1. Turbocharge SQL Performance with Oracle Database 12c Philip Moore Senior Data Architect and Developer

2. 222 © 84.51° 2015 | Public We believe in making people’s lives easier by putting the customer at the center of everything we do WHO IS 84.51°

3. 333 © 84.51° 2015 | Public 84.51° HELPS COMPANIES PUT THE CUSTOMER AT THE CENTER OF EVERY DECISION With insights from the data, we personalize the experience, making your business more relevant to consumers who matter most Second, we work with you to change the organization, embedding the principle of shopper first In so doing, we grow measurable brand value for our clients

4. 444 © 84.51° 2015 | Public PLACING THE CUSTOMER AT THE CENTER WORKS JV Partnership began in 2003 10% 0%

5. 555 © 84.51° 2015 | Public 5 WHO AM I? Philip Moore Exadata Data Warehouse Architect / Lead Oracle Developer – 84.51° 15+ years Oracle data warehousing (since Oracle 8i) 5+ years with Oracle Exadata (v2, x2, x3) Oracle Certified Professional ‑ PL/SQL Developer ‑ 10g DBA Oracle SQL Certified Expert ? Experience with: SQL Server Teradata Netezza Greenplum PostgreSQL

6. 666 Before we start on 12c… Let’s Talk about: 84.51’s Oracle Data Warehouse

7. 777 © 84.51° 2015 | Public 7 ETL Analyst ad-hoc queries (no-SLA) Loyalty Campaign Mailer ETL Products –– The Shop Pre-canned queries with SLA Oracle Exadata runs the heart of our Data Warehouse. It has enabled us to deliver things we could not do before. We use an Active-Active Disaster Recovery configuration –– using a dual ETL/ELT strategy to load data into our Primary site and to the D/R and Products site Primary SiteD/R and Products Site WE USE ORACLE EXADATA ETL

8. 888 © 84.51° 2015 | Public 8 Fact Table 200+ Billion rows Logically — We use a traditional Kimball star schema Example client model: Customers 300+ Million rows Terminals 500+ rows Stores 15,000+ rows Dates 4,700+ rows Products 8+ Million rows 84.51 - Data Warehouse Point of Sale Data Model

9. 999 © 84.51° 2015 | Public 9 Physically — we use the following strategies For Dimensions Oracle Advanced Compression (COMPRESS FOR OLTP) Primary Keys are enforced — in RELY mode Oracle DIMENSION objects Exadata Hybrid Columnar Compression (HCC) — QUERY HIGH We will put the latest 2 years (“Hot” data) into the In-Memory Column Store Partition by RANGE on our date field, 1 week per partition “Soft” Referential Integrity for Foreign Key relationships NUMBER for Numbers DATE for Dates VARCHAR2 for Characters For Facts Proper data types for our data NOT NULL and CHECK constraints to validate data and help CBO PHYSICAL MODEL STRATEGIES FOR EXADATA

10. 10 Now Let’s Talk Oracle 12c New Features that will speed up the Data Warehouse

11. 11 © 84.51° 2015 | Confidential 12c – New Data Warehousing Features These new features will accelerate your Data Warehouse SQL queries Oracle In-Memory Column Store In-Memory Aggregation (Vector Group By) Attribute Clustering Adaptive Optimization Approximate Aggregation

12. 12 © 84.51° 2015 | Public 12  Query plans are not longer set in stone!  Before 12c – the Optimizer’s decision was locked – meaning that no matter how bad its cardinality estimates were for each phase of execution – it continued down the ill-advised path.  The Optimizer can now change path in mid-query execution – for: o Join Paths –Helps recover from stale stats causing nested loop joins with large driving tables for example. o Parallel Query Distribution Methods (Broadcast / Hash) –This helps your query recover from high skew – preventing an unbalanced  This feature will take queries than run in hours in 11gR2 run in minutes or less! ADAPTIVE OPTIMIZATION The Cost-Based Optimizer just got a whole lot smarter!

13. 13 © 84.51° 2015 | Public 13  Data is organized in columnar, compressed format  Ideal for Data Warehousing (Decision Support Systems) – with analytical workloads  Oracle In-Memory excels at: o Predicate Filtering (due to SIMD instructions and IMCU “pruning”) o Bloom Filters for large joins o Full Table Scans o Additive Aggregation (hopefully non-additive as well soon)  You will typically see 10-50x performance improvement for non-Exadata, and about 5-10x with Exadata. Note: with Exadata – In-Memory doesn’t shine until you have a highly concurrent workload…  With Engineered Systems – you can choose how the data is spread across RAC Nodes: o Distribution (default) – (best for fact tables) o Duplication – consider this for Dimension tables ORACLE IN-MEMORY COLUMN STORE Revolutionizing Analytical Query Performance

14. 14 © 84.51° 2015 | Public 14  Also known as “I.M.A.” – or “Vector Group By”  The methodology was taken from Oracle OLAP – and fitted into In-Memory with use by SQL for the first time  The data is aggregated as it is scanned – reducing TEMP usage to zero in most cases – allowing for blazing fast aggregation for additive measures.  The decision is Cost-Based – and by default only kicks in if your fact table has 10 million or more rows.  You can force Vector Group By in your star aggregation query by using the “/*+ vector_transform */” hint  We have seen 16x performance improvement with Vector Group By as compared to the “traditional” Hash Group By aggregation method. IN-MEMORY AGGREGATION A Smarter way to Aggregate

15. 15 © 84.51° 2015 | Public 15  For the first time – the way data is stored and sorted in a table (or partition) is declarative  Any direct-path INSERTs or “ALTER TABLE … MOVE” commands which load or reorganize the table will obey the table-level attribute clustering directives (stored in the data dictionary)  Storing data that logically belongs together helps improve performance, compression ratios, and improved concurrency for users of your system  You can use “Join Attribute Clustering” to organize your fact data by dimensional attributes. This can greatly speed up star queries.  You can choose between two options: o Linear (basic ORDER BY) – allowing for better compression – benefitting In-Memory as well as Oracle Exadata Hybrid Columnar Compression o Interleaved – a.k.a “Z-Order Curve Fitting” – ideal for hierarchical aggregation via star queries  Can be combined with Zone Maps (on Exadata) – to facilitate zone pruning and dramatically reduce I/O as a result ATTRIBUTE CLUSTERING Storing similar “fact” rows together

16. 16 © 84.51° 2015 | Public 16 ATTRIBUTE CLUSTERING Example – from Oracle 12c Data Warehousing Guide documentation For Linear-Ordered tables – the data is simply sorted in the order of the column(s) you specify For Interleaved-Ordered tables – the data is arranged so that the data is contiguous in a multi- dimensional manner – meaning that contiguous regions contain data for similar “Country” and “Category” values

17. 17 © 84.51° 2015 | Public 17  This technique helps solve the difficult: “Count Distinct Problem” – see: https://en.wikipedia.org/wiki/Count-distinct_problem https://en.wikipedia.org/wiki/Count-distinct_problem  HyperLogLog provides about 97%+ accuracy while using far fewer resources than traditional “COUNT (DISTINCT expr)” aggregation  HyperLogLog is distributable – meaning that it can be merged between parallel slaves – effectively making Distinct Counts “additive” in nature.  APPROX_COUNT_DISTINCT provides nearly 10x query performance improvement when used for star queries which perform Distinct Counts with hierarchical aggregation  The more Distinct Counts you do in your query – the more it shines  This feature is near and dear to my heart – after learning about HyperLogLog – I requested (via an SR enhancement request via “My Oracle Support”) that Oracle adopt the “APPROX_COUNT_DISTINCT” function based upon Flajolet’s algorithm.  Oracle put the customer first and graciously added this revolutionary new feature  Many more approximate features could (will) be coming to an Oracle release near you! APPROXIMATE AGGREGATION Using the brilliant: HyperLogLog algorithm

18. 18 THANK YOU!