Introduction to Machine Learning for Oracle Database Professionals
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This document summarizes a presentation on practical machine learning for database administrators. It discusses using machine learning to classify PL/SQL code as good or bad, classify database schemas, cluster SQL statements, and detect anomalies in database workloads. The presentation covers what machine learning is, why it can be useful for databases, and provides examples of applying machine learning to common DBA problems like code classification. It describes building a naive Bayes classification model in Oracle to classify PL/SQL code, including extracting text features, training and testing the model, and assessing performance.
Introduction to Machine Learning for Oracle Database Professionals
- 1. Practical Machine Learning for DBAs Alex Gorbachev Las Vegas, NV April 2014
- 2. Alex Gorbachev • Chief Technology Officer at Pythian • Blogger • Cloudera Champion of Big Data • OakTable Network member • Oracle ACE Director • Founder of BattleAgainstAnyGuess.com • Founder of Sydney Oracle Meetup • IOUG Director of Communities • EVP, Ottawa Oracle User Group
- 3. Agenda • What’s Machine Learning – Typical Machine Learning applications • Why using Oracle Database for Machine Learning • Practical examples – Classifying PL/SQL code – Classifying database schemas into good and bad – SQL statements clustering – Detecting anomalies in database workload
- 4. What is Machine Learning?
- 5. data magic
- 8. building simplified models of the universe using probabilistic models
- 9. Tom Mitchell’s definition • Machine Learning is the study of computer algorithms that improve automatically through experience. ! • A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.
- 10. Why is it useful?
- 11. Why is it useful?
- 12. Why is it useful?
- 13. Why is it useful?
- 14. Classes of ML algorithms • Supervised learning – Input: data + known facts; Output - predictions • Unsupervised learning – Input: data; Output – hypothesis ! – Other less common algorithms such as reinforcement learning, recommenders and etc
- 15. Supervised Learning: Linear Regression
- 18. Unsupervised Learning: Anomaly Detection
- 19. Machine Learning workflow • Gather • Clean & transform • Explore • Model • Interpret • Produce value } today’s focus
- 20. Why Machine Learning in Oracle Database?
- 21. Machine Learning in Oracle DB? • That’s where the data is • Data in an RDBMS is often clean • Easy to transform data with SQL • Powerful algorithms implemented – Oracle Data Mining option – Analytic SQL
- 22. Machine Learning by example Applying Machine Learning to the business of DBAs
- 23. Problem: Detect bad PL/SQL • Goal: automated PL/SQL code grading – Classify as Good or Bad • Typical classification task – Assignment of labels to the set of unlabeled items based on prior observations
- 24. Classification process • Parse input data • Extract features – Manually or automatically or they are clearly defined (if row is an item, columns may be features) • Train – calculate model based on labeled input • Verify – test model on labeled input • Apply labels to unlabeled input ! • Classification is supervised learning
- 25. Features definition - easy task?
- 26. Kittens vs …
- 28. PL/SQL code features • Automatically extract words from the text as features (tokenize) – EASY TO AUTOMATE • Assign features intelligently – Code size – Author – Percent of comment lines – Presence of specific code patterns – DIFFICULT TO AUTOMATE
- 29. Classification model workflow 1. Create Oracle Text policy (define lexer) 2. Configure and build the model on training set 3. Apply model to the testing set 4. Assess model performance 5. Adjust model settings/features/size and repeat
- 30. Basic probability lesson • p(A) is the probability that A is true A is false A is true Area is 1
- 31. Basic probability lesson • p(A) is the probability that A is true • Axioms of Probability
- 32. Basic probability lesson • p(A) is the probability that A is true • Axioms of Probability ! ! ! ! • Bayes Law
- 33. How Bayes Law can work for us? ! ! ! • A – presence of a feature like WHEN OTHERS THEN NULL in PL/SQL • B – bad PL/SQL code A B Area is 1 B|A
- 34. PL/SQL data source • OBJECT_ID – case ID • CODE – text column • TARGET_VALUE – 0 is good and 1 is bad • Training set – where mod(object_id, 10) < 5 • Testing set – where mod(object_id, 10) >= 5
- 35. Oracle Text policy begin begin ctx_ddl.drop_policy('plsql_nb_policy'); exception when others then null; end; begin ctx_ddl.drop_preference('plsql_nb_lexer'); exception when others then null; end; ctx_ddl.create_preference ('plsql_nb_lexer’, 'BASIC_LEXER'); ctx_ddl.create_policy ('plsql_nb_policy', lexer=>'plsql_nb_lexer'); end; /
- 36. Model settings CREATE TABLE plsql_nb_settings ( setting_name VARCHAR2(30), setting_value VARCHAR2(4000)); BEGIN -- Populate settings table INSERT INTO plsql_svm_settings VALUES (dbms_data_mining.algo_name, dbms_data_mining.algo_naive_bayes); INSERT INTO plsql_nb_settings VALUES (dbms_data_mining.prep_auto, dbms_data_mining.prep_auto_on); INSERT INTO plsql_nb_settings VALUES (dbms_data_mining.odms_text_policy_name, 'plsql_nb_policy'); -- INSERT INTO plsql_nb_settings VALUES -- (dbms_data_mining.NABS_PAIRWISE_THRESHOLD,0.01); -- INSERT INTO plsql_nb_settings VALUES -- (dbms_data_mining.NABS_SINGLETON_THRESHOLD,0.01); COMMIT; END; /
- 37. Build model DECLARE xformlist dbms_data_mining_transform.TRANSFORM_LIST; BEGIN BEGIN DBMS_DATA_MINING.DROP_MODEL('PLSQL_NB'); EXCEPTION WHEN OTHERS THEN NULL; END; ! dbms_data_mining_transform.SET_TRANSFORM( xformlist, 'code', null, 'code', null, 'TEXT(TOKEN_TYPE:NORMAL)'); ! DBMS_DATA_MINING.CREATE_MODEL( model_name => 'PLSQL_NB', mining_function => dbms_data_mining.classification, data_table_name => 'plsql_build', case_id_column_name => 'object_id', target_column_name => 'target_value', settings_table_name => 'plsql_nb_settings', xform_list => xformlist); END; /
- 38. Test model SELECT target_value AS actual_target, PREDICTION(plsql_nb USING *) AS predicted_target, COUNT(*) AS cases_count FROM plsql_test GROUP BY target_value, PREDICTION(plsql_nb USING *) ORDER BY 1, 2;
- 39. Demo
- 40. 40
- 41. Skyline and Oculus by Etsy blackbox anomaly detection 41
- 42. Thanks and Q&A Contact info [email protected] +1-877-PYTHIAN To follow us pythian.com/blog @alexgorbachev @pythian linkedin.com/company/pythian