Parquet vs ORC vs ORC with Snappy

Question

I am running a few tests on the storage formats available with Hive and using Parquet and ORC as major options. I included ORC once with default compression and once with Sn

Answer 1

Both Parquet and ORC have their own advantages and disadvantages. But I simply try to follow a simple rule of thumb - "How nested is your Data and how many columns are there". If you follow the Google Dremel you can find how parquet is designed. They user a hierarchal tree-like structure to store data. More the nesting deeper the tree.

But ORC is designed for a flattened file store. So if your Data is flattened with fewer columns, you can go with ORC, otherwise, parquet would be fine for you. Compression on flattened Data works amazingly in ORC.

We did some benchmarking with a larger flattened file, converted it to spark Dataframe and stored it in both parquet and ORC format in S3 and did querying with **Redshift-Spectrum **.

Size of the file in parquet: ~7.5 GB and took 7 minutes to write
Size of the file in ORC: ~7.1. GB and took 6 minutes to write
Query seems faster in ORC files.

Soon we will do some benchmarking for nested Data and update the results here.