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From Philipp Krause <philippkrause.m...@googlemail.com>
Subject Re: Local join instead of data exchange - co-located blocks
Date Mon, 19 Mar 2018 20:02:30 GMT
I'd like to provide a small example for our purpose. The last post may 
be a bit confusing, so here's a very simple example in the attached pdf 
file. I hope, it's understandable. Otherwise, please give me a short 
feedback.

Basically, I only want each data node to join all it's local blocks. Is 
there a range mapping needed or is it possible to easily join all local 
blocks (regardless of its content) since everything is already 
"prepared"? Maybe you can clarify this for me.

As you can see in the example, the tables are not partitioned by ID. The 
files are manually prepared by the help of the modulo function. So I 
don't have a range like [0,10], but something like 0,5,10,15 etc.

I hope, I didn't make it too complicated and confusing. I think, the 
actual idea behind this is really simple and I hope you can help me to 
get this working.

Best regards and thank you very much for your time!
Philipp


Am 18.03.2018 um 17:32 schrieb Philipp Krause:
>
> Hi! At the moment the data to parquet (block) mapping is based on a 
> simple modulo function: Id % #data_nodes. So with 5 data nodes all 
> rows with Id's 0,5,10,... are written to Parquet_0, Id's 1,4,9 are 
> written to Parquet_1 etc. That's what I did manually. Since the 
> parquet file size and the block size are both set to 64MB, each 
> parquet file will result in one block when I transfer the parquet 
> files to HDFS. By default, HDFS distributes the blocks randomly. For 
> test purposes I transferred corresponding blocks from Table_A and 
> Table_B to the same data node (Table_A - Block_X with Id's 0,5,10 and 
> Table_B - Block_Y with Id's 0,5,10). In this case, they are 
> transferred to data_node_0 because the modulo function (which I want 
> to implement in the scheduler) returns 0 for these Id's. This is also 
> done manually at the moment.
>
> 1.) DistributedPlanner: For first, upcoming tests I simply changed the 
> first condition in the DistributedPlanner to true to avoid exchange nodes.
>
> 2.) The scheduler: That's the part I'm currently struggling with. For 
> first tests, block replication is deactivated. I'm not sure how / 
> where to implement the modulo function for scan range to host mapping. 
> Without the modulo function, I had to implement a hard coded mapping 
> (something like "range" 0-0, 5-5, 10-10 -> Data_node_0 etc.). Is that 
> correct? Instead I would like to use a slightly more flexible solution 
> by the help of this modulo function for the host mapping.
>
> I would be really grateful if you could give me a hint for the 
> scheduling implementation. I try to go deeper through the code meanwhile.
>
> Best regards and thank you in advance
> Philipp
>
>
> Am 14.03.2018 um 08:06 schrieb Philipp Krause:
>> Thank you very much for these information! I'll try to implement 
>> these two steps and post some updates within the next days!
>>
>> Best regards
>> Philipp
>>
>> 2018-03-13 5:38 GMT+01:00 Alexander Behm <alex.behm@cloudera.com 
>> <mailto:alex.behm@cloudera.com>>:
>>
>>     Cool that you working on a research project with Impala!
>>
>>     Properly adding such a feature to Impala is a substantial effort,
>>     but hacking the code for an experiment or two seems doable.
>>
>>     I think you will need to modify two things: (1) the planner to
>>     not add exchange nodes, and (2) the scheduler to assign the
>>     co-located scan ranges to the same host.
>>
>>     Here are a few starting points in the code:
>>
>>     1) DistributedPlanner
>>     https://github.com/apache/impala/blob/master/fe/src/main/java/org/apache/impala/planner/DistributedPlanner.java#L318
>>     <https://github.com/apache/impala/blob/master/fe/src/main/java/org/apache/impala/planner/DistributedPlanner.java#L318>
>>
>>     The first condition handles the case where no exchange nodes need
>>     to be added because the join inputs are already suitably partitioned.
>>     You could hack the code to always go into that codepath, so no
>>     exchanges are added.
>>
>>     2) The scheduler
>>     https://github.com/apache/impala/blob/master/be/src/scheduling/scheduler.cc#L226
>>     <https://github.com/apache/impala/blob/master/be/src/scheduling/scheduler.cc#L226>
>>
>>     You'll need to dig through and understand that code so that you
>>     can make the necessary changes. Change the scan range to host
>>     mapping to your liking. The rest of the code should just work.
>>
>>     Cheers,
>>
>>     Alex
>>
>>
>>     On Mon, Mar 12, 2018 at 6:55 PM, Philipp Krause
>>     <philippkrause.mail@googlemail.com
>>     <mailto:philippkrause.mail@googlemail.com>> wrote:
>>
>>         Thank you very much for your quick answers!
>>         The intention behind this is to improve the execution time
>>         and (primarily) to examine the impact of block-co-location
>>         (research project) for this particular query (simplified):
>>
>>         select A.x, B.y, A.z from tableA as A inner join tableB as B
>>         on A.id=B.id
>>
>>         The "real" query includes three joins and the data size is in
>>         pb-range. Therefore several nodes (5 in the test environment
>>         with less data) are used (without any load balancer).
>>
>>         Could you give me some hints what code changes are required
>>         and which files are affected? I don't know how to give Impala
>>         the information that it should only join the local data
>>         blocks on each node and then pass it to the "final" node
>>         which receives all intermediate results. I hope you can help
>>         me to get this working. That would be awesome!
>>
>>         Best regards
>>         Philipp
>>
>>         Am 12.03.2018 um 18:38 schrieb Alexander Behm:
>>>         I suppose one exception is if your data lives only on a
>>>         single node. Then you can set num_nodes=1 and make sure to
>>>         send the query request to the impalad running on the same
>>>         data node as the target data. Then you should get a local join.
>>>
>>>         On Mon, Mar 12, 2018 at 9:30 AM, Alexander Behm
>>>         <alex.behm@cloudera.com <mailto:alex.behm@cloudera.com>>
wrote:
>>>
>>>             Such a specific block arrangement is very uncommon for
>>>             typical Impala setups, so we don't attempt to recognize
>>>             and optimize this narrow case. In particular, such an
>>>             arrangement tends to be short lived if you have the HDFS
>>>             balancer turned on.
>>>
>>>             Without making code changes, there is no way today to
>>>             remove the data exchanges and make sure that the
>>>             scheduler assigns scan splits to nodes in the desired
>>>             way (co-located, but with possible load imbalance).
>>>
>>>             In what way is the current setup unacceptable to you? Is
>>>             this pre-mature optimization? If you have certain
>>>             performance expectations/requirements for specific
>>>             queries we might be able to help you improve those. If
>>>             you want to pursue this route, please help us by posting
>>>             complete query profiles.
>>>
>>>             Alex
>>>
>>>             On Mon, Mar 12, 2018 at 6:29 AM, Philipp Krause
>>>             <philippkrause.mail@googlemail.com
>>>             <mailto:philippkrause.mail@googlemail.com>> wrote:
>>>
>>>                 Hello everyone!
>>>
>>>                 In order to prevent network traffic, I'd like to
>>>                 perform local joins on each node instead of
>>>                 exchanging the data and perform a join over the
>>>                 complete data afterwards. My query is basically a
>>>                 join over three three tables on an ID attribute. The
>>>                 blocks are perfectly distributed, so that e.g. Table
>>>                 A - Block 0 and Table B - Block 0 are on the same
>>>                 node. These blocks contain all data rows with an ID
>>>                 range [0,1]. Table A - Block 1  and Table B - Block
>>>                 1 with an ID range [2,3] are on another node etc. So
>>>                 I want to perform a local join per node because any
>>>                 data exchange would be unneccessary (except for the
>>>                 last step when the final node recevieves all results
>>>                 of the other nodes). Is this possible?
>>>                 At the moment the query plan includes multiple data
>>>                 exchanges, although the blocks are already perfectly
>>>                 distributed (manually).
>>>                 I would be grateful for any help!
>>>
>>>                 Best regards
>>>                 Philipp Krause
>>>
>>>
>>>
>>
>>
>>
>


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