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Oracle 12c New Features For Better Performance


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Oracle 12cR1 and 12cR2 came with some great features for better performance and scaling. In this session we will talk about some of the new features that might improve performance greatly: Optimizer changes, adaptive plans improvements, changes to statistics gathering and we'll get to know Oracle 12cR2 new sharding option

On the agenda:
- Oracle Database In Memory (Column Store)
- Oracle Sharding (
- Optimizer changes in 12c
- Statistics changes in 12c.

Presented first at ilOUG - Israel Oracle User Group meetup in February 2017.
[including promised hidden slide.. :) ]

Published in: Technology
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Oracle 12c New Features For Better Performance

  1. 1. Zohar Elkayam Twitter: @realmgic Oracle 12c New Features for Better Performance
  2. 2. Who am I? • Zohar Elkayam, CTO at Brillix • Programmer, DBA, team leader, database trainer, public speaker, and a senior consultant for over 19 years • Oracle ACE Associate • Member of ilOUG – Israel Oracle User Group • Blogger – and 2
  3. 3. About Brillix • We offer complete, integrated end-to-end solutions based on best-of- breed innovations in database, security and big data technologies • We provide complete end-to-end 24x7 expert remote database services • We offer professional customized on-site trainings, delivered by our top-notch world recognized instructors 3
  4. 4. Some of Our Customers
  5. 5. Agenda •Database In Memory (column store) – •Oracle Database Sharding – •Optimizer and Statistics changes – 12c
  6. 6. Our Goal for Today •Getting to know some of Oracle 12cR1 and 12cR2 new features around performance •Not a lot of syntax today – mainly concepts •Way too many slides, let’s try to catch ‘em all…
  7. 7. Oracle Database In-Memory (Column Store)
  8. 8. What is an In Memory Database? • In memory databases are management systems that keeps the data in a non-persistent storage (RAM) for faster access Examples: • AeroSpike • SQLite • MemcacheDB • Oracle TimesTen and Oracle Coherence
  9. 9. What is a Column Store Database? • Column Store databases are management systems that use data managed in a columnar structure format for better analysis of single column data (i.e. aggregation). Data is saved and handled as columns instead of rows. Examples: • Apache Cassandra • Apache HBase • Apache Parquet • Sybase IQ • HP Vertica
  10. 10. How Records are Organized? • This is a logical table in RDBMS • Its physical organization is just like the logical one: column by column, row by row Row 1 Row 2 Row 3 Row 4 Col 1 Col 2 Col 3 Col 4
  11. 11. Query Data • When we query data, records are read at the order they are organized in the physical structure • Even when we query a single column, we still need to read the entire table and extract the column Row 1 Row 2 Row 3 Row 4 Col 1 Col 2 Col 3 Col 4 Select Col2 From MyTable Select * From MyTable
  12. 12. How Does Column Stores Keep Data Organization in row store Organization in column store Select Col2 From MyTable
  13. 13. Row Format vs. Column Format
  14. 14. In Memory Option Breakthrough • In memory option introduces a dual format database • Tables can be accessed as row format and column format at the same time – the Optimizer is aware to the new format so: • OLTP continue using the old row format • Analytic queries start using the column format
  15. 15. Oracle In Memory Option •Column data is pure in memory format: it’s non- persistent and require no logging, archiving or backup •Data changes are simultaneously changed in both formats so data is consistent and current •Application code requires no changes – just turn on and start using
  16. 16. In Memory Option – Good To Know • It is Not “In Memory Database” – it’s an accelerator to the regular database • It is Not “Column Store Database” – column organized data is non-persistent* • In Memory Option requires more memory than the data you plan to load to the memory: no LRU mechanism • Not related to Oracle Times-Ten or Oracle Coherence
  17. 17. Oracle Buffer Cache and Memory Management •Oracle buffer cache can keep data blocks in memory for optimization •Blocks are removed from memory based on their usability (LRU) •If data is smaller than available memory, we can use Oracle 12c new features: Full Database Caching
  18. 18. Full Database Caching • Full Database Caching: Implicit default and automatic mode in which an internal calculation determines if the database can be fully cached • Force Full Database Caching: This mode requires the DBA to execute the ALTER DATABASE FORCE FULL DATABASE CACHING command • Neither Full Database Caching nor Force Full Database Caching forces prefetch of data into the memory
  19. 19. What’s new In 12cR2? •In memory support for Active Data Guard configuration •In memory virtual columns and expressions •In memory FastStart •Automatic Data Optimization Support for In-Memory Column Store
  20. 20. Oracle Sharding
  21. 21. Scaling Databases •Why would we want to scale our database • Performance • Elasticity • Global data distribution •Possible solutions: • Scaling up – adding more hardware • Scaling out – the Oracle way, using RAC • Scaling out using sharding
  22. 22. What Is Sharding? •Sharding is a way of horizontal scaling (horizontal partitioning) •Instead of scaling the database infrastructures, we scale out the data itself •Not a new concept: MongoDB, Cassandra, MySQL… •Starting with Oracle 12.2 we can use Sharded Database Architecture (SDA) as part of Oracle Global Data Services (GDS) architecture
  23. 23. Global Data Services (GDS)
  24. 24. Sharded Database Architecture (SDA) •Part of the Global Data Services (GDS) architecture •Databases in the logical database doesn’t share any physical resources or clusterware software •Databases can reside in different geo-locations •Application must be compatible with sharded behavior
  25. 25. Benefits of Sharding • Linear Scalability - eliminates performance bottlenecks and makes it possible to linearly scale performance by adding shards • Fault Containment - Sharding is a shared nothing hardware infrastructure that eliminates single points of failure • Geographical Distribution of Data - store data close to its users • Rolling Upgrades – changes to one shard at a time does not affect other shards • Simplicity of Cloud Deployment - supports on-premises, cloud, and hybrid deployment models
  26. 26. Why RDBMS Sharding? •Unlike NoSQL sharding, Oracle Shards still support • Relational schemas • ACID transactions properties and read consistency • SQL and other programmatic interfaces • Complex data types • Database partitioning • Advanced security • High Availability features • And more…
  27. 27. The Big Picture
  28. 28. Server A – Non-Sharded Sharding Methods •We can use two methods of sharding data: • Sharded tables: data exist is one shared • Duplicated tables: data exist in all shareds SDB – Sharded (Logical) Database Server B Server C Server D Shard 1 Shard 2 Shard 3
  29. 29. Example – Sharded Table Creation CREATE SHARDED TABLE customers ( cust_id NUMBER NOT NULL , name VARCHAR2(50) , address VARCHAR2(250) , region VARCHAR2(20) , class VARCHAR2(3) , signup DATE CONSTRAINT cust_pk PRIMARY KEY(cust_id) ) PARTITION BY CONSISTENT HASH (cust_id) TABLESPACE SET ts1 PARTITIONS AUTO;
  30. 30. Example – Duplicated Table Creation CREATE DUPLICATED TABLE Products ( StockNo NUMBER PRIMARY KEY , Description VARCHAR2(20) , Price NUMBER(6,2)) );
  31. 31. Sharded Table Families •We can shard multiple tables to the same database shard using table families •All tables in a table family must have the same equi- partition sharding key: • Using Reference partitions • Using the PARENT clause
  34. 34. Non-Table Objects •We can create non-table objects in the logical databases • Schema objects: users, roles, views, indexes, synonyms, functions, procedures, and packages • Non-schema objects: tablespaces, tablespace sets, directories, and contexts •Objects will be created on all shards
  35. 35. DDL Execution • The application schema name and all objects name must be identical on all shards • DDL on sharded table must be done from the Shared catalog database or using GDS command line tool (GDSCTL) • Changes are automatically propagated to all shards SQL> CONNECT SYS@SH_CATALOG SQL> ALTER SESSION ENABLE SHARD DDL; SQL> CREATE USER <app_name>... SQL> GRANT CREATE TABLE TO <app_name>... SQL> CREATE DUPLICATED TABLE <name>... SQL> CREATE SHARDED TABLE <name>... GDSCTL> sql "CREATE USER ..." GDSCTL> sql "CREATE TABLESPACE SET ..."
  36. 36. Sharding Physical Structure •Physical data distribution based on chunks – each chunk is one table partition •Each chunk is located on a different tablespace •Tablespaces are defined using tablespace sets (tablespace templates)
  37. 37. Resharding and Hotspots Handling • Adding/Removing shards or hotspot elimination requires chunk movement (automatically or manually) • This will generate an RMAN backup, restore and recovery of the chunk (tablespace) in the new node. Old chunk will be automatically removed once done. • We can also split hotsposts using GDSCTL split command GDSCTL> MOVE CHUNK -CHUNK 12 -SOURCE sh01 -TARGET sh12 GDSCTL> SPLIT CHUNK -CHUNK 12
  38. 38. Sharding High Availability •Data replication with Data Guard is a crucial component in SDB environment • High availability, disaster recovery, read offloading • Replication deployment performed fully automatically • The logical unit of data replication is a shardgroup
  39. 39. High Availability Setup Example GDSCTL> create shardcatalog -database shdard01:1521:repo -chunks 12 -user mygdsadmin/<pwd> -sdb sharddb -region london,Amsterdam –repl DG –sharding system -protectmode maxavailability ... GDSCTL> add shardgroup -shardgroup shardgrp1 -deploy_as primary -region london GDSCTL> add shardgroup -shardgroup shardgrp2 -deploy_as active_standby -region london GDSCTL> add shardgroup -shardgroup shardgrp3 -deploy_as active_standby -region amsterdam
  40. 40. Session Routing (single shard) • Application must be compatible with sharding architecture • When connecting to the database, the application must provide the sharding key (and super key) to the connection • All SQL operations in this session are related to the specified sharding key (shard) • To work on another sharding key value, the application needs to create a new session
  41. 41. Statement Routing/Cross-Shard Query •Client connection to the Coordinator (Catalog) Database is required • No sharding key necessary in the connect descriptor •Cross-shard SQL are executed via DB Link to Shards • Partition and Shard pruning
  42. 42. Optimizer Changes and Adaptive Query Optimization +
  43. 43. Adaptive Query Optimization Adaptive Query Optimization Adaptive Plans Adaptive Statistics At compile time At run timeJoin Methods Parallel distribution Methods
  44. 44. Adaptive Execution Plans (12.1) • Allows the Optimizer to make runtime adjustments to execution plans and to discover additional information that can lead to better statistics • Good SQL execution without intervention • Final plan decision is based on rows seen during execution • Bad effects of skew eliminated
  45. 45. Adaptive Execution Plans: Join Methods • Join method decision deferred until runtime • Default plan is computed using available statistics • Alternate sub-plans are pre-computed and stored in the cursor • Statistic collectors are inserted at key points in the plan • Final decision is based on statistics collected during execution • Possible sub-plans are nested loop joins or hash joins and vice versa
  46. 46. Displaying the Default Plan •Explain plan command always shows default plan •Example shows a nested loops join as default plan •No statistics collector shown in plan
  47. 47. Displaying the Final Plan • After the statement has completed use DBMS_XPLAN.DISPLAY_CURSOR to see the final plan selected • Example shows that hash join picked at execution time • Again the statistics collector is not visible in the plan
  48. 48. Displaying Plan With +adaptive & +report Formats • Additional information displayed on why operations are inactive can be seen with format parameter ‘+report’
  49. 49. Adaptive Execution Plans In V$SQL
  50. 50. Dynamic Statistics (12.1  •During compilation optimizer decides if statistics are sufficient to generate a good plan or not •Dynamic statistics are used to compensate for missing, stale, or incomplete statistics •They can be used for table scans, index access, joins and group by •One type of dynamic statistics is dynamic sampling
  51. 51. Dynamic Statistics • Dynamic sampling has a new level 11(AUTO) • Decision to use dynamic sampling depends on the complexity of predicate, existing statistics and total execution time • Dynamic statistics shared among queries
  52. 52. Adaptive Statistics/Statistics Feedback Re-optimization Pre 12c: • During execution optimizer estimates are compared to execution statistics • If statistics vary significantly then a new plan will be chosen for subsequent executions based on execution statistics • Re-optimization uses statistics gathered from previous executions Re-optimization in 12c • Join statistics are also monitored • Works with adaptive cursor sharing for statement with binds • New Column in V$SQL IS_REOPTIMIZABLE • Information found at execution time is persisted as SQL Plan Directives
  53. 53. Statistics Feedback
  54. 54. Re-optimization – indicator in V$SQL •New column in V$SQL: IS_REOPTIMIZABLE •Indicates that the statement will be re-parsed on the next execution
  55. 55. More Optimizer Changes… •Adaptive Statistics/Statistics Feedback (12.1) •Concurrent Execution of UNION and UNION ALL Branches (12.1) •Cost-Based OR Expansion Transformation (12.2) •Enhanced Join Elimination (12.2) •Approximate Query Processing (12.1 + 12.2)
  56. 56. Statistics +
  57. 57. Histograms • Histograms tell the Optimizer about the data distribution in a Column for better cardinality estimations • Default create histogram on any column that has been used in the WHERE clause or GROUP BY of a statement AND has a data skew • Oracle 12c changes histograms methods: • Top-Frequency (new) • Height balanced (obsolete) • Hybrid (new)
  58. 58. Histograms: Top Frequency • Traditionally a frequency histogram is only created if NDV < 254 • But if a small number of values occupies most of the rows (>99% rows), creating a frequency histograms on that small set of values is very useful even though NDV is greater than 254 • Ignores the unpopular values to create a better quality histogram for popular values • Built using the same technique used for frequency histograms • Only created with AUTO_SAMPLE_SIZE
  59. 59. Top Frequency Histogram Example • Table PRODUCT_SALES contains information on Christmas ornament sales • It has 1.78 million rows • There are 620 distinct TIME_IDs • But 99.9% of the rows have less than 254 distinct TIME_IDs TIME_ID column perfect candidate for top-frequency histogram
  60. 60. Height Balanced Histograms (obsolete) •A height balanced histogram is created if the number of distinct values in a column (NDV) is greater than 254 values. This is now obsolete. Height balanced histogram
  61. 61. Hybrid Histograms •Hybrid histogram is created if the number of distinct values in a column (NDV) is greater than 254 values but uses actual frequencies of bucket endpoints Hybrid histogram
  62. 62. Hybrid Histograms • Similar to height balanced histogram as created if the NDV >254 • Store the actual frequencies of bucket endpoints in histograms • No values are allowed to spill over multiple buckets • More endpoint values can be squeezed in a histogram • Achieves the same effect as increasing the # of buckets • Only created with AUTO_SAMPLE_SIZE
  63. 63. Height-balanced versus Hybrid Histogram Oracle Database 11g Oracle Database 12c
  64. 64. Session Private Statistics for GTT’s • GTT’s had only one set of statistics that were shared among all sessions even though the table could contain different data in different sessions • Starting Oracle 12c, GTT’s now have session private statistics, which is a different set of statistics for each session • Queries against GTT use statistics from their own session • Improves the performance and manageability of GTT’s • Reduces the possibility of errors in the cardinality estimates for GTT’s and ensures that the optimizer has the data to generate optimal execution plans
  65. 65. Online Statistics Gathering for Bulk Loads • Table statistics are gathered automatically during bulk loads: • CREATE TABLE AS SELECT • INSERT INTO … SELECT • Improved performance: avoids an additional table scan to gather table statistics • Improved manageability: no user intervention is required to gather statistics after a bulk load • To disable use hint: NO_GATHER_OPTIMIZER_STATISTICS
  66. 66. Optimizer Statistics Advisor (12.2) • Optimizer Statistics Advisor is built-in diagnostic software that analyzes the quality of statistics and statistics-related tasks
  67. 67. Optimizer Statistics Advisor (12.2) •The advisor automatically diagnoses problems in the existing practices for gathering statistics •The advisor does not gather a new or alternative set of optimizer statistics •The output of the advisor is a report of findings and recommendations
  68. 68. What Can Go Wrong With Statistic Gathering? •Legacy scripts may not keep pace with new best practices, which can change from release to release •Resources are wasted on unnecessary statistics gathering •Statistics can sometimes be missing, stale, or incorrect •Automatic statistics gathering jobs do not guarantee accurate and up-to-date statistics
  69. 69. Optimizer Statistics Advisor: Output Example ---------------------------------------------------------------------------------------------------- GENERAL INFORMATION ------------------------------------------------------------------------------- Task Name : MY_TASK Execution Name : EXEC_52 Created : 12-07-16 11:31:40 Last Modified : 12-07-16 11:32:37 ------------------------------------------------------------------------------- SUMMARY ------------------------------------------------------------------------------- For execution EXEC_52 of task MY_TASK, the Statistics Advisor has 6 finding(s). The findings are related to the following rules: USECONCURRENT, AVOIDSETPROCEDURES, USEDEFAULTPARAMS, USEGATHERSCHEMASTATS, AVOIDSTALESTATS, UNLOCKNONVOLATILETABLE. Please refer to the finding section for detailed information. ------------------------------------------------------------------------------- FINDINGS ------------------------------------------------------------------------------- ...
  70. 70. Optimizer Statistics Advisor: Output Example (2) ------------------------------------------------------------------------------- FINDINGS ------------------------------------------------------------------------------- Rule Name: UseConcurrent Rule Description: Use Concurrent preference for Statistics Collection Finding: The CONCURRENT preference is not used. Recommendation: Set the CONCURRENT preference. Example: dbms_stats.set_global_prefs('CONCURRENT', 'ALL'); Rationale: The system's condition satisfies the use of concurrent statistics gathering. Using CONCURRENT increases the efficiency of statistics gathering. ---------------------------------------------------- ...
  71. 71. More Statistics Features •Concurrent statistics gathering (12.1) •Automatic Column Group Detection for extended statistics (12.2) •Enhancements to Incremental Statistics •Enhancements to System Statistics •More…
  72. 72. Q&A
  73. 73. Summary •We talked about DBIM and the column store solution •We overviewed the new Sharding solution •We looked into new Optimizer and Statistics changes •12c has a lot to offer us, try it – use it! •12cR2 release date for on-prem usage: March 15, 2017 (March 1st for Exadata)
  74. 74. What Did We NOT Talk About •SQL Plan Management framework • Automatic Plan Evolution • Enhanced Auto Capture • Capture from AWR Repository •Indexing, Partitioning, and many other performance related new features…
  75. 75. Thank You Zohar Elkayam twitter: @realmgic