January 2015 - users-list

analyse performance slow query
by Pierre-Adrien Coustillas 16 Jan '15

16 Jan '15

Hi, I try to analyze the slowness of a query with EXPLAIN and stethoscope The query is : SELECT count (*) FROM ( SELECT id_unique, siteniveau1, siteniveau2, '0000-00-00', 0, 0, MAX( debutvisite ), 0, 0 FROM tmp __ at __ lm __visites_ agregees _id_unique GROUP BY id_unique,siteniveau1,siteniveau2 ) tbl ; the beginning of explain is ( there is 272 tuples ) +============================================================================================================================================================================================+ | function user.s2_5{ autoCommit = true }(A0: str ,A1: bte ,A2: bte ,A3: bte ,A4: bte ): void ; | | barrier X_429 := language . dataflow (); | | X_7 := sql . mvc (); | | X_55:bat[: oid ,: oid ] := sql . tid (X_7," sys "," tmp __ at __ lm __visites_ agregees _id_unique",0,8); | | X_70:bat[: oid ,: int ] := sql . bind (X_7," sys "," tmp __ at __ lm __visites_ agregees _id_unique","id_unique",0,0,8); | | (X_78:bat[: oid ,: oid ] ,X_79:bat[: oid ,: int ] ) := sql . bind (X_7," sys "," tmp __ at __ lm __visites_ agregees _id_unique","id_unique",2,0,8); | | X_161 := sql .delta(X_70,X_78,X_79); | | X_169 := algebra . leftfetchjoin (X_55,X_161); | | X_96:bat[: oid ,: int ] := sql . bind (X_7," sys "," tmp __ at __ lm __visites_ agregees _id_unique","siteniveau1",0,0,8); | | (X_111:bat[: oid ,: oid ] ,X_112:bat[: oid ,: int ] ) := sql . bind (X_7," sys "," tmp __ at __ lm __visites_ agregees _id_unique","siteniveau1",2,0,8); | | X_184 := sql .delta(X_96,X_111,X_112); | | X_193 := algebra . leftfetchjoin (X_55,X_184); | | X_129:bat[: oid ,: int ] := sql . bind (X_7," sys "," tmp __ at __ lm __visites_ agregees _id_unique","siteniveau2",0,0,8); | | (X_144:bat[: oid ,: oid ] ,X_145:bat[: oid ,: int ] ) := sql . bind (X_7," sys "," tmp __ at __ lm __visites_ agregees _id_unique","siteniveau2",2,0,8); | | X_201 := sql .delta(X_129,X_144,X_145); | | X_209 := algebra . leftfetchjoin (X_55,X_201); | | (X_224,X_225,X_226) := group. subgroup (X_209); | | (X_248,X_249,X_250) := group. subgroup (X_193,X_224); | | (X_272,X_273,X_274) := group. subgroupdone (X_169,X_248); | | X_275 := algebra . leftfetchjoin (X_273,X_169); | | X_304 := algebra . leftfetchjoin (X_273,X_248); | | X_331 := algebra . leftfetchjoin (X_304,X_249); group. subgroupdone (X_169,X_248); set 10 minutes to run . Why ? how to understand this function ? Thank Pierre -- 1G6 52 route de bischwiller 67300 Schiltigheim Société de Services et de Formations en Logiciels Libres http ://1g6. biz Tél : 06 64 63 70 35

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MSVCR80.dll Error Question
by Zola Javzmaa 16 Jan '15

16 Jan '15

Hello All, I am having trouble installing Monetdb on to my computer. Would anybody have any suggestions to fixing it? I'm on a Window 7, 64 bit operating system and I get the following error message when I try to load the server program: "Error message: This application has failed to start because MSVCR80.dll was not found. Re-installing the application might fix the problem." Thanks, Zola

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rename database
by Pierre-Adrien Coustillas 15 Jan '15

15 Jan '15

Hi, Is it possible to rename a database ? I thought to do like this: monetdb stop database mv /var/lib/monetdb/database /var/lib/monetdb/new_database monetdb start new_database it's okay ? Thank Pierre -- 1G6 52 route de bischwiller 67300 Schiltigheim Société de Services et de Formations en Logiciels Libres http://1g6.biz Tél : 06 64 63 70 35

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MonetDB Usage for spherical clustering
by Jiří Nádvorník 14 Jan '15

14 Jan '15

Dear MonetDB support, My name is Jiri Nadvornik and I am working for Czech astronomical institute (http://www.asu.cas.cz/en/about/about-the-institute) For more than a year I’m trying to generate identifiers for astronomical objects used when creating light curves for these objects. In general, I’m working with spherical coordinates and based on Euclidean parameters (and possibly other parameters, like magnitude) I am clustering these objects to assign multiple observations to one real object. A bottleneck when doing this in a relational database (like PostgreSQL) is not the finding of the neighbors as such (~ ms in a loop), but the inefficiency of working with disk IO when doing so. So after a little analysis of what could be done, I am quite interested into storing the data (mainly spherical coordinates) in an array DB, physically partition regions of the sky to multiple disks and then use the clustering on these partitions in parallel. My question no. 1 for you is whether MonetDB has some kind of support for spherical indexing (PgSphere, Q3C, HealPix, …) to ingest and correctly distribute the astronomical data. I need to control the way of how the data will be distributed to physical partitions. If question no.1 can be somehow answered as true, then question no 2. is: What is the easiest way to install MonetDB, ingest my data and test them? P.S.: Our processed dataset contains now cca 3e8 observations (entries for clustering) which resolve to about 6e6 real objects. We expect that number to double when we process all of our data. Thank you for your answer. Kind regards, Jiri Nadvornik Astronomical Institute of the Academy of Sciences of the Czech Republic nadvornik.ji(a)gmail.com

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Bug in Query Plan??
by Vijay Krishna 09 Jan '15

09 Jan '15

Hi, I have been studying on multiple join performances in MonetDB. I am running the latest version of MonetDB Oct2014 - SP1 (MonetDB-11.19.3) on a CentOS machine with 128 GB RAM. All my tables have 100 to 300 columns and around 35 lakh rows. I have the following query with 5 joins and a few criteria. SELECT * FROM table1 INNER JOIN table2 on table1.col1=table2.col1 LEFT JOIN table3 ON table1.col2=table3.col1 LEFT JOIN table3 ON table1.col3=table3.col1 LEFT JOIN table4 ON table2.col2=table4.col1 LEFT JOIN table5 ON table2.col3=table5.col1 WHERE (((table2.col10 like '%a%' OR table5.col11 like '%s%') and ((table2.col12 like '%w%' *and table4.col13 like '%k'*) OR table2.col14 like '%h%') and (table1.col21 = 1) AND (table1.col22 IN (1,2,3))) AND (((table1.col23 >= 15842000000000000) AND (table1.col23 <= 15842999999999999)) OR ((table1.col23 >= 0) AND (table1.col23 <= 999999999999)))) ORDER BY 1 DESC LIMIT 10 OFFSET 0; This query has only 2000 matching rows and it returns the result in *2.9 seconds*. In the same query, if I remove one criteria alone (the bolded part), the result would still match only 2000 rows, but the result would come in *300 milliseconds* even. I used the PLAN statement and found that the query plan that the executor takes for the first query is a much complex path and *it performs 3 joins for all the 35 lakh rows* (i.e. before executing the criteria even) and hence the time has spiked to 3 seconds. May be the hot data of 35 lakh tuples would have not fit into the memory?? Not sure. But if I remove the highlighted criteria, the executor evaluates the criteria first and *it performs the joins for only 7000 rows* and hence it is faster 1000 times. Is this a bug? Or is there any way to optimise my query to match the executor to choose the right plan? Any help much appreciated. I could even demo this. Thanks & Regards, Vijayakrishna.P. Mobile : (+91) 9500402305.

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Is there a way to limit worker threads on Windows
by Bryan Senseman 06 Jan '15

06 Jan '15

All, I have tried various approaches, but nothing changes the ‘using 8 threads’ message displayed at server startup. I have a client (with a client) that is running MonetDB on a 2cpu windows box and getting various errors, I’m trying to duplicate them but cannot and am concerned it might be that my hardware (read laptop) is too “powerful” to create the error condition. I really do not want to light up a virtual machine just to reduce threads. Thanx, Bryan -- ------------------------------ This transmission is confidential and intended solely for the use of the recipient named above. It may contain confidential, proprietary, or legally privileged information. If you are not the intended recipient, you are hereby notified that any unauthorized review, use, disclosure or distribution is strictly prohibited. If you have received this transmission in error, please contact the sender by reply e-mail and delete the original transmission and all copies from your system.

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no_mitosis_optimizer - is it advisable?
by Vijay Krishna 05 Jan '15

05 Jan '15

Hi, I have been working on join performances with MonetDB. I tried using various optimizer pipelines. Few costly queries which took 15 seconds with the default pipeline, returned results as fast as 5 seconds with the 'no_mitosis_optimizer' pipeline. I am looking to study more on mitosis optimizer. Is there any reference on what does it do? >From the mserver5 man page, I got this - "forcefully activate mitosis even on small tables, i.e., split small tables in as many (tiny) pieces as there are cores (threads) available;" So, does this mean with the mitosis optimizer, the tables are split and processed? If so, then why are queries slower with mitosis optimizer in the pipeline? Also, from the monetdb man page, I was alerted that "Changing this setting is discouraged at all times." What is the disadvantage of changing the optimizer pipeline to something other than the 'default_pipe'? Though the 'no_mitosis_optimizer' is stable, is it worth for production? Any help much appreciated. Thanks & Regards, Vijayakrishna.P. Mobile : (+91) 9500402305.

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