I need logic for below scenario which needs to be implemented using Pig scripts. Can anyone please help in providing some ideas on how to do this.
Input contains a column groupName with some data like others and unknown. This data needs to be replaced by its previous record data.
Input:
id,groupName
123,casc0001
124,casc0002
125,sale0001
126,unknown
127,nave9876
128,casc0001
129,sale0002
130,others
131,casc0004
132,unknown
133,unknown
134,others
135,nave1234
output:
123,casc0001
124,casc0002
125,sale0001
126,sale0001
127,nave9876
128,casc0001
129,sale0002
130,sale0002
131,casc0004
132,casc0004
133,casc0004
134,casc0004
135,nave1234
In the above input 126,unknown to be replaced with 125,sale0001. 130,others need to be replaced by 129,sale0002. 132,unknown 133,unknown 134,others to be replaced with 131,casc0004.
--Edit--
I tried lead function in Pig. But it is used only to compare n rows at a time. Which cannot solve this completely.
Another logic which is working, but looking for optimized one.
Cogroup for the same data set (like Dataset and Dataset_self)
-Filter Dataset.id=Dataset_self.id or Dataset_self.groupname='others' or Dataset_self.groupname='unknown'
-Generate IdDiff like (Dataset_self.id-Dataset.id), CASE when id=id then ( id, group) else (id_self,group)
-Foreach (group id){
ordered = order by id,diff,group;
limited = ordered limit 1;
generate limited ;
}
This is going to be a complicated problem on a distributed system like hadoop, especially that your file is going to be split between nodes. In your case what if 126 happens to be the first record in a new split. Then you will need to trace the previous file split which is most likely on a different node. Lets say you come up with a MapReduce program to do this, in all likelyhood it would an extremely slow and inefficient way to do it. The solution might be simpler if you are in a single node system where the splittable property of your input format is false, and the nuber of reducers is set to 1.
In that case you could almost make the argument that a traditional database like Oracle or Terra data might be a better fit for your problem as you have lead or lag functions readily available which could be used to do exactly what u need.
Related
I am trying to reduce the size of my data and I cannot make it work. I have data points taken every minute over 1 month. I want to reduce this data to have one sample for every hour. The problem is: Some of my runs have "NA" value, so I delete these rows. There is not exactly 60 points for every hour - it varies.
I have a 'Timestamp' column. I have used this to make a 'datehour' column which has the same value if the data set has the same date and hour. I want to average all the values with the same 'datehour' value.
How can I do this? I have tried using the if and for loop below, but it takes so long to run.
Thanks for all your help! I am new to Julia and come from a Matlab background.
======= CODE ==========
uniquedatehour=unique(datehour,1)
index=[]
avedata=reshape([],0,length(alldata[1,:]))
for j in uniquedatehour
for i in 1:length(datehour)
if datehour[i]==j
index=vcat(index,i)
else
rows=alldata[index,:]
rows=convert(Array{Float64,2},rows)
avehour=mean(rows,1)
avedata=vcat(avedata,avehour)
index=[]
continue
end
end
end
There are several layers to optimizing this code. I am assuming that your data is sorted on datehour (your code assumes this).
Layer one: general recommendation
Wrap your code in a function. Executing code in global scope in Julia is much slower than within a function. By wrapping it make sure to either pass data to your function as arguments or if data is in global scope it should be qualified with const;
Layer two: recommendations to your algorithm
Statement like [] creates an array of type Any which is slow, you should use type qualifier like index=Int[] to make it fast;
Using vcat like index=vcat(index,i) is inefficient, it is better to do push!(index, i) in place;
It is better to preallocate avedata with e.g. fill(NA, length(uniquedatehour), size(alldata, 2)) and assign values to an existing matrix than to do vcat on it;
Your code will produce incorrect results if I am not mistaken as it will not catch the last entry of uniquedatehour vector (assume it has only one element and check what happens - avedata will have zero rows)
Line rows=convert(Array{Float64,2},rows) is probably not needed at all. If alldata is not Matrix{Float64} it is better to convert it at the beginning with Matrix{Float64}(alldata);
You can change line rows=alldata[index,:] to a view like view(alldata, index, :) to avoid allocation;
In general you can avoid creation of index vector as it is enough that you remember start s and end e position of the range of the same values and then use range s:e to select rows you want.
If you correct those things please post your updated code and maybe I can help further as there is still room for improvement but requires a bit different algorithmic approach (but maybe you will prefer option below for simplicity).
Layer three: how I would do it
I would use DataFrames package to handle this problem like this:
using DataFrames
df = DataFrame(alldata) # assuming alldata is Matrix{Float64}, otherwise convert it here
df[:grouping] = datehour
agg = aggregate(df, :grouping, mean) # maybe this is all what you need if DataFrame is OK for you
Matrix(agg[2:end]) # here is how you can convert DataFrame back to a matrix
This is not the fastest solution (as it converts to a DataFrame and back but it is much simpler for me).
I have a use-case with Hadoop where I would like my output files to be split by key. At the moment I have the reducer simply outputting each value in the iterator. For example, here's some python streaming code:
for line in sys.stdin:
data = line.split("\t")
print data[1]
This method works for a small dataset (around 4GB). Each output file of the job only contains the values for one key.
However, if I increase the size of the dataset (over 40GB) then each file contains a mixture of keys, in sorted order.
Is there an easier way to solve this? I know that the output will be in sorted order and I could simply do a sequential scan and add to files. But it seems that this shouldn't be necessary since Hadoop sorts and splits the keys for you.
Question may not be the clearest, so I'll clarify if anyone has any comments. Thanks
Ok then create a custom jar implementation of your MapReduce solution and go for MultipleTextOutputFormat to be the OutputFormat used as explained here. You just have to emit the filename (in your case the key) as the key in your reducer and the entire payload as the value, and your data will be written in the file named as your key.
I want to write a map reduce to compare two large file in hdfs. any thoughts how to achieve that. Or if there is nay other way to do the comparison because file size is very large, so thought map-reduce would be an ideal approach.
Thanks for your help.
You may do this in 2 steps.
First make the line number to be the part of text files:
Say initial file looks like:
I am awesome
He is my best friend
Now, convert this to something like this:
1,I am awesome
2,He is my best friend
This may well be done by a MapReduce job itself or some other tool.
2. Now write a MapReduce step where in mapper emit the line number as the key and rest of the actual sentence as value. Then in reducer just compare the values. As and when it doesn't match emit out the line number (the key) and the payloads, whatever you may want here. Also if the count of the values is just 1 then also it is a mismatch.
EDIT: Better approach
Better still what you can do is, just emit the complete line read at a time in the mapper as the key and make the value a number, say 1. So taking my above example your mapper output would be as follows:
< I am awesome,1 >
< He is my best friend,1 >
And in reducer just check the count of values, if it isn't 2, you have a mismatch.
But there is one catch in this approach, if there is a possibility of exactly same line occurring at two different places then instead of checking for the length of values for a given key in reducer, you should be checking it to be a multiple of 2.
One possible solution could be, put line number as count in map job.
There are two files like below :
File 1:
I am here --Line 1
I am awesome -- Line 2
You are my best friend -- Line 3
File 2 also similar kind
Now your map job output should be like , < I am awesome, 2>...
Once you done with Map job for both the file, you have two record(key,value) which has same value to reduce.
At the time of reduce, you can either compare the counter or generate the output as , and so on. If the line is exist in the different location too than out put could be which indicates that this line is mismatch.
I have a solution for comparing files with keys. In your case if you know that your ID's are unique, you could emit the ID's as keys in the map, the entire record as value. Lets say your file has ID,Line1 then emit as key and as value from mapper.
In the shuffle and sort phase, the ID's will be sorted and you will get an iterator with data from both the files. ie, the records from both files with same ID will end up in same iterator.
Then in the reducer, compare both the values from the iterator and if they match move on with next record. Else, if they do not match write them to an output.
I have done this and it worked like a charm.
Scenario - No matching key
If there is no matching ID between two files, they will have only one iterator value.
Scenario 2 - Duplicate keys
If the files have duplicate keys, the iterator will have more than 2 values.
Note: You should compare the values only when the iterator has only 2 values.
**Tip:**The iterator will not have values in order always. To identify the value from a particular file, in the mapper add a small indicator at the end of the line like Line1;file1
Line1;file2
Then on the reducer you will be able to identify which value belongs to which mapper.
In pig I massaged my data into something like:
(a,{(b,c),(d,e),(f,g)})
(h,{(i,j),(k,l)})
where the first item is the group and the bag are other values related to the group. I would like to get it into the following format:
(a,b,c,d,e,f,g)
(h,i,j,k,l)
I got to where I am now with
grunt> j = foreach G {
>> o = order myvar by second;
>> generate group, o.(first,second);
>> };
So the tuples in the bag are currently ordered. If I do something like mystuff = foreach j generate group, flatten($1); I get it all flattened and un-grouped.
Is this possible in pig, and if so what command should I be looking at?
There is no way I can that can do what you want out of the box. You really need to use a user-defined function for this. I know it sucks because you have to write Java or Python code, but you'll find several situations where Pig just doesn't go far enough. Pig can be considered a data flow language and not so much of a programming language, which is why UDFs play such an important role: they bridge the gap.
My suggestion is you write a UDF that takes in the group and value bag as parameters. Do the ordering/sorting in the UDF and also the flattening.
The other thing you want to be careful about is that now your rows will have different numbers of columns and Pig doesn't really like this. If you are just immediately outputting it, you can probably get away with this. You might want to consider having your UDF write out the list in a tab-delimited string or something that is preformatted. This isn't that big of a deal... feel free to ignore my advice here.
I do have to deal with very large plain text files (over 10 gigabytes, yeah I know it depends what we should call large), with very long lines.
My most recent task involves some line editing based on data from another file.
The data file (which should be modified) contains 1500000 lines, each of them are e.g. 800 chars long. Each line is unique, and contains only one identity number, each identity number is unique)
The modifier file is e.g. 1800 lines long, contains an identity number, and an amount and a date which should be modified in the data file.
I just transformed (with Vim regex) the modifier file to sed, but it's very inefficient.
Let's say I have a line like this in the data file:
(some 500 character)id_number(some 300 character)
And I need to modify data in the 300 char part.
Based on the modifier file, I come up with sed lines like this:
/id_number/ s/^\(.\{650\}\).\{20\}/\1CHANGED_AMOUNT_AND_DATA/
So I have 1800 lines like this.
But I know, that even on a very fast server, if I do a
sed -i.bak -f modifier.sed data.file
It's very slow, because it has to read every pattern x every line.
Isn't there a better way?
Note: I'm not a programmer, had never learnt (in school) about algorithms.
I can use awk, sed, an outdated version of perl on the server.
My suggested approaches (in order of desirably) would be to process this data as:
A database (even a simple SQLite-based DB with an index will perform much better than sed/awk on a 10GB file)
A flat file containing fixed record lengths
A flat file containing variable record lengths
Using a database takes care of all those little details that slow down text-file processing (finding the record you care about, modifying the data, storing it back to the DB). Take a look for DBD::SQLite in the case of Perl.
If you want to stick with flat files, you'll want to maintain an index manually alongside the big file so you can more easily look up the record numbers you'll need to manipulate. Or, better yet, perhaps your ID numbers are your record numbers?
If you have variable record lengths, I'd suggest converting to fixed-record lengths (since it appears only your ID is variable length). If you can't do that, perhaps any existing data will not ever move around in the file? Then you can maintain that previously mentioned index and add new entries as necessary, with the difference is that instead of the index pointing to record number, you now point to the absolute position in the file.
I suggest you a programm written in Perl (as I am not a sed/awk guru and I don't what they are exactly capable of).
You "algorithm" is simple: you need to construct, first of all, an hashmap which could give you the new data string to apply for each ID. This is achieved reading the modifier file of course.
Once this hasmap in populated you may browse each line of your data file, read the ID in the middle of the line, and generate the new line as you've described above.
I am not a Perl guru too , but I think that the programm is quite simple. If you need help to write it, ask for it :-)
With perl you should use substr to get id_number, especially if id_number has constant width.
my $id_number=substr($str, 500, id_number_length);
After that if $id_number is in range, you should use substr to replace remaining text.
substr($str, -300,300, $new_text);
Perl's regular expressions are very fast, but not in this case.
My suggestion is, don't use database. Well written perl script will outperform database in order of magnitude in this sort of task. Trust me, I have many practical experience with it. You will not have imported data into database when perl will be finished.
When you write 1500000 lines with 800 chars it seems 1.2GB for me. If you will have very slow disk (30MB/s) you will read it in a 40 seconds. With better 50 -> 24s, 100 -> 12s and so. But perl hash lookup (like db join) speed on 2GHz CPU is above 5Mlookups/s. It means that your CPU bound work will be in seconds and you IO bound work will be in tens of seconds. If it is really 10GB numbers will change but proportion is same.
You have not specified if data modification changes size or not (if modification can be done in place) thus we will not assume it and will work as filter. You have not specified what format of your "modifier file" and what sort of modification. Assume that it is separated by tab something like:
<id><tab><position_after_id><tab><amount><tab><data>
We will read data from stdin and write to stdout and script can be something like this:
my $modifier_filename = 'modifier_file.txt';
open my $mf, '<', $modifier_filename or die "Can't open '$modifier_filename': $!";
my %modifications;
while (<$mf>) {
chomp;
my ($id, $position, $amount, $data) = split /\t/;
$modifications{$id} = [$position, $amount, $data];
}
close $mf;
# make matching regexp (use quotemeta to prevent regexp meaningful characters)
my $id_regexp = join '|', map quotemeta, keys %modifications;
$id_regexp = qr/($id_regexp)/; # compile regexp
while (<>) {
next unless m/$id_regexp/;
next unless $modifications{$1};
my ($position, $amount, $data) = #{$modifications{$1}};
substr $_, $+[1] + $position, $amount, $data;
}
continue { print }
On mine laptop it takes about half minute for 1.5 million rows, 1800 lookup ids, 1.2GB data. For 10GB it should not be over 5 minutes. Is it reasonable quick for you?
If you start think you are not IO bound (for example if use some NAS) but CPU bound you can sacrifice some readability and change to this:
my $mod;
while (<>) {
next unless m/$id_regexp/;
$mod = $modifications{$1};
next unless $mod;
substr $_, $+[1] + $mod->[0], $mod->[1], $mod->[2];
}
continue { print }
You should almost certainly use a database, as MikeyB suggested.
If you don't want to use a database for some reason, then if the list of modifications will fit in memory (as it currently will at 1800 lines), the most efficient method is a hashtable populated with the modifications as suggested by yves Baumes.
If you get to the point where even the list of modifications becomes huge, you need to sort both files by their IDs and then perform a list merge -- basically:
Compare the ID at the "top" of the input file with the ID at the "top" of the modifications file
Adjust the record accordingly if they match
Write it out
Discard the "top" line from whichever file had the (alphabetically or numerically) lowest ID and read another line from that file
Goto 1.
Behind the scenes, a database will almost certainly use a list merge if you perform this alteration using a single SQL UPDATE command.
Good deal on the sqlloader or datadump decision. That's the way to go.