带重排的 MongoDB

Redis and MongoDB can be used together with good results. A company well-known for running MongoDB and Redis (along with MySQL and Sphinx) is Craiglist. See this presentation from Jeremy Zawodny.

MongoDB is interesting for persistent, document oriented, data indexed in various ways. Redis is more interesting for volatile data, or latency sensitive semi-persistent data.

Here are a few examples of concrete usage of Redis on top of MongoDB.

• Pre-2.2 MongoDB does not have yet an expiration mechanism. Capped collections cannot really be used to implement a real TTL. Redis has a TTL-based expiration mechanism, making it convenient to store volatile data. For instance, user sessions are commonly stored in Redis, while user data will be stored and indexed in MongoDB. Note that MongoDB 2.2 has introduced a low accuracy expiration mechanism at the collection level (to be used for purging data for instance).

• Redis provides a convenient set datatype and its associated operations (union, intersection, difference on multiple sets, etc ...). It is quite easy to implement a basic faceted search or tagging engine on top of this feature, which is an interesting addition to MongoDB more traditional indexing capabilities.

• Redis supports efficient blocking pop operations on lists. This can be used to implement an ad-hoc distributed queuing system. It is more flexible than MongoDB tailable cursors IMO, since a backend application can listen to several queues with a timeout, transfer items to another queue atomically, etc ... If the application requires some queuing, it makes sense to store the queue in Redis, and keep the persistent functional data in MongoDB.

• Redis also offers a pub/sub mechanism. In a distributed application, an event propagation system may be useful. This is again an excellent use case for Redis, while the persistent data are kept in MongoDB.

Because it is much easier to design a data model with MongoDB than with Redis (Redis is more low-level), it is interesting to benefit from the flexibility of MongoDB for main persistent data, and from the extra features provided by Redis (low latency, item expiration, queues, pub/sub, atomic blocks, etc ...). It is indeed a good combination.

Please note you should never run a Redis and MongoDB server on the same machine. MongoDB memory is designed to be swapped out, Redis is not. If MongoDB triggers some swapping activity, the performance of Redis will be catastrophic. They should be isolated on different nodes.

Obviously there are far more differences than this, but for an extremely high overview:

For use-cases:

• Redis is often used as a caching layer or shared whiteboard for distributed computation.
• MongoDB is often used as a swap-out replacement for traditional SQL databases.

Technically:

• Redis is an in-memory db with disk persistence (the whole db needs to fit in RAM).
• MongoDB is a disk-backed db which only needs enough RAM for the indexes.

There is some overlap, but it is extremely common to use both. Here's why:

• MongoDB can store more data cheaper.
• Redis is faster for the entire dataset.
• MongoDB's culture is "store it all, figure out access patterns later"
• Redis's culture is "carefully consider how you'll access data, then store"
• Both have open source tools that depend on them, many of which are used together.

Redis can be used as a replacement for a traditional datastore, but it's most often used with another normal "long" data store, like Mongo, Postgresql, MySQL, etc.

Redis works excellently with MongoDB as a caching server. Here is what happens.

Anytime that mongoose issues a cache query, it will first go over to the cache server.

The cache server will check to see if that exact query has ever been issued before.

If it hasn’t then the cache server will take the query, send it over to mongodb and Mongo will execute the query.

We will then take the result of that query, it then goes back to the cache server, the cache server will store the result of the query on itself.

It will say anytime I execute that query, I get this response and so its going to maintain a record between queries that are issued and responses that come back from those queries.

The cache server will take the response and send it back to mongoose, mongoose will give it to express and it eventually ends up inside the application.

Anytime that the same exact query is issued again, mongoose will send the same query to the cache server, but if the cache server sees that this query was issued before it will not send the query onto mongodb, instead its going to take the response to the query it got the last time and immediately send it back over to mongoose. There is no indices here, no full table scan, nothing.

We are doing a simple lookup to say has this query been executed? Yes? Okay, take the request and send it back immediately and don’t send anything to mongo.

We have the mongoose server, the cache server (Redis) and Mongodb.

On the cache server there might be a datastore with key value type of data store where all the keys are some type of query issued before and the value the result of that query.

So maybe we are looking up a bunch of blogposts by _id.

So maybe the keys in here are the _id of the records we have looked up before.

So lets imagine that mongoose issues a new query where it tries to find a blogpost with _id of 123, the query flows into the cache server, the cache server will check to see if it has a result for any query that was looking for an _id of 123.

If it does not exist in the cache server, this query is taken and sent on to the mongodb instance. Mongodb will execute the query, get a response and send it back.

This result is sent back over to the cache server who takes that result and immediately sends it back to mongoose so we get as fast a response as possible.

Right after that, the cache server will also take the query issued, and add that on to its collection of queries that have been issued and take the result of the query and store it right up against the query.

So we can imagine that in the future we issue the same query again, it hits the cache server, it looks at all the keys it has and says oh I already found that blogpost, it doesn’t reach out to mongo, it just takes the result of the query and sends it directly to mongoose.

We are not doing complex query logic, no indices, nothing like that. Its as fast as possible. Its a simple key value lookup.

Thats an overview of how the cache server (Redis) works with MongoDB.

Now there are other concerns. Are we caching data forever? How do we update records?

We don’t want to always be storing data in the cache and be reading from the cache.

The cache server is not used for any write actions. The cache layer is only used for reading data. If we ever write data, writing will always go over to the mongodb instance and we need to ensure that anytime we write data we clear any data stored on the cache server that is related to the record we just updated in Mongo.