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From Boris Tyukin <bo...@boristyukin.com>
Subject Re: "broadcast" tablet replication for kudu?
Date Mon, 23 Jul 2018 14:20:05 GMT
Hi Todd,

Are you saying that your earlier comment below is not longer valid with
Impala 2.11 and if I replicate a table to all our Kudu nodes Impala can
benefit from this?

"
*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.*
"

On Mon, Jul 23, 2018 at 9:46 AM Todd Lipcon <todd@cloudera.com> wrote:

> Are you on the latest release of Impala? It switched from using Thrift for
> RPC to a new implementation (actually borrowed from kudu) which might help
> broadcast performance a bit.
>
> Todd
>
> On Mon, Jul 23, 2018, 6:43 AM Boris Tyukin <boris@boristyukin.com> wrote:
>
>> sorry to revive the old thread but I am curious if there is a good way to
>> speed up requests to frequently used tables in Kudu.
>>
>> On Thu, Apr 12, 2018 at 8:19 AM Boris Tyukin <boris@boristyukin.com>
>> wrote:
>>
>>> bummer..After reading your guys conversation, I wish there was an easier
>>> way...we will have the same issue as we have a few dozens of tables which
>>> are used very frequently in joins and I was hoping there was an easy way to
>>> replicate them on most of the nodes to avoid broadcasts every time
>>>
>>> On Thu, Apr 12, 2018 at 7:26 AM, Clifford Resnick <
>>> cresnick@mediamath.com> wrote:
>>>
>>>> The table in our case is 12x hashed and ranged by month, so the
>>>> broadcasts were often to all (12) nodes.
>>>>
>>>> On Apr 12, 2018 12:58 AM, Mauricio Aristizabal <mauricio@impact.com>
>>>> wrote:
>>>> Sorry I left that out Cliff, FWIW it does seem to have been broadcast..
>>>>
>>>>
>>>>
>>>> Not sure though how a shuffle would be much different from a broadcast
>>>> if entire table is 1 file/block in 1 node.
>>>>
>>>> On Wed, Apr 11, 2018 at 8:52 PM, Cliff Resnick <cresny@gmail.com>
>>>> wrote:
>>>>
>>>>> From the screenshot it does not look like there was a broadcast of the
>>>>> dimension table(s), so it could be the case here that the multiple smaller
>>>>> sends helps. Our dim tables are generally in the single-digit millions
and
>>>>> Impala chooses to broadcast them. Since the fact result cardinality is
>>>>> always much smaller, we've found that forcing a [shuffle] dimension join
is
>>>>> actually faster since it only sends dims once rather than all to all
nodes.
>>>>> The degenerative performance of broadcast is especially obvious when
the
>>>>> query returns zero results. I don't have much experience here, but it
does
>>>>> seem that Kudu's efficient predicate scans can sometimes "break" Impala's
>>>>> query plan.
>>>>>
>>>>> -Cliff
>>>>>
>>>>> On Wed, Apr 11, 2018 at 5:41 PM, Mauricio Aristizabal <
>>>>> mauricio@impact.com> wrote:
>>>>>
>>>>>> @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
>>>>>>>> <https://twitter.com/impactradius> | Facebook
>>>>>>>> <https://www.facebook.com/pages/Impact-Radius/153376411365183>
|
>>>>>>>> LinkedIn <https://www.linkedin.com/company/impact-radius-inc->
>>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> --
>>>>>>> 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/>
>>>>>> <https://twitter.com/impactmartech>
>>>>>>
>>>>>
>>>>>
>>>>
>>>>
>>>> --
>>>> 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/>
>>>> <https://twitter.com/impactmartech>
>>>>
>>>
>>>

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