Actually, thinking more on this question, Matei: I'd definitely say support for Avro. There's a lot of interest in this!!
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.xmlreaderis > 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 <http://nabble.com/>. >>> > >> >>> > > >>> > > >>> > >>> > >>> >>> >> > > >
