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Ruby Gem for smarter importing of CSV Files as Array(s) of Hashes, with optional features for processing large files in parallel, embedded comments, unusual field- and record-separators, flexible mapping of CSV-headers to Hash-keys
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smarter_csv is a Ruby Gem for smarter importing of CSV Files as Array(s) of Hashes, suitable for direct processing with Mongoid or ActiveRecord, and parallel processing with Resque or Sidekiq.

smarter_csv has lots of features:

  • able to process large CSV-files
  • able to chunk the input from the CSV file to avoid loading the whole CSV file into memory
  • return a Hash for each line of the CSV file, so we can quickly use the results for either creating MongoDB or ActiveRecord entries, or further processing with Resque
  • able to pass a block to the process method, so data from the CSV file can be directly processed (e.g. Resque.enqueue )
  • allows to have a bit more flexible input format, where comments are possible, and col_sep,row_sep can be set to any character sequence, including control characters.
  • able to re-map CSV "column names" to Hash-keys of your choice (normalization)
  • able to ignore "columns" in the input (delete columns)
  • able to eliminate nil or empty fields from the result hashes (default)

NOTE; This Gem is only for importing CSV files - writing of CSV files is not supported.


Ruby's CSV library's API is pretty old, and it's processing of CSV-files returning Arrays of Arrays feels 'very close to the metal'. The output is not easy to use - especially not if you want to create database records from it. Another shortcoming is that Ruby's CSV library does not have good support for huge CSV-files, e.g. there is no support for 'chunking' and/or parallel processing of the CSV-content (e.g. with Resque or Sidekiq),

As the existing CSV libraries didn't fit my needs, I was writing my own CSV processing - specifically for use in connection with Rails ORMs like Mongoid, MongoMapper or ActiveRecord. In those ORMs you can easily pass a hash with attribute/value pairs to the create() method. The lower-level Mongo driver and Moped also accept larger arrays of such hashes to create a larger amount of records quickly with just one call.


The two main choices you have in terms of how to call SmarterCSV.process are:

  • calling process with or without a block
  • passing a :chunk_size to the process method, and processing the CSV-file in chunks, rather than in one piece.

Example 1a: How SmarterCSV processes CSV-files as array of hashes:

Please note how each hash contains only the keys for columns with non-null values.

 $ cat pets.csv
 first name,last name,dogs,cats,birds,fish
 $ irb
 > require 'smarter_csv'
  => true 
 > pets_by_owner = SmarterCSV.process('/tmp/pets.csv')
  => [ {:first_name=>"Dan", :last_name=>"McAllister", :dogs=>"2"},
       {:first_name=>"Lucy", :last_name=>"Laweless", :cats=>"5"}, 
       {:first_name=>"Miles", :last_name=>"O'Brian", :fish=>"21"}, 
       {:first_name=>"Nancy", :last_name=>"Homes", :dogs=>"2", :birds=>"1"} 

Example 1b: How SmarterCSV processes CSV-files as chunks, returning arrays of hashes:

Please note how the returned array contains two sub-arrays containing the chunks which were read, each chunk containing 2 hashes. In case the number of rows is not cleanly divisible by :chunk_size, the last chunk contains fewer hashes.

 > pets_by_owner = SmarterCSV.process('/tmp/pets.csv', {:chunk_size => 2, :key_mapping => {:first_name => :first, :last_name => :last}})
   => [ [ {:first=>"Dan", :last=>"McAllister", :dogs=>"2"}, {:first=>"Lucy", :last=>"Laweless", :cats=>"5"} ], 
        [ {:first=>"Miles", :last=>"O'Brian", :fish=>"21"}, {:first=>"Nancy", :last=>"Homes", :dogs=>"2", :birds=>"1"} ]

Example 1c: How SmarterCSV processes CSV-files as chunks, and passes arrays of hashes to a given block:

Please note how the given block is passed the data for each chunk as the parameter (array of hashes), and how the process method returns the number of chunks when called with a block

 > total_chunks = SmarterCSV.process('/tmp/pets.csv', {:chunk_size => 2, :key_mapping => {:first_name => :first, :last_name => :last}}) do |chunk|
     chunk.each do |h|   # you can post-process the data from each row to your heart's content, and also create virtual attributes:
       h[:full_name] = [h[:first],h[:last]].join(' ')  # create a virtual attribute
       h.delete(:first) ; h.delete(:last)              # remove two keys
     puts chunk.inspect   # we could at this point pass the chunk to a Resque worker..

   [{:dogs=>"2", :full_name=>"Dan McAllister"}, {:cats=>"5", :full_name=>"Lucy Laweless"}]
   [{:fish=>"21", :full_name=>"Miles O'Brian"}, {:dogs=>"2", :birds=>"1", :full_name=>"Nancy Homes"}]
    => 2 

Example 2: Reading a CSV-File in one Chunk, returning one Array of Hashes:

filename = '/tmp/input_file.txt' # TAB delimited file, each row ending with Control-M
recordsA = SmarterCSV.process(filename, {:col_sep => "\t", :row_sep => "\cM"})  # no block given

=> returns an array of hashes

Example 3: Populate a MySQL or MongoDB Database with SmarterCSV:

# without using chunks:
filename = '/tmp/some.csv'
n = SmarterCSV.process(filename, {:key_mapping => {:unwanted_row => nil, :old_row_name => :new_name}}) do |array|
      # we're passing a block in, to process each resulting hash / =row (the block takes array of hashes)
      # when chunking is not enabled, there is only one hash in each array
      MyModel.create( array.first )

 => returns number of chunks / rows we processed 

Example 4: Populate a MongoDB Database in Chunks of 100 records with SmarterCSV:

# using chunks:
filename = '/tmp/some.csv'
n = SmarterCSV.process(filename, {:chunk_size => 100, :key_mapping => {:unwanted_row => nil, :old_row_name => :new_name}}) do |chunk|
      # we're passing a block in, to process each resulting hash / row (block takes array of hashes)
      # when chunking is enabled, there are up to :chunk_size hashes in each chunk
      MyModel.collection.insert( chunk )   # insert up to 100 records at a time

 => returns number of chunks we processed

Example 5: Reading a CSV-like File, and Processing it with Resque:

filename = '/tmp/strange_db_dump'   # a file with CRTL-A as col_separator, and with CTRL-B\n as record_separator (hello iTunes)
n = SmarterCSV.process(filename, {:col_sep => "\cA", :row_sep => "\cB\n", :comment_regexp => /^#/,
        :chunk_size => 100 , :key_mapping => {:export_date => nil, :name => :genre}}) do |chunk|
    Resque.enque( ResqueWorkerClass, chunk ) # pass chunks of CSV-data to Resque workers for parallel processing
=> returns number of chunks


The process method reads and processes a "generalized" CSV file and returns the contents either as an Array of Hashes, or an Array of Arrays, which contain Hashes, or processes Chunks of Hashes via a given block.

SmarterCSV.process(filename, options={}, &block)

The options and the block are optional.

SmarterCSV.process supports the following options:

  • :col_sep : column separator , which defaults to ','
  • :row_sep : row separator or record separator , defaults to system's $/ , which defaults to "\n"
  • :quote_char : quotation character , defaults to '"'
  • :comment_regexp : regular expression which matches comment lines , defaults to /^#/ (see NOTE about the CSV header)
  • :chunk_size : if set, determines the desired chunk-size (defaults to nil, no chunk processing)
  • :key_mapping : a hash which maps headers from the CSV file to keys in the result hash (default: nil)
  • :downcase_header : downcase all column headers (default: true)
  • :strings_as_keys : use strings instead of symbols as the keys in the result hashes (default: false)
  • :remove_empty_values : remove values which have nil or empty strings as values (default: true)
  • :remove_zero_values : remove values which have a numeric value equal to zero / 0 (default: false)
  • :remove_values_matching : removes key/value pairs if value matches given regular expressions (default: nil) , e.g. /^\$0.0+$/ to match $0.00 , or /^#VALUE!$/ to match errors in Excel spreadsheets
  • :convert_values_to_numeric : converts strings containing Integers or Floats to the appropriate class (default: true)
  • :remove_empty_hashes : remove / ignore any hashes which don't have any key/value pairs (default: true)

NOTES about CSV Headers:

  • as this method parses CSV files, it is assumed that the first line of any file will contain a valid header
  • the first line with the CSV header may or may not be commented out according to the :comment_regexp
  • any occurences of :comment_regexp or :row_sep will be stripped from the first line with the CSV header
  • any of the keys in the header line will be downcased, spaces replaced by underscore, and converted to Ruby symbols before being used as keys in the returned Hashes

NOTES on Key Mapping:

  • keys in the header line of the file can be re-mapped to a chosen set of symbols, so the resulting Hashes can be better used internally in your application (e.g. when directly creating MongoDB entries with them)
  • if you want to completely delete a key, then map it to nil or to '', they will be automatically deleted from any result Hash

NOTES on the use of Chunking and Blocks:

  • chunking can be VERY USEFUL if used in combination with passing a block to File.read_csv FOR LARGE FILES
  • if you pass a block to File.read_csv, that block will be executed and given an Array of Hashes as the parameter.
  • if the chunk_size is not set, then the array will only contain one Hash.
  • if the chunk_size is > 0 , then the array may contain up to chunk_size Hashes.
  • this can be very useful when passing chunked data to a post-processing step, e.g. through Resque

See also:


Add this line to your application's Gemfile:

gem 'smarter_csv'

And then execute:

$ bundle

Or install it yourself as:

$ gem install smarter_csv


1.0.1 (2012-07-30)

  • added the following options:

    • :downcase_header
    • :strings_as_keys
    • :remove_zero_values
    • :remove_values_matching
    • :remove_empty_hashes
    • :convert_values_to_numeric
  • renamed the following options:

    • :remove_empty_fields => :remove_empty_values

1.0.0 (2012-07-29)

  • renamed SmarterCSV.process_csv to SmarterCSV.process.

1.0.0.pre1 (2012-07-29)

Reporting Bugs / Feature Requests

Please open an Issue on GitHub if you have feedback, new feature requests, or want to report a bug. Thank you!


  1. Fork it
  2. Create your feature branch (git checkout -b my-new-feature)
  3. Commit your changes (git commit -am 'Added some feature')
  4. Push to the branch (git push origin my-new-feature)
  5. Create new Pull Request
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