@Jacques, Steven probably could confirm whether my understanding of the code is correct or not. From the code, it seems we enforce the checking that only a column with unique value across all the files would be considered for pruning.
I just tried two simple cases with TPC-H sample data. It seems to be the case; the table that meets this unique value requirement shows partition pruning works, while the table that has one file that violates this requirement does not show so. I think the code imposes such requirement due to the current way how partition pruning works : we build a set of value vectors for each input files before partition pruning does the interpreter evaluation. Those value vectors assume that each input file should have single value; otherwise it would not fit into this model. I agree with you that we actually should do the partitioning if any of the files are excludable. That sounds like we should change the partition pruning rule such that the filter evaluation is moved into ParquetGroupScan itself. On Sun, Oct 25, 2015 at 4:32 PM, Jacques Nadeau <jacq...@dremio.com> wrote: > Jinfeng hit the nail on the head. If you have Parquet files with single > value columns (and have Parquet footer metadata stats), Drill will > automatically leverage the partitioning with zero additional setup > required. > > Jinfeng, based on what you said, it sounds as if we don't apply > partitioning unless all the files read are partitioned (single value > columns). We should actually do the partitioning if any of the files are > excludable, right? > > -- > Jacques Nadeau > CTO and Co-Founder, Dremio > > On Wed, Oct 21, 2015 at 1:45 PM, Chris Mathews <math...@uk2.net> wrote: > >> Thanks guys this is very helpful. >> I now need to go away and do some more research into this. >> >> Cheers -- Chris >> ____________________ >> Sent from my iPhone >> >> > On 21 Oct 2015, at 21:32, Jinfeng Ni <jinfengn...@gmail.com> wrote: >> > >> > For each column in the parquet files, Drill will check column metadata >> > and see if min == max across all parquet files. If yes, that indicates >> > this column has a unique value for all the files, and Drill will use >> > that column as partitioning columns. >> > >> > The partitioning column could be a column specified in "PARTITION BY" >> > clause of CTAS; in such case, CTAS will ensure each created parquet >> > file will have unique value for the column. If the parquet files are >> > not created by CTAS PARTITION BY, but some column happens to be unique >> > across all the parquet files, then it will be treated as partitioning >> > column as well. >> > >> > You may get more detail in the code [1]. >> > >> > [1] >> https://github.com/apache/drill/blob/master/exec/java-exec/src/main/java/org/apache/drill/exec/store/parquet/ParquetGroupScan.java#L324 >> > >> > >> >> On Wed, Oct 21, 2015 at 12:18 PM, Chris Mathews <math...@uk2.net> >> wrote: >> >> We create a JSON format schema for the Parquet file using the Avro >> specification and use this schema when loading data. >> >> >> >> Is there anything special we have to do to flag a column as a >> partitioning column ? >> >> Sorry I don’t understand your answer. What do you mean by ‘discover the >> columns with a single value’ ? >> >> >> >> Cheers — Chris >> >> >> >>> On 21 Oct 2015, at 20:02, Mehant Baid <baid.meh...@gmail.com> wrote: >> >>> >> >>> The information is stored in the footer of the parquet files. Drill >> reads the metadata information stored in the parquet footer to discover the >> columns with a single value and treats them as partitioning columns. >> >>> >> >>> Thanks >> >>> Mehant >> >>> >> >>>> On 10/21/15 11:52 AM, Chris Mathews wrote: >> >>>> 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 <baid.meh...@gmail.com> 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 >> >> >>
