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From Mauricio Aristizabal <mauri...@impact.com>
Subject Re: "broadcast" tablet replication for kudu?
Date Wed, 11 Apr 2018 21:41:49 GMT
@Todd not to belabor the point, but when I suggested breaking up small dim
tables into multiple parquet files (and in this thread's context perhaps
partition kudu table, even if small, into multiple tablets), it was to
speed up joins/exchanges, not to parallelize the scan.

For example recently we ran into this slow query where the 14M record
dimension fit into a single file & block, so it got scanned on a single
node though still pretty quickly (300ms), however it caused the join to
take 25+ seconds and bogged down the entire query.  See highlighted
fragment and its parent.

So we broke it into several small files the way I described in my previous
post, and now join and query are fast (6s).

-m





On Fri, Mar 16, 2018 at 3:55 PM, Todd Lipcon <todd@cloudera.com> wrote:

> I suppose in the case that the dimension table scan makes a non-trivial
> portion of your workload time, then yea, parallelizing the scan as you
> suggest would be beneficial. That said, in typical analytic queries,
> scanning the dimension tables is very quick compared to scanning the
> much-larger fact tables, so the extra parallelism on the dim table scan
> isn't worth too much.
>
> -Todd
>
> On Fri, Mar 16, 2018 at 2:56 PM, Mauricio Aristizabal <
> mauricio@impactradius.com> wrote:
>
>> @Todd I know working with parquet in the past I've seen small dimensions
>> that fit in 1 single file/block limit parallelism of
>> join/exchange/aggregation nodes, and I've forced those dims to spread
>> across 20 or so blocks by leveraging SET PARQUET_FILE_SIZE=8m; or similar
>> when doing INSERT OVERWRITE to load them, which then allows these
>> operations to parallelize across that many nodes.
>>
>> Wouldn't it be useful here for Cliff's small dims to be partitioned into
>> a couple tablets to similarly improve parallelism?
>>
>> -m
>>
>> On Fri, Mar 16, 2018 at 2:29 PM, Todd Lipcon <todd@cloudera.com> wrote:
>>
>>> On Fri, Mar 16, 2018 at 2:19 PM, Cliff Resnick <cresny@gmail.com> wrote:
>>>
>>>> Hey Todd,
>>>>
>>>> Thanks for that explanation, as well as all the great work you're
>>>> doing  -- it's much appreciated! I just have one last follow-up question.
>>>> Reading about BROADCAST operations (Kudu, Spark, Flink, etc. ) it seems the
>>>> smaller table is always copied in its entirety BEFORE the predicate is
>>>> evaluated.
>>>>
>>>
>>> That's not quite true. If you have a predicate on a joined column, or on
>>> one of the columns in the joined table, it will be pushed down to the
>>> "scan" operator, which happens before the "exchange". In addition, there is
>>> a feature called "runtime filters" that can push dynamically-generated
>>> filters from one side of the exchange to the other.
>>>
>>>
>>>> But since the Kudu client provides a serialized scanner as part of the
>>>> ScanToken API, why wouldn't Impala use that instead if it knows that the
>>>> table is Kudu and the query has any type of predicate? Perhaps if I
>>>> hash-partition the table I could maybe force this (because that complicates
>>>> a BROADCAST)? I guess this is really a question for Impala but perhaps
>>>> there is a more basic reason.
>>>>
>>>
>>> Impala could definitely be smarter, just a matter of programming
>>> Kudu-specific join strategies into the optimizer. Today, the optimizer
>>> isn't aware of the unique properties of Kudu scans vs other storage
>>> mechanisms.
>>>
>>> -Todd
>>>
>>>
>>>>
>>>> -Cliff
>>>>
>>>> On Fri, Mar 16, 2018 at 4:10 PM, Todd Lipcon <todd@cloudera.com> wrote:
>>>>
>>>>> On Fri, Mar 16, 2018 at 12:30 PM, Clifford Resnick <
>>>>> cresnick@mediamath.com> wrote:
>>>>>
>>>>>> I thought I had read that the Kudu client can configure a scan for
>>>>>> CLOSEST_REPLICA and assumed this was a way to take advantage of data
>>>>>> collocation.
>>>>>>
>>>>>
>>>>> Yea, when a client uses CLOSEST_REPLICA it will read a local one if
>>>>> available. However, that doesn't influence the higher level operation
of
>>>>> the Impala (or Spark) planner. The planner isn't aware of the replication
>>>>> policy, so it will use one of the existing supported JOIN strategies.
Given
>>>>> statistics, it will choose to broadcast the small table, which means
that
>>>>> it will create a plan that looks like:
>>>>>
>>>>>
>>>>>                                    +-------------------------+
>>>>>                                    |                         |
>>>>>                         +---------->build      JOIN          |
>>>>>                         |          |                         |
>>>>>                         |          |              probe      |
>>>>>                  +--------------+  +-------------------------+
>>>>>                  |              |                  |
>>>>>                  | Exchange     |                  |
>>>>>             +----+ (broadcast   |                  |
>>>>>             |    |              |                  |
>>>>>             |    +--------------+                  |
>>>>>             |                                      |
>>>>>       +---------+                                  |
>>>>>       |         |                        +-----------------------+
>>>>>       |  SCAN   |                        |                       |
>>>>>       |  KUDU   |                        |   SCAN (other side)   |
>>>>>       |         |                        |                       |
>>>>>       +---------+                        +-----------------------+
>>>>>
>>>>> (hopefully the ASCII art comes through)
>>>>>
>>>>> In other words, the "scan kudu" operator scans the table once, and
>>>>> then replicates the results of that scan into the JOIN operator. The
"scan
>>>>> kudu" operator of course will read its local copy, but it will still
go
>>>>> through the exchange process.
>>>>>
>>>>> For the use case you're talking about, where the join is just looking
>>>>> up a single row by PK in a dimension table, ideally we'd be using an
>>>>> altogether different join strategy such as nested-loop join, with the
inner
>>>>> "loop" actually being a Kudu PK lookup, but that strategy isn't implemented
>>>>> by Impala.
>>>>>
>>>>> -Todd
>>>>>
>>>>>
>>>>>
>>>>>>  If this exists then how far out of context is my understanding of
>>>>>> it? Reading about HDFS cache replication, I do know that Impala will
choose
>>>>>> a random replica there to more evenly distribute load. But especially
>>>>>> compared to Kudu upsert, managing mutable data using Parquet is painful.
>>>>>> So, perhaps to sum thing up, if nearly 100% of my metadata scan are
single
>>>>>> Primary Key lookups followed by a tiny broadcast then am I really
just
>>>>>> splitting hairs performance-wise between Kudu and HDFS-cached parquet?
>>>>>>
>>>>>> From:  Todd Lipcon <todd@cloudera.com>
>>>>>> Reply-To: "user@kudu.apache.org" <user@kudu.apache.org>
>>>>>> Date: Friday, March 16, 2018 at 2:51 PM
>>>>>>
>>>>>> To: "user@kudu.apache.org" <user@kudu.apache.org>
>>>>>> Subject: Re: "broadcast" tablet replication for kudu?
>>>>>>
>>>>>> It's worth noting that, even if your table is replicated, Impala's
>>>>>> planner is unaware of this fact and it will give the same plan regardless.
>>>>>> That is to say, rather than every node scanning its local copy, instead
a
>>>>>> single node will perform the whole scan (assuming it's a small table)
and
>>>>>> broadcast it from there within the scope of a single query. So, I
don't
>>>>>> think you'll see any performance improvements on Impala queries by
>>>>>> attempting something like an extremely high replication count.
>>>>>>
>>>>>> I could see bumping the replication count to 5 for these tables since
>>>>>> the extra storage cost is low and it will ensure higher availability
of the
>>>>>> important central tables, but I'd be surprised if there is any measurable
>>>>>> perf impact.
>>>>>>
>>>>>> -Todd
>>>>>>
>>>>>> On Fri, Mar 16, 2018 at 11:35 AM, Clifford Resnick <
>>>>>> cresnick@mediamath.com> wrote:
>>>>>>
>>>>>>> Thanks for that, glad I was wrong there! Aside from replication
>>>>>>> considerations, is it also recommended the number of tablet servers
be odd?
>>>>>>>
>>>>>>> I will check forums as you suggested, but from what I read after
>>>>>>> searching is that Impala relies on user configured caching strategies
using
>>>>>>> HDFS cache.  The workload for these tables is very light write,
maybe a
>>>>>>> dozen or so records per hour across 6 or 7 tables. The size of
the tables
>>>>>>> ranges from thousands to low millions of rows so so sub-partitioning
would
>>>>>>> not be required. So perhaps this is not a typical use-case but
I think it
>>>>>>> could work quite well with kudu.
>>>>>>>
>>>>>>> From: Dan Burkert <danburkert@apache.org>
>>>>>>> Reply-To: "user@kudu.apache.org" <user@kudu.apache.org>
>>>>>>> Date: Friday, March 16, 2018 at 2:09 PM
>>>>>>> To: "user@kudu.apache.org" <user@kudu.apache.org>
>>>>>>> Subject: Re: "broadcast" tablet replication for kudu?
>>>>>>>
>>>>>>> The replication count is the number of tablet servers which Kudu
>>>>>>> will host copies on.  So if you set the replication level to
5, Kudu will
>>>>>>> put the data on 5 separate tablet servers.  There's no built-in
broadcast
>>>>>>> table feature; upping the replication factor is the closest thing.
 A
>>>>>>> couple of things to keep in mind:
>>>>>>>
>>>>>>> - Always use an odd replication count.  This is important due
to how
>>>>>>> the Raft algorithm works.  Recent versions of Kudu won't even
let you
>>>>>>> specify an even number without flipping some flags.
>>>>>>> - We don't test much much beyond 5 replicas.  It *should* work,
but
>>>>>>> you may run in to issues since it's a relatively rare configuration.
 With
>>>>>>> a heavy write workload and many replicas you are even more likely
to
>>>>>>> encounter issues.
>>>>>>>
>>>>>>> It's also worth checking in an Impala forum whether it has features
>>>>>>> that make joins against small broadcast tables better?  Perhaps
Impala can
>>>>>>> cache small tables locally when doing joins.
>>>>>>>
>>>>>>> - Dan
>>>>>>>
>>>>>>> On Fri, Mar 16, 2018 at 10:55 AM, Clifford Resnick <
>>>>>>> cresnick@mediamath.com> wrote:
>>>>>>>
>>>>>>>> The problem is, AFIK, that replication count is not necessarily
the
>>>>>>>> distribution count, so you can't guarantee all tablet servers
will have a
>>>>>>>> copy.
>>>>>>>>
>>>>>>>> On Mar 16, 2018 1:41 PM, Boris Tyukin <boris@boristyukin.com>
>>>>>>>> wrote:
>>>>>>>> I'm new to Kudu but we are also going to use Impala mostly
with
>>>>>>>> Kudu. We have a few tables that are small but used a lot.
My plan is
>>>>>>>> replicate them more than 3 times. When you create a kudu
table, you can
>>>>>>>> specify number of replicated copies (3 by default) and I
guess you can put
>>>>>>>> there a number, corresponding to your node count in cluster.
The downside,
>>>>>>>> you cannot change that number unless you recreate a table.
>>>>>>>>
>>>>>>>> On Fri, Mar 16, 2018 at 10:42 AM, Cliff Resnick <cresny@gmail.com>
>>>>>>>> wrote:
>>>>>>>>
>>>>>>>>> We will soon be moving our analytics from AWS Redshift
to
>>>>>>>>> Impala/Kudu. One Redshift feature that we will miss is
its ALL
>>>>>>>>> Distribution, where a copy of a table is maintained on
each server. We
>>>>>>>>> define a number of metadata tables this way since they
are used in nearly
>>>>>>>>> every query. We are considering using parquet in HDFS
cache for these, and
>>>>>>>>> Kudu would be a much better fit for the update semantics
but we are worried
>>>>>>>>> about the additional contention.  I'm wondering if having
a Broadcast, or
>>>>>>>>> ALL, tablet replication might be an easy feature to add
to Kudu?
>>>>>>>>>
>>>>>>>>> -Cliff
>>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>
>>>>>>
>>>>>> --
>>>>>> Todd Lipcon
>>>>>> Software Engineer, Cloudera
>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>> --
>>>>> Todd Lipcon
>>>>> Software Engineer, Cloudera
>>>>>
>>>>
>>>>
>>>
>>>
>>> --
>>> Todd Lipcon
>>> Software Engineer, Cloudera
>>>
>>
>>
>>
>> --
>> *MAURICIO ARISTIZABAL*
>> Architect - Business Intelligence + Data Science
>> mauricio@impactradius.com(m)+1 323 309 4260 <(323)%20309-4260>
>> 223 E. De La Guerra St. | Santa Barbara, CA 93101
>> <https://maps.google.com/?q=223+E.+De+La+Guerra+St.+%7C+Santa+Barbara,+CA+93101&entry=gmail&source=g>
>>
>> Overview <http://www.impactradius.com/?src=slsap> | Twitter
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>>
>
>
>
> --
> Todd Lipcon
> Software Engineer, Cloudera
>



-- 
Mauricio Aristizabal
Architect - Data Pipeline
*M * 323 309 4260
*E  *mauricio@impact.com  |  *W * https://impact.com
<https://www.linkedin.com/company/608678/>
<https://www.facebook.com/ImpactMarTech/>
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