Hello Martin,
   Thanks for response. Is there any recommendation on what should be mode of running monetdb server? Given I will be dealing with 100 times bigger than this data ( but similar in nature ). Since the memory is sufficient ( 128GB physical ) to have relevant data  in memory after first call.  Does the decision depend on the properties of data? Should I first ANALYZE the data and check certain patterns to get the best usage of 16 cores that I will be having?

Regards,
Manish 

On Thu, Mar 23, 2017 at 6:35 AM, Martin Kersten <martin.kersten@cwi.nl> wrote:
No it doesn;t. See the traces, the plans are different.
Blocking operators limit multithreaded work and at some point
the cores fight over resources.

regards, Martin
On 23/03/2017 01:54, Manish gupta wrote:
Dear All,
    Sorry for spamming. Can anyone suggest how single thread is faster than 4 threads through trace output I attached in previous append. I want to confirm that in general multi thread performance should be faster for monetdb, and it should scale with
increasing cores.

Regards,
Mohit.

On Tue, Mar 21, 2017 at 9:23 PM, Manish gupta <gahoimnshg@gmail.com <mailto:gahoimnshg@gmail.com>> wrote:

    Hello Jennie,
       Thanks for responding. Here are the answers for the questions you had asked

    1. you can prefix your queries with the TRACE keyword to see where the time went
    I am attaching Trace output. 4.trace is TRACE from 4 threads (  --set "gdk_nr_threads=4" ), 1.trace files is TRACE from 1 threads monet server run (  --set "gdk_nr_threads=1"). I was expecting 4 threads run to be ~2X faster, but its slower than 1
    thread run.

    2. are the times from running the queries once, e.g. for the first time?  have you tried to run the queries repeatedly to see if there are any changes in the execution time?
    No, I am taking these snapshots after 3-4 times prior runs, so that the data is in memory, and no IO,

    3. are there any other applications running on your windows machine?
    No, apart from Windows, there is no other application running on the machine.

    4. does your windows run in a VM or on physical hardware?
    Its running on Physical hardware on my windows machine.

    Please let me know if more info is required. In the meantime, I am trying to use tomograph utility.

    Regards,
    Mohit

    On Tue, Mar 21, 2017 at 1:25 PM, Ying Zhang <Y.Zhang@cwi.nl <mailto:Y.Zhang@cwi.nl>> wrote:

        Hai Manish,

        For the use case you described here, using merge table won’t help the performance, because, as Martin mentioned, MonetDB would already use all cores for the single base table in your merge table.

        However, the performance degradation is a bit surprising.  Some following up questions:
        1. you can prefix your queries with the TRACE keyword to see where the time went
        2. are the times from running the queries once, e.g. for the first time?  have you tried to run the queries repeatedly to see if there are any changes in the execution time?
        3. are there any other applications running on your windows machine?
        4. does your windows run in a VM or on physical hardware?

        Regards,
        Jennie

        > On 19 Mar 2017, at 04:25, Manish gupta <gahoimnshg@gmail.com <mailto:gahoimnshg@gmail.com>> wrote:
        >
        > Dear all,
        >     I am checking the performance of merge table vs single table on single machine with 4 cores. The fact table contains ~10million records. I am using # MonetDB 5 server v11.25.9 "Dec2016-SP2" on windows machine ( core i7 16GB physical ram ).
        > I have created a merge table which contains fact table and 3 blank copies of fact table. I am seeing surprisingly slow performance of merge table queries compared to base table queries.
        >
        > Base table query
        > =================
        > sql>select sum(f_1_1_1), count(*), f_1_1_7,f_1_1_5 from t3760_279_1_1 group by f_1_1_7,f_1_1_5 order by  sum(f_1_1_1) desc limit 10;
        > +-----------+------+---------+---------+
        > | L2        | L5   | f_1_1_7 | f_1_1_5 |
        > +===========+======+=========+=========+
        > | 125885351 |   14 | SALE    | 8084869 |
        > | 125629268 |   15 | SALE    | 6852518 |
        > | 121733416 |   16 | SALE    | 5440605 |
        > | 110328868 |   13 | SALE    | 4842411 |
        > | 100848236 |   11 | SALE    | 8068509 |
        > | 100719291 |   16 | SALE    | 5822736 |
        > |  93127875 |   11 | SALE    | 6529612 |
        > |  91034094 |   13 | SALE    | 3401293 |
        > |  90766181 |   10 | SALE    | 8084900 |
        > |  88848574 |   10 | SALE    | 2660811 |
        > +-----------+------+---------+---------+
        > 10 tuples (44.2s)
        >
        > Merge Table Query
        > =================
        > sql>select sum(f_1_1_1), count(*), f_1_1_7,f_1_1_5 from mt279_1_1 group by f_1_1_7,f_1_1_5 order by  sum(f_1_1_1) desc limit 10;
        > +-----------+------+---------+---------+
        > | L2        | L5   | f_1_1_7 | f_1_1_5 |
        > +===========+======+=========+=========+
        > | 125885351 |   14 | SALE    | 8084869 |
        > | 125629268 |   15 | SALE    | 6852518 |
        > | 121733416 |   16 | SALE    | 5440605 |
        > | 110328868 |   13 | SALE    | 4842411 |
        > | 100848236 |   11 | SALE    | 8068509 |
        > | 100719291 |   16 | SALE    | 5822736 |
        > |  93127875 |   11 | SALE    | 6529612 |
        > |  91034094 |   13 | SALE    | 3401293 |
        > |  90766181 |   10 | SALE    | 8084900 |
        > |  88848574 |   10 | SALE    | 2660811 |
        > +-----------+------+---------+---------+
        > 10 tuples (1m 51s)
        >
        >
        > For the same results ( because merge table is containing the base table and 3 blank copies of base table ), merge table query is taking more than 2.5 times the base table runtime. Why is it so?
        >
        > Thanks,
        > Mohit
        >
        >
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