Hi community,

Since CarbonData has global dictionary feature, currently when loading data to 
CarbonData, it requires two times of scan of the input data. First scan is to 
generate dictionary, second scan to do actual data encoding and write to carbon 
files. Obviously, this approach is simple, but this approach has at least two 
1. involve unnecessary IO read. 
2. need two jobs for MapReduce application to write carbon files

To solve this, we need single-pass data loading solution, as discussed earlier, 
and now community is developing it (CARBONDATA-401, PR310). 

In this post, I want to discuss the OutputFormat part, I think there will be 
two OutputFormat for CarbonData. 
1. DictionaryOutputFormat, which is used for the global dictionary generation. 
(This should be extracted from CarbonColumnDictGeneratRDD)
2. TableOutputFormat, which is used for writing CarbonData files.

When carbon has these output formats, it is more easier to integrate with 
compute framework like spark, hive, mapreduce.
And in order to make data loading faster, user can choose different solution 
based on its scenario as following
Scenario 1:  First load is small (can not cover most dictionary)

run two jobs that use DictionaryOutputFormat and TableOutputFormat accordingly, 
in first few loads
after some loads, it becomes like Scenario 2, run one job that use 
TableOutputFormat with single-pass
Scenario 2: First load is big (can cover most dictionary)

for first load
if the bigest column cardinality > 10K, run two jobs using two output formats
otherwise, run one job that use TableOutputFormat with single-pass
for subsequent load, run one job that use TableOutputFormat with single-pass
What do yo think this idea?


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