Open antomgarre opened 5 years ago
buggtb
commented
5 years ago We have a branch we add stuff to. Mondrian 3/8 is hardly maintained these days either, small bug fixes or db support, I'd consider them to be feature complete for our needs. Switching back to Mondrian 3 would be a PITA for our existing users, but there may be scope to write a 2nd backend later in the year for Saiku 4
antomgarre
commented
5 years ago I tried to compile mondrian 4.x to fix a (very old) issue with vertica that prevents saiku to work with it (https://jira.pentaho.com/plugins/servlet/mobile#issue/MONDRIAN-2249) but did not succeed. Looks like nobody has compiled that version for a long time, it is full of dust:)
In mondrian 3/8 it works so I thought it could be possible to cherry pick the fix and apply it to 4.x...
buggtb
commented
5 years ago Bruno has a branch that compiles and might be able to help as he's working on kylin support at the moment
On Tue, 26 Feb 2019, 22:12 antomgarre, notifications@github.com wrote:
I tried to compile mondrian 4.x to fix a (very old) issue with vertica that prevents saiku to work with it ( https://jira.pentaho.com/plugins/servlet/mobile#issue/MONDRIAN-2249) but did not succeed. Looks like nobody has compiled that version for a long time, it is full of dust:)
In mondrian 3/8 it works so I thought it could be possible to cherry pick the fix and apply it to 4.x...
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antomgarre
commented
5 years ago Thank you, I could clone Bruno's branch and compile it, and fix the vertica integration problem with INT/LONG types. I am testing it but looks ok.
buggtb
commented
5 years ago Sweet! PR’s are welcome! ;)
On 27 February 2019 at 13:00:11, antomgarre (notifications@github.com) wrote:
Thank you, I could clone Bruno's branch and compile it, and fix the vertica integration problem with INT/LONG types. I am testing it but looks ok.
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antomgarre
commented
5 years ago Of course! :) https://github.com/OSBI/saiku-mondrian/pull/1
Thank you!
antomgarre
commented
5 years ago Hello Tom!
After having fixed the mondiran bug with Saiku+Vertica, the performance is sweet, works great with 1 large dimension, but looks like it falls drastically when using a few large dimensions (100k-150k) BD queries are resolved fast, but saiku takes a long time to process each query, and it makes a lot of separate queries.
Do you think on any optimization can be done? something like 'delegate more work to DB'?
Thank you!
El mié., 27 feb. 2019 a las 14:07, Tom Barber (notifications@github.com) escribió:
Sweet! PR’s are welcome! ;)
On 27 February 2019 at 13:00:11, antomgarre (notifications@github.com) wrote:
Thank you, I could clone Bruno's branch and compile it, and fix the vertica integration problem with INT/LONG types. I am testing it but looks ok.
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buggtb
commented
5 years ago Hi Antonio,
Can you do me a favour, take the MDX from one of the slow queries, then rewrite the cross joins so you remove the * and instead write them long form and use Crossjoin() in a test MDX query and see what the execution time is like.
Thanks
Tom
On 17 June 2019 at 08:34:50, Antonio Martín Garre (notifications@github.com) wrote:
Hello Tom!
After having fixed the mondiran bug with Saiku+Vertica, the performance is sweet, works great with 1 large dimension, but looks like it falls drastically when using a few large dimensions (100k-150k) BD queries are resolved fast, but saiku takes a long time to process each query, and it makes a lot of separate queries.
Do you think on any optimization can be done? something like 'delegate more work to DB'?
Thank you!
El mié., 27 feb. 2019 a las 14:07, Tom Barber (notifications@github.com) escribió:
Sweet! PR’s are welcome! ;)
On 27 February 2019 at 13:00:11, antomgarre (notifications@github.com) wrote:
Thank you, I could clone Bruno's branch and compile it, and fix the vertica integration problem with INT/LONG types. I am testing it but looks ok.
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antomgarre
commented
5 years ago Hi Tom!
I will ,but the problem is not in the database. All queries take much less than 1s. The problem is that the query is partitioned in segments, small queries like:
DEBUG [mondrian.sql] 47: Segment.load: executing sql [select "column1 as c1, column2 as c2... columnN as CN where "BIG_Dimension1"."Dim1_SK" in (163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180)
There are a lot of SQLs like this, If dimension "BIG_Dimension1" has 100.000 rows....
May be there is a way to make less segments (with more elements in the where clause) ?
Regards,
El lun., 17 jun. 2019 a las 9:47, Tom Barber (notifications@github.com) escribió:
Hi Antonio,
Can you do me a favour, take the MDX from one of the slow queries, then rewrite the cross joins so you remove the * and instead write them long form and use Crossjoin() in a test MDX query and see what the execution time is like.
Thanks
Tom
On 17 June 2019 at 08:34:50, Antonio Martín Garre ( notifications@github.com) wrote:
Hello Tom!
After having fixed the mondiran bug with Saiku+Vertica, the performance is sweet, works great with 1 large dimension, but looks like it falls drastically when using a few large dimensions (100k-150k) BD queries are resolved fast, but saiku takes a long time to process each query, and it makes a lot of separate queries.
Do you think on any optimization can be done? something like 'delegate more work to DB'?
Thank you!
El mié., 27 feb. 2019 a las 14:07, Tom Barber (notifications@github.com) escribió:
Sweet! PR’s are welcome! ;)
On 27 February 2019 at 13:00:11, antomgarre (notifications@github.com) wrote:
Thank you, I could clone Bruno's branch and compile it, and fix the vertica integration problem with INT/LONG types. I am testing it but looks ok.
— You are receiving this because you commented. Reply to this email directly, view it on GitHub https://github.com/OSBI/saiku/issues/692#issuecomment-467852823, or mute the thread <
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A. Einstein
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buggtb
commented
5 years ago Yeah I appreciate the database is fast. There's a few things to try but the crossjoin test does affect SQL performance so it's a good one to test first up.
On Mon, Jun 17, 2019, 09:38 Antonio Martín Garre notifications@github.com wrote:
Hi Tom!
I will ,but the problem is not in the database. All queries take much less than 1s. The problem is that the query is partitioned in segments, small queries like:
DEBUG [mondrian.sql] 47: Segment.load: executing sql [select "column1 as c1, column2 as c2... columnN as CN where "BIG_Dimension1"."Dim1_SK" in (163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180)
There are a lot of SQLs like this, If dimension "BIG_Dimension1" has 100.000 rows....
May be there is a way to make less segments (with more elements in the where clause) ?
Regards,
El lun., 17 jun. 2019 a las 9:47, Tom Barber (notifications@github.com) escribió:
Hi Antonio,
Can you do me a favour, take the MDX from one of the slow queries, then rewrite the cross joins so you remove the * and instead write them long form and use Crossjoin() in a test MDX query and see what the execution time is like.
Thanks
Tom
On 17 June 2019 at 08:34:50, Antonio Martín Garre ( notifications@github.com) wrote:
Hello Tom!
After having fixed the mondiran bug with Saiku+Vertica, the performance is sweet, works great with 1 large dimension, but looks like it falls drastically when using a few large dimensions (100k-150k) BD queries are resolved fast, but saiku takes a long time to process each query, and it makes a lot of separate queries.
Do you think on any optimization can be done? something like 'delegate more work to DB'?
Thank you!
El mié., 27 feb. 2019 a las 14:07, Tom Barber (<notifications@github.com ) escribió:
Sweet! PR’s are welcome! ;)
On 27 February 2019 at 13:00:11, antomgarre (notifications@github.com) wrote:
Thank you, I could clone Bruno's branch and compile it, and fix the vertica integration problem with INT/LONG types. I am testing it but looks ok.
— You are receiving this because you commented. Reply to this email directly, view it on GitHub https://github.com/OSBI/saiku/issues/692#issuecomment-467852823, or mute the thread <
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A. Einstein
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antomgarre
commented
5 years ago I have done the test MDX, with a few columns using the crossjoin. The results are similar.
Queries are a little bit faster, but there are again many, following this pattern:
... 2019-06-17 10:41:22,122 DEBUG [mondrian.sql] 86: , exec 580 ms 2019-06-17 10:41:22,153 DEBUG [mondrian.sql] 86: , exec+fetch 609 ms, 1047 rows 2019-06-17 10:42:02,700 DEBUG [mondrian.sql] 87: Segment.load: executing sql [select ... where dim_sk in (883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899, 900) and ... 2019-06-17 10:42:03,169 DEBUG [mondrian.sql] 87: , exec 473 ms 2019-06-17 10:42:03,184 DEBUG [mondrian.sql] 87: , exec+fetch 484 ms, 1030 rows 2019-06-17 10:42:46,919 DEBUG [mondrian.sql] 88: Segment.load: executing sql [select ... where dim_sk in (901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 915, 916, 917, 918) and ... 2019-06-17 10:42:47,450 DEBUG [mondrian.sql] 88: , exec 540 ms 2019-06-17 10:42:47,465 DEBUG [mondrian.sql] 88: , exec+fetch 546 ms, 1018 rows 2019-06-17 10:43:31,590 DEBUG [mondrian.sql] 89: Segment.load: executing sql [select ... where dim_sk in (919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936) and ... 2019-06-17 10:43:32,090 DEBUG [mondrian.sql] 89: , exec 483 ms 2019-06-17 10:43:32,090 DEBUG [mondrian.sql] 89: , exec+fetch 500 ms, 546 rows 2019-06-17 10:44:18,106 DEBUG [mondrian.sql] 90: Segment.load: executing sql [select ... where dim_sk in (937, 938, 939, 940, 941, 942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954) and ... 2019-06-17 10:44:18,622 DEBUG [mondrian.sql] 90: , exec 523 ms 2019-06-17 10:44:18,637 DEBUG [mondrian.sql] 90: , exec+fetch 531 ms, 803 rows 2019-06-17 10:45:09,184 DEBUG [mondrian.sql] 91: Segment.load: executing sql [select ... where dim_sk in (955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972) and ... 2019-06-17 10:45:09,809 DEBUG [mondrian.sql] 91: , exec 614 ms 2019-06-17 10:45:09,825 DEBUG [mondrian.sql] 91: , exec+fetch 641 ms, 987 rows 2019-06-17 10:45:58,997 DEBUG [mondrian.sql] 92: Segment.load: executing sql [select ... where dim_sk in (973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990) and ... 2019-06-17 10:45:59,544 DEBUG [mondrian.sql] 92: , exec 526 ms 2019-06-17 10:45:59,559 DEBUG [mondrian.sql] 92: , exec+fetch 562 ms, 859 rows 2019-06-17 10:46:51,137 DEBUG [mondrian.sql] 93: Segment.load: executing sql [select ... where dim_sk in (991, 992, 993, 994, 995, 996, 997, 998, 999, 1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008) and ... 2019-06-17 10:46:51,653 DEBUG [mondrian.sql] 93: , exec 507 ms 2019-06-17 10:46:51,669 DEBUG [mondrian.sql] 93: , exec+fetch 532 ms, 738 rows 2019-06-17 10:47:46,606 DEBUG [mondrian.sql] 94: Segment.load: executing sql [select ... where dim_sk in (1009, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023, 1024, 1025, 1026) and ... 2019-06-17 10:47:47,122 DEBUG [mondrian.sql] 94: , exec 521 ms 2019-06-17 10:47:47,137 DEBUG [mondrian.sql] 94: , exec+fetch 531 ms, 403 rows 2019-06-17 10:48:42,434 DEBUG [mondrian.sql] 95: Segment.load: executing sql [select ... where dim_sk in (1027, 1028, 1029, 1030, 1031, 1032, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044) and ...
....
El lun., 17 jun. 2019 a las 10:42, Tom Barber (notifications@github.com) escribió:
Yeah I appreciate the database is fast. There's a few things to try but the crossjoin test does affect SQL performance so it's a good one to test first up.
On Mon, Jun 17, 2019, 09:38 Antonio Martín Garre <notifications@github.com
wrote:
Hi Tom!
I will ,but the problem is not in the database. All queries take much less than 1s. The problem is that the query is partitioned in segments, small queries like:
DEBUG [mondrian.sql] 47: Segment.load: executing sql [select "column1 as c1, column2 as c2... columnN as CN where "BIG_Dimension1"."Dim1_SK" in (163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180)
There are a lot of SQLs like this, If dimension "BIG_Dimension1" has 100.000 rows....
May be there is a way to make less segments (with more elements in the where clause) ?
Regards,
El lun., 17 jun. 2019 a las 9:47, Tom Barber (<notifications@github.com ) escribió:
Hi Antonio,
Can you do me a favour, take the MDX from one of the slow queries, then rewrite the cross joins so you remove the * and instead write them long form and use Crossjoin() in a test MDX query and see what the execution time is like.
Thanks
Tom
On 17 June 2019 at 08:34:50, Antonio Martín Garre ( notifications@github.com) wrote:
Hello Tom!
After having fixed the mondiran bug with Saiku+Vertica, the performance is sweet, works great with 1 large dimension, but looks like it falls drastically when using a few large dimensions (100k-150k) BD queries are resolved fast, but saiku takes a long time to process each query, and it makes a lot of separate queries.
Do you think on any optimization can be done? something like 'delegate more work to DB'?
Thank you!
El mié., 27 feb. 2019 a las 14:07, Tom Barber (< notifications@github.com ) escribió:
Sweet! PR’s are welcome! ;)
On 27 February 2019 at 13:00:11, antomgarre ( notifications@github.com) wrote:
Thank you, I could clone Bruno's branch and compile it, and fix the vertica integration problem with INT/LONG types. I am testing it but looks ok.
— You are receiving this because you commented. Reply to this email directly, view it on GitHub https://github.com/OSBI/saiku/issues/692#issuecomment-467852823, or mute the thread <
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Antonio Martín Garre antonio.martin.garre@gmail.com
"En los momentos de crisis, sólo la imaginación es más importante que el conocimiento"
Hello!
Saiku uses mondrian 4 , but this version looks like it is no longer mantained. Is this correct? If so, is there any way to use mondrian 3 (or 8)?
Regards,