Parquet vs ORC vs ORC with Snappy

问题:

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 Snappy.

I have read many a documents that state Parquet to be better in time/space complexity as compared to ORC but my tests are opposite to the documents I went through.

Follows some details of my data.

Table A- Text File Format- 2.5GB

Table B - ORC - 652MB

Table C - ORC with Snappy - 802MB

Table D - Parquet - 1.9 GB

Parquet was worst as far as compression for my table is concerned.

My tests with the above tables yielded following results.

Row count operation

Text Format Cumulative CPU - 123.33 sec

Parquet Format Cumulative CPU - 204.92 sec

ORC Format Cumulative CPU - 119.99 sec 

ORC with SNAPPY Cumulative CPU - 107.05 sec

Sum of a column operation

Text Format Cumulative CPU - 127.85 sec   

Parquet Format Cumulative CPU - 255.2 sec   

ORC Format Cumulative CPU - 120.48 sec   

ORC with SNAPPY Cumulative CPU - 98.27 sec

Average of a column operation

Text Format Cumulative CPU - 128.79 sec

Parquet Format Cumulative CPU - 211.73 sec    

ORC Format Cumulative CPU - 165.5 sec   

ORC with SNAPPY Cumulative CPU - 135.45 sec 

Selecting 4 columns from a given range using where clause

Text Format Cumulative CPU -  72.48 sec 

Parquet Format Cumulative CPU - 136.4 sec       

ORC Format Cumulative CPU - 96.63 sec 

ORC with SNAPPY Cumulative CPU - 82.05 sec 

Does that mean ORC is faster then Parquet? Or there is something that I can do to make it work better with query response time and compression ratio?

Thanks!

回答1:

I would say, that both of these formats have their own advantages.

Parquet might be better if you have highly nested data, because it stores its elements as a tree like Google Dremel does (See here).
Apache ORC might be better if your file-structure is flattened.

And as far as I know parquet does not support Indexes yet. ORC comes with a light weight Index and since Hive 0.14 an additional Bloom Filter which might be helpful the better query response time especially when it comes to sum operations.

The Parquet default compression is SNAPPY. Are Table A - B - C and D holding the same Dataset? If yes it looks like there is something shady about it, when it only compresses to 1.9 GB

回答2:

You are seeing this because:

• Hive has a vectorized ORC reader but no vectorized parquet reader.

• Spark has a vectorized parquet reader and no vectorized ORC reader.

• Spark performs best with parquet, hive performs best with ORC.

I've seen similar differences when running ORC and Parquet with Spark.

Vectorization means that rows are decoded in batches, dramatically improving memory locality and cache utilization.

(correct as of Hive 2.0 and Spark 2.1)

回答3:

We did some benchmark comparing the different file formats (Avro, JSON, ORC, and Parquet) in different use cases.

https://www.slideshare.net/oom65/file-format-benchmarks-avro-json-orc-parquet

The data is all publicly available and benchmark code is all open source at:

https://github.com/apache/orc/tree/branch-1.4/java/bench

回答4:

Both of them have their advantages. We use Parquet at work together with Hive and Impala, but just wanted to point a few advantages of ORC over Parquet: during long-executing queries, when Hive queries ORC tables GC is called about 10 times less frequently. Might be nothing for many projects, but might be crucial for others.

ORC also takes much less time, when you need to select just a few columns from the table. Some other queries, especially with joins, also take less time because of vectorized query execution, which is not available for Parquet

Also, ORC compression is sometimes a bit random, while Parquet compression is much more consistent. It looks like when ORC table has many number columns - it doesn't compress as well. It affects both zlib and snappy compression

回答5:

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.