BTW one other thing — in your experience, Diana, which non-text InputFormats would be most useful to support in Python first? Would it be Parquet or Avro, simple SequenceFiles with the Hadoop Writable types, or something else? I think a per-file text input format that does the stuff we did here would also be good.
Matei On Mar 18, 2014, at 3:27 PM, Matei Zaharia <matei.zaha...@gmail.com> wrote: > Hi Diana, > > This seems to work without the iter() in front if you just return > treeiterator. What happened when you didn’t include that? Treeiterator should > return an iterator. > > Anyway, this is a good example of mapPartitions. It’s one where you want to > view the whole file as one object (one XML here), so you couldn’t implement > this using a flatMap, but you still want to return multiple values. The MLlib > example you saw needs Python 2.7 because unfortunately that is a requirement > for our Python MLlib support (see > http://spark.incubator.apache.org/docs/0.9.0/python-programming-guide.html#libraries). > We’d like to relax this later but we’re using some newer features of NumPy > and Python. The rest of PySpark works on 2.6. > > In terms of the size in memory, here both the string s and the XML tree > constructed from it need to fit in, so you can’t work on very large > individual XML files. You may be able to use a streaming XML parser instead > to extract elements from the data in a streaming fashion, without every > materializing the whole tree. > http://docs.python.org/2/library/xml.sax.reader.html#module-xml.sax.xmlreader > is one example. > > Matei > > On Mar 18, 2014, at 7:49 AM, Diana Carroll <dcarr...@cloudera.com> wrote: > >> Well, if anyone is still following this, I've gotten the following code >> working which in theory should allow me to parse whole XML files: (the >> problem was that I can't return the tree iterator directly. I have to call >> iter(). Why?) >> >> import xml.etree.ElementTree as ET >> >> # two source files, format <data> <country name="...">...</country>...</data> >> mydata=sc.textFile("file:/home/training/countries*.xml") >> >> def parsefile(iterator): >> s = '' >> for i in iterator: s = s + str(i) >> tree = ET.fromstring(s) >> treeiterator = tree.getiterator("country") >> # why to I have to convert an iterator to an iterator? not sure but >> required >> return iter(treeiterator) >> >> mydata.mapPartitions(lambda x: parsefile(x)).map(lambda element: >> element.attrib).collect() >> >> The output is what I expect: >> [{'name': 'Liechtenstein'}, {'name': 'Singapore'}, {'name': 'Panama'}] >> >> BUT I'm a bit concerned about the construction of the string "s". How big >> can my file be before converting it to a string becomes problematic? >> >> >> >> On Tue, Mar 18, 2014 at 9:41 AM, Diana Carroll <dcarr...@cloudera.com> wrote: >> Thanks, Matei. >> >> In the context of this discussion, it would seem mapParitions is essential, >> because it's the only way I'm going to be able to process each file as a >> whole, in our example of a large number of small XML files which need to be >> parsed as a whole file because records are not required to be on a single >> line. >> >> The theory makes sense but I'm still utterly lost as to how to implement it. >> Unfortunately there's only a single example of the use of mapPartitions in >> any of the Python example programs, which is the log regression example, >> which I can't run because it requires Python 2.7 and I'm on Python 2.6. >> (aside: I couldn't find any statement that Python 2.6 is unsupported...is >> it?) >> >> I'd really really love to see a real life example of a Python use of >> mapPartitions. I do appreciate the very simple examples you provided, but >> (perhaps because of my novice status on Python) I can't figure out how to >> translate those to a real world situation in which I'm building RDDs from >> files, not inline collections like [(1,2),(2,3)]. >> >> Also, you say that the function called in mapPartitions can return a >> collection OR an iterator. I tried returning an iterator by calling >> ElementTree getiterator function, but still got the error telling me my >> object was not an iterator. >> >> If anyone has a real life example of mapPartitions returning a Python >> iterator, that would be fabulous. >> >> Diana >> >> >> On Mon, Mar 17, 2014 at 6:17 PM, Matei Zaharia <matei.zaha...@gmail.com> >> wrote: >> Oh, I see, the problem is that the function you pass to mapPartitions must >> itself return an iterator or a collection. This is used so that you can >> return multiple output records for each input record. You can implement most >> of the existing map-like operations in Spark, such as map, filter, flatMap, >> etc, with mapPartitions, as well as new ones that might do a sliding window >> over each partition for example, or accumulate data across elements (e.g. to >> compute a sum). >> >> For example, if you have data = sc.parallelize([1, 2, 3, 4], 2), this will >> work: >> >> >>> data.mapPartitions(lambda x: x).collect() >> [1, 2, 3, 4] # Just return the same iterator, doing nothing >> >> >>> data.mapPartitions(lambda x: [list(x)]).collect() >> [[1, 2], [3, 4]] # Group together the elements of each partition in a >> single list (like glom) >> >> >>> data.mapPartitions(lambda x: [sum(x)]).collect() >> [3, 7] # Sum each partition separately >> >> However something like data.mapPartitions(lambda x: sum(x)).collect() will >> *not* work because sum returns a number, not an iterator. That’s why I put >> sum(x) inside a list above. >> >> In practice mapPartitions is most useful if you want to share some data or >> work across the elements. For example maybe you want to load a lookup table >> once from an external file and then check each element in it, or sum up a >> bunch of elements without allocating a lot of vector objects. >> >> Matei >> >> >> On Mar 17, 2014, at 11:25 AM, Diana Carroll <dcarr...@cloudera.com> wrote: >> >> > "There’s also mapPartitions, which gives you an iterator for each >> > partition instead of an array. You can then return an iterator or list of >> > objects to produce from that." >> > >> > I confess, I was hoping for an example of just that, because i've not yet >> > been able to figure out how to use mapPartitions. No doubt this is >> > because i'm a rank newcomer to Python, and haven't fully wrapped my head >> > around iterators. All I get so far in my attempts to use mapPartitions is >> > the darned "suchnsuch is not an iterator" error. >> > >> > def myfunction(iterator): return [1,2,3] >> > mydata.mapPartitions(lambda x: myfunction(x)).take(2) >> > >> > >> > >> > >> > >> > On Mon, Mar 17, 2014 at 1:57 PM, Matei Zaharia <matei.zaha...@gmail.com> >> > wrote: >> > Here’s an example of getting together all lines in a file as one string: >> > >> > $ cat dir/a.txt >> > Hello >> > world! >> > >> > $ cat dir/b.txt >> > What's >> > up?? >> > >> > $ bin/pyspark >> > >>> files = sc.textFile(“dir”) >> > >> > >>> files.collect() >> > [u'Hello', u'world!', u"What's", u'up??’] # one element per line, not >> > what we want >> > >> > >>> files.glom().collect() >> > [[u'Hello', u'world!'], [u"What's", u'up??’]] # one element per file, >> > which is an array of lines >> > >> > >>> files.glom().map(lambda a: "\n".join(a)).collect() >> > [u'Hello\nworld!', u"What's\nup??”] # join back each file into a single >> > string >> > >> > The glom() method groups all the elements of each partition of an RDD into >> > an array, giving you an RDD of arrays of objects. If your input is small >> > files, you always have one partition per file. >> > >> > There’s also mapPartitions, which gives you an iterator for each partition >> > instead of an array. You can then return an iterator or list of objects to >> > produce from that. >> > >> > Matei >> > >> > >> > On Mar 17, 2014, at 10:46 AM, Diana Carroll <dcarr...@cloudera.com> wrote: >> > >> > > Thanks Matei. That makes sense. I have here a dataset of many many >> > > smallish XML files, so using mapPartitions that way would make sense. >> > > I'd love to see a code example though ...It's not as obvious to me how >> > > to do that as I probably should be. >> > > >> > > Thanks, >> > > Diana >> > > >> > > >> > > On Mon, Mar 17, 2014 at 1:02 PM, Matei Zaharia <matei.zaha...@gmail.com> >> > > wrote: >> > > Hi Diana, >> > > >> > > Non-text input formats are only supported in Java and Scala right now, >> > > where you can use sparkContext.hadoopFile or .hadoopDataset to load data >> > > with any InputFormat that Hadoop MapReduce supports. In Python, you >> > > unfortunately only have textFile, which gives you one record per line. >> > > For JSON, you’d have to fit the whole JSON object on one line as you >> > > said. Hopefully we’ll also have some other forms of input soon. >> > > >> > > If your input is a collection of separate files (say many .xml files), >> > > you can also use mapPartitions on it to group together the lines because >> > > each input file will end up being a single dataset partition (or map >> > > task). This will let you concatenate the lines in each file and parse >> > > them as one XML object. >> > > >> > > Matei >> > > >> > > On Mar 17, 2014, at 9:52 AM, Diana Carroll <dcarr...@cloudera.com> wrote: >> > > >> > >> Thanks, Krakna, very helpful. The way I read the code, it looks like >> > >> you are assuming that each line in foo.log contains a complete json >> > >> object? (That is, that the data doesn't contain any records that are >> > >> split into multiple lines.) If so, is that because you know that to be >> > >> true of your data? Or did you do as Nicholas suggests and have some >> > >> preprocessing on the text input to flatten the data in that way? >> > >> >> > >> Thanks, >> > >> Diana >> > >> >> > >> >> > >> On Mon, Mar 17, 2014 at 12:09 PM, Krakna H <shankark+...@gmail.com> >> > >> wrote: >> > >> Katrina, >> > >> >> > >> Not sure if this is what you had in mind, but here's some simple >> > >> pyspark code that I recently wrote to deal with JSON files. >> > >> >> > >> from pyspark import SparkContext, SparkConf >> > >> >> > >> >> > >> >> > >> from operator import add >> > >> import json >> > >> >> > >> >> > >> >> > >> import random >> > >> import numpy as np >> > >> >> > >> >> > >> >> > >> >> > >> def concatenate_paragraphs(sentence_array): >> > >> >> > >> >> > >> >> > >> return ' '.join(sentence_array).split(' ') >> > >> >> > >> >> > >> >> > >> >> > >> logFile = 'foo.json' >> > >> conf = SparkConf() >> > >> >> > >> >> > >> >> > >> conf.setMaster("spark://cluster-master:7077").setAppName("example").set("spark.executor.memory", >> > >> "1g") >> > >> >> > >> >> > >> >> > >> >> > >> >> > >> >> > >> >> > >> sc = SparkContext(conf=conf) >> > >> >> > >> >> > >> >> > >> logData = sc.textFile(logFile).cache() >> > >> >> > >> >> > >> >> > >> num_lines = logData.count() >> > >> print 'Number of lines: %d' % num_lines >> > >> >> > >> >> > >> >> > >> >> > >> >> > >> >> > >> >> > >> # JSON object has the structure: {"key": {'paragraphs': [sentence1, >> > >> sentence2, ...]}} >> > >> tm = logData.map(lambda s: (json.loads(s)['key'], >> > >> len(concatenate_paragraphs(json.loads(s)['paragraphs'])))) >> > >> >> > >> >> > >> >> > >> >> > >> >> > >> >> > >> >> > >> tm = tm.reduceByKey(lambda _, x: _ + x) >> > >> >> > >> >> > >> >> > >> >> > >> >> > >> >> > >> >> > >> op = tm.collect() >> > >> for key, num_words in op: >> > >> >> > >> >> > >> >> > >> print 'state: %s, num_words: %d' % (state, num_words) >> > >> >> > >> >> > >> >> > >> >> > >> >> > >> >> > >> >> > >> >> > >> >> > >> >> > >> >> > >> On Mon, Mar 17, 2014 at 11:58 AM, Diana Carroll [via Apache Spark User >> > >> List] <[hidden email]> wrote: >> > >> I don't actually have any data. I'm writing a course that teaches >> > >> students how to do this sort of thing and am interested in looking at a >> > >> variety of real life examples of people doing things like that. I'd >> > >> love to see some working code implementing the "obvious work-around" >> > >> you mention...do you have any to share? It's an approach that makes a >> > >> lot of sense, and as I said, I'd love to not have to re-invent the >> > >> wheel if someone else has already written that code. Thanks! >> > >> >> > >> Diana >> > >> >> > >> >> > >> On Mon, Mar 17, 2014 at 11:35 AM, Nicholas Chammas <[hidden email]> >> > >> wrote: >> > >> There was a previous discussion about this here: >> > >> >> > >> http://apache-spark-user-list.1001560.n3.nabble.com/Having-Spark-read-a-JSON-file-td1963.html >> > >> >> > >> How big are the XML or JSON files you're looking to deal with? >> > >> >> > >> It may not be practical to deserialize the entire document at once. In >> > >> that case an obvious work-around would be to have some kind of >> > >> pre-processing step that separates XML nodes/JSON objects with newlines >> > >> so that you can analyze the data with Spark in a "line-oriented >> > >> format". Your preprocessor wouldn't have to parse/deserialize the >> > >> massive document; it would just have to track open/closed tags/braces >> > >> to know when to insert a newline. >> > >> >> > >> Then you'd just open the line-delimited result and deserialize the >> > >> individual objects/nodes with map(). >> > >> >> > >> Nick >> > >> >> > >> >> > >> On Mon, Mar 17, 2014 at 11:18 AM, Diana Carroll <[hidden email]> wrote: >> > >> Has anyone got a working example of a Spark application that analyzes >> > >> data in a non-line-oriented format, such as XML or JSON? I'd like to >> > >> do this without re-inventing the wheel...anyone care to share? Thanks! >> > >> >> > >> Diana >> > >> >> > >> >> > >> >> > >> >> > >> If you reply to this email, your message will be added to the >> > >> discussion below: >> > >> http://apache-spark-user-list.1001560.n3.nabble.com/example-of-non-line-oriented-input-data-tp2750p2752.html >> > >> To start a new topic under Apache Spark User List, email [hidden email] >> > >> To unsubscribe from Apache Spark User List, click here. >> > >> NAML >> > >> >> > >> >> > >> View this message in context: Re: example of non-line oriented input >> > >> data? >> > >> Sent from the Apache Spark User List mailing list archive at Nabble.com. >> > >> >> > > >> > > >> > >> > >> >> >> >
