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Recently I came across learning hadoop, all I found was only example to read text data and calculate wordcount. More or less all examples were of same task. Please help me understand is it the only use case of hadoop? Please provide me some references for more real use cases, or where I can understand and write where hadoop can be used.
Thanks
I can try to outline a few directions restricting myself to MapReduce:
a) ETL - data transformations. Here hadoop shines since latency is not important, but scalability is
b) Hive / Pig. There are cases when we need actually SQL or SQL like functionality over big data sets, but can not afford commercial MPP database
c) Log processing of different kinds.
d) Deep analytics - when we simply want to run java code over massive data volumes. Mahaout is used in many cases as machine learning library.
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It can be while creating a table or while using other queries like Inserting, updating, deleting on a table.
I understood that using options like BloomFilter, BlockCache can have an impact. But I would like to know the other techniques that will improve the overall throughput. Also can anyone show how to add a BloomFilter on a Hbase table. I'd like to try it for practicing.
Any help is appreciated.
You question is too general. In order to know how to properly build you DataStore in HBase you should understand its internal logic of the storage and how data is distributed across the regions. This is probably the main place for start. I would recommend you to get acquainted with LSM-tree and how HBase implements it in this article. After this I would advice you to read about the proper design of the data schema here as it would play the main role in your performance. Correct schema with good key would make your data properly distributed across the nodes and would avoid you from having such thing as hotspotting. Then you can start looking through optimization techniques like blume filters, BlockCache, custom secondary indexes and other stuff.
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Or HBase does not have anything to do with this process?
I have read that Hbase works on top of hadoop, and I have seen some diagrams that shows Hbase as part of the MapReduce part of Hadoop, but I have not found anything concrete about my question.
The Map/Reduce framework itself doesn't rely on HBase. It would be interesting to see pointers to the diagrams you mention.
You can communicate with HBase in your map/reduce code, if you like (e.g. look up values by key).
HBase does work "on top of Hadoop": it stores its data in HDFS, relies on ZooKeeper, and its servers can run on the same cluster.
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I have a complex query used in ETL process (SQL-based). It is too big to fit here but in general few inner joins between several tables and some BL using window functions and other 'goodies'.
I need to port it to Hadoop MapReduce. just dump all the tables in FROM cases to CSV format and bring files jto HDFS.
Then write MapReduce jobs that copy the logic implemented in SQL.
I wonder: are there any best-practices/recommendations/pitfalls I should be aware of while porting SQL to MapReduce?
Googling in my case was no good as the results were either too specific or some scientific papers with no practical tips.
You can look at sqoop as one of the option for transferring data between Hadoop and structured datastores.
Also, this link could be helpful- http://www.ibm.com/developerworks/library/bd-sqltohadoop1/
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I am in the process of creating a survey engine that will store millions of responses to various large surveys.
There are various agencies that will have 10-100 users each. Each will be able to administer a 3000+ question survey. There will be multiple agencies as well.
If each agency was to have hundreds of thousands of sessions each with 3000+ responses, I'm thinking that hadoop would be a good candidate to get the sessions and their response data to run various analyses on (aggregations etc).
The sessions, survey questions, and responses are all currently held in a sql database. I was thinking that I would keep that and put the data in parallel. So when a new session is taken under an agency, it is then added to the hadoop 'file', such that when the entire dataset is called up it would be included.
Would this implementation work well with hadoop or am I still well within the limits of a relational database?
I don't think anyone is going to be able to tell you definitively, yes or no here. I also don't think I fully grasp what your program will be doing from the wording of the question, however, in general, Hadoop Map/Reduce excels at batch processing huge volumes of data. It is not meant to be an interactive (aka real-time) tool. So if your system:
1) Will be running scheduled jobs to analyze survey results, generate trends, summarize data, etc.....then yes, M/R would be a good fit for this.
2) Will allow users to search through surveys by specifying what they are interested in and get reports in real-time based on their input....then no, M/R would probably not be the best tool for this. You might want to take a look at HBase. I haven't used it yet, but Hive is a query based tool but I am not sure how "real-time" that can get. Also, Drill is an up and coming project that looks promising for interactively querying big data.
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Newbie here with Hadoop. Concept wise, it is pretty simple to understand, however, one of the real challenge is how to model the problem to be solved in the map-reduce architecture. Suppose my data contains two parts (all in oracle):
1. Rather static data that doesn't change much
2. Fresh data collected everyday.
and currently the data processing is basically read the fresh data, find and use the corresponding static data (or metadata) and apply some algorithm on it and dump it back to Oracle.
How do I model such application paradigm? Do I save/store the static data as part of distributed cache? What if that data is pretty big?
Basically I am looking for more examples like the following:
http://stevekrenzel.com/finding-friends-with-mapreduce
Thanks,
Basically the requirement is to do join on two data sets. MapReduce programming requires a different way of thinking than normal programming. Here are some references to join and some other patterns on top of MapReduce
Data-Intensive Text Processing with MapReduce
MapReduce Design Patterns
Section 8.3 in Hadoop - The Definitive Guide
Coming back to join, it can multiple ways based on the amount of data and how the data is. The above references have more about the same.
We are collecting real life use cases here : http://hadoopilluminated.com/hadoop_book/Hadoop_Use_Cases.html
we already have good coverage of multiple domains, and will continue to add to it.
(disclaimer : I am a co-author of this free hadoop book)
I would look at the following article about Map/Reduce patterns, which should give you a nice idea of common algorithms and their translation in the Map/Reduce world.
More generally, I don't think there's a magical formula to translate a problem into a set of Map/Reduce, you have to ask yourself questions that vary from dataset to dataset, looking at existing examples is a good thing, and you should definitely try to implement something on a little toy problem.
Also if you have issues abstracting your problem to a set of Map/Reduce jobs, you could also use for example Hive which works like a relational database with a few tweaks, and generates Map/Reduce jobs for you without having to worry too much about what happens.