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Finding mongoDB records in batches (using mongoid ruby adapter)

Using rails 3 and mongoDB with the mongoid adapter, how can I batch finds to the mongo DB? I need to grab all the records in a particular mongo DB collection and index them in solr (initial index of data for searching).

The problem I'm having is that doing Model.all grabs all the records and stores them into memory. Then when I process over them and index in solr, my memory gets eaten up and the process dies.

What I'm trying to do is batch the find in mongo so that I can iterate over 1,000 records at a time, pass them to solr to index, and then process the next 1,000, etc...

The code I currently have does this:

Model.all.each do |r|   Sunspot.index(r) end 

For a collection that has about 1.5 million records, this eats up 8+ GB of memory and kills the process. In ActiveRecord, there is a find_in_batches method that allows me to chunk up the queries into manageable batches that keeps the memory from getting out of control. However, I can't seem to find anything like this for mongoDB/mongoid.

I would LIKE to be able to do something like this:

Model.all.in_batches_of(1000) do |batch|   Sunpot.index(batch) end 

That would alleviate my memory problems and query difficulties by only doing a manageable problem set each time. The documentation is sparse, however, on doing batch finds in mongoDB. I see lots of documentation on doing batch inserts but not batch finds.

like image 595
Dan L Avatar asked Aug 12 '11 14:08

Dan L


1 Answers

With Mongoid, you don't need to manually batch the query.

In Mongoid, Model.all returns a Mongoid::Criteria instance. Upon calling #each on this Criteria, a Mongo driver cursor is instantiated and used to iterate over the records. This underlying Mongo driver cursor already batches all records. By default the batch_size is 100.

For more information on this topic, read this comment from the Mongoid author and maintainer.

In summary, you can just do this:

Model.all.each do |r|   Sunspot.index(r) end 
like image 197
Ryan McGeary Avatar answered Sep 29 '22 09:09

Ryan McGeary