the verb_dict.txt contains things like that
abash,abash,abashed,abashed,abashes,abashing
abate,abate,abated,abated,abates,abating
abide,abide,abode,abode,abides,abiding
......
Shakespeare text just 5MB article
every word have 6 tense the question is once met different tense in the text but need to count to original tenst
Use learned RDD operations to merge the verb pairs that are from the same verb. E.g. (work, 100),
(works,50), (working,150) ----> (work, 300).
my idea is group things like [(verb_in_different_tense,(original_tense,count))] I don't know if that works just give a think about it
My code shows below:
val shakes = sc.textFile("shakespeare.txt") to create rdd from txt file
val shakes1 = shakes.filter(l => l.length > 0) remove empty line
val shakes2 = shakes1.map(x=>x.replaceAll("""[\p{Punct}]""","")) remove the punctuations
val shakes3 = shakes2.flatMap(line=>line.split(" ")) split by space will make each words into one partition
val shakes4 = shakes3.filter(_.nonEmpty) get the nonEmpty line
val shakes5 = shakes4.filter(w=>w == w.toLowerCase()) to lower case
This is done for Shakespeare.txt file
then need to Use learned RDD operations to merge the verb pairs that are from the same verb. E.g. (work, 100),
(works,50), (working,150) ----> (work, 300).
can anyone give me specific steps to deal with this question please?
There are another questions related to this topic and they are related to NLP tasks. What you need here is to extract the lemmas of those words and put them in another column and then group by that column.
Take a look to https://en.wikipedia.org/wiki/Lemma_(morphology)
You can use the Stanford NLP library to perform the lemmatizer to your words(tokens). An example of how you can use in Java that you can use without problems in Scala: https://stanfordnlp.github.io/stanfordnlp/lemma.html
In this repo you can see how you can use Stanford CoreNLP in Spark https://github.com/databricks/spark-corenlp.
Or you can use the annotation from SparkNLP project https://nlp.johnsnowlabs.com/docs/en/annotators
Related
So, I'm reading data from a JSON file and creating a DataFrame. Usually, I would use
sqlContext.read.json("//line//to//some-file.json")
Problem is that my JSON file isn't consistent. So, for each line in the file, there are 2 JSONs. Each line looks like this
{...data I don't need....}, {...data I need....}
I only need my DataFrame to be formed from the data I need, i.e. the second JSON of each line. So I read each line as a string and substring the part that I need, like so
val lines = sc.textFile(link, 2)
val part = lines.map( x => x.substring(x.lastIndexOf('{')).trim)
I want to get all the elements in 'part' as an Array[String] then turn the Array[String] into one string and make the DataFrame. Like so
val strings = part .collect() //doesn't work
val strings = part.take(1000) //works
val jsonStr = "[".concat(strings.mkString(", ")).concat("]")
The problem is, if I call part.collect(), it doesn't work but if I call part.take(N) it works. However, I'd like to get all my data and not just the first N.
Also, if I try part.take(part.count().toInt) it still doesn't work.
Any Ideas??
EDIT
I realized my problem after a good sleep. It was a silly mistake on my part. The very last line of my input file had a different format from the rest of the file.
So part.take(N) would work for all N less than part.count(). That's why part.collect() wasn't working.
Thanks for the help though!
TL;DR: I have a large file that I iterate over three times to get three different sets of counts out. Is there a way to get three maps out in one pass over the data?
Some more detail:
I'm trying to compute PMI between words and features that are listed in a large file. My pipeline looks something like this:
val wordFeatureCounts = sc.textFile(inputFile).flatMap(line => {
val word = getWordFromLine(line)
val features = getFeaturesFromLine(line)
for (feature <- features) yield ((word, feature), 1)
})
And then I repeat this to get word counts and feature counts separately:
val wordCounts = sc.textFile(inputFile).flatMap(line => {
val word = getWordFromLine(line)
val features = getFeaturesFromLine(line)
for (feature <- features) yield (word, 1)
})
val featureCounts = sc.textFile(inputFile).flatMap(line => {
val word = getWordFromLine(line)
val features = getFeaturesFromLine(line)
for (feature <- features) yield (feature, 1)
})
(I realize I could just iterate over wordFeatureCounts to get the wordCounts and featureCounts, but that doesn't answer my question, and looking at running times in practice I'm not sure it's actually faster to do it that way. Also note that there are some reduceByKey operations and other stuff that I do with this after the counts are computed that aren't shown, as they aren't relevant to the question.)
What I would really like to do is something like this:
val (wordFeatureCounts, wordCounts, featureCounts) = sc.textFile(inputFile).flatMap(line => {
val word = getWordFromLine(line)
val features = getFeaturesFromLine(line)
val wfCounts = for (feature <- features) yield ((word, feature), 1)
val wCounts = for (feature <- features) yield (word, 1)
val fCounts = for (feature <- features) yield (feature, 1)
??.setOutput1(wfCounts)
??.setOutput2(wCounts)
??.setOutput3(fCounts)
})
Is there any way to do this with spark? In looking for how to do this, I've seen questions about multiple outputs when you're saving the results to disk (not helpful), and I've seen a bit about accumulators (which don't look like what I need), but that's it.
Also note that I can't just yield all of these results in one big list, because I need three separate maps out. If there's an efficient way to split a combined RDD after the fact, that could work, but the only way I can think of to do this would end up iterating over the data four times, instead of the three I currently do (once to create the combined map, then three times to filter it into the maps I actually want).
It is not possible to split an RDD into multiple RDDs. This is understandable if you think about how this would work under the hood. Say you split RDD x = sc.textFile("x") into a = x.filter(_.head == 'A') and b = x.filter(_.head == 'B'). Nothing happens so far, because RDDs are lazy. But now you print a.count. So Spark opens the file, and iterates through the lines. If the line starts with A it counts it. But what do we do with lines starting with B? Will there be a call to b.count in the future? Or maybe it will be b.saveAsTextFile("b") and we should be writing these lines out somewhere? We cannot know at this point. Splitting an RDD is just not possible with the Spark API.
But nothing stops you from implementing something if you know what you want. If you want to get both a.count and b.count you can map lines starting with A into (1, 0) and lines with B into (0, 1) and then sum up the tuples elementwise in a reduce. If you want to save lines with B into a file while counting lines with A, you could use an aggregator in a map before filter(_.head == 'B').saveAsTextFile.
The only generic solution is to store the intermediate data somewhere. One option is to just cache the input (x.cache). Another is to write the contents into separate directories in a single pass, then read them back as separate RDDs. (See Write to multiple outputs by key Spark - one Spark job.) We do this in production and it works great.
This is one of the major disadvantages of Spark over traditional map-reduce programming. An RDD/DF/DS can be transformed into another RDD/DF/DS but you cannot map an RDD into multiple outputs. To avoid recomputation you need to cache the results into some intermediate RDD and then run multiple map operations to generate multiple outputs. The caching solution will work if you are dealing with reasonable size data. But if the data is large compared to the memory available the intermediate outputs will be spilled to disk and the advantage of caching will not be that great. Check out the discussion here - https://issues.apache.org/jira/browse/SPARK-1476. This is an old Jira but relevant. Checkout out the comment by Mridul Muralidharan.
Spark needs to provide a solution where a map operation can produce multiple outputs without the need to cache. It may not be elegant from the functional programming perspective but I would argue, it would be a good compromise to achieve better performance.
I was also quite disappointed to see that this is a hard limitation of Spark over classic MapReduce. I ended up working around it by using multiple successive maps in which I filter out the data I need.
Here's a schematic toy example that performs different calculations on the numbers 0 to 49 and writes both to different output files.
from functools import partial
import os
from pyspark import SparkContext
# Generate mock data
def generate_data():
for i in range(50):
yield 'output_square', i * i
yield 'output_cube', i * i * i
# Map function to siphon data to a specific output
def save_partition_to_output(part_index, part, filter_key, output_dir):
# Initialise output file handle lazily to avoid creating empty output files
file = None
try:
for key, data in part:
if key != filter_key:
# Pass through non-matching rows and skip
yield key, data
continue
if file is None:
file = open(os.path.join(output_dir, '{}-part{:05d}.txt'.format(filter_key, part_index)), 'w')
# Consume data
file.write(str(data) + '\n')
yield from []
finally:
if file is not None:
file.close()
def main():
sc = SparkContext()
rdd = sc.parallelize(generate_data())
# Repartition to number of outputs
# (not strictly required, but reduces number of output files).
#
# To split partitions further, use repartition() instead or
# partition by another key (not the output name).
rdd = rdd.partitionBy(numPartitions=2)
# Map and filter to first output.
rdd = rdd.mapPartitionsWithIndex(partial(save_partition_to_output, filter_key='output_square', output_dir='.'))
# Map and filter to second output.
rdd = rdd.mapPartitionsWithIndex(partial(save_partition_to_output, filter_key='output_cube', output_dir='.'))
# Trigger execution.
rdd.count()
if __name__ == '__main__':
main()
This will create two output files output_square-part00000.txt and output_cube-part00000.txt with the desired output splits.
Hopefully I'm stupid and this will be easy.
I have a dataframe containing the columns 'url' and 'referrer'.
I want to extract all the referrers that contain the top level domain 'www.mydomain.com' and 'mydomain.co'.
I can use
val filteredDf = unfilteredDf.filter(($"referrer").contains("www.mydomain."))
However, this pulls out the url www.google.co.uk search url that also contains my web domain for some reason. Is there a way, using scala in spark, that I can filter out anything with google in it while keeping the correct results I have?
Thanks
Dean
You can negate predicate using either not or ! so all what's left is to add another condition:
import org.apache.spark.sql.functions.not
df.where($"referrer".contains("www.mydomain.") &&
not($"referrer".contains("google")))
or separate filter:
df
.where($"referrer".contains("www.mydomain."))
.where(!$"referrer".contains("google"))
You may use a Regex. Here you can find a reference for the usage of regex in Scala. And here you can find some hints about how to create a proper regex for URLs.
Thus in your case you will have something like:
val regex = "PUT_YOUR_REGEX_HERE".r // something like (https?|ftp)://www.mydomain.com?(/[^\s]*)? should work
val filteredDf = unfilteredDf.filter(regex.findFirstIn(($"referrer")) match {
case Some => true
case None => false
} )
This solution requires a bit of work but is the safest one.
I have a dataset of employees and their leave-records. Every record (of type EmployeeRecord) contains EmpID (of type String) and other fields. I read the records from a file and then transform into PairRDDFunctions:
val empRecords = sc.textFile(args(0))
....
val empsGroupedByEmpID = this.groupRecordsByEmpID(empRecords)
At this point, 'empsGroupedByEmpID' is of type RDD[String,Iterable[EmployeeRecord]]. I transform this into PairRDDFunctions:
val empsAsPairRDD = new PairRDDFunctions[String,Iterable[EmployeeRecord]](empsGroupedByEmpID)
Then, I go for processing the records as per the logic of the application. Finally, I get an RDD of type [Iterable[EmployeeRecord]]
val finalRecords: RDD[Iterable[EmployeeRecord]] = <result of a few computations and transformation>
When I try to write the contents of this RDD to a text file using the available API thus:
finalRecords.saveAsTextFile("./path/to/save")
the I find that in the file every record begins with an ArrayBuffer(...). What I need is a file with one EmployeeRecord in each line. Is that not possible? Am I missing something?
I have spotted the missing API. It is well...flatMap! :-)
By using flatMap with identity, I can get rid of the Iterator and 'unpack' the contents, like so:
finalRecords.flatMap(identity).saveAsTextFile("./path/to/file")
That solves the problem I have been having.
I also have found this post suggesting the same thing. I wish I saw it a bit earlier.
I have about 1500 files on S3 (each file looks like this:)
Format :
UserId \t ItemId:Score,ItemdId:Score,ItemId:Score \n
UserId \t ItemId:Score,ItemdId:Score,ItemId:Score \n
I read the file as:
import scala.io.Source
val FileRead = Source.fromFile("/home/home/testdataFile1").mkString
Here is an example of what I get:
1152 401368:1.006,401207:1.03
1184 401230:1.119,40049:1.11,40029:1.31
How do I compute the average and standard deviation of the variable 'Score'?
While it's not explicit in the question, Apache Spark is a good tool for doing this in a distributed way. I assume you have set up a Spark cluster. Read the files into an RDD:
val lines: RDD[String] = sc.textFile("s3n://bucket/dir/*")
Pick out the "score" somehow:
val scores: RDD[Double] = lines.map(_.split(":").last.toDouble).cache
.cache saves it in memory. This avoids re-reading the files all the time, but can use a lot of RAM. Remove it if you want to trade speed for RAM.
Calculate the metrics:
val count = scores.count
val mean = scores.sum / count
val devs = scores.map(score => (score - mean) * (score - mean))
val stddev = Math.sqrt(devs.sum / (count - 1))
This question is not new, so maybe I can update the answers.
There are stddev functions (stddev, stddev_pop, and stddev_smap) is SparkSQL (import org.apache.spark.sql.functions) since spark version >= 1.6.0.
I use Apache Commons Math for this stuff (http://commons.apache.org/proper/commons-math/userguide/stat.html), albeit from Java. You can stream stuff through the SummaryStatistics class so you aren't limited to the size of memory. Scala to Java interop should allow you to do this, but I haven't tried it. You should be able to each your way through the File line by line and stream the stuff through an instance of SummaryStatistics. How hard could it be in Scala?
Lookie here, someone is off and Scala-izing the whole thing: https://code.google.com/p/scalalab/wiki/ApacheCommonMathsLibraryInScalaLab
I don't think that storage space should be an issue so I would try putting all of the values into an array of doubles then adding up all of the values then use that and the number of elements in the array to calculate the mean.Then add up all of the absolute values of the differences between the value in the mean and divide that by the number of elements. Then take the square root.