Iterate on 2 Data Sources in PIG - hadoop

Question

I have 2 data sources
1) Params.txt which has the following content
item1
item2
item2
.
.
.
itemN
2) Data.txt which which has following content
he names (aliases) of relations A, B, and C are case sensitive.
The names (aliases) of fields f1, f2, and f3 are case sensitive.
Function names PigStorage and COUNT are case sensitive.
Keywords LOAD, USING, AS, GROUP, BY, FOREACH, GENERAT
and DUMP are case insensitive. They can also be written
The task is to see if each of N items of param file exist in each line of data file.
this is the pseudocode for the same
FOREACH d IN data:
FOREACH PARAM IN PARAMS:
IF PARAM IN d:
GENERATE PARAM,1
Is something of this sort possible in PIG scripting, if yes could you please point me in that direction.
Thanks

Answer 1

This is possible in Pig, but Pig is perhaps an unusual language to solve the problem!
I would approach the problem like this:
Load in Params.txt
Load in Data.txt and tokenise each line (assuming you're happy to split the text on spaces - you might need to think about what to do with punctuation)
Flatten the bag from tokenise to get one "word" per record in the relation.
Join the Params and Data relations. An inner join would give you words that are only in both.
Group the data and then count the occurrence of each word.
params = LOAD 'Params.txt' USING PigStorage() AS (param_word:chararray);
data = LOAD 'Data.txt' USING PigStorage() AS (line:chararray);
token_data = FOREACH data GENERATE TOKENIZE(line) AS words:{(word:chrarray)};
token_flat = FOREACH token_data GENERATE FLATTEN(words) AS (word);
joined = JOIN params BY param_word, token_flat BY word;
word_count = FOREACH (GROUP joined BY params::param_word) GENERATE
group AS param_word,
COUNT(joined) AS param_word_count;