Do CouchDB or MongoDB support aggregation?
CouchDB uses map/reduce for this. Aggregation happens during the reduce stage.
For general information on CouchDB's map/reduce system, aka views, see:
http://guide.couchdb.org/editions/1/en/views.html
http://wiki.apache.org/couchdb/Introduction_to_CouchDB_views
And for some examples of simulating SQL-style aggregation functions and those CouchDB provides, see:
http://guide.couchdb.org/editions/1/en/cookbook.html#aggregate
http://wiki.apache.org/couchdb/Built-In_Reduce_Functions
From MongoDB's point of view, yes.
See: http://www.mongodb.org/display/DOCS/Aggregation
Note there are limitations there (there is work being done to provide greater support for aggregrations, see here). Typically, you may need to use MapReduce (see Mongo docs here) especially on larger resultsets.
Related
Which one is good mongodb aggregate or mongodb functions
what i mean to say : mongodb aggregation or mongodb functional which one is preforable and which one gives good performance.
Mongodb functions
Defines a custom aggregation function or expression in JavaScript.
Whereas aggregation is a prebuilt supported operations.
Executing JavaScript inside an aggregation expression may decrease performance. Only use the $function operator if the provided pipeline operators cannot fulfill your application's needs.
If you have a logic which needs to be customised not supported out of the box, go for functions.
Reference
Some blogs says that mapreduce is slower than aggregation. So which one is ideal to use?
If you go through the official document you can clearly see it written:
For most aggregation operations, the Aggregation Pipeline provides better performance and more coherent interface. However, map-reduce operations provide some flexibility that is not presently available in the aggregation pipeline.
The MongoDB documentation says about map-reduce:
For most aggregation operations, the Aggregation Pipeline provides better performance and more coherent interface. However, map-reduce operations provide some flexibility that is not presently available in the aggregation pipeline.
Does it mean that there are some aggregation operations that cannot be performed in the usual MongoDB aggregation framework but are possible using map-reduce?
In particular I'm looking for an example of map-reduce that cannot be implemented in the MongoDB aggregation framework
Thanks!
An example of "flexibility". Basically, if you have any logic, that does not fit into standard aggregation operators, map-reduce is the only option to do it serverside.
I have two collections called Company_Details and Company_Ranks...Comp_ID is common in two collections. How do I merge these two collections to get complete details of a company.
Please help me
Thanks
Satyam
To make long story short, you either do that on client-side or consider the benefits of embedding those documents.
MongoDB does not support joins, as opposed to relational databases. This is both a pro and a con. It has helped MongoDB's developers to focus on scalability which is much harder to implement when you have joins and transactions.
You can follow the DBRef specification. Lots of drivers support DBRef and do the composition seamlessly for you. You can even do that manually. But most importantly, you can take advantage of embedding documents.
Embedding documents in MongoDB is a unique ability over relational databases. Meaning, you can create one collection consisting of compound documents. You'll enjoy atomicity, as there is no "partial success", and data locality: spinning disks are better in accessing data in sequence.
If querying is your motive and you don't want to change your schema. Then, try Apache Drill which allows you to query with SQLs. Then perform the full join, inner join, etc whatever you want. You can check for drill with MongoDB.
With MongoDB Version 3.2 and higher we got now the $lookup Command, which is the "same" as a Join in a RDBMS.
With that you can easy Query between your 2 Collections and get the Information you want.
For further Details Checkt out the Documentation
https://docs.mongodb.com/manual/reference/operator/aggregation/lookup/
I wanted to know about some examples or scenarios related to Mongo DB which can be done by map-reduce but not aggregation framework ?
Map-reduce is considered to be very powerful tool/mechanism of aggregating data. Then can some of you please share few scenarios where it is not possible for map-reduce to do it ?
Thanks & Best Regards.
In MongoDB currently aggregation framework is limited to 16MB of returned results.
MapReduce can write its output to a collection and has no size limitations.
MapReduce can group entire documents, aggregation framework works on field level. MapReduce can map keys to values and values to keys which can't be done any other way. MapReduce can also call/use various JavaScript built-in functions where aggregation is limited to functions and expressions which are built-in to its framework.