I've got a socket from which I'm reading XML data. However, this socket will spit out multiple different XML documents, so I can't simply parse all the output I receive.
Is there a good way, preferably using the Python standard library, for me to parse multiple XML documents? In other words, if I end up getting
<foo/>
<bar/>
then is there a way to either get multiple DOM objects or have a SAX parser simply work on such a stream?
If you get separate documents, you'll need something to divide them; and if you have that, you can simply split the stream before you parse the individual documents.
Another possibility would be to wrap that into another document, so each XML document is actually a subdocument of a parent's you create (and wrap around) just for that purpose.
Related
I have to process XML files that contain potentially large (up to 2GB) content. In these files , the 'large' part of the content is not spread over the whole file but is contained in one single element (an encrypted file, hex encoded).
I have no leverage on the source of the files, so I need to deal with that situation.
A requirement is to keep a small memory foot print (< 500MB). I was able to read and process the file's contents in streaming mode using xml.sax which is doing it's job just fine.
The problem is, that these files also need to be validated against an XML schema definition (.xsd file), which seems not to be supported by xml.sax.
I found some up-to-date libraries for schema validation like xmlschema but none for doing the validation in a streaming/lazy fashion.
Can anyone recommend a way to do this?
Many schema processors (such as Xerces and Saxon) operate in streaming mode, so there's no need to hold the data in memory while it's being validated. However, a 2Gb single text node is stretching Java's limits on the size of strings and arrays, and even a streaming processor is quite likely to want to hold the whole of a single node in memory.
If there are no validation constraints on the content of this text node (e.g. you don't need to validate that it is valid xs:base64Binary) then I would suggest using a schema validator (such as Saxon) that accepts SAX input, and supplying the input via a SAX filter that eliminates or condenses the long text value. A SAX parser supplies text to the ContentHandler in multiple chunks so there should be no limit in the SAX parser on the size of a text node. Saxon will try and combine the multiple chunks into a single string (or char array) and may fail at this stage either because of Java limits or because of the amount of memory available; but if your filter cuts out the big text node, this won't happen.
Michael Kay's answer had this nice idea of a content filter that can condense long text. This helped me solve my problem.
I ended up writing a simple text shrinker that pre-processes an XML file for me by reducing the text content size in named tags (like: "only keep the first 64 bytes of the text in the 'Data' and 'CipherValue' elements, don't touch anything else").
The resulting file then is small enought to feed it into a validator like xmlschema.
If anyone needs something similar: here is the code of the shrinker
If you use this, be careful
This indeed changes the content of the XML and could potentially cause problems, if the XML schema definition contains things like min or max length checks for the affected elements.
I am trying to parse an XML file using Python. But the problem is that the XML file size is around 30GB. So, it's taking hours to execute:
tree = ET.parse('Posts.xml')
In my XML file, there are millions of child elements of the root. Is there any way to make it faster? I don't need all the children to parse. Even the first 100,000 would be fine. All I need is to set a limit for the depth to parse.
You'll want an XML parsing mechanism that doesn't load everything into memory.
You can use ElementTree.iterparse or you could use Sax.
Here is a page with some XML processing tutorials for Python.
UPDATE: As #marbu said in the comment, if you use ElementTree.iterparse be sure to use it in such a way that you get rid of elements in memory when you've finished processing them.
I have a couple of gigantic XML files (10GB-40GB) that have a very simple structure: just a single root node containing multiple row nodes. I'm trying to parse them using SAX in Python, but the extra processing I have to do for each row means that the 40GB file takes an entire day to complete. To speed things up, I'd like to use all my cores simultaneously. Unfortunately, it seems that the SAX parser can't deal with "malformed" chunks of XML, which is what you get when you seek to an arbitrary line in the file and try parsing from there. Since the SAX parser can accept a stream, I think I need to divide my XML file into eight different streams, each containing [number of rows]/8 rows and padded with fake opening and closing tags. How would I go about doing this? Or — is there a better solution that I might be missing? Thank you!
You can't easily split the SAX parsing into multiple threads, and you don't need to: if you just run the parse without any other processing, it should run in 20 minutes or so. Focus on the processing you do to the data in your ContentHandler.
My suggested way is to read the whole XML file into an internal format and do the extra processing afterwards. SAX should be fast enough to read 40GB of XML in no more than an hour.
Depending on the data you could use a SQLite database or HDF5 file for intermediate storage.
By the way, Python is not really multi-threaded (see GIL). You need the multiprocessing module to split the work into different processes.
I have a very large XML file with 40,000 tag elements.
When i am using element tree to parse this file it's giving errors due to memory.
So is there any module in python that can read the xml file in data chunks without loading the entire xml into memory?And How i can implement that module?
Probably the best library for working with XML in Python is lxml, in this case you should be interested in iterparse/iterwalk.
This is a problem that people usually solve using sax.
If your huge file is basically a bunch of XML documents aggregated inside and overall XML envelope, then I would suggest using sax (or plain string parsing) to break it up into a series of individual documents that you can then process using lxml.etree.
I have a directory full (~103, 104) of XML files from which I need to extract the contents of several fields.
I've tested different xml parsers, and since I don't need to validate the contents (expensive) I was thinking of simply using xml.parsers.expat (the fastest one) to go through the files, one by one to extract the data.
Is there a more efficient way? (simple text matching doesn't work)
Do I need to issue a new ParserCreate() for each new file (or string) or can I reuse the same one for every file?
Any caveats?
Thanks!
Usually, I would suggest using ElementTree's iterparse, or for extra-speed, its counterpart from lxml. Also try to use Processing (comes built-in with 2.6) to parallelize.
The important thing about iterparse is that you get the element (sub-)structures as they are parsed.
import xml.etree.cElementTree as ET
xml_it = ET.iterparse("some.xml")
event, elem = xml_it.next()
event will always be the string "end" in this case, but you can also initialize the parser to also tell you about new elements as they are parsed. You don't have any guarantee that all children elements will have been parsed at that point, but the attributes are there, if you are only interested in that.
Another point is that you can stop reading elements from iterator early, i.e. before the whole document has been processed.
If the files are large (are they?), there is a common idiom to keep memory usage constant just as in a streaming parser.
The quickest way would be to match strings (with, e.g., regular expressions) instead of parsing XML - depending on your XMLs this could actually work.
But the most important thing is this: instead of thinking through several options, just implement them and time them on a small set. This will take roughly the same amount of time, and will give you real numbers do drive you forward.
EDIT:
Are the files on a local drive or network drive? Network I/O will kill you here.
The problem parallelizes trivially - you can split the work among several computers (or several processes on a multicore computer).
If you know that the XML files are generated using the ever-same algorithm, it might be more efficient to not do any XML parsing at all. E.g. if you know that the data is in lines 3, 4, and 5, you might read through the file line-by-line, and then use regular expressions.
Of course, that approach would fail if the files are not machine-generated, or originate from different generators, or if the generator changes over time. However, I'm optimistic that it would be more efficient.
Whether or not you recycle the parser objects is largely irrelevant. Many more objects will get created, so a single parser object doesn't really count much.
One thing you didn't indicate is whether or not you're reading the XML into a DOM of some kind. I'm guessing that you're probably not, but on the off chance you are, don't. Use xml.sax instead. Using SAX instead of DOM will get you a significant performance boost.