Use Cases of Row Pattern Matching in Oracle 12c
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This document discusses use cases for Oracle's MATCH_RECOGNIZE clause for row pattern matching. It begins with an introduction to the speaker and an overview of Trivadis, an IT consulting firm. The bulk of the document outlines an agenda for a webinar on MATCH_RECOGNIZE, including brief explanations of its syntax and examples of how it can be used for tasks like grouping contiguous sequences, grouping by time ranges, merging date ranges, and analyzing hierarchical data.
Use Cases of Row Pattern Matching in Oracle 12c
- 1. BASEL BERN BRUGG DÜSSELDORF FRANKFURT A.M. FREIBURG I.BR. GENF HAMBURG KOPENHAGEN LAUSANNE MÜNCHEN STUTTGART WIEN ZÜRICH Use Cases of Row Pattern Matching Webinar on MATCH_RECOGNIZE as a versatile SQL tool Kim Berg Hansen Senior Consultant
- 2. • Danish geek • SQL & PL/SQL developer since 2000 • Developer at Trivadis AG since 2016 http://www.trivadis.dk • Oracle Certified Expert in SQL • Oracle ACE • Blogger at http://www.kibeha.dk • SQL quizmaster at http://plsqlchallenge.oracle.com • Likes to cook • Reads sci-fi • Chairman of local chapter of Danish Beer Enthusiasts About me Use Cases of Row Pattern Matching2 2/23/2016
- 3. About Trivadis © Trivadis – The Company3 2/23/2016 Trivadis is a market leader in IT consulting, system integration, solution engineering and the provision of IT services focusing on and technologies in Switzerland, Germany, Austria and Denmark. We offer our services in the following strategic business fields: Trivadis Services takes over the interacting operation of your IT systems. O P E R A T I O N
- 4. COPENHAGEN MUNICH LAUSANNE BERN ZURICH BRUGG GENEVA HAMBURG DÜSSELDORF FRANKFURT STUTTGART FREIBURG BASEL VIENNA With over 600 specialists and IT experts in your region © Trivadis – The Company4 2/23/2016 14 Trivadis branches and more than 600 employees 260 Service Level Agreements Over 4,000 training participants Research and development budget: EUR 5.0 million Financially self-supporting and sustainably profitable Experience from more than 1,900 projects per year at over 800 customers
- 5. 2/23/2016 © Trivadis – The Company5 Technology on its own won't help you. You need to know how to use it properly.
- 6. Agenda Use Cases of Row Pattern Matching6 2/23/2016 Brief syntax overview 1. Grouping contiguous sequences 2. Grouping by time range (I) 3. Grouping by time range (II) 4. Merge date ranges 5. Group with limit on aggregate (bin fitting I) 6. Fill bins to near equal size (bin fitting II) 7. Network hickups 8. Tablespace growth spurts 9. Hierarchical child count Brief summary Q & A
- 7. Use Cases of Row Pattern Matching7 2/23/2016 Brief syntax overview
- 8. What‘s it look like Think in Patterns8 2/23/2016 SELECT * FROM Ticker MATCH_RECOGNIZE ( PARTITION BY symbol ORDER BY tstamp MEASURES STRT.tstamp AS start_tstamp, FINAL LAST(DOWN.tstamp) AS bottom_tstamp, FINAL LAST(UP.tstamp) AS end_tstamp, MATCH_NUMBER() AS match_num, CLASSIFIER() AS var_match ALL ROWS PER MATCH AFTER MATCH SKIP TO LAST UP PATTERN (STRT DOWN+ UP+) DEFINE DOWN AS DOWN.price < PREV(DOWN.price), UP AS UP.price > PREV(UP.price) ) MR ORDER BY MR.symbol, MR.match_num, MR.tstamp Example from Data Warehousing Guide chapter on SQL For Pattern Matching
- 9. The result Think in Patterns9 2/23/2016 SYMBOL TSTAMP START_TST BOTTOM_TS END_TSTAM MATCH_NUM VAR_ PRICE ---------- --------- --------- --------- --------- ---------- ---- ---------- ACME 05-APR-11 05-APR-11 06-APR-11 10-APR-11 1 STRT 25 ACME 06-APR-11 05-APR-11 06-APR-11 10-APR-11 1 DOWN 12 ACME 07-APR-11 05-APR-11 06-APR-11 10-APR-11 1 UP 15 ACME 08-APR-11 05-APR-11 06-APR-11 10-APR-11 1 UP 20 ACME 09-APR-11 05-APR-11 06-APR-11 10-APR-11 1 UP 24 ACME 10-APR-11 05-APR-11 06-APR-11 10-APR-11 1 UP 25 ACME 10-APR-11 10-APR-11 12-APR-11 13-APR-11 2 STRT 25 ACME 11-APR-11 10-APR-11 12-APR-11 13-APR-11 2 DOWN 19 ACME 12-APR-11 10-APR-11 12-APR-11 13-APR-11 2 DOWN 15 ACME 13-APR-11 10-APR-11 12-APR-11 13-APR-11 2 UP 25 ACME 14-APR-11 14-APR-11 16-APR-11 18-APR-11 3 STRT 25 ACME 15-APR-11 14-APR-11 16-APR-11 18-APR-11 3 DOWN 14 ACME 16-APR-11 14-APR-11 16-APR-11 18-APR-11 3 DOWN 12 ACME 17-APR-11 14-APR-11 16-APR-11 18-APR-11 3 UP 14 ACME 18-APR-11 14-APR-11 16-APR-11 18-APR-11 3 UP 24 The output of the example in the previous slide
- 10. Elements Use Cases of Row Pattern Matching10 2/23/2016 PARTITION BY – like analytics split data to work on one partition at a time ORDER BY – in which order shall rows be tested whether they match the pattern MEASURES – the information we want returned from the match ALL ROWS / ONE ROW PER MATCH – return aggregate or detailed info for match AFTER MATCH SKIP … – when match found, where to start looking for new match PATTERN – regexp like syntax of pattern of defined row classifiers to match SUBSET – „union“ a set of classifications into one classification variable DEFINE – definition of classification of rows FIRST, LAST, PREV, NEXT – navigational functions CLASSIFIER(), MATCH_NUMBER() – identification functions
- 11. Use Cases of Row Pattern Matching11 2/23/2016 9 use cases
- 12. 9 Demos Use Cases of Row Pattern Matching12 2/23/2016
- 13. Use Cases of Row Pattern Matching13 2/23/2016 Brief summary
- 14. MATCH_RECOGNIZE - A “swiss army knife” tool Use Cases of Row Pattern Matching14 2/23/2016 Brilliant when applied “BI style” like stock ticker analysis examples But applicable to many other cases too When you have some problem crossing row boundaries and feel you have to “stretch” even the capabilities of analytics, try a pattern based approach: – Rephrase (in natural language) your requirements in terms of what classifies the rows you are looking for – Turn that into pattern matching syntax classifying individual rows in DEFINE and how the classified rows should appear in PATTERN As with analytics, it might feel daunting at first, but once you start using pattern matching, it will become just another tool in your SQL toolbox
- 15. Use Cases of Row Pattern Matching15 2/23/2016 Further information Demo scripts used in this webinar – http://bit.ly/patternmatchsamples Follow Stew Ashton’s blog posts on the topic – https://stewashton.wordpress.com/category/match_recognize/ Documentation in Data Warehousing Guide – http://docs.oracle.com/database/121/DWHSG/pattern.htm
- 16. Q & A Kim Berg Hansen Senior Consultant [email protected] 2/23/2016 Use Cases of Row Pattern Matching16