Thank Mehant; yes we did look at doing this, but the advantages of using the new PARTITION BY feature is that the partitioned columns are automatically detected during any subsequent queries. This is a major advantage as our customers are using the Tableau BI tool, and knowing details such as the exact partition levels and directories is not an option.
By the way, having created a table using PARTITION BY and CTAS ,how does a query know how to action the pruning ? Where is this information stored for the query to access the tables/files efficiently ? Cheers — Chris > On 21 Oct 2015, at 19:37, Mehant Baid <[email protected]> wrote: > > In addition to the auto partitioning done by CTAS, Drill also supports > directory based pruning. You could load data into different(nested) > directories underneath the top level table location and use the 'where' > clause to get the pruning performance benefits. Following is a typical example > > Table location: /home/user/table_name > Within this you could create nested directory structure of the form > /home/user/table_name/2010/jan > /home/user/table_name/2010/feb > ... > /home/user/table_name/2010/dec > > /home/user/table_name/2011/jan > ... > /home/user/table_name/2011/dec > > Given this directory structure you could have a query that looks like > > select col1 from dfs.`/home/user/table_name` where dir0 = 2011 and dir1 = jan; > > This would prune out scanning the parquet files under the other directories. > > Thanks > Mehant > On 10/21/15 11:26 AM, Chris Mathews wrote: >> We have an existing ETL framework processing machine generated data, which >> we are updating to write Parquet files out directly to HDFS using >> AvroParquetWriter for access by Drill. >> >> Some questions: >> >> How do we take advantage of Drill’s partition pruning capabilities with >> PARTITION BY if we are not using CTAS to load the Parquet files ? >> >> It seems there is no way of taking advantage of these features if the >> Parquet files are created externally to CTAS - am I correct ? >> >> If I am, then is there any way using a Drill API of programatically loading >> our data into Parquet files and utilise Drill's parallelisation techniques >> using CTAS, or do we have to write the data out to a file and then load that >> file again as input to a CTAS command ? >> >> Another potential issue is that we are constantly writing Parquet files out >> to HDFS directories so the data in these files eventually appears as >> additional data in a Drill query - so how can we do this with CTAS ? Does >> CTAS append to an existing directory structure or does it insist on a new >> table name each time it is executed ? >> >> What I am getting at here is that there seem to be performance enhancement >> features available to Drill when the Parquet files are created using an >> existing file as input to a CTAS that are not possible otherwise. With the >> volumes of data we are talking about it is not really an option to write the >> files out, form them to then be read back in again for conversion using >> CTAS; which is why we write the Parquet files out directly to HDFS and >> append them to existing directories. >> >> Am I missing something obvious here - quite possibly yes ? >> >> Thanks for any help. >> >> Cheers — Chris >> >
