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Big Data - Cartesian

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Occasional Contributor
Posts: 5

Big Data - Cartesian

Hi, I have a very simple SQL procedure (Cartesian) from two tables:

  1. Base – 400K Rows, 2 columns
  2. Dict – 25 mio Rows, 2 columns:

proc sql;

create table Final as

select a.*, b.* from Base a

join Dict b

on trim(a.Name_2) contains trim(b.Name_1)

;

quit;

 

it works, but very slowly. My server needs one month to process this. Do you have an idea how to speed it?

Super User
Posts: 5,256

Re: Big Data - Cartesian

Lots of RAM, and fast disks for saswork location.
If you care to rewrite it I would assume that a data step with a hash table technique is much faster.
A general thought (that doesn't necessarily help this specific query) is to always try to avoid function call in the join criteria. Make sure that your source data is already trimmed.
Data never sleeps
PROC Star
Posts: 1,561

Re: Big Data - Cartesian

You can also define your library as a binary compressed Spde library. This will reduce IOs. And may add threads.
The contains operator is certainly slowing things down. Would the find function and the t parameter be faster?
What about loading your tables in RAM using sasfile?
PROC Star
Posts: 1,561

Re: Big Data - Cartesian

Do you have the fullstimer log output?
PROC Star
Posts: 1,561

Re: Big Data - Cartesian

You can influence proc sql to use a hash method by adding option magic=103. You can also use proc sql option buffersize= to read data in bulk.
PROC Star
Posts: 1,561

Re: Big Data - Cartesian

So many possibilities...
Occasional Contributor
Posts: 5

Re: Big Data - Cartesian

 LinusH,

 

In my opinion, HASH TABLES will not work with contains. Columns name_1 and name_2 are string and it searches the contents.

 

ChrisNZ,

 

I checked FIND and INDEX and they were slower. What is magic=103 and  option buffersize=? Have you got sample query?

I think that this is a text mining problem and I thought about using SAS Sentiment analytyst, what do you think?

Super User
Posts: 17,785

Re: Big Data - Cartesian

As defined currently there's not much of any type of analysis. 

 

Your doing a lookup, but it depends on the output you want. 

 

ASSUMING your dictionary is single words and your other dataset are phrases, maybe a custom lookup and splitting the data into words rather than leaving it as phrases would be faster. You do have to be careful though, is Apples the same as Apple, and what about Applebee, the restaurant. It's not an exact science. 

PROC Star
Posts: 1,561

Re: Big Data - Cartesian

[ Edited ]

Alright I ran a few tests on my PC.

 

Firstly, the process is CPU-bound for me, so no point trying to optimise I/Os through better use of RAM or buffers.

 

Secondly, the contains operator is as fast as function index(trim()), while funtion find() is slower. So SAS added a new function that is slower than the old one? Why?

 

Thirdly I tried a few data step matching logics to see how they compared to the SQL join: Scanning an array, scanning the table directly and scanning a hash table. The array scan is 20% faster than SQL, the point= direct access 20% slower (but on par with SQL when data is loaded in memory with sasfile), and the hash match is 95% faster than SQL.

 

We have a clear winner here.

 

More benchmarks and performance tips in

 

https://www.amazon.com/High-Performance-SAS-Coding-Christian-Graffeuille/dp/1512397490

 

I may reuse this example in a future edition!

 

Log below.

PROC Star
Posts: 1,561

Re: Big Data - Cartesian

 

1051  data PHRASES;
1052    length PHRASE $2000;
1053    PHRASE=repeat('x',1000);
1054    do I=1 to 1e5; output; end ;
1055   run;

NOTE: The data set WORK.PHRASES has 100000 observations and 2 variables.
NOTE: DATA statement used (Total process time):
      real time           0.30 seconds
      user cpu time       0.06 seconds
      system cpu time     0.26 seconds
      memory              186.01k
      OS Memory           11888.00k
      Timestamp           31/07/2016 05:34:07 PM


1056
1057  data BRANDS;  length BRAND $40;
1058    BRAND=repeat('y',25);
1059    do J=1 to 199; output; end ;
1060    BRAND=repeat('x',25); output;
1061   run;

NOTE: The data set WORK.BRANDS has 200 observations and 2 variables.
NOTE: DATA statement used (Total process time):
      real time           0.01 seconds
      user cpu time       0.00 seconds
      system cpu time     0.00 seconds
      memory              181.54k
      OS Memory           11888.00k
      Timestamp           31/07/2016 05:34:07 PM


1062
1063  proc sql;
1064    create table OUT_SQL_CONTAINS as
1065    select * from PHRASES, BRANDS
1066    where trim(PHRASES.PHRASE) contains trim(BRANDS.BRAND);
NOTE: The execution of this query involves performing one or more Cartesian product joins that
      can not be optimized.
NOTE: Table WORK.OUT_SQL_CONTAINS created, with 100000 rows and 4 columns.

1067  quit;
NOTE: PROCEDURE SQL used (Total process time):
      real time           28.02 seconds
      user cpu time       27.44 seconds
      system cpu time     0.48 seconds
      memory              539.80k
      OS Memory           11888.00k
      Timestamp           31/07/2016 05:34:35 PM


1068
1069  proc sql;
1070    create table OUT_SQL_INDEX as
1071    select * from PHRASES, BRANDS
1072    where index(trim(PHRASES.PHRASE),trim(BRANDS.BRAND));
NOTE: The execution of this query involves performing one or more Cartesian product joins that
      can not be optimized.
NOTE: Table WORK.OUT_SQL_INDEX created, with 100000 rows and 4 columns.

1073  quit;
NOTE: PROCEDURE SQL used (Total process time):
      real time           27.99 seconds
      user cpu time       27.33 seconds
      system cpu time     0.53 seconds
      memory              552.67k
      OS Memory           11888.00k
      Timestamp           31/07/2016 05:35:03 PM


1074
1075  proc sql;
1076    create table OUT_SQL_FIND1 as
1077    select * from PHRASES, BRANDS
1078    where find(trim(PHRASES.PHRASE),trim(BRANDS.BRAND));
NOTE: The execution of this query involves performing one or more Cartesian product joins that
      can not be optimized.
NOTE: Table WORK.OUT_SQL_FIND1 created, with 100000 rows and 4 columns.

1079  quit;
NOTE: PROCEDURE SQL used (Total process time):
      real time           37.12 seconds
      user cpu time       36.59 seconds
      system cpu time     0.42 seconds
      memory              552.67k
      OS Memory           11888.00k
      Timestamp           31/07/2016 05:35:40 PM


1080
1081  proc sql;
1082    create table OUT_SQL_FIND2 as
1083    select * from PHRASES, BRANDS
1084    where find(PHRASES.PHRASE,BRANDS.BRAND,'t');
NOTE: The execution of this query involves performing one or more Cartesian product joins that
      can not be optimized.
NOTE: Table WORK.OUT_SQL_FIND2 created, with 100000 rows and 4 columns.

1085  quit;
NOTE: PROCEDURE SQL used (Total process time):
      real time           34.66 seconds
      user cpu time       34.14 seconds
      system cpu time     0.60 seconds
      memory              528.54k
      OS Memory           11888.00k
      Timestamp           31/07/2016 05:36:15 PM


1086
1087  data OUT_DS_ARRAY;
1088    array BRANDS [200] $40 _temporary_;
1089    if _N_=1 then do K=1 to 200;
1090      set BRANDS;
1091      BRANDS[K]=BRAND;
1092    end;
1093    set PHRASES;
1094    do K=1 to 200;
1095      if index(trim(PHRASE), trim(BRANDS[K])) then do;
1096        BRAND=BRANDS[K];
1097        output;
1098      end;
1099    end;
1100    drop K;
1101  run;

NOTE: There were 200 observations read from the data set WORK.BRANDS.
NOTE: There were 100000 observations read from the data set WORK.PHRASES.
NOTE: The data set WORK.OUT_DS_ARRAY has 100000 observations and 4 variables.
NOTE: DATA statement used (Total process time):
      real time           24.82 seconds
      user cpu time       24.11 seconds
      system cpu time     0.70 seconds
      memory              369.45k
      OS Memory           11888.00k
      Timestamp           31/07/2016 05:36:40 PM


1102
1103  data OUT_DS_POINT;
1104    set PHRASES;
1105    do K=1 to 200;
1106      set BRANDS point=K;
1107      if index(trim(PHRASE), trim(BRAND)) then output;
1108    end;
1109    drop K;
1110  run;

NOTE: There were 100000 observations read from the data set WORK.PHRASES.
NOTE: The data set WORK.OUT_DS_POINT has 100000 observations and 4 variables.
NOTE: DATA statement used (Total process time):
      real time           31.10 seconds
      user cpu time       28.56 seconds
      system cpu time     2.46 seconds
      memory              326.67k
      OS Memory           11888.00k
      Timestamp           31/07/2016 05:37:11 PM


1111
1112  data OUT_DS_HASH;
1113    set PHRASES;
1114    if _N_=1 then do;
1115      dcl hash BRANDS(dataset:'BRANDS');
1116      BRANDS.definekey('BRAND');
1117      BRANDS.definedata('BRAND','J');
1118      BRANDS.definedone();
1119      declare hiter IT('BRANDS');
1120      if 0 then set BRANDS;
1121    end;
1122    RC=IT.first();
1123    do while (RC = 0);
1124      if index(trim(PHRASE), trim(BRAND)) then output;
1125      RC=IT.next();
1126    end;
1127    drop RC;
1128  run;

NOTE: There were 200 observations read from the data set WORK.BRANDS.
NOTE: There were 100000 observations read from the data set WORK.PHRASES.
NOTE: The data set WORK.OUT_DS_HASH has 100000 observations and 4 variables.
NOTE: DATA statement used (Total process time):
      real time           0.98 seconds
      user cpu time       0.56 seconds
      system cpu time     0.42 seconds
      memory              701.21k
      OS Memory           11888.00k
      Timestamp           31/07/2016 05:37:12 PM


 

 

Occasional Contributor
Posts: 5

Re: Big Data - Cartesian

Hi,

Chris, I appreciate your help. You are amazing!

I tested your query. In my test Contain is still faster than INDEX & HASH TABLES. Maybe I’m doing something wrong?

On the another hand, I had problem, because I had to divide table into 2 smaller.  

 

Contain: 14 min

OUT_DS_HASH: 21min + 22 min = 43 min

 

Log below:

499  /****************************************/

500  /* Standard Query */

501  /****************************************/

502

503      proc sql;

504      create table F_Standard as

505      select a.*, b.* from Phrases a

506      join Brands b

507      on trim(a.PHRASE) contains trim(b.BRAND)

508      ;

NOTE: The execution of this query involves performing one or more Cartesian product joins that

      can not be optimized.

NOTE: Compressing data set WORK.F_STANDARD decreased size by 62.50 percent.

      Compressed is 6 pages; un-compressed would require 16 pages.

NOTE: Table WORK.F_STANDARD created, with 775 rows and 3 columns.

 

509      quit;

NOTE: PROCEDURE SQL used (Total process time):

      real time           13:58.42

      cpu time            13:58.16

 

 

510

511  /****************************************/

512  /* New Query */

513  /****************************************/

514

593    data OUT_DS_HASH;

594      set PHRASES;

595      if _N_=1 then do;

596        dcl hash BRANDS(dataset:'BRANDS');

597        BRANDS.definekey('BRAND');

598        BRANDS.definedata('BRAND','J');

599        BRANDS.definedone();

600        declare hiter IT('BRANDS');

601        if 0 then set BRANDS;

602      end;

603      RC=IT.first();

604      do while (RC = 0);

605        if index(trim(PHRASE), trim(BRAND)) then output;

606        RC=IT.next();

607      end;

608      drop RC;

609    run;

 

ERROR: Hash object added 17301488 items when memory failure occurred.

FATAL: Insufficient memory to execute DATA step program. Aborted during the EXECUTION phase.

ERROR: The SAS System stopped processing this step because of insufficient memory.

NOTE: There were 1 observations read from the data set WORK.PHRASES.

WARNING: The data set WORK.OUT_DS_HASH may be incomplete.  When this step was stopped there

         were 0 observations and 3 variables.

WARNING: Data set WORK.OUT_DS_HASH was not replaced because this step was stopped.

NOTE: DATA statement used (Total process time):

      real time           16.72 seconds

      cpu time            16.41 seconds

532

533  /****************************************/

534  /* New Query part 2 */

535  /****************************************/

536

537  data Brands_00;

538  set Brands;

539  RANDOM_INT = INT(RANUNI(0)*1000000);

540  run;

 

NOTE: There were 21597444 observations read from the data set WORK.BRANDS.

NOTE: The data set WORK.BRANDS_00 has 21597444 observations and 3 variables.

NOTE: Compressing data set WORK.BRANDS_00 decreased size by 31.68 percent.

      Compressed is 175669 pages; un-compressed would require 257113 pages.

NOTE: DATA statement used (Total process time):

      real time           16.05 seconds

      cpu time            16.02 seconds

 

 

541

542  data Brands_01;

543  set Brands_00;

544  where RANDOM_INT > 500000;

545  drop RANDOM_INT;

546  run;

 

NOTE: There were 10800201 observations read from the data set WORK.BRANDS_00.

      WHERE RANDOM_INT>500000;

NOTE: The data set WORK.BRANDS_01 has 10800201 observations and 2 variables.

NOTE: Compressing data set WORK.BRANDS_01 decreased size by 30.13 percent.

      Compressed is 81138 pages; un-compressed would require 116132 pages.

NOTE: DATA statement used (Total process time):

      real time           8.95 seconds

      cpu time            8.79 seconds

 

 

547

548  data Brands_02;

549  set Brands_00;

550  where RANDOM_INT <= 500000;

551  drop RANDOM_INT;

552  run;

 

NOTE: There were 10797243 observations read from the data set WORK.BRANDS_00.

      WHERE RANDOM_INT<=500000;

NOTE: The data set WORK.BRANDS_02 has 10797243 observations and 2 variables.

NOTE: Compressing data set WORK.BRANDS_02 decreased size by 30.13 percent.

      Compressed is 81117 pages; un-compressed would require 116100 pages.

NOTE: DATA statement used (Total process time):

      real time           8.57 seconds

      cpu time            8.54 seconds

 

 

553

554    data OUT_DS_HASH_01;

555      set PHRASES;

556      if _N_=1 then do;

557        dcl hash BRANDS_01(dataset:'BRANDS_01');

558        BRANDS_01.definekey('BRAND');

559        BRANDS_01.definedata('BRAND','J');

560        BRANDS_01.definedone();

561        declare hiter IT('BRANDS_01');

562        if 0 then set BRANDS_01;

563      end;

564      RC=IT.first();

565      do while (RC = 0);

566        if index(trim(PHRASE), trim(BRAND)) then output;

567        RC=IT.next();

568      end;

569      drop RC;

570    run;

 

NOTE: There were 10800201 observations read from the data set WORK.BRANDS_01.

NOTE: There were 330 observations read from the data set WORK.PHRASES.

NOTE: The data set WORK.OUT_DS_HASH_01 has 399 observations and 3 variables.

NOTE: Compressing data set WORK.OUT_DS_HASH_01 decreased size by 50.00 percent.

      Compressed is 4 pages; un-compressed would require 8 pages.

NOTE: DATA statement used (Total process time):

      real time           21:21.56

      cpu time            21:19.62

 

 

571

572    data OUT_DS_HASH_02;

573      set PHRASES;

574      if _N_=1 then do;

575        dcl hash BRANDS_02(dataset:'BRANDS_02');

576        BRANDS_02.definekey('BRAND');

577        BRANDS_02.definedata('BRAND','J');

578        BRANDS_02.definedone();

579        declare hiter IT('BRANDS_02');

580        if 0 then set BRANDS_02;

581      end;

582      RC=IT.first();

583      do while (RC = 0);

584        if index(trim(PHRASE), trim(BRAND)) then output;

585        RC=IT.next();

586      end;

587      drop RC;

588    run;

 

NOTE: There were 10797243 observations read from the data set WORK.BRANDS_02.

NOTE: There were 330 observations read from the data set WORK.PHRASES.

NOTE: The data set WORK.OUT_DS_HASH_02 has 376 observations and 3 variables.

NOTE: Compressing data set WORK.OUT_DS_HASH_02 decreased size by 50.00 percent.

      Compressed is 4 pages; un-compressed would require 8 pages.

NOTE: DATA statement used (Total process time):

      real time           22:08.78

      cpu time            22:02.82

 

 

589

590  Data OUT_DS_HASH;

591  set OUT_DS_HASH_02 OUT_DS_HASH_01;

592  run;

 

NOTE: There were 376 observations read from the data set WORK.OUT_DS_HASH_02.

NOTE: There were 399 observations read from the data set WORK.OUT_DS_HASH_01.

NOTE: The data set WORK.OUT_DS_HASH has 775 observations and 3 variables.

NOTE: Compressing data set WORK.OUT_DS_HASH decreased size by 62.50 percent.

      Compressed is 6 pages; un-compressed would require 16 pages.

NOTE: DATA statement used (Total process time):

      real time           0.01 seconds

      cpu time            0.01 seconds

 

 

 

 

Occasional Contributor
Posts: 5

Re: Big Data - Cartesian

Maybe, I wasn't clear enogh Smiley Sad

I' m looking for company names in texts, so dictionary is not single words. For example:

"A global leader in the beverage industry, the <Coca-Cola company> offers hundreds of brands, including soft drinks, fruit ..."
Super User
Posts: 17,785

Re: Big Data - Cartesian

Ok, but why? Sentiment analysis looks for positive/negative connotations to text AFAIK. Is that related to your problem? 

 

SAS Text Analytics tool, an add on for SAS EM may be beneficial but I doubt it. 

 

Can each statement meet multiple company names or do you expect a single one per observation?

PROC Star
Posts: 1,561

Re: Big Data - Cartesian

 

You need to provide more information about your data, or a good replication of similar fake data and cardinality if you want us to replicate your problem. Also please use option fullstimer.

.

Your new number of observations is radically different from what you first mentioned, but I get even better results for the hash table, which is now 10,000 times faster than SQL.

 


36   options fullstimer;
37
38   data PHRASES;
39     length PHRASE $2000;
40     PHRASE=repeat('x',1000);
41     do I=1 to 300; output; end ;
42    run;

NOTE: The data set WORK.PHRASES has 300 observations and 2 variables.
NOTE: DATA statement used (Total process time):
      real time           0.01 seconds
      user cpu time       0.00 seconds
      system cpu time     0.01 seconds
      memory              186.10k
      OS Memory           6768.00k
      Timestamp            1/08/2016 10:10:44 PM


43
44   data BRANDS;  
length BRAND $40; 45 BRAND=repeat('y',25); 46 do J=1 to 1e6; output; end ; 47 BRAND=repeat('x',25); output; 48 run; NOTE: The data set WORK.BRANDS has 1000001 observations and 2 variables. NOTE: DATA statement used (Total process time): real time 0.17 seconds user cpu time 0.09 seconds system cpu time 0.07 seconds memory 181.64k OS Memory 6768.00k Timestamp 1/08/2016 10:10:44 PM 49 50 proc sql; 51 create table OUT_SQL_CONTAINS as 52 select * from PHRASES, BRANDS 53 where trim(PHRASES.PHRASE) contains trim(BRANDS.BRAND); NOTE: The execution of this query involves performing one or more Cartesian product joins that can not be optimized. NOTE: Table WORK.OUT_SQL_CONTAINS created, with 300 rows and 4 columns. 54 quit; NOTE: PROCEDURE SQL used (Total process time): real time 6:49.74 user cpu time 6:48.03 system cpu time 0.21 seconds memory 1041.61k OS Memory 7280.00k Timestamp 1/08/2016 10:17:34 PM 55 56 data OUT_DS_HASH; 57 set PHRASES; 58 if _N_=1 then do; 59 dcl hash BRANDS(dataset:'BRANDS'); 60 BRANDS.definekey('BRAND'); 61 BRANDS.definedata('BRAND','J'); 62 BRANDS.definedone(); 63 declare hiter IT('BRANDS'); 64 if 0 then set BRANDS; 65 end; 66 RC=IT.first(); 67 do while (RC = 0); 68 if index(trim(PHRASE), trim(BRAND)) then output; 69 RC=IT.next(); 70 end; 71 drop RC; 72 run; NOTE: There were 1000001 observations read from the data set WORK.BRANDS. NOTE: There were 300 observations read from the data set WORK.PHRASES. NOTE: The data set WORK.OUT_DS_HASH has 300 observations and 4 variables. NOTE: DATA statement used (Total process time): real time 0.32 seconds user cpu time 0.25 seconds system cpu time 0.06 seconds memory 741.91k OS Memory 6768.00k Timestamp 1/08/2016 10:17:34 PM

How SAS can iterate through a hash table 300 million times in 0.3s is beyond me. Is this right?

That's one iteration every other CPU clock cycle! This sounds implausible.

Also very odd is that the data step only used 7 MB of RAM. The hash table alone is 40+ MB.

Is this right? What is going on?

 

Super User
Posts: 6,933

Re: Big Data - Cartesian

You defined brands as 1 million repeats of the same value and one repeat of another value. Since you also defined BRAND as key in the hash, you end up with only 2 items in there.

So you have only 600 iterations in the loop.

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