I am using Scala to parse CSV files. Some of these files have fields which are non-textual data like images or octet-streams. I would like to use Apache Spark's textFile()
method to split up the CSV into rows, and
split(",[ ]*(?=([^\"]*\"[^\"]*\")*[^\"]*$)")
to split the row into fields. Unfortunatly this does not work with files that have these mentioned binary fields. There are two problems: 1) The octet-streams can contain newlines which make textFile()
split rows which should be one, and 2) The octet-streams contain commas and/or double quotes which are not escaped and mess up my schema.
The files are usually big, couple of MBs up to couple of 100MBs. I have to take the CSV's as they are, although I could preprocess them.
All I want to achieve is a working split function so I can ignore the field with the octet-stream. Nevertheless, a great bonus would be to extract the textual information in the octet-stream.
So how would I go forward to solve my problems?
Edit: A typical record obtained with cat
, the newlines are from the file, not for cosmetic purposes (shortened):
7,url,user,02/24/2015 02:29:00 AM,03/22/2015 03:12:36 PM,octet-stream,27156,"MSCF^@^@^@^@�,^@^@^@^@^@^@D^@^@^@^@^@^@^@^C^A^A^@^C^@^D^@^@^@^@^@^T^@^@^@^@^@^P^@�,^@^@^X=^@^@^@^@^@^@^@^@^@^@�^@^@^@^E^@^A^@��^A^@^@^@^@^@^@^@WF6�!^@Info.txt^@=^B^@^@��^A^@^@^@WF7�^@^@List.xml^@^�^@^@��^A^@^@^@WF:�^@^@Filename.txt^@��>��
^@�CK�]�r��^Q��T�^O�^@�-�j�]��FI�Ky��Ei�Je^K""!�^Qx @�*^U^?�^_�;��ħ�^LI^@$(�^Q���b��\N����t�����+������ȷgvM�^L̽�LǴL�^L��^ER��w^Ui^M��^X�Kޓ�^QJȧ��^N~��&�x�bB��D]1�^B|^G���g^SyG�����:����^_P�^T�^_�����U�|B�gH=��%Z^NY���,^U�^VI{��^S�^U�!�^Lpw�T���+�a�z�l������b����w^K��or��pH� ��ܞ�l��z�^\i=�z�:^C�^S!_ESCW��ESC""��g^NY2��s�� u���X^?�^R^R+��b^]^Ro�r���^AR�h�^D��^X^M�^]ޫ���ܰ�^]���0^?��^]�92^GhCx�DN^?
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ESC��(��E�j�π쬖���2{^U&b\��P^S�`^O^XdL�^ 6bu��FD��^@^@^@^@","field_x, data",field_y,field_z
Expected output would be an array
("7","url","user","02/24/2015 02:29:00 AM","03/22/2015 03:12:36 PM","octet-stream","27156","field_x, data",field_y",field_z")
Or, but this is probably another question, such an array (like running strings
on the octet-stream field):
("7","url","user","02/24/2015 02:29:00 AM","03/22/2015 03:12:36 PM","octet-stream","27156","Info.txt List.xml Filename.txt","field_x, data",field_y",field_z")
Edit 2: Every file that has a binary field also contains a length field for it. So instead of splitting directly I can walk left to right through my record and extract the fields. This is certainly a great improvement of my current situation but problem 1) still persists. How can I split those files reliably?
I took a closer look at the files and a header looks like this:
RecordId, Field_A, Content_Type, Content_Length, Content, Field_B
(Where Content_Type can be "octet-stream", Content_Length the number of bytes in the Content field, and Content obviously the data). And good for me, the value of Field_B is predictable, let's assume for a certain file it's always "Hello World".
So instead of using Spark's default behaviour splitting on newlines, how can I achieve that Spark is only splitting on newlines following "Hello World"? (I also edited the question title since the focus of the question changed)