The document discusses cardinality and how it is estimated for queries. It provides examples of how the cardinality is calculated for different predicates on columns in a sample table. It also describes cases where the cardinality estimate may be incorrect, such as when the statistics are not up to date or the predicate values are outside the range of data in the column.
Basic Query Tuning Primer - Pg West 2009mattsmiley
Intro to query tuning in Postgres, for beginners or intermediate software developers. Lists your basic toolkit, common problems, a series of examples. Assumes the audience knows basic SQL but has little or no experience with reading or adjusting execution plans. Accompanies 45-90 minute talk; meant to encourage Q/A.
New Tuning Features in Oracle 11g - How to make your database as boring as po...Sage Computing Services
One of the key problems that have haunted Oracle sites since the introduction of the cost based optimiser is the ability to provide a stable level of performance over time. The very responsiveness of the CBO to factors such as changes in statistics and initialisation parameters can lead to sudden changes in performance levels. Oracle 11g is set to introduce a number of features that will assist the DBA in providing a stable environment for mission critical applications. Excitement is for out of work time, (and for developers). The aim of most database administrators is to have as boring a working life as possible. Oracle 11g may help us achieve those aims.
This presentation discusses some of those features including:
Capture and replay of workload
Automatic SGA tuning
Managing and fixing plans
The 11g Automatic Tuning Advisor
Basic Query Tuning Primer - Pg West 2009mattsmiley
Intro to query tuning in Postgres, for beginners or intermediate software developers. Lists your basic toolkit, common problems, a series of examples. Assumes the audience knows basic SQL but has little or no experience with reading or adjusting execution plans. Accompanies 45-90 minute talk; meant to encourage Q/A.
New Tuning Features in Oracle 11g - How to make your database as boring as po...Sage Computing Services
One of the key problems that have haunted Oracle sites since the introduction of the cost based optimiser is the ability to provide a stable level of performance over time. The very responsiveness of the CBO to factors such as changes in statistics and initialisation parameters can lead to sudden changes in performance levels. Oracle 11g is set to introduce a number of features that will assist the DBA in providing a stable environment for mission critical applications. Excitement is for out of work time, (and for developers). The aim of most database administrators is to have as boring a working life as possible. Oracle 11g may help us achieve those aims.
This presentation discusses some of those features including:
Capture and replay of workload
Automatic SGA tuning
Managing and fixing plans
The 11g Automatic Tuning Advisor
This one is about advanced indexing in PostgreSQL. It guides you through basic concepts as well as through advanced techniques to speed up the database.
All important PostgreSQL Index types explained: btree, gin, gist, sp-gist and hashes.
Regular expression indexes and LIKE queries are also covered.
This is a brief introduction to how R can be useful in the manufacturing sector to calculate the frequency of faults and then developing the model so that preventive maintenance can be done
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Most of the time we see finished SQL queries, either in code repositories, blog posts of talk slides. This talk focus on the process of how to write an SQL query, from a problem statement expressed in English to code review and long term maintenance of SQL code.
DN 2017 | Multi-Paradigm Data Science - On the many dimensions of Knowledge D...Dataconomy Media
Gaining insight from data is not as straightforward as we often wish it would be – as diverse as the questions we’re asking are the quality and the quantity of the data we may have at hand. Any attempt to turn data into knowledge thus strongly depends on it dealing with big or not-so-big data, high- or low-dimensional data, exact or fuzzy data, exact or fuzzy questions, and the goal being accurate prediction or understanding. This presentation emphasizes the need for a multi-paradigm data science to tackle all the challenges we are facing today and may be facing in the future. Luckily, solutions are starting to emerge...
Managing Statistics for Optimal Query PerformanceKaren Morton
Half the battle of writing good SQL is in understanding how the Oracle query optimizer analyzes your code and applies statistics in order to derive the “best” execution plan. The other half of the battle is successfully applying that knowledge to the databases that you manage. The optimizer uses statistics as input to develop query execution plans, and so these statistics are the foundation of good plans. If the statistics supplied aren’t representative of your actual data, you can expect bad plans. However, if the statistics are representative of your data, then the optimizer will probably choose an optimal plan.
Matt Smiley
This is a basic primer aimed primarily at developers or DBAs new to Postgres. The format is a Q/A style tour with examples, based on common questions and pitfalls. Begin with a quick tour of relevant parts of the postgres catalog, with an aim to answer simple but important questions like:
How many rows does the optimizer think my table has?
When was it last analyzed?
Which other tables also have a column named "foo"?
How often is this index used?
This one is about advanced indexing in PostgreSQL. It guides you through basic concepts as well as through advanced techniques to speed up the database.
All important PostgreSQL Index types explained: btree, gin, gist, sp-gist and hashes.
Regular expression indexes and LIKE queries are also covered.
This is a brief introduction to how R can be useful in the manufacturing sector to calculate the frequency of faults and then developing the model so that preventive maintenance can be done
How to write SQL queries | pgDay Paris 2019 | Dimitri FontaineCitus Data
Most of the time we see finished SQL queries, either in code repositories, blog posts of talk slides. This talk focus on the process of how to write an SQL query, from a problem statement expressed in English to code review and long term maintenance of SQL code.
DN 2017 | Multi-Paradigm Data Science - On the many dimensions of Knowledge D...Dataconomy Media
Gaining insight from data is not as straightforward as we often wish it would be – as diverse as the questions we’re asking are the quality and the quantity of the data we may have at hand. Any attempt to turn data into knowledge thus strongly depends on it dealing with big or not-so-big data, high- or low-dimensional data, exact or fuzzy data, exact or fuzzy questions, and the goal being accurate prediction or understanding. This presentation emphasizes the need for a multi-paradigm data science to tackle all the challenges we are facing today and may be facing in the future. Luckily, solutions are starting to emerge...
Managing Statistics for Optimal Query PerformanceKaren Morton
Half the battle of writing good SQL is in understanding how the Oracle query optimizer analyzes your code and applies statistics in order to derive the “best” execution plan. The other half of the battle is successfully applying that knowledge to the databases that you manage. The optimizer uses statistics as input to develop query execution plans, and so these statistics are the foundation of good plans. If the statistics supplied aren’t representative of your actual data, you can expect bad plans. However, if the statistics are representative of your data, then the optimizer will probably choose an optimal plan.
Matt Smiley
This is a basic primer aimed primarily at developers or DBAs new to Postgres. The format is a Q/A style tour with examples, based on common questions and pitfalls. Begin with a quick tour of relevant parts of the postgres catalog, with an aim to answer simple but important questions like:
How many rows does the optimizer think my table has?
When was it last analyzed?
Which other tables also have a column named "foo"?
How often is this index used?
This paper describes the evolution of the Plan table and DBMSX_PLAN in 11g and some of the features that can be used to troubelshoot SQL performance effectively and efficiently.
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RMD24 | Retail media: hoe zet je dit in als je geen AH of Unilever bent? Heid...BBPMedia1
Grote partijen zijn al een tijdje onderweg met retail media. Ondertussen worden in dit domein ook de kansen zichtbaar voor andere spelers in de markt. Maar met die kansen ontstaan ook vragen: Zelf retail media worden of erop adverteren? In welke fase van de funnel past het en hoe integreer je het in een mediaplan? Wat is nu precies het verschil met marketplaces en Programmatic ads? In dit half uur beslechten we de dilemma's en krijg je antwoorden op wanneer het voor jou tijd is om de volgende stap te zetten.
Putting the SPARK into Virtual Training.pptxCynthia Clay
This 60-minute webinar, sponsored by Adobe, was delivered for the Training Mag Network. It explored the five elements of SPARK: Storytelling, Purpose, Action, Relationships, and Kudos. Knowing how to tell a well-structured story is key to building long-term memory. Stating a clear purpose that doesn't take away from the discovery learning process is critical. Ensuring that people move from theory to practical application is imperative. Creating strong social learning is the key to commitment and engagement. Validating and affirming participants' comments is the way to create a positive learning environment.
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2. Agenda Cardinality The Cardinality under Various Prediction Case study: incorrect high value
3. Cardinality The estimated number of rows a query is expected to return. Number of rows in table x Predicate selectivity
4. Imaging there are 1200 people attend the dba weekly meeting, They are randomly born in 12 month and comes from 10 citites, given the data the even distributed. create table audience as select mod(rownum-1,12) + 1 month_no, mod(rownum-1,10) + 1 city_no from all_objects where rownum <= 1200;
5. sid@CS10G> select month_no, count(*) from audience group by month_no order by month_no; MONTH_NO COUNT(*) ---------- ---------- 1 100 2 100 3 100 4 100 5 100 6 100 7 100 8 100 9 100 10 100 11 100 12 100 12 rows selected.
6. sid@CS10G> select city_no, count(*) from audience group by city_no order by city_no; CITY_NO COUNT(*) ---------- ---------- 1 120 2 120 3 120 4 120 5 120 6 120 7 120 8 120 9 120 10 120 10 rows selected.
8. Critical info NDK: number of distinct keys Density = 1/NDK, (0.1 = 1/10) (0.083333333 = 1/12) NUM_BUCKETS=1, there is no histogram gather NUM_BUCKETS > 1, histogram gathered
9. select month_no from audience where month_no=12; Cardinality 1200 * (1/12) = 100
10. sid@CS10G> select month_no from audience where month_no=12; 100 rows selected. Execution Plan ---------------------------------------------------------- Plan hash value: 2423062965 ------------------------------------------------------------------- | Id | Operation | Name | Rows | Bytes | Cost (%CPU)| ------------------------------------------------------------------- | 0 | SELECT STATEMENT | | 100 | 300 | 3 (0)| |* 1 | TABLE ACCESS FULL| AUDIENCE | 100 | 300 | 3 (0)| ------------------------------------------------------------------- Predicate Information (identified by operation id): --------------------------------------------------- 1 - filter("MONTH_NO"=12)
11. select month_no from audience where city_no=1; Cardinality 1200 * (1/10) = 120
12. sid@CS10G> select month_no from audience where city_no=1; 120 rows selected. Execution Plan ---------------------------------------------------------- Plan hash value: 2423062965 ------------------------------------------------------------------- | Id | Operation | Name | Rows | Bytes | Cost (%CPU)| ------------------------------------------------------------------- | 0 | SELECT STATEMENT | | 120 | 720 | 3 (0)| |* 1 | TABLE ACCESS FULL| AUDIENCE | 120 | 720 | 3 (0)| ------------------------------------------------------------------- Predicate Information (identified by operation id): --------------------------------------------------- 1 - filter("CITY_NO"=1)
13. select month_no from audience where month_no > 9; Cardinality 1200 * ( (12-9)/(12-1) ) = 327
14. sid@CS10G> select month_no from audience where month_no > 9; 300 rows selected. Execution Plan ---------------------------------------------------------- Plan hash value: 2423062965 ------------------------------------------------------------------- | Id | Operation | Name | Rows | Bytes | Cost (%CPU)| ------------------------------------------------------------------- | 0 | SELECT STATEMENT | | 327 | 981 | 3 (0)| |* 1 | TABLE ACCESS FULL| AUDIENCE | 327 | 981 | 3 (0)| ------------------------------------------------------------------- Predicate Information (identified by operation id): --------------------------------------------------- 1 - filter("MONTH_NO">9)
15. Equality: If Out of range explain plan set statement_id = '12' for select month_no from audience where month_no = 12; explain plan set statement_id = '13' for select month_no from audience where month_no = 13; explain plan set statement_id = '14' for select month_no from audience where month_no = 14; explain plan set statement_id = '15' for select month_no from audience where month_no = 15; explain plan set statement_id = '16' for select month_no from audience where month_no = 16; explain plan set statement_id = '17' for select month_no from audience where month_no = 17; explain plan set statement_id = '18' for select month_no from audience where month_no = 18; explain plan set statement_id = '19' for select month_no from audience where month_no = 19; explain plan set statement_id = '20' for select month_no from audience where month_no = 20;
16. sid@CS10G> select statement_id, cardinality from plan_table where id=1 order by statement_id; STATEMENT_ID CARDINALITY ------------ ----------- 12 100 13 91 14 82 15 73 1664 17 55 18 45 19 36 20 27 9 rows selected.
17. Range: If Out of range explain plan set statement_id = '12' for select month_no from audience where month_no > 12; explain plan set statement_id = '13' for select month_no from audience where month_no > 13; explain plan set statement_id = '14' for select month_no from audience where month_no > 14; explain plan set statement_id = '15' for select month_no from audience where month_no > 15; explain plan set statement_id = '16' for select month_no from audience where month_no > 16; explain plan set statement_id = '17' for select month_no from audience where month_no > 17; explain plan set statement_id = '18' for select month_no from audience where month_no > 18; explain plan set statement_id = '19' for select month_no from audience where month_no > 19; explain plan set statement_id = '20' for select month_no from audience where month_no > 20;
18. sid@CS10G> select statement_id, cardinality from plan_table where id=1 order by statement_id; STATEMENT_ID CARDINALITY ------------ ----------- 12 100 13 91 14 82 15 73 1664 17 55 18 45 19 36 20 27 9 rows selected.
19. The far from low/high range, the less you are to find data
20. Bad news If you have sequence, or time-based column in predicate(such as last modified date:last_mod_dt), and haven’t been keeping the statistics up to date The Cardinality will drop as time passes, if you using equality and range on that column
21. sid@CS10G> select month_no from audience where month_no > 16; no rows selected Execution Plan ---------------------------------------------------------- Plan hash value: 2423062965 -------------------------------------+-----------------------------------+ | Id | Operation | Name | Rows | Bytes | Cost | Time | -------------------------------------+-----------------------------------+ | 0 | SELECT STATEMENT | | | | 3 | | | 1 | TABLE ACCESS FULL | AUDIENCE| 64 | 192 | 3 | 00:00:01 | -------------------------------------+-----------------------------------+ Predicate Information: ---------------------- 1 - filter("MONTH_NO"=16)
22. Using 10053 event to confirm sid@CS10G> @53on alter session set events '10053 trace name context forever, level 1'; Session altered. sid@CS10G> explain plan for select month_no from audience where month_no > 16; Explained. sid@CS10G> @53off sid@CS10G> @tracefile TRACEFILE ---------------------------------------------------------------------------------------------------- /home/u02/app/oracle/product/11.1.0/db_1/admin/cs10g/udump/cs10g_ora_24947.trc
23. SINGLE TABLE ACCESS PATH ----------------------------------------- BEGIN Single Table Cardinality Estimation ----------------------------------------- Column (#1): MONTH_NO(NUMBER) AvgLen: 3.00 NDV: 12 Nulls: 0 Density: 0.083333 Min: 1 Max: 12 Using prorated density: 0.05303 of col #1 as selectivity of out-of-range value pred Table: AUDIENCE Alias: AUDIENCE Card: Original: 1200 Rounded: 64 Computed: 63.64 Non Adjusted: 63.64 ----------------------------------------- END Single Table Cardinality Estimation -----------------------------------------
24. Between: If Out of range explain plan set statement_id = '12' for select month_no from audience where month_nobetween 13 and 15; explain plan set statement_id = '14' for select month_no from audience where month_nobetween 14 and 16; explain plan set statement_id = '15' for select month_no from audience where month_nobetween 15 and 17; explain plan set statement_id = '16' for select month_no from audience where month_no between 13 and 20; explain plan set statement_id = '17' for select month_no from audience where month_no between 14 and 21; explain plan set statement_id = '18' for select month_no from audience where month_no between 15 and 22; explain plan set statement_id = '19' for select month_no from audience where month_no between 16 and 23;
25. sid@CS10G> select statement_id, cardinality from plan_table where id=1 order by statement_id; STATEMENT_ID CARDINALITY ------------ ----------- 12 100 13 100 14 100 15 100 16 100 17 100 18 100 19 100 20 100 9 rows selected.
27. SELECT count('1') RECCOUNT FROM Test_ILM_INTERACTIONt0 JOIN Test_ILM_INTERACTION_TYPEt1 ON t0.INTERACTION_TYP = t1.INTERACTION_TYP JOIN Test_ILM_INTERACTION_REFt3 ON t0.interaction_uuid = t3.interaction_uuid WHERE t1.IS_VIEWABLE = 1 AND ((t0.DOMAIN_NME = 'DOMAIN_A') or (T0.DOMAIN_NME = 'DOMAIN_B' AND T0.APPLICATION_NME = 'APPLICATION_C')) AND (t3.REF_CDE = 'BK_NUMBER' AND t3.REF_KEY_VALUE = '2389301444') AND t0.INTERACTION_DT BETWEEN TO_DATE('01-06-2011 16:00:00', 'DD-MM-YYYY HH24:MI:SS') AND TO_DATE('16-06-2011 15:59:59', 'DD-MM-YYYY HH24:MI:SS')
28. ---------------------------------------------------------------------+---------------- | Id | Operation | Name | Rows | Cost | ---------------------------------------------------------------------+---------------- | 0 | SELECT STATEMENT | | | 13 | | 1 | SORT AGGREGATE | | 1 | | | 2 | NESTED LOOPS | | 1 | 13 | | 3 | NESTED LOOPS | | 1 | 12 | | 4 | INDEX RANGE SCAN | Test_ILM_INTERACTION_IDX3 | 4 | 4 | | 5 | INDEX UNIQUE SCAN | Test_ILM_INTERACTION_REF_PK| 1 | 2 | | 6 | TABLE ACCESS BY INDEX ROWID | Test_ILM_INTERACTION_TYPE| 1 | 1 | | 7 | INDEX UNIQUE SCAN | Test_ILM_INTERACTION_TYPE_PK| 1 | 0 | ---------------------------------------------------------------------+---------------- Predicate Information (identified by operation id): --------------------------------------------------- 4 - access("T0"."INTERACTION_DT">=TO_DATE('2011-06-01 05:00:00', 'yyyy-mm-dd hh24:mi:ss') AND "T0"."INTERACTION_DT"<=TO_DATE('2011-06-16 04:59:59', 'yyyy-mm-dd hh24:mi:ss')) filter("T0"."DOMAIN_NME"='DOMAIN_A' OR "T0"."APPLICATION_NME"='APPLICATION_C' AND "T0"."DOMAIN_NME"='DOMAIN_B') 5 - access("T0"."INTERACTION_UUID"="T3"."INTERACTION_UUID" AND "T3"."REF_CDE"='BL_NUMBER' AND "T3"."REF_KEY_VALUE"=2389301444') 6 - filter("T1"."IS_VIEWABLE"=1) 7 - access("T0"."INTERACTION_TYP"="T1"."INTERACTION_TYP")