I recently wanted to process some large data in nodejs and then put them into mongodb, at first I tried to process data in memory (javascript arrays),but when I decide to use node cluster, I figured out that by using node cluster it is not good way to use cache object. however, it seems impractical solution to store data in arrays even for short time. I think it would be better to store it in some memory databases such as redis.
so my first question is whether I can use redis via node cluster or not? and whether redis is the best solution or not?
thanks in advance.
When using the cluster module, an external data store is by far the simplest way of sharing state. Think of each worker as an individual client accessing the data store. Redis happens to be perfectly fit for the job, but the same can be said for just about any database. For short term storage, redis and memcached are both popular solutions. For arrays, redis beats memcached because it has an array like type that you can push to and pop from.
An extremely popular library for Node.js connecting to redis is node-redis. I've personally used it and highly recommend the API.
Related
I have been reading through some instructions on using redis and memcache. Can someone input on which would be more suitable?
Our scenario, here is to cache results from a database query and store it for a period of time, if the same value is called it should be used from the cache if its within the time interval.
Can someone share an example of how this can be achieved . just a simple example would do it?
Both Redis and Memcache should work for your use case. But Redis has more features compared to Memcache. Redis can provide persistence of cached data, Memcahe doesn't have persistence. Redis can provide high availability as well as clustering. Memcache doesn't have clusering and HA (some libraries impose HA, clustering from client side, but that is less reliable). Redis provides more data structures and features. Overall, Redis is a better choice as it is kind of Memcache++++.
I have a nodejs project that using couchbase as database.
Just wonder if I store the temporary data in
1.redis
or in
2.couchbase directly.
As I know there is socket delay for couchbase, I think store temporary data in redis while store the permanent data in couchbase is better.
Is there any person has the experience on this?
Your comment welcome
I'm a big Redis fan, but in this situation I would use Couchbase only.
Couchbase is rather efficient, and comparable to the performance of memcached when the working set of your data fits in memory. Most of the time, an extra caching layer on top of Couchbase is not useful.
That said, if you really need a caching layer, or simply some storage for temporary data, you can simply create a memcached bucket hosted in the Couchbase cluster. So you would have an "eventually persistent" bucket for your persistent data, and a memcached bucket for the temporary data.
The bucket types are described here:
http://docs.couchbase.com/couchbase-manual-2.5/cb-admin/#data-storage
In that context, adding Redis as a extra storage layer does not really make sense.
Couchbase has a managed cache built into it, even for Couchbase buckets. So it already has a caching layer and adding another one on top just sounds superfluous.
I am not sure what you mean by a socket delay in Couchbase. Can you perhaps explain more about that? That is not something I have ever seen before and sticks out as suspect to me. I would try and troubleshoot this and figure out what that is before looking to add redis to the mix and have yet another layer to manage and code against. Without know more about the socket delay, it is difficult to make more recommendations.
It's an old question, but I'll have my take at it as well, if nothing else then for the people coming across it via google, just as I did.
I agree with he accepted answer, in that CouchBase has the most recently used Documents in RAM. In that aspect, it does the same as Redis. The advantage of CouchBase is of course that the data can reliably spill over the RAM limit, and the server disk limit, automatically, by adding more nodes.
However, I have a project where I am considering using Redis along side CouchBase. It's basically thought as a caching server, but for the "calculated" items. Such as html-snippets or other things. CouchBase is a fantastic document store, but making lists and other structures, doesn't come that easy, especially not without a lot of views. So I'm thinking to use Redis as a temporary datastore for the ad-hoc data manipulation needed, and CouchBase as the main datastore.
I am looking for a way to share the same data structure (which contains functions, so JSON is not an option) across all cluster instances within NodeJS. I have a data structure called 'Users' that tracks user sessions and contains functions that they have access to. I need to be able to share this datastructure across all node processes, or I need an alternative design pattern. Does anyone know of any solutions to this issue? Thanks
I realize this is old and answered, but it may be beneficial to others to note an alternative. The recommended way to handle a situation as this is to place this data structure with its functions in a separate file and require it when needed. This will essentially pull in the "code/functions" and you store (serialize/deserialize) the data itself in any data store.
There are multiple options for setting up proper IPC (inter process communication) on nodejs:
using a document/key-value storage solution like Redis (key-value) or MongoDB (NoSQL Document-Storage)
using the integrated IPC functionality of the cluster module (see send method)
Deciding which one of those solutions fits best depends on your requirements and your project setup. For our last project, i decided to use both methods:
IPC for triggering jobs and dispatching partial tasks to different nodejs instances
Redis for centralized session- and api-token management
If you are using Express, i highly recommend you use the Redis middleware connect-redis. This session middleware automatically handles centralized session management for express based applications (which also means you can store complex JS objects and have access to them from all your instances).
The Setup:
Imagine a 'twitter like' service where a user submits a post, which is then read by many (hundreds, thousands, or more) users.
My question is regarding the best way to architect the cache & database to optimize for quick access & many reads, but still keep the historical data so that users may (if they want) see older posts. The assumption here is that 90% of users would only be interested in the new stuff, and that the old stuff will get accessed occasionally. The other assumption here is that we want to optimize for the 90%, and its ok if the older 10% take a little longer to retrieve.
With this in mind, my research seems to strongly point in the direction of using a cache for the 90%, and then to also store the posts in another longer-term persistent system. So my idea thus far is to use Redis for the cache. The advantages is that Redis is very fast, and also it has built in pub/sub which would be perfect for publishing posts to many people. And then I was considering using MongoDB as a more permanent data store to store the same posts which will be accessed as they expire off of Redis.
Questions:
1. Does this architecture hold water? Is there a better way to do this?
2. Regarding the mechanism for storing posts in both the Redis & MongoDB, I was thinking about having the app do 2 writes: 1st - write to Redis, it then is immediately available for the subscribers. 2nd - after successfully storing to Redis, write to MongoDB immediately. Is this the best way to do it? Should I instead have Redis push the expired posts to MongoDB itself? I thought about this, but I couldn't find much information on pushing to MongoDB from Redis directly.
It is actually sensible to associate Redis and MongoDB: they are good team players. You will find more information here:
MongoDB with redis
One critical point is the resiliency level you need. Both Redis and MongoDB can be configured to achieve an acceptable level of resiliency, and these considerations should be discussed at design time. Also, it may put constraint on the deployment options: if you want master/slave replication for both Redis and MongoDB you need at least 4 boxes (Redis and MongoDB should not be deployed on the same machine).
Now, it may be a bit simpler to keep Redis for queuing, pub/sub, etc ... and store the user data in MongoDB only. Rationale is you do not have to design similar data access paths (the difficult part of this job) for two stores featuring different paradigms. Also, MongoDB has built-in horizontal scalability (replica sets, auto-sharding, etc ...) while Redis has only do-it-yourself scalability.
Regarding the second question, writing to both stores would be the easiest way to do it. There is no built-in feature to replicate Redis activity to MongoDB. Designing a daemon listening to a Redis queue (where activity would be posted) and writing to MongoDB is not that hard though.
We have an application for iOS which has a chat feature. Currently it works with long poll. And now we are trying to modify it to work with sockets. When it comes to socket, we have started for a research and it seems that one of the best option is using nodejs with socket.io. Then we have used redis pub/sub to manage the message delivery and storage.
After a few researching on redis, the recommended usage suggests the stored data should fit on memory. But, we have a little big database. We would like to store the whole chat history. So we have started to plan to use redis as a cache database, which will store the online user's chat history (may be not whole of them) and write the actual conversation after getting offline from redis to mongodb/simpledb (or instantly both of them).
So as a summary, we are about to decide to use nodejs and redis pub/sub to deliver messages, redis as a cache database, and mongodb to store the whole conversation.
What do you think about the design? Is this acceptable? Or, if there is a better way you can suggest, can you please explain a little more?
Thanks in advance.
For a chat system, you're thinking big. If you think you're going to reach a million users then go for it. Consider also availability - how will your system deal with failure of a machine?