Another vote on this, support for simple SequenceFiles and/or Avro would be terrific, as using plain text can be very space-inefficient, especially for numerical data.
-- Jeremy On Mar 19, 2014, at 5:24 PM, Nicholas Chammas <nicholas.cham...@gmail.com> wrote: > I'd second the request for Avro support in Python first, followed by Parquet. > > > On Wed, Mar 19, 2014 at 2:14 PM, Evgeny Shishkin <itparan...@gmail.com> wrote: > > On 19 Mar 2014, at 19:54, Diana Carroll <dcarr...@cloudera.com> wrote: > >> Actually, thinking more on this question, Matei: I'd definitely say support >> for Avro. There's a lot of interest in this!! >> > > Agree, and parquet as default Cloudera Impala format. > > > > >> On Tue, Mar 18, 2014 at 8:14 PM, Matei Zaharia <matei.zaha...@gmail.com> >> wrote: >> 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. >>>> > >> >>>> > > >>>> > > >>>> > >>>> > >>>> >>>> >>>> >>> >> >> > >
