Can anyone give example use cases of when you would benefit from using Redis and MongoDB in conjunction with each other?
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.
Related
I'm having a MongoDB database, holding tens of millions of documents.
Let's say I want to query a single value out of each document (see image below: target key under 0 key under references key)
so it's a 3rd level nested key, and only if the referenceType equals "CopiedFrom" (references level doesn't exists in all documents)
there's ~10M documents that will answer this condition, and this is a one-time query.
The DBA in my org tells me this database is transactional (and not for reporting) and serves many clients in production, hence, a query like i'm asking will put great load on the system and will compromise production response times.
I don't have much experience with MongoDB and cannot evaluate this claim (besides the fact that it's absurd to have historical data you cannot effectivly access).
Is he right, or he's exaggerating?
knowing this can help me deal with his claim, and get the data i need.
thanks!
Your use case is addressed by adding dedicated hidden nodes to the replica set for analytics queries. See here for example.
The DBA is generally correct in that an expensive analytical query is unsuitable for executing against servers that serve transactional workloads.
In my project, main database is mongodb and for caching, i have redis.
Now for long and more complex queries, it is obviously better that i use redis to cache them.
But i'm wondering if i should cache simple queries like lookup by id, or lookup by some other mongodb indexed field? Does it make sense to use redis for this kind of indexed lookup ?
or should i just not cache this kind of query because mongodb already has good caching mechanism internally?
Is it faster looking up to mongodb indexed field or is it faster to lookup to redis?
Lookup in Redis is definitely faster (because of the key-value nature of Redis).
MongoDB Can't cache queries' results:
MongoDB is a Database and can't cache the result of queries for you because data may change anytime. So managing the cache is the responsibility of the Developer.
But also the MongoDB has some good internal mechanisms to use the RAM for better performance. (check this Question for more info)
DataBase query is expensive:
When you are executing a query in MongoDB, there will be many processes to find data, even on simple queries. But Redis can find a key very, very fast. So it's clear that you must use Redis for keeping things and use MongoDB only for permanent storage and queries.
My recommendation:
It's recommended to cache any high-usage or heavy query's results in the Redis, Memcached, or other key-value in-memory storage.
(It doesn't make sense to look up a simple post in Database/MongoDB a thousand times per day. It's just wasting of resources. The first duty of Cache systems is to keep high-usage data closer)
Also attention you must have a good "cache invalidation" mechanism to update cached data in Redis.
I recommend use the write-through technique to keep models and data in Redis.
I hope this helps.
as the title suggests, include out the map-reduce framework
if i want to trigger an event to run a consistency check or security operations before a record is inserted, how can i do that with MongoDB?
MongoDB does not support triggers, but people have created solutions around them, mostly using the oplog, though this will only help you if you are running with replica sets, as the oplog is a capped collection that keeps track of data changes for the purposes of replication.
For a nodejs solution see: https://www.npmjs.org/package/mongo-watch or see an earlier SO thread: How to listen for changes to a MongoDB collection?
If you are concerned with consistency, read about write concern in mongoDB. http://docs.mongodb.org/manual/core/write-concern/ You can be as relaxed or as strict as you want by setting insert write concern levels, from fire and hope to getting an acknowledgement from all members of the replica set.
So, if you want to run a consistency check before inserting data, you probably will have to move that logic to the client application and set your write concern level to a level that will ensure consistency.
MongoDb does not have triggers or stored procedures. While there are solutions that some have used to try to emulate the behavior, as it is not a built-in feature, you'll need to decide whether the solutions are effective for you. Searching for "triggers and mongodb" should find dozens. All depend on the oplog and replicas.
But, given the nature of MongoDb and a typical 3 tier architecture, I would expect that at the point of data insertion, which could be on a web server for example, you would run, on the web server, the necessary consistency and security checks. You wouldn't allow a client such as a mobile application to directly set data into the database collection without some checks.
Many drivers for MongoDb and extended libraries have validation and consistency checks built in already, so there is less to do. Using unique indexes for some fields can also provide a level of consistency that you cannot do from the driver alone. Look at calls like findAndModify which make atomic updates.
When storing events in an event store, the order in which the events are stored is very important especially when projecting the events later to restore an entities current state.
MongoDB seems to be a good choice for persisting the event store, given its speed and flexibel schema (and it is often recommended as such) but there is no such thing as a transaction in MongoDB meaning the correct event order can not be garanteed.
Given that fact, should you not use MongoDB if you are looking for a consistent event store but rather stick with a conventional RDMS, or is there a way around this problem?
I'm not familiar with the term "event store" as you are using it, but I can address some of the issues in your question. I believe it is probably reasonable to use MongoDB for what you want, with a little bit of care.
In MongoDB, each document has an _id field which is by default in ObjectId format, which consists of a server identifier, and then a timestamp and then a sequence counter. So you can sort on that field and you'll get your objects in their creation order, provided the ObjectIds are all created on the same machine.
Most MongoDB client drivers create the _id field locally before sending an insert command to the database. So if you have multiple clients connecting to the database, sorting by _id won't do what you want since it will sort first by server-hash, which is not what you want.
But if you can convince your MongoDB client driver to not include the _id in the insert command, then the server will generate the ObjectId for each document and they will have the properties you want. Doing this will depend on what language you're working in since each language has its own client driver. Read the driver docs carefully or dive into their source code -- they're all open source. Most drivers also include a way to send a raw command to the server. So if you construct an insert command by hand this will certainly allow you to do what you want.
This will break down if your system is so massive that a single database server can't handle all of your write traffic. The MongoDB solution to needing to write thousands of records per second is to set up a sharded database. In this case the ObjectIds will again be created by different machines and won't have the nice sorting property you want. If you're concerned about outgrowing a single server for writes, you should look to another technology that provides distributed sequence numbers.
We started with Redis, storing active data, logged in users, etc. We're using some pubsub too for realtime data passing.
Recently we added Mongo to fit our geo spatial needs, and it seems great for non-active data too.
How should these two work together? It is dumb to use both? Is it dumb to pass chunks of data from mongo to redis when they becomes active?
Our thoughts were that we might store everything in mongo but then pass user data from mongo to redis when a user is active and the data is likely to be accessed. I know Mongo does some cacheing like this on its own, we are new to both of them and just want to know how they should be used together, if at all.
Thanks!!
It is dumb to use both? Is it dumb to pass chunks of data from mongo to redis when they becomes active?
So I feel like there's actually a legitimate to test and validate this question. Redis is basically an "in-memory" DB, so how much better can you do by giving that RAM to Mongo?
Historically, we've used the Memcache/MySQL combo to basically "add RAM" to MySQL and limit the amount of writing it needed to do. We did this simply because it was complicated to shard MySQL.
However, MongoDB provides a sharding mechanism. So you can "add RAM" to a problem (along with "adding disks") simply by adding more shards.
Thanks to the way memory-mapped files work, MongoDB tends to keep recently used data in memory. So if you're pulling recent data into Redis, that data is probably also in memory on the MongoDB side, so it's not clear that you benefit from having it in two places.
Is it dumb ...
That's hard to say without some testing and analysis. MongoDB doesn't really have the pub/sub mechanism, but it does tend to have fast query times, so it may be appropriate in specific spots.