I’m sorry that I haven’t deeply understood HBase and Hadoop MapReduce, but I think you can help me to find the way of using them, or maybe you could propose frameworks I need.
Part I
There is 1st stream of records that I have to store somewhere. They should be accessible by some keys depending on them. Several records could have the same key. There are quite a lot of them. I have to delete old records by timeout.
There is also 2nd stream of records, that is very intensive too. For each record (argument-record) I need to: get all records from 1st strem with that argument-record’s key, find first corresponding record, delete it from 1st stream storage, return the result (res1) of merging these two records.
Part II
The 3rd stream of records is like 1st. Records should be accessable by keys (differ from that ones of part I). Several records as usual will have the same key. There are not so many of them like in the 1st stream. I have to delete old records by timeout.
For each res1 (argument-record) I have to: get all records from 3rd strem with that record’s another key, map these records having res1 as parameter, reduce into result. 3rd stream records should stay unmodified in storage.
The records with the same key are prefered to be stored at the same node, and procedures that get records by the key and make some actions based on given argument-record are preferred to be run on the node where that records are.
Are HBase and Hadoop MapReduce applicable in my case? And how such app should look like (base idea)? If the answer is no, is there frameworks to buld such app?
Please, ask questions, if you couldn’t get what I want.
I am relating to the storage backend technologies. Front end accepting records can be stateless and thereof trivially scalable.
We have streams of records and we want to join them on the fly. Some of records should be persisted why some (as far as I understood – 1st stream) are transient.
If we take scalability and persistence out of equation – it can be implemented in single java process using HashMap for randomly accessible data and TreeMap for data we want to store sorted
Now let see how it can be mapped into NoSQL technologies to gain scalability and performance we need.
HBase is distributed sorted map. So it can be good candidate for stream 2. If we used our key as hbase table key – we will gain data locality for the records with the same key.
MapReduce on top of HBase is also available.
Stream 1 looks like transient randomly accessed data. I think it does not make sense to pay a price of persistence for those records – so distributed in memory hashtable should do. For example: http://memcached.org/ Probably element of storage there will be list of records with the same key.
I still not 100% sure about 3rd stream requirements but need for secondary index (if it known beforehand) can be implemented on application level as another distributed map.
In a nutshell – my suggestion to pick up HBase for data you want to persist and store sorted and consider some more lightweight solutions for transient (but still considerable big) data.