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. >>> > >> >>> > > >>> > > >>> > >>> > >>> >>> >>> >> > >
