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From Jacques Nadeau <jacq...@dremio.com>
Subject Re: [DISCUSS] Remove required type
Date Wed, 23 Mar 2016 00:40:59 GMT
Re Performance:

I think the main question is what portion of people's data is actually
marked as non-nullable in Parquet files? (We already treat json, avro,
kudu, and hbase (except row key) as nullable. We do treat csv as
non-nullable (array) but I think these workloads start with conversion to
Parquet.)  Early on, we typically benchmarked Drill using required fields
in Parquet. At the time, we actually hacked the Pig code to get something
to even generate this format. (I believe, to this day, Pig only generates
nullable fields in Parquet.) After some time, we recognized that basically
every tool was producing Parquet files that were nullable and ultimately
moved the benchmark infrastructure to using nullable types to do a better
job of representing real-world workloads.

Based on my (fuzzy) recollection, working with nullable types had a 10-15%
performance impact versus working on required types so I think there is a
performance impact but I think the population of users who have
non-nullable Parquet files are small. If I recall, I believe Impala also
creates nullable Parquet files. Not sure what Spark does. I believe Hive
has also made this change recently or is doing it (deprecating non-nulls in
their internals).

If we move forward with this, I would expect there initially would be a
decrease in performance when data is held as non-nullable given we
previously observed this. However, I believe the reduction in code
complexity would leads us to improve other things more quickly. Which leads
me to...

Re: Why

Drill suffers from code complexity. This hurts forward progress. One
example is the fact that we have to generate all nullable permutations of
functions. (For example, if we have three arguments, we have to generate 8
separate functions to work with the combination of argument nullabilities).
This leads to vastly more reliance on compile-time templating which is a
maintenance headache. Additionally, it makes the runtime code generation
more complicated and error prone. Testing is also more expensive because we
now have twice as many paths to both validate and maintain.  Realistically,
we should try to move to more columnar algorithms, which would provide a
bigger lift than this declared schema nullability optimization. This is
because many workloads have rare nulls so we can actually optimize better
at the batch level. Creating three code paths (nullable observed non-null,
nullable observed null and non-null) make this substantially more
complicated. We want to invest in this area but the code complexity of
nullable versus required makes this tasks less likely to happen/harder. So,
in essence, I'm arguing that it is a small short-term loss that leads to
better code quality and ultimately faster performance.

Do others have real-world observations on the frequency of required fields
in Parquet files?

thanks,
Jacques



--
Jacques Nadeau
CTO and Co-Founder, Dremio

On Tue, Mar 22, 2016 at 3:08 PM, Parth Chandra <parthc@apache.org> wrote:

> I'm not entirely convinced that this would have no performance impact. Do
> we have any experiments?
>
>
> On Tue, Mar 22, 2016 at 1:36 PM, Jacques Nadeau <jacques@dremio.com>
> wrote:
>
> > My suggestion is we use explicit observation at the batch level. If there
> > are no nulls we can optimize this batch. This would ultimately improve
> over
> > our current situation where most parquet and all json data is nullable so
> > we don't optimize. I'd estimate that the vast majority of Drills
> workloads
> > are marked nullable whether they are or not. So what we're really
> > suggesting is deleting a bunch of code which is rarely in the execution
> > path.
> > On Mar 22, 2016 1:22 PM, "Aman Sinha" <amansinha@apache.org> wrote:
> >
> > > I was thinking about it more after sending the previous concerns.
> Agree,
> > > this is an execution side change...but some details need to be worked
> > out.
> > > If the planner indicates to the executor that a column is non-nullable
> > (e.g
> > > a primary key),  the run-time generated code is more efficient since it
> > > does not have to check the null bit.  Are you thinking we would use the
> > > existing nullable vector and add some additional metadata (at a record
> > > batch level rather than record level) to indicate non-nullability ?
> > >
> > >
> > > On Tue, Mar 22, 2016 at 12:27 PM, Jacques Nadeau <jacques@dremio.com>
> > > wrote:
> > >
> > > > Hey Aman, I believe both Steven and I were only suggesting removal
> only
> > > > from execution, not planning. It seems like your concerns are all
> > related
> > > > to planning. Iit seems like the real tradeoffs in execution are
> > nominal.
> > > > On Mar 22, 2016 9:03 AM, "Aman Sinha" <amansinha@apache.org> wrote:
> > > >
> > > > > While it is true that there is code complexity due to the required
> > > type,
> > > > > what would we be trading off ?  some important considerations:
> > > > >   - We don't currently have null count statistics which would need
> to
> > > be
> > > > > implemented for various data sources
> > > > >   - Primary keys in the RDBMS sources (or rowkeys in hbase) are
> > always
> > > > > non-null, and although today we may not be doing optimizations to
> > > > leverage
> > > > > that,  one could easily add a rule that converts  WHERE primary_key
> > IS
> > > > NULL
> > > > > to a FALSE filter.
> > > > >
> > > > >
> > > > > On Tue, Mar 22, 2016 at 7:31 AM, Dave Oshinsky <
> > > doshinsky@commvault.com>
> > > > > wrote:
> > > > >
> > > > > > Hi Jacques,
> > > > > > Marginally related to this, I made a small change in PR-372
> > > > (DRILL-4184)
> > > > > > to support variable widths for decimal quantities in Parquet.
 I
> > > found
> > > > > the
> > > > > > (decimal) vectoring code to be very difficult to understand
> > (probably
> > > > > > because it's overly complex, but also because I'm new to Drill
> code
> > > in
> > > > > > general), so I made a small, surgical change in my pull request
> to
> > > > > support
> > > > > > keeping track of variable widths (lengths) and null booleans
> within
> > > the
> > > > > > existing fixed width decimal vectoring scheme.  Can my changes
be
> > > > > > reviewed/accepted, and then we discuss how to fix properly
> > long-term?
> > > > > >
> > > > > > Thanks,
> > > > > > Dave Oshinsky
> > > > > >
> > > > > > -----Original Message-----
> > > > > > From: Jacques Nadeau [mailto:jacques@dremio.com]
> > > > > > Sent: Monday, March 21, 2016 11:43 PM
> > > > > > To: dev
> > > > > > Subject: Re: [DISCUSS] Remove required type
> > > > > >
> > > > > > Definitely in support of this. The required type is a huge
> > > maintenance
> > > > > and
> > > > > > code complexity nightmare that provides little to no benefit.
As
> > you
> > > > > point
> > > > > > out, we can do better performance optimizations though null
count
> > > > > > observation since most sources are nullable anyway.
> > > > > > On Mar 21, 2016 7:41 PM, "Steven Phillips" <steven@dremio.com>
> > > wrote:
> > > > > >
> > > > > > > I have been thinking about this for a while now, and I
feel it
> > > would
> > > > > > > be a good idea to remove the Required vector types from
Drill,
> > and
> > > > > > > only use the Nullable version of vectors. I think this
will
> > greatly
> > > > > > simplify the code.
> > > > > > > It will also simplify the creation of UDFs. As is, if a
> function
> > > has
> > > > > > > custom null handling (i.e. INTERNAL), the function has
to be
> > > > > > > separately implemented for each permutation of nullability
of
> the
> > > > > > > inputs. But if drill data types are always nullable, this
> > wouldn't
> > > > be a
> > > > > > problem.
> > > > > > >
> > > > > > > I don't think there would be much impact on performance.
In
> > > practice,
> > > > > > > I think the required type is used very rarely. And there
are
> > other
> > > > > > > ways we can optimize for when a column is known to have
no
> nulls.
> > > > > > >
> > > > > > > Thoughts?
> > > > > > >
> > > > > >
> > > > > >
> > > > > >
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