The following code works quite well, but when I already have two existing bags (with their alias, suppose S1 and S2 for representing two existing bags for two sets), wondering how to call UDF setDifference to generate set differences? I think if I manually construct an additional bag, using my already existing input bags (S1 and S2), it will be additional overhead?
register datafu-1.2.0.jar;
define setDifference datafu.pig.sets.SetDifference();
-- ({(3),(4),(1),(2),(7),(5),(6)} \t {(1),(3),(5),(12)})
A = load 'input.txt' AS (B1:bag{T:tuple(val:int)},B2:bag{T:tuple(val:int)});
F1 = foreach A generate B1;
F2 = foreach A generate B2;
differenced = FOREACH A {
-- input bags must be sorted
sorted_b1 = ORDER B1 by val;
sorted_b2 = ORDER B2 by val;
GENERATE setDifference(sorted_b1,sorted_b2);
}
-- produces: ({(2),(4),(6),(7)})
DUMP differenced;
Update:
Question is, suppose I have two bags already, how to call UDF setDifference to get set differences? Do I need to build another super bag which contains the two separate bags? Thanks.
thanks in advance,
Lin
I don't see any overhead issue with the UDF invocation.
Ref : http://datafu.incubator.apache.org/docs/datafu/guide/set-operations.html, we have a example for using SetDifference method.
As per API (http://datafu.incubator.apache.org/docs/datafu/1.2.0/datafu/pig/sets/SetDifference.html) SetDifference method takes bags as input and emits the difference between them.
N.B. Do note that the input bags have to be sorted.
In the example snippet shared, I don't see the need of below code snippet
F1 = foreach A generate B1;
F2 = foreach A generate B2;
Related
I am learning Hadoop pig and I always stuck at referencing the elements.please find the below example.
groupwordcount: {group: chararray,words: {(bag_of_tokenTuples_from_line::token: chararray)}}
Can somebody please explain how to reference the elements if we have nested tuples and bags.
Any Links for better understanding the nested referrencing would be great help.
Let's do a simple Demonstration to understand this problem.
say a file 'a.txt' stored at '/tmp/a.txt' folder in HDFS
A = LOAD '/tmp/a.txt' using PigStorage(',') AS (name:chararray,term:chararray,gpa:float);
Dump A;
(John,fl,3.9)
(John,fl,3.7)
(John,sp,4.0)
(John,sm,3.8)
(Mary,fl,3.8)
(Mary,fl,3.9)
(Mary,sp,4.0)
(Mary,sm,4.0)
Now let's group by this Alias 'A' on the basis of some parameter say name and term
B = GROUP A BY (name,term);
dump B;
((John,fl),{(John,fl,3.7),(John,fl,3.9)})
((John,sm),{(John,sm,3.8)})
((John,sp),{(John,sp,4.0)})
((Mary,fl),{(Mary,fl,3.9),(Mary,fl,3.8)})
((Mary,sm),{(Mary,sm,4.0)})
((Mary,sp),{(Mary,sp,4.0)})
describe B;
B: {group: (name: chararray,term: chararray),A: {(name: chararray,term: chararray,gpa: float)}}
now it has become the problem statement that you have asked. Let me demonstrate you how to access elements of group tuple or element of A tuple or both
C = foreach B generate group.name,group.term,A.name,A.term,A.gpa;
dump C;
(John,fl,{(John),(John)},{(fl),(fl)},{(3.7),(3.9)})
(John,sm,{(John)},{(sm)},{(3.8)})
(John,sp,{(John)},{(sp)},{(4.0)})
(Mary,fl,{(Mary),(Mary)},{(fl),(fl)},{(3.9),(3.8)})
(Mary,sm,{(Mary)},{(sm)},{(4.0)})
(Mary,sp,{(Mary)},{(sp)},{(4.0)})
So we accessed all elements by this way.
hope this helped
I have fields C1C2C3C4 (no delimter present)in a raw file, I have to generate output which should look like C1,C2,C3,C4.Using PIG script.
Given :- size of C1=C2=C3=C4= 4bytes.
This should be straightforward with these steps:
Load the data as is
Generate four new columns, using the SUBSTRING function
For example, you should be able to extract c2 as:
SUBSTRING(inputstring, 5, 8)
Extending Dennis's Answer.
Assuming the field is stored as chararray
A = LOAD 'data.txt' as (f1:chararray);
B = FOREACH A GENERATE
SUBSTRING(f1,0,2) as A1,
SUBSTRING(f1,2,4) as A2,
SUBSTRING(f1,4,6) as A3,
SUBSTRING(f1,6,8) as A4;
DUMP B;
I am trying to build a pig script that takes in a textbook file and divides it into chapters and then compares the words in each chapter and returns only words that show up in all chapters and counts them. The chapters are Delimited fairly easily by CHAPTER - X.
Here's what I have so far:
lines = LOAD '../../Alice.txt' AS (line:chararray);
lineswithoutspecchars = FOREACH lines GENERATE REPLACE(line,'([^a-zA-Z\\s]+)','') as line;
words = FOREACH lineswithoutspecchars GENERATE FLATTEN(TOKENIZE(line)) as word;
grouped = GROUP words BY word;
wordcount = FOREACH grouped GENERATE group, COUNT(words);
DUMP wordcount;
Sorry that this question is probably way too simple compared to what I normally ask on stackoverflow and I googled around for it but perhaps I am not using the correct keywords. I am brand new to PIG and trying to learn it for a new job assignment.
Thanks in advance!
A bit lengthy but you will get the result. You could cut down unnecessary relations based on your file though. Provided appropriate comments in teh script.
Input File:
Pig does not know whether integer values in baseball are stored as ASCII strings, Java
serialized values, binary-coded decimal, or some other format. So it asks the load func-
tion, because it is that function’s responsibility to cast bytearrays to other types. In
general this works nicely, but it does lead to a few corner cases where Pig does not know
how to cast a bytearray. In particular, if a UDF returns a bytearray, Pig will not know
how to perform casts on it because that bytearray is not generated by a load function.
CHAPTER - X
In a strongly typed computer language (e.g., Java), the user must declare up front the
type for all variables. In weakly typed languages (e.g., Perl), variables can take on values
of different type and adapt as the occasion demands.
CHAPTER - X
In this example, remember we are pretending that the values for base_on_balls and
ibbs turn out to be represented as integers internally (that is, the load function con-
structed them as integers). If Pig were weakly typed, the output of unintended would
be records with one field typed as an integer. As it is, Pig will output records with one
field typed as a double. Pig will make a guess and then do its best to massage the data
into the types it guessed.
Pig Script:
A = LOAD 'file' as (line:chararray);
B = FOREACH A GENERATE REPLACE(line,'([^a-zA-Z\\s]+)','') as line;
//we need to split on CHAPTER X but the above load function would give us a tuple for each newline. so
group everything and convert that bag to string which will give a single tuple with _ as delimiter.
C = GROUP B ALL;
D = FOREACH C GENERATE BagToString(B) as (line:chararray);
//now we dont have any commas so convert our delimiter CHAPTER X to comma. We do this becuase if we pass this
to TOKENIZE it would split that into separarte column that would be useful to RANK it.
E = FOREACH D GENERATE REPLACE(line,'_CHAPTER X_',',') AS (line:chararray);
F = FOREACH E GENERATE REPLACE(line,'_',' ') AS (line:chararray); //remove the delimiter created by BagToString
//create separate columns
G = FOREACH F GENERATE FLATTEN(TOKENIZE(line,',')) AS (line:chararray);
//we need to rank each chapter so that would be easy when you are doing the count of each word.
H = RANK G;
J = FOREACH H GENERATE rank_G,FLATTEN(TOKENIZE(line)) as (line:chararray);
J1 = GROUP J BY (rank_G, line);
J2 = FOREACH J1 GENERATE COUNT(J) AS (cnt:long),FLATTEN(group.line) as (word:chararray),FLATTEN(group.rank_G) as (rnk:long);
//So J2 result will not have duplicate word within each chapter now.
//So if we group it by word and then filter teh count of that by 2 we are sure that the word is present in all chapters.
J3 = GROUP J2 BY word;
J4 = FOREACH J3 GENERATE SUM(J2.cnt) AS (sumval:long),COUNT(J2) as (cnt:long),FLATTEN(group) as (word:chararray);
J5 = FILTER J4 BY cnt > 2;
J6 = FOREACH J5 GENERATE word,sumval;
dump J6;
//result in order word,count across chapters
Output:
(a,8)
(In,5)
(as,6)
(the,9)
(values,4)
I am working on a solution to the following problem:
Given an arbitrary text document written in English, write a program that will generate a concordance, i.e. an alphabetical list of all word occurrences, labeled with word frequencies.
Bonus: label each word with the sentence numbers in which each occurrence appeared.
Now, I have the first part of this exercise completed. I am stuck on the bonus part.
Can someone please help me out? I am using Hadoop Pig on Cloudera Live. Here is what the sample output is suppose to look like including the bonus.
a. a {2:1,1}
b. all {1:1}
c. alphabetical {1:1}
d. an {2:1,1}
e. appeared {1:2}
Wordcount.pig script does the word count and the other one puts it in alphabetical order.
Wordcount.pig
--Load data
lines = LOAD '/user/cloudera/gettysburg.txt' AS (line:chararray);
-- Create list
words = FOREACH lines GENERATE FLATTEN(TOKENIZE(line)) as word;
-- Count occurances
grouped = GROUP words BY word;
--Generate wordcout
wordcount = FOREACH grouped GENERATE group, COUNT(words);
--Save output
STORE wordcount into '/user/cloudera/output';
WORDCOUNTALPHABETIZE.PIG
--Load unsorted data file
unsortedData = LOAD '/user/cloudera/output/UnsortedList.txt' AS (words:chararray, frequency:int);
DUMP unsortedData;
--Put data in alphabetical order
sortedData = ORDER unsortedData BY words ASC, frequency;
DUMP sortedData;
--Save output
STORE sortedData into '/user/cloudera/output2';
Thanks,
Anne
Could be achieved with UDF Enumerate(Datafu) which would be useful to generate sequence number for each tuple in a bag. can you try this?
register datafu-1.1.0.jar;
define Enumerate datafu.pig.bags.Enumerate('1');
A = LOAD '/home/hduser/a22.dat' as (line:chararray);
Z = FOREACH A GENERATE FLATTEN(TOKENIZE(line,'.')) as (word:chararray); // generate line_number with rank
Z1 = RANK Z;
Z2 = FOREACH Z1 GENERATE rank_Z,FLATTEN(TOKENIZE(word)) as (word:chararray); // line_number,word
Z3 = RANK Z2; // rank used to maintain the word order
Z4 = GROUP Z3 by rank_Z; // grouped by line_number to generate word_number for each line
Z5 = foreach Z4 {
sorted = order Z3 by rank_Z2;
generate group, sorted;
} //ordered to maintain word order
Z6 = foreach Z5 generate FLATTEN(Enumerate(sorted)) as (l:int,word_no:int,word:chararray,line_no:int); //generate word_number
Z7 = GROUP Z6 BY word;
Z8 = FOREACH Z7 GENERATE group,Z6.line_no,Z6.word_no,COUNT(Z6); // output in order word,line_number,word_number,count_of_each_word
For word nation below is the output:
(nation,{(16),(13),(25),(16)},{(2),(2),(4),(1)},4)
in the order (word,{(word_number1,word_number2,word_number3,word_number4},{line_number1,line_number2,line_number3,line_number4},count_of_each_word)
I'd like to use Apache Pig to build a large key -> value mapping, look things up in the map, and iterate over the keys. However, there does not even seem to be syntax for doing these things; I've checked the manual, wiki, sample code, Elephant book, Google, and even tried parsing the parser source. Every single example loads map literals from a file... and then never uses them. How can you use Pig's maps?
First, there doesn't seem to be a way to load a 2-column CSV file into a map directly. If I have a simple map.csv:
1,2
3,4
5,6
And I try to load it as a map:
m = load 'map.csv' using PigStorage(',') as (M: []);
dump m;
I get three empty tuples:
()
()
()
So I try to load tuples and then generate the map:
m = load 'map.csv' using PigStorage(',') as (key:chararray, val:chararray);
b = foreach m generate [key#val];
ERROR 1000: Error during parsing. Encountered " "[" "[ "" at line 1, column 24.
...
Many variations on the syntax also fail (e.g., generate [$0#$1]).
OK, so I munge my map into Pig's map literal format as map.pig:
[1#2]
[3#4]
[5#6]
And load it up:
m = load 'map.pig' as (M: []);
Now let's load up some keys and try lookups:
k = load 'keys.csv' as (key);
dump k;
3
5
1
c = foreach k generate m#key; /* Or m[key], or... what? */
ERROR 1000: Error during parsing. Invalid alias: m in {M: map[ ]}
Hrm, OK, maybe since there are two relations involved, we need a join:
c = join k by key, m by /* ...um, what? */ $0;
dump c;
ERROR 1068: Using Map as key not supported.
c = join k by key, m by m#key;
dump c;
Error 1000: Error during parsing. Invalid alias: m in {M: map[ ]}
Fail. How do I refer to the key (or value) of a map? The map schema syntax doesn't seem to let you even name the key and value (the mailing list says there's no way to assign types).
Finally, I'd just like to be able to find all they keys in my map:
d = foreach m generate ...oh, forget it.
Is Pig's map type half-baked? What am I missing?
Currently pig maps need the key to a chararray (string) that you supply and not a variable which contains a string. so in map#key the key has to be constant string that you supply (eg: map#'keyvalue').
The typical use case of this is to load a complex data structure one of the element being a key value pair and later in a foreach statement you can refer to a particular value based on the key you are interested in.
http://pig.apache.org/docs/r0.9.1/basic.html#map-schema
In Pig version 0.10.0 there is a new function available called "TOMAP" (http://pig.apache.org/docs/r0.10.0/func.html#tomap) that converts its odd (chararray) parameters to keys and even parameters to values. Unfortunately I haven't found it to be that useful, though, since I typically deal with arbitrary dicts of varying lengths and keys.
I would find a TOMAP function that took a tuple as a single argument, instead of a variable number of parameters, to be much more useful.
This isn't a complete solution to your problem, but the availability of TOMAP gives you some more options for your constructing a real solution.
Great question!
I personally do not like Maps in Pig. They have a place in traditional programming languages like Java, C# etc, wherein its really handy and fast to lookup a key in the map. On the other hand, Maps in Pig have very limited features.
As you rightly pointed, one can not lookup variable key in the Map in Pig. The key needs to be Constant. e.g. myMap#'keyFoo' is allowed but myMap#$SOME_VARIABLE is not allowed.
If you think about it, you do not need Map in Pig. One usually loads the data from some source, transforms it, joins it with some other dataset, filter it, transform it and so on. JOIN actually does a good job of looking up the variable keys in the data.
e.g. data1 has 2 columns A and B and data2 has 3 columns X, Y, Z. If you join data1 BY A with data2 BY Z, JOIN does the work of a Map (from traditional language) which maps value of column Z to value of column B (via column A). So data1 essentially represents a Map A -> B.
So why do we need Map in Pig?
Usually Hadoop data are the dumps of different data sources from Traditional languages. If original data sources contain Maps, the HDFS data would contain a corresponding Map.
How can one handle the Map data?
There are really 2 use cases:
Map keys are constants.
e.g. HttpRequest Header data contains time, server, clientIp as the keys in Map. to access value of a particular key, one case access them with Constant key.
e.g. header#'clientIp'.
Map keys are variables.
In these cases, you would most probably would want to JOIN the Map keys with some other data set. I usually convert the Map to Bag using UDF MapToBag, which converts map data into Bag of 2 field tuples (key, value). Once map data is converted to Bag of tuples, its easy to join it with other data sets.
I hope this helps.
1)If you want to load map data it should be like "[programming#SQL,rdbms#Oracle]"
2)If you want to load tuple data it should be like "(first_name_1234,middle_initial_1234,last_name_1234)"
3)If you want to load bag data it should be like"{(project_4567_1),(project_4567_2),(project_4567_3)}"
my file pigtest.csv like this
1234|emp_1234#company.com|(first_name_1234,middle_initial_1234,last_name_1234)|{(project_1234_1),(project_1234_2),(project_1234_3)}|[programming#SQL,rdbms#Oracle]
4567|emp_4567#company.com|(first_name_4567,middle_initial_4567,last_name_4567)|{(project_4567_1),(project_4567_2),(project_4567_3)}|[programming#Java,OS#Linux]
my schema:
a = LOAD 'pigtest.csv' using PigStorage('|') AS (employee_id:int, email:chararray, name:tuple(first_name:chararray, middle_name:chararray, last_name:chararray), project_list:bag{project: tuple(project_name:chararray)}, skills:map[chararray]) ;
b = FOREACH a GENERATE employee_id, email, name.first_name, project_list, skills#'programming' ;
dump b;
I think you need to think in term of relations and the map is just one field of one record. Then you can apply some operations on the relations, like joining the two sets data and mapping:
Input
$ cat data.txt
1
2
3
4
5
$ cat mapping.txt
1 2
2 4
3 6
4 8
5 10
Pig
mapping = LOAD 'mapping.txt' AS (key:CHARARRAY, value:CHARARRAY);
data = LOAD 'data.txt' AS (value:CHARARRAY);
-- list keys
mapping_keys =
FOREACH mapping
GENERATE key;
DUMP mapping_keys;
-- join mapping to data
mapped_data =
JOIN mapping BY key, data BY value;
DUMP mapped_data;
Output
> # keys
(1)
(2)
(3)
(4)
(5)
> # mapped data
(1,2,1)
(2,4,2)
(3,6,3)
(4,8,4)
(5,10,5)
This answer could also help you if you just want to do a simple look up:
pass-a-relation-to-a-pig-udf-when-using-foreach-on-another-relation
You can load up any data and then convert and store in key value format to read for later use
data = load 'somedata.csv' using PigStorage(',')
STORE data into 'folder' using PigStorage('#')
and then read as a mapped data.