On Apr 28, 2016 14:33, "Johann Spies" <johann.spies@xxxxxxxxx> wrote:
>
> I have several large (7GB+) xml files to get into an SQL database.
>
> The xml-files can contain up to 500 000 subrecords which I want to be able to query in the database.
>
> They are too large to do something like this:
>
>
> insert into rawxml (xml) select XMLPARSE (DOCUMENT CONVERT_FROM(PG_READ_BINARY_FILE('FOO.xml' ), 'UTF8'));
>
> If it were possible, each file would be one huge record in the table which can then be unpacked using XPATH.
>
>
> The options I am considering is :
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> 1. Unpack the individual records (will be more than 50 million) using something like python with lxml and psycopg2 and insert them after dropping all indexes and triggers on the table
>
> 2. Unpack the individual records and write a (very) large tsv-file and then insert it using 'copy'
>
The fastest way I found is to combine these two. Using iterparse from lxml combined with load_rows and COPY from py-postgresql:
http://python.projects.pgfoundry.org/docs/1.1/driver.html#copy-statements
That way you can stream the data.
> It would be convenient If I could use the present xml files as 'foreign tables' and parse them using the xpath-capabilities of PostgreSQL.
>
> Is this possible?
>
There is a multicorn fdw for that:
https://github.com/Kozea/Multicorn/blob/master/python/multicorn/xmlfdw.py
But I never tried it. It looks like it loads all rows in a python list.
Groeten, Arjen
