How can I customize HbaseStorage for pig script? Actually I want to perform some business logic on the data before loading it to the pig script. It would be something like custom storage on top of HbaseStorage.
e.g I've my row key has structure like this A_B_C. Currently, I'm passing A_B_C key in HbaseStorage in my pig script but I want to perform some logic like filtering etc against key like A_B_C_D before serving input data to actual pig script. How is it possible
You may have to end up looking at the HBaseStorage java class and implementing your own classes based on that. Depending on how the HBaseStorage and associated classes have been written, this could vary from being easy (just extend HBaseStorage itself and overwrite where necessary) to a real headache.
You then have to ensure that the .jar containing your code is on the pig classpath.
I find HbaseStorage to be a real pain, so I write regular Java MR jobs to query HBase and create custom sequence files, which I then use from Pig with a simple custom loader. I find this saves a ton of time since the sequence file can be re-used many times throughout the day to get quick results, rather than scanning everything in Hbase for every Pig script.
Related
I'm currently working on DataStage IBM and here's my problem:
I have to get a n numbers of datasets that's going to be in a folder and I have to append them in one DataSet (.ds).
Since I don't know how many datasets I will have and neither they full name, I can't use a DataStage job to deal with them. All I know is they will have the same metadata (because they will be generated in the same job).
I think I have to use a Shell Cmd to append them but I'm not a UNIX guy.
Thank you for everyone who reads so far.
You can use the same job. Specify Append mode (rather than Override) for the target Data Set; each time you run the job data will be added to the same Data Set. Be careful not to inadvertently create duplicates by processing the same source data twice. Use parameters to specify the source.
I have a set of Hadoop flows that were written before we started using Hive. When we added Hive, we configured the data files as external tables. Now we're thinking about rewriting the flows to output their results using HCatalog. Our main motivation to make the change is to take advantage of the dynamic partitioning.
One of the hurdles I'm running into is that some of our reducers generate multiple data sets. Today this is done with side-effect files, so we write out each record type to its own file in a single reduce step, and I'm wondering what my options are to do this with HCatalog.
One option obviously is to have each job generate just a single record type, reprocessing the data once for each type. I'd like to avoid this.
Another option for some jobs is to change our schema so that all records are stored in a single schema. Obviously this option works well if the data was just broken apart for poor-man's partitioning, since HCatalog will take care of partitioning the data based on the fields. For other jobs, however, the types of records are not consistent.
It seems that I might be able to use the Reader/Writer interfaces to pass a set of writer contexts around, one per schema, but I haven't really thought it through (and I've only been looking at HCatalog for a day, so I may be misunderstanding the Reader/Writer interface).
Does anybody have any experience writing to multiple schemas in a single reduce step? Any pointers would be much appreciated.
Thanks.
Andrew
As best I can tell, the proper way to do this is to use the MultiOutputFormat class. The biggest help for me was the TestHCatMultiOutputFormat test in Hive.
Andrew
I want to figure out whether there is any software or algorithm which can help in identifying the maps and reduces in a given code on its own.
This is what that happens when you run Hive or Pig queries. You just submit your queries and they automatically get converted into corresponding MR jobs and you get the result without having to do anything additional. Have a look at ANTLR(ANother Tool for Language Recognition) which automatically parses a given Hive query into the corresponding MR job. ANTLR is a parser generator for reading, processing, executing, or translating structured text or binary files.
Do you need something else?Apologies if I have get it wrong.
I am a newbie on the MR and Hadoop front.
I wrote an MR for finding missing's in csv file and it is working fine.
now I have an usecase where i need to parse a csv file and code it with the regarding category.
ex: "11,abc,xyz,51,61,78","11,adc,ryz,41,71,38",.............
now this has to be replaced as "1,abc,xyz,5,6,7","1,adc,ryz,4,7,3",.............
here i am doing a mod of 10 but there will be different cases of mod's.
data size is in gb's.
I want to know how to replace the content in-place for the input. Is this achievable with MR?
Basically i have not seen any file handling or writing based hadoop examples any where.
At this point i do not want to go to HBase or other db tools.
You can not replace data in place, since HDFS files are append only, and can not be edited.
I think simplest way to achiece your goal is to register your data in the Hive as external table, and write your trnasformation in HQL.
Hive is a system sitting aside of hadoop and translating your queries to MR Jobs.
Its usage is not serious infrastructure decision as HBASE usage
Our environment is heavy into storing data in hive. I find myself currently working on something that it outside the scope though. I have a mapreduce written, but it requires a lot of direct user inputs for information that could easily be scraped from Hive. That said, when I query hive for extended table data, all of the extended information is thrown out in 1 or 2 columns as a giant blob of almost-JSON. Is there either a convenient way to parse this information, or better yet, get it directly in a more direct manor?
Alternatively, if I could get pointed to documentation on manually using the CombinedHiveInputFormat, that would simplify my code a lot more. But it seems like that InputFormat is solely used inside of Hive, using it's custom structs.
Ultimately, what I want is to know table names, columns (not including partitions), and partition locations for the split a mapper is working on. If there is yet another way to accomplish this, I am eager to know.