What database is good enough for logging application?

I am writing a web application with nodeJS that can be used by other applications to store logs and accessed later in a web interface or by applications themselves providing an API. Similar to Graylog2 but schema free.

I've already tried couchDB in which each document would be a log doc but since I'm not really using revisions it seems to me I'm not using its all features. And beside that I think if the logs exceeds a limit it would be pretty hard to manage in couchDB.

What I'm really looking for, is a big array of logs that can be sorted, filtered, searched and capped on. Then the last events of it accessed. It should be schema free and writing to it should be non-blocking.

I'm considering using Cassandra(I'm not really familiar with it) due to the points here said. MongoDB seems good here too, since Graylog2 uses in mongoDB, in here it has some good points about it.

I've already have seen this question, but not satisfied with the answers.

Edit: For some reasons I can't use Cassandra in production, now I'm trying MongoDB.

One more reason to use mongoDB : http://www.slideshare.net/WombatNation/logging-app-behavior-to-mongo-db

More edits:

It is similar to graylog2, but the difference I want to make that instead of having a message field, having fileds defined by the client, which is why I want it to be schema free, and because of that, I may need to query in the user defined fields. We can build it on SQL, but querying on the user defined fields would be reinventing wheel. Same 开发者_如何学编程goes with files.

Technically what I'm looking for is to get rich statistical data in the end, or easy debugging and a lot of other stuff that we can't get out of the logs.

Where shall it be stored and how shall it be retrieved?

I guess it depends on how much data you are dealing with. If you have a huge amount (terabytes and petabytes per day) of logs then Apache Kafka, which is designed to allow data to be PULLED by HDFS in parallel, is a interesting solution - still in the incubation stage. I believe if you want to consume Kafka messages with MongoDb, you'd need to develop your own adapter to ingest it as a consumer of a particular Kafka topic. Although MongoDb data (e.g. shards and replicas) is distributed, it may be a sequential process to ingest each message. So, there may be a bottleneck or even race conditions depending on the rate and size of message traffic. Kafka is optimized to pump and append that data to HDFS nodes using message brokers FAST. Then once it is in HDFS you can map/reduce to analyze your information in a variety of ways.

If MongoDb can handle the ingestion load, then it is an excellent, scalable, real-time solution to find information, particularly documents. Otherwise, if you have more time to process data (i.e. batch processes that take hours and sometimes days), then Hadoop or some other Map Reduce database is warranted. Finally, Kafka can distribute that load of messages and hookup that fire-hose to a variety of consumers. Overall, these new technologies spread the load and huge amounts of data across cheap hardware using software to manage failure and recover with a very low probability of losing data.

Even with a small amount of data, MongoDb is a nice option to traditional relational database solutions which require more overhead of developer resources to design, build and maintain.

General Approach

You have a lot of work ahead of you. Whichever database you use, you have many features which you must build on top of the DB foundation. You have done good research about all of your options. It sounds like you suspect that all have pros and cons but all are imperfect. Your suspicion is correct. At this point it is probably time to start writing code.

You could just choose one arbitrarily and start building your application. If your guess was correct that the pros and cons balance out and it's all about the same, then why not simply start building immediately? When you hit difficulty X on your database, remember that it gave you convenience Y and Z and that's just life.

You could also establish the fundamental core of your application and implement various prototypes on each of the databases. That might give you true insight to help discriminate between the databases for your specific application. For example, besides the interface, indexing, and querying questions, what about deployment? What about backups? What about maintenance and security? Maybe "wasting" time to build the same prototype on each platform will make the answer very clear for you.

Notes about CouchDB

I suppose CouchDB is "NoSQL" if you say so. Other things which are "no SQL" include bananas, poems, and cricket. It is not a very meaningful word. We have general-purpose languages and domain-specific languages; similarly CouchDB is a domain-specific database. It can save you time if you need the following features:

• Built-in web API: clients may query directly
• Incremental map-reduce: CouchDB runs the job once, but you can query repeatedly at no cost. Updates to the data set are immediately reflected in the map/reduce result without full re-processing
• Easy to start small but expand to large clusters without changing application code.

Have you considered Apache Kafka?

Kafka is a distributed messaging system developed at LinkedIn for collecting and delivering high volumes of log data with low latency. Our system incorporates ideas from existing log aggregators and messaging systems, and is suitable for both offline and online message consumption.