I am a newbie to analyze Images using Apache Pig.
Can anyone suggest me how to load and process the images??
I know for textfiles,
alias = load '/user/Pavan/sample.txt' using PigStorage(" ");
How to do with images??
You have a few options, which really depend on the kind of manipulation you're looking to do:
1) Write a custom load function
Pig can be used for images, but you'd need to write a custom load function, which could be more than you're looking to do.
2) Use a Sequence File (my recommendation)
You could also convert the image to a Sequence File, which Pig has a loader file for, available in the Piggybank JAR. There are also load functions and store functions for reading and writing Sequence Files available via Twitter's Elephant Bird package.
Here's an article about using Sequence Files on Hadoop for astronomical categorization tasks.
3) Go with MapReduce.
Depending on the nature of your task, you may be better off in native MapReduce.
Related
I have a simple text file which contains list of folders on some FTP servers. Each line is a separate folder. Each folder contains couple of thousand images. I want to connect to each folder, store all files inside that foder in a SequenceFile and then remove that folder from FTP server. I have written a simple pig UDF for this. Here it is:
dirs = LOAD '/var/location.txt' USING PigStorage();
results = FOREACH dirs GENERATE download_whole_folder_into_single_sequence_file($0);
/* I don't need results bag. It is just a dummy bag */
The problem is I'm not sure if each line of input is processed in separate mapper. The input file is not a huge file just couple of hundred lines. If it were pure Map/Reduce then I would use NLineInputFormat and process each line in a separate Mapper. How can I achieve the same thing in pig?
Pig lets you write your own load functions, which let you specify which InputFormat you'll be using. So you could write your own.
That said, the job you described sounds like it would only involve a single map-reduce step. Since using Pig wouldn't reduce complexity in this case, and you'd have to write custom code just to use Pig, I'd suggest just doing it in vanilla map-reduce. If the total file size is Gigabytes or less, I'd just do it all directly on a single host. It's simpler not to use map reduce if you don't have to.
I typically use map-reduce to first load data into HDFS, and then Pig for all data processing. Pig doesn't really add any benefits over vanilla hadoop for loading data IMO, it's just a wrapper around InputFormat/RecordReader with additional methods you need to implement. Plus it's technically possible with Pig that your loader will be called multiple times. That's a gotcha you don't need to worry about using Hadoop map-reduce directly.
I have files in Mainframe. I want these data to be pushed to Hadoop(HDFS)/HIVE.
I can use Sqoop for the Mainframe DB2 database and import it to HIVE, but what about files (like COBOL,VASM etc.)
Is there any custom flume source that I can write or some alternative tool to use here?
COBOL is a programming language, not a file format. If what you need is to export files produced by COBOL programs, you can use the same technique as if those files were produced by C, C++, Java, Perl, PL/I, Rexx, etc.
In general, you will have three different data sources: flat files, VSAM files, and a DBMS such as DB2 or IMS.
DMBSs have export utilities to copy the data into flat files. Keep in mind that data in DB2 will likely be normalized and thus you likely need the contents of related tables in order to make sense of the data.
VSAM files can be exported to flat files via the IDCAMS utility.
I would strongly suggest you get the files into a text format before transferring them to another box with a different code page. Trying to deal with mixed text (which must have its code page translated) and binary (which must not have its code page translated but which likely must be converted from big endian to little endian) is harder than doing the conversion up front.
The conversion can likely be done via the SORT utility on the mainframe. Mainframe SORT utilities tend to have extensive data manipulation functions. There are other mechanisms you could use (other utilities, custom code written in the language of your choice, purchased packages) but this is what we tend to do in these circumstances.
Once you have your flat files converted such that all data is text, you can transfer them to your Hadoop boxes via FTP or SFTP or FTPS.
This isn't an exhaustive coverage of the topic, but it will get you started.
Syncsort has been processing mainframe data for 40 years (approx 50% of mainframes already run the software) they have a specific product called DMX-H which can source mainframe data, handle the data type conversions, import the cobol copy books and load it directly into HDFS.
Syncsort also recently contributed a new feature enhancement to the Apache Hadoop core
I suggest you contact them at www.syncsort.com
They were showing this in a demo at a recent Cloudera roadshow.
Update for 2018:
There are a number of commercial products that help to move data from the mainframe to distributed platforms. Here is a list of ones that I have run into for those that are interested. All of them take data on Z as described in the question and will do some transformation and enable movement of the data to other platforms. Not an exact match, but, the industry has changed and the goal of moving data for analysis to other platforms is growing. Data Virtualization Manager provides the most robust tooling for transforming the data from what I've seen.
SyncSort IronStream
IBM Common Data Provider
Correlog
IBM Data Virtualization Manager
Why not : hadoop fs -put <what> <where>?
Transmission of cobol layout files can be done through above discussed options. However actual mapping them to Hive table is a complex task as cobol layout has complex formats as depending clause, variable length, etc.,
I have tried to create custom serde to achieve, although it is still in initial stages. But here is the link, which might give you some idea how to deserialize according to your requirements.
https://github.com/rbheemana/Cobol-to-Hive
Not pull, but push: use the Co:Z Launcher from Dovetailed Technologies.
For example (JCL excerpt):
//FORWARD EXEC PGM=COZLNCH
//STDIN DD *
hadoop fs -put <(fromfile /u/me/data.csv) /data/data.csv
# Create a catalog table
hive -f <(fromfile /u/me/data.hcatalog)
/*
where /u/me/data.csv (the mainframe-based data that you want in Hadoop) and /u/me/data.hcatalog (corresponding HCatalog file) are z/OS UNIX file paths.
For a more detailed example, where the data happens to be log records, see Extracting logs to Hadoop.
Cobrix might be able to solve it for you. It is an open-source COBOL data source for Spark and can parse the files you mentioned.
I am currently starting a project titled "Cloud computing for time series mining algorithms using Hadoop".
The data which I have is hdf files of size over a terabyte.In hadoop as I know that we should have text files as input for further processing (map-reduce task). So I have one option that I convert all my .hdf files to text files which is going to take a lot of time.
Or I find a way of how to use raw hdf files in map reduce programmes.
So far I have not been successful in finding any java code which reads hdf files and extract data from them.
If somebody has a better idea of how to work with hdf files I will really appreciate such help.
Thanks
Ayush
Here are some resources:
SciHadoop (uses netCDF but might be already extended to HDF5).
You can either use JHDF5 or the lower level official Java HDF5 interface to read out data from any HDF5 file in the map-reduce task.
For your first option, you could use a conversion tool like HDF dump to dump HDF file to text format. Otherwise, you can write a program using Java library for reading HDF file and write it to text file.
For your second option, SciHadoop is a good example of how to read Scientific datasets from Hadoop. It uses NetCDF-Java library to read NetCDF file. Hadoop does not support POSIX API for file IO. So, it uses an extra software layer to translate POSIX call of NetCDF-java library to HDFS(Hadoop) API calls. If SciHadoop does not already support HDF files, you might go along a little harder path and develop a similar solution yourself.
If you do not find any java code and can do in other languages then you can use hadoop streaming.
SciMATE http://www.cse.ohio-state.edu/~wayi/papers/SciMATE.pdf is a good option. It is developed based on a variant of MapReduce, which has been shown to perform a lot of scientific applications much more efficiently than Hadoop.
I want to use Apache pig to transform/join data in two files, but I want to implement it step by step, which means, test it from real data, but with a small size(10 lines for example), is it possible to use pig that read from STDIN and output to STDOUT?
Basically Hadoop supports Streaming in various ways, but Pig originally lacked support for loading data through streaming. However there are some solutions.
You can check out HStreaming:
A = LOAD 'http://myurl.com:1234/index.html' USING HStream('\n') AS (f1, f2);
The answer is no. The data needs to be out in the cluster on data nodes before any MR job can even run over the data.
However if you are using a small sample of data and are just wanting to do something simple you could use Pig in local mode and just write stdin to a local file and run it through your script.
But the bigger question becomes why are you wanting to use MR/Pig on a stream of data? It was and is not intended for that type of use.
Apache Pig can load data from Hadoop sequence files using the PiggyBank SequenceFileLoader:
REGISTER /home/hadoop/pig/contrib/piggybank/java/piggybank.jar;
DEFINE SequenceFileLoader org.apache.pig.piggybank.storage.SequenceFileLoader();
log = LOAD '/data/logs' USING SequenceFileLoader AS (...)
Is there also a library out there that would allow writing to Hadoop sequence files from Pig?
It's just a matter of implementing a StoreFunc to do so.
This is possible now, although it will become a fair bit easier once Pig 0.7 comes out, as it includes a complete redesign of the Load/Store interfaces.
The "Hadoop expansion pack" Twitter is about to open source open-sourced at github, includes code for generating Load and Store funcs based on Google Protocol Buffers (building on Input/Output formats for same -- you already have those for sequence files, obviously). Check it out if you need examples of how to do some of the less trivial stuff. It should be fairly straightforward though.
This seemed to work for me. https://github.com/kevinweil/elephant-bird/pull/73