can determine its type.
Create your relationships/indexes on your writes
One thing to remember is because there are no implicit indexes in Redis, you will generally need to setup your indexes/relationships needed for reading yourself during your writes. Basically you need to think about all your query requirements up front and ensure you set up the necessary relationships at write time. The above RedisStackOverflow source code is a good example that shows this.
Note: the ServiceStack.Redis C# provider assumes you have a unique field called Id that is its primary key. You can configure it to use a different field with the ModelConfig.Id() config mapping.
Redis Persistance
2) Redis supports 2 types persistence modes out-of-the-box RDB and Append Only File (AOF). RDB writes routine snapshots whilst the Append Only File acts like a transaction journal recording all the changes in-between snapshots - I recommend adding both until your comfortable with what each does and what your application needs. You can read all Redis persistence at http://redis.io/topics/persistence.
Note Redis also supports trivial replication you can read more about at: http://redis.io/topics/replication
Redis loves RAM
3) Since Redis operates predominantly in memory the most important resource is that you have enough RAM to hold your entire dataset in memory + a buffer for when it snapshots to disk. Redis is very efficient so even a small AWS instance will be able to handle a lot of load - what you want to look for is having enough RAM.
Visualizing your data with the Redis Admin UI
Finally if you're using the ServiceStack C# Redis Client I recommend installing the Redis Admin UI which provides a nice visual view of your entities. You can see a live demo of it at:
http://servicestack.net/RedisAdminUI/AjaxClient/