I am pretty sure a modified/similar discussion might have already been done here but I want to present the exact problem i am facing with possible solution from my side. Then I want to hear from you guys that what would be better approach or how can I approve my logic.
PROBLEM
I have a huge file which contains lines. Each line is in following format <weight>,<some_name>. Now what I have to do is to add the weight of all the objects which has same name. The problem is
I don't know how frequent some_name exist in the file. it could appear only once or all of the millions could be it
It is not ordered
I am using File Stream (java specific, but it doesn't matter)
SOLUTION 1: Assuming that I have huge ram, What i am planning to do is to read file line by line and use the name as key in my hash_map. If its already there, sum it up otherwise add. It will cost me m ram (m = numer of lines in file) but overall processing would be fast
SOLUTION 2: Assuming that I don't have huge ram, I am going to do in batches. Read first 10,000 in hashtable, sum it up and dump it into the file. Do the for rest of the file. Once done processing file, I will start reading processed files and will repease this process to sum it up all.
What do you guys suggest here ?
Beside your suggestions, Can I do parallel file reading of the file ? I have access to FileInputStream here, Can i work with fileInputStream to make reading of file more efficient ?
The second approach is not going to help you: in order to produce the final output, you need sufficient amount of RAM to hold all keys from the file, along with a single Integer representing the count. Whether you're going to get to it in one big step or by several iterations of 10K rows at a time does not change the footprint that you would need at the end.
What would help is partitioning the keys in some way, e.g. by the first character of the key. If the name starts in a letter, process the file 26 times, the first time taking only the weights for keys starting in 'A' and ignoring all other keys, the second time taking only 'B's, and so on. This will let you end up with 26 files that do not intersect.
Another valid approach would be using an external sorting algorithm to transform an unordered file to an ordered one. This would let you walk the ordered file, calculate totals as you go, and write them to an output, even without the need for an in-memory table.
As far as optimizing the I/O goes, I would recommend using the newBufferedReader(Path path,Charset c) method of the java.nio.file.Files class: it gives you a BufferedReader that is optimized for reading efficiency.
Is the file static when you do this computation? If so, then you could disk sort the file based on the name and add up the consecutive entries.
I am running a series of MapReduce jobs on EMR. However, the 3rd MapReduce job needs the data output from the 2nd MapReduce job, and the output is essentially over a million key-value pairs (both the key and the value are less than 1KB). Is there a good way to store this information in a distributed store on the same machine as the EMR so the subsequent jobs can access the information? I looked at DistributedCache, but it's more for storing files? I am not sure if Hadoop is optimized for storing a million tiny files..
Or maybe I can somehow use another MapReduce job to combine all of the key-value pairs into ONE output file, and then put that entire file into DistributedCache.
Please advise. Thanks!
Usually, the output of a map reduce job is stored in HDFS (or S3). The number of reducers of this job determines the number of output files. How come you have a million of tiny files? Do you run a million reducers? I'm not so sure.
So if you define a single reducer for your 2nd job, you'll automatically end up with a single output file, which will be stored in HDFS. Your 3rd job will be able to access and process this file as input. If the 2nd job needs multiple reducers, you'll have multiple output files. 1 million key-value pairs with key and value of 1 KB each give you a < 2 GB file. With a HDFS block size of 64 MB, you'll end up with result files with size N*64 MB, which will allow the 3rd job to process the blocks in parallel (multiple mappers).
You should use DistributedCache only if the whole file needs to be read in every single mapper. However with a size of max. 2 GB it is a rather flawed approach.
I am using Hadoop to analyze a very uneven distribution of data. Some keys have thousands of values, but most have only one. For example, network traffic associated with IP addresses would have many packets associated with a few talkative IPs and just a few with most IPs. Another way of saying this is that the Gini index is very high.
To process this efficiently, each reducer should either get a few high-volume keys or a lot of low-volume keys, in such a way as to get a roughly even load. I know how I would do this if I were writing the partition process: I would take the sorted list of keys (including all duplicate keys) that was produced by the mappers as well as the number of reducers N and put splits at
split[i] = keys[floor(i*len(keys)/N)]
Reducer i would get keys k such that split[i] <= k < split[i+1] for 0 <= i < N-1 and split[i] <= k for i == N-1.
I'm willing to write my own partitioner in Java, but the Partitioner<KEY,VALUE> class only seems to have access to one key-value record at a time, not the whole list. I know that Hadoop sorts the records that were produced by the mappers, so this list must exist somewhere. It might be distributed among several partitioner nodes, in which case I would do the splitting procedure on one of the sublists and somehow communicate the result to all other partitioner nodes. (Assuming that the chosen partitioner node sees a randomized subset, the result would still be approximately load-balanced.) Does anyone know where the sorted list of keys is stored, and how to access it?
I don't want to write two map-reduce jobs, one to find the splits and another to actually use them, because that seems wasteful. (The mappers would have to do the same job twice.) This seems like a general problem: uneven distributions are pretty common.
I've been thinking about this problem, too. This is the high-level approach I would take if someone forced me.
In addition to the mapper logic you have in place to solve your business problem, code some logic to gather whatever statistics you'll need in the partitioner to distribute key-value pairs in a balanced manner. Of course, each mapper will only see some of the data.
Each mapper can find out its task ID and use that ID to build a unique filename in a specified hdfs folder to hold the gathered statistics. Write this file out in the cleanup() method which runs at the end of the task.
use lazy initialization in the partitioner to read all files in the specified hdfs directory. This gets you all of the statistics gathered during the mapper phase. From there you're left with implementing whatever partitioning logic you need to correctly partition the data.
This all assumes that the partitioner isn't called until all mappers have finished, but that's the best I've been able to do so far.
In best of my understanding - there is no single place in MR processing where all keys are present. More then this - there is no guarantee that single machine can store this data.
I think this problem does not have ideal solution in current MR framework. I think so because to have ideal solution - we have to wait for the end of last mapper and only then analyze key distribution and parametrize partitioner with this knowledge.
This approach will significantly complicate the system and raise latency.
I think good approximation might be to do random sampling over data to get the idea of the keys distribution and then make partiotioner to work according to it.
As far as I understand Terasort implementation is doing something very similar : http://sortbenchmark.org/YahooHadoop.pdf
given large datasets that don't fit in memory, is there any library or api to perform sort in Java?
the implementation would possibly be similar to linux utility sort.
Java provides a general-purpose sorting routine which can be used as part of the larger solution to your problem. A common approach to sort data that's too large to all fit in memory is this:
1) Read as much data as will fit into main memory, let's say it's 1 Gb
2) Quicksort that 1 Gb (here's where you'd use Java's built-in sort from the Collections framework)
3) Write that sorted 1 Gb to disk as "chunk-1"
4) Repeat steps 1-3 until you've gone through all the data, saving each data chunk in a separate file. So if your original data was 9 Gb, you will now have 9 sorted chunks of data labeled "chunk-1" thru "chunk-9"
5) You now just need a final merge sort to merge the 9 sorted chunks into a single fully sorted data set. The merge sort will work very efficiently against these pre-sorted chunks. It will essentially open 9 file readers (one for each chunk), plus one file writer (for output). It then compares the first data element in each read file and selects the smallest value, which is written to the output file. The reader from which that selected value came advances to its next data element, and the 9-way comparison process to find the smallest value is repeated, again writing the answer to the output file. This process repeats until all data has been read from all the chunk files.
6) Once step 5 has finished reading all the data you are done -- your output file now contains a fully sorted data set
With this approach you could easily write a generic "megasort" utility of your own that takes a filename and maxMemory parameter and efficiently sorts the file by using temp files. I'd bet you could find at least a few implementations out there for this, but if not you can just roll your own as described above.
The most common way to handle large datasets is in memory (you can buy a server with 1 TB these days) or in a database.
If you are not going to use a database (or buy more memory) you can write it yourself fair easily.
There are libraries which may help which perform Map-Reduce functions but they may add more complexity than they save.
Points:
We process thousands of flat files in a day, concurrently.
Memory constraint is a major issue.
We use thread for each file process.
We don't sort by columns. Each line (record) in the file is treated as one column.
Can't Do:
We cannot use unix/linux's sort commands.
We cannot use any database system no matter how light they can be.
Now, we cannot just load everything in a collection and use the sort mechanism. It will eat up all the memory and the program is gonna get a heap error.
In that situation, how would you sort the records/lines in a file?
It looks like what you are looking for is
external sorting.
Basically, you sort small chunks of data first, write it back to the disk and then iterate over those to sort all.
As other mentionned, you can process in steps.
I would like to explain this with my own words (differs on point 3) :
Read the file sequentially, process N records at a time in memory (N is arbitrary, depending on your memory constraint and the number T of temporary files that you want).
Sort the N records in memory, write them to a temp file. Loop on T until you are done.
Open all the T temp files at the same time, but read only one record per file. (Of course, with buffers). For each of these T records, find the smaller, write it to the final file, and advance only in that file.
Advantages:
The memory consumption is as low as you want.
You only do the double of disk accesses comparing to a everything-in-memory policy. Not bad! :-)
Exemple with numbers:
Original file with 1 million records.
Choose to have 100 temp files, so read and sort 10 000 records at a time, and drop these in their own temp file.
Open the 100 temp file at a time, read the first record in memory.
Compare the first records, write the smaller and advance this temp file.
Loop on step 5, one million times.
EDITED
You mentionned a multi-threaded application, so I wonder ...
As we seen from these discussions on this need, using less memory gives less performance, with a dramatic factor in this case. So I could also suggest to use only one thread to process only one sort at a time, not as a multi-threaded application.
If you process ten threads, each with a tenth of the memory available, your performance will be miserable, much much less than a tenth of the initial time. If you use only one thread, and queue the 9 other demands and process them in turn, you global performance will be much better, you will finish the ten tasks much faster.
After reading this response :
Sort a file with huge volume of data given memory constraint
I suggest you consider this distribution sort. It could be huge gain in your context.
The improvement over my proposal is that you don't need to open all the temp files at once, you only open one of them. It saves your day! :-)
You can read the files in smaller parts, sort these and write them to temporrary files. Then you read two of them sequentially again and merge them to a bigger temporary file and so on. If there is only one left you have your file sorted. Basically that's the Megresort algorithm performed on external files. It scales quite well with aribitrary large files but causes some extra file I/O.
Edit: If you have some knowledge about the likely variance of the lines in your files you can employ a more efficient algorithm (distribution sort). Simplified you would read the original file once and write each line to a temporary file that takes only lines with the same first char (or a certain range of first chars). Then you iterate over all the (now small) temporary files in ascending order, sort them in memory and append them directly to the output file. If a temporary file turns out to be too big for sorting in memory, you can reapeat the same process for this based on the 2nd char in the lines and so on. So if your first partitioning was good enough to produce small enough files, you will have only 100% I/O overhead regardless how large the file is, but in the worst case it can become much more than with the performance wise stable merge sort.
In spite of your restriction, I would use embedded database SQLITE3. Like yourself, I work weekly with 10-15 millions of flat file lines and it is very, very fast to import and generate sorted data, and you only need a little free of charge executable (sqlite3.exe). For example: Once you download the .exe file, in a command prompt you can do this:
C:> sqlite3.exe dbLines.db
sqlite> create table tabLines(line varchar(5000));
sqlite> create index idx1 on tabLines(line);
sqlite> .separator '\r\n'
sqlite> .import 'FileToImport' TabLines
then:
sqlite> select * from tabLines order by line;
or save to a file:
sqlite> .output out.txt
sqlite> select * from tabLines order by line;
sqlite> .output stdout
I would spin up an EC2 cluster and run Hadoop's MergeSort.
Edit: not sure how much detail you would like, or on what. EC2 is Amazon's Elastic Compute Cloud - it lets you rent virtual servers by the hour at low cost. Here is their website.
Hadoop is an open-source MapReduce framework designed for parallel processing of large data sets. A job is a good candidate for MapReduce when it can be split into subsets that can be processed individually and then merged together, usually by sorting on keys (ie the divide-and-conquer strategy). Here is its website.
As mentioned by the other posters, external sorting is also a good strategy. I think the way I would decide between the two depends on the size of the data and speed requirements. A single machine is likely going to be limited to processing a single file at a time (since you will be using up available memory). So look into something like EC2 only if you need to process files faster than that.
You could use the following divide-and-conquer strategy:
Create a function H() that can assign each record in the input file a number. For a record r2 that will be sorted behind a record r1 it must return a larger number for r2 than for r1. Use this function to partition all the records into separate files that will fit into memory so you can sort them. Once you have done that you can just concatenate the sorted files to get one large sorted file.
Suppose you have this input file where each line represents a record
Alan Smith
Jon Doe
Bill Murray
Johnny Cash
Lets just build H() so that it uses the first letter in the record so you might get up to 26 files but in this example you will just get 3:
<file1>
Alan Smith
<file2>
Bill Murray
<file10>
Jon Doe
Johnny Cash
Now you can sort each individual file. Which would swap "Jon Doe" and "Johnny Cash" in <file10>. Now, if you just concatenate the 3 files you'll have a sorted version of the input.
Note that you divide first and only conquer (sort) later. However, you make sure to do the partitioning in a way that the resulting parts which you need to sort don't overlap which will make merging the result much simpler.
The method by which you implement the partitioning function H() depends very much on the nature of your input data. Once you have that part figured out the rest should be a breeze.
If your restriction is only to not use an external database system, you could try an embedded database (e.g. Apache Derby). That way, you get all the advantages of a database without any external infrastructure dependencies.
Here is a way to do it without heavy use of sorting in-side Java and without using DB.
Assumptions : You have 1TB space and files contain or start with unique number, but are unsorted
Divide the files N times.
Read those N files one by one, and create one file for each line/number
Name that file with corresponding number.While naming keep a counter updated to store least count.
Now you can already have the root folder of files marked for sorting by name or pause your program to give you the time to fire command on your OS to sort the files by names. You can do it programmatically too.
Now you have a folder with files sorted with their name, using the counter start taking each file one by one, put numbers in your OUTPUT file, close it.
When you are done you will have a large file with sorted numbers.
I know you mentioned not using a database no matter how light... so, maybe this is not an option. But, what about hsqldb in memory... submit it, sort it by query, purge it. Just a thought.
You can use SQL Lite file db, load the data to the db and then let it sort and return the results for you.
Advantages: No need to worry about writing the best sorting algorithm.
Disadvantage: You will need disk space, slower processing.
https://sites.google.com/site/arjunwebworld/Home/programming/sorting-large-data-files
You can do it with only two temp files - source and destination - and as little memory as you want.
On first step your source is the original file, on last step the destination is the result file.
On each iteration:
read from the source file into a sliding buffer a chunk of data half size of the buffer;
sort the whole buffer
write to the destination file the first half of the buffer.
shift the second half of the buffer to the beginning and repeat
Keep a boolean flag that says whether you had to move some records in current iteration.
If the flag remains false, your file is sorted.
If it's raised, repeat the process using the destination file as a source.
Max number of iterations: (file size)/(buffer size)*2
You could download gnu sort for windows: http://gnuwin32.sourceforge.net/packages/coreutils.htm Even if that uses too much memory, it can merge smaller sorted files as well. It automatically uses temp files.
There's also the sort that comes with windows within cmd.exe. Both of these commands can specify the character column to sort by.
File sort software for big file https://github.com/lianzhoutw/filesort/ .
It is based on file merge sort algorithm.
If you can move forward/backward in a file (seek), and rewrite parts of the file, then you should use bubble sort.
You will have to scan lines in the file, and only have to have 2 rows in memory at the moment, and then swap them if they are not in the right order. Repeat the process until there are no files to swap.