Best way to work with large amounts of CSV data quickly

Question

I have large CSV datasets (10M+ lines) that need to be processed. I have two other files that need to be referenced for the output—they contain data that amplifies what we know about the millions of lines in the CSV file. The goal is to output a new CSV file that has each record merged with the additional information from the other files.

Imagine that the large CSV file has transactions but the customer information and billing information is recorded in two other files and we want to output a new CSV that has each transaction linked to the customer ID and account ID, etc.

A colleague has a functional program written in Java to do this but it is very slow. The reason is that the CSV file with the millions of lines has to be walked through many, many, many times apparently.

My question is—yes, I am getting to it—how should I approach this in Ruby? The goal is for it to be faster (18+ hours right now with very little CPU activity)

Can I load this many records into memory? If so, how should I do it?

I know this is a little vague. Just looking for ideas as this is a little new to me.

Answer 1

Here is some ruby code I wrote to process large csv files (~180mb in my case).

https://gist.github.com/1323865

A standard FasterCSV.parse pulling it all into memory was taking over an hour. This got it down to about 10 minutes.

The relevant part is this:

lines = []
IO.foreach('/tmp/zendesk_tickets.csv') do |line|
  lines << line
  if lines.size >= 1000
    lines = FasterCSV.parse(lines.join) rescue next
    store lines
    lines = []
  end
end
store lines

IO.foreach doesn't load the entire file into memory and just steps through it with a buffer. When it gets to 1000 lines, it tries parsing a csv and inserting just those rows. One tricky part is the "rescue next". If your CSV has some fields that span multiple lines, you may need to grab a few more lines to get a valid parseable csv string. Otherwise the line you're on could be in the middle of a field.

In the gist you can see one other nice optimization which uses MySQL's update ON DUPLICATE KEY. This allows you to insert in bulk and if a duplicate key is detected it simply overwrites the values in that row instead of inserting a new row. You can think of it like a create/update in one query. You'll need to set a unique index on at least one column for this to work.

Answer 2

how about using a database.

jam the records into tables, and then query them out using joins.

the import might take awhile, but the DB engine will be optimized for the join and retrieval part...