Best Practices – Extreme Performance with Data Warehousing on Oracle Database
3 likes2,915 views
The document discusses best practices for data warehousing performance on Oracle Database. It covers using Oracle Exadata Database Machine for mixed workloads including data warehousing. Key strategies discussed are partitioning tables for pruning and parallelism, using hybrid columnar compression for storage savings and faster scans, and enabling auto parallelism and queuing for optimal parallel query processing.
Ad
More Related Content
What's hot (20)
Similar to Best Practices – Extreme Performance with Data Warehousing on Oracle Database (20)
Ad
More from Edgar Alejandro Villegas (20)
Ad
Recently uploaded (20)
Best Practices – Extreme Performance with Data Warehousing on Oracle Database
- 1. <Insert Picture Here> Best Practices – Extreme Performance with Data Warehousing on Oracle Database Rekha Balwada, Principal Product Manager Levi Norman, Product Marketing Director
- 2. 2 Agenda • Oracle Exadata Database Machine • The Three Ps of Data Warehousing • Power • Partitioning • Parallel • Workload Management on a Data Warehouse • Data Loading
- 3. 3 Oracle Exadata Database Machine Best Machine For… Mixed Workloads • Warehousing • OLTP • DB Consolidation All Tiers • Disk • Flash • Memory DB Consolidation Lower Costs Increase Utilization Reduce Management Tier Unification Cost of Disk IOs of Flash Speed of DRAM
- 4. 4 Oracle Exadata Standardized and Simple to Deploy • All Database Machines Are The Same • Delivered Ready-to-Run • Thoroughly Tested • Highly Supportable • No Unique Config Issues • Identical to Config used by Oracle Engineering • Runs Existing OLTP and DW Applications • 30 Years of Oracle DB Capabilities • No Exadata Certification Required • Leverages Oracle Ecosystem • Skills, Knowledge Deploy in Days, Base, People, & Partners Not Months
- 5. 5 Oracle Exadata Innovation Exadata Storage Server Software Intelligent Storage • Smart Scan query offload • Scale-out storage + + + Hybrid Columnar Compression • 10x compression for warehouses • 15x compression for archives Compressed primary standby test dev’t backup Uncompressed Smart Flash Cache • Accelerates random I/O up to 30x • Doubles data scan rate Data remains compressed for scans and in Flash Benefits Multiply
- 6. 6 Exadata in the Marketplace Rapid Adoption In All Geographies and Industries
- 7. 7 Best Practices for Data Warehousing 3 Ps - Power, Partitioning, Parallelism • Power - A Balanced Hardware Configuration • Weakest link defines throughput • Partition larger tables or fact tables • Facilitates data load, data elimination & join performance • Enables easier Information Lifecycle Management • Parallel Execution should be used • Instead of one process doing all the work, multiple processes work concurrently on smaller units • Parallel degree should be power of 2 Goal – Minimize amount of data accessed & use the most efficient joins
- 8. 8 Disk Array 1 Disk Array 2 Disk Array 3 Disk Array 4 Disk Array 5 Disk Array 6 Disk Array 7 Disk Array 8 FC-Switch1 FC-Switch2 HBA1 HBA2 HBA1 HBA2 HBA1 HBA2 HBA1 HBA2 Balanced Configuration “The Weakest Link” Defines Throughput CPU Quantity and Speed dictate number of HBAs capacity of interconnect HBA Quantity and Speed dictate number of Disk Controllers Speed and quantity of switches Controllers Quantity and Speed dictate number of Disks Speed and quantity of switches Disk Quantity and Speed
- 9. 9 • 14 High Performance low-cost storage servers Oracle Exadata Database Machine Hardware Architecture Database Grid Intelligent Storage Grid InfiniBand Network • Redundant 40Gb/s switches • Unified server & storage network • 8 Dual-processor x64 database servers • 2 Eight-processor x64 database servers Scaleable Grid of Compute and Storage servers Eliminates long-standing tradeoff between Scalability, Availability, Cost • 100 TB High Performance disk or 504 TB High Capacity disk • 5.3 TB PCI Flash •Data mirrored across storage servers
- 10. 10 Partitioning • First level of partitioning • Goal: enable partitioning pruning/simplify data management • Most typical range or interval partitioning on date column • How do you decide to use first level? • Second level of partitioning • Goal: multi-level pruning/improve join performance • Most typical hash or list • How do you decide to use second level?
- 11. 11 Sales Table SALES_Q3_1998 SELECT sum(s.amount_sold) FROM sales s WHERE s.time_id BETWEEN to_date(’01-JAN-1999’,’DD-MON-YYYY’) AND to_date(’31-DEC-1999’,’DD-MON-YYYY’); Q: What was the total sales for the year 1999? Partition Pruning SALES_Q4_1998 SALES_Q1_1999 SALES_Q2_1999 SALES_Q3_1999 SALES_Q4_1999 SALES_Q1_2000 Only the 4 relevant partitions are accessed
- 12. 12 Monitoring Partition Pruning Static Pruning Sample plan Only 4 partitions are touched – 9, 10, 11, & 12 SALES_Q1_1999, SALES_Q2_1999, SALES_Q3_1999, SALES_Q4_1999
- 13. 13 Monitoring Partition Pruning Static Pruning • Simple Query : SELECT COUNT(*) FROM RHP_TAB WHERE CUST_ID = 9255 AND TIME_ID = „2008-01-01‟; • Why do we see so many numbers in the Pstart / Pstop columns for such a simple query?
- 14. 14 Numbering of Partitions • An execution plan show partition numbers for static pruning • Partition numbers used can be relative and/or absolute 14 Table Partition 1 Partition 5 Partition 10 Sub-part 1 Sub-part 2 Sub-part 1 Sub-part 2 Sub-part 1 Sub-part 2 : : 1 2 9 10 19 20
- 15. 15 Monitoring Partition Pruning Static Pruning • Simple Query : SELECT COUNT(*) FROM RHP_TAB WHERE CUST_ID = 9255 AND TIME_ID = „2008-01-01‟; • Why do we see so many numbers in the Pstart / Pstop columns for such a simple query? Overall partition # range partition # Sub-partition #
- 16. 16 • Advanced pruning mechanism for complex queries • Recursive statement evaluates the relevant partitions • Look for word „KEY‟ in PSTART/PSTOP columns in the plan SELECT sum(amount_sold) FROM sales s, times t WHERE t.time_id = s.time_id AND t.calendar_month_desc IN (‘MAR-04’,‘APR-04’,‘MAY-04’); Sales Table May 2004 June 2004 Jul 2004 Jan 2004 Feb 2004 Mar 2004 Apr 2004 Times Table Monitoring Partition Pruning Dynamic Partition Pruning
- 17. 17 Sample explain plan output Monitoring Partition Pruning Dynamic Partition Pruning Sample Plan
- 18. 18 SELECT sum(amount_sold) FROM sales s, customer c WHERE s.cust_id=c.cust_id; Both tables have the same degree of parallelism and are partitioned the same way on the join column (cust_id) Sales Range partition May 18th 2008 Customer Hash Partitioned Sub part 1 A large join is divided into multiple smaller joins, each joins a pair of partitions in parallel Part 1 Sub part 2 Sub part 3 Sub part 4 Part 2 Part 3 Part 4 Sub part 2 Sub part 3 Sub part 4 Sub part 1 Part 1 Part 2 Part 3 Part 4 Partition Wise Join
- 19. 19 Monitoring of Partition-Wise Join Partition Hash All above the join method Indicates it’s a partition-wise join
- 20. 20 Hybrid Columnar Compression Featured in Exadata V2 Warehouse Compression • 10x average storage savings • 10x reduction in Scan IO Archive Compression • 15x average storage savings – Up to 70x on some data • For cold or historical data Optimized for Speed Optimized for Space Smaller Warehouse Faster Performance Reclaim 93% of Disks Keep Data Online Can mix OLTP and hybrid columnar compression by partition for ILM
- 21. 21 Hybrid Columnar Compression • Hybrid Columnar Compressed Tables • New approach to compressed table storage • Useful for data that is bulk loaded and queried • Light update activity • How it Works • Tables are organized into Compression Units (CUs) • CUs larger than database blocks • ~ 32K • Within Compression Unit, data organized by column instead of row • Column organization brings similar values close together, enhancing compression Compression Unit 10x to 15x Reduction
- 22. 22 Warehouse Compression Built on Hybrid Columnar Compression • 10x average storage savings • 100 TB Database compresses to 10 TB • Reclaim 90 TB of disk space • Space for 9 more „100 TB‟ databases • 10x average scan improvement – 1,000 IOPS reduced to 100 IOPS 100 TB 10 TB
- 23. 23 Archive Compression Built on Hybrid Columnar Compression • Compression algorithm optimized for max storage savings • Benefits any application with data retention requirements • Best approach for ILM and data archival • Minimum storage footprint • No need to move data to tape or less expensive disks • Data is always online and always accessible • Run queries against historical data (without recovering from tape) • Update historical data • Supports schema evolution (add/drop columns)
- 24. 24 Archive Compression ILM and Data Archiving Strategies • OLTP Applications • Table Partitioning • Heavily accessed data • Partitions using OLTP Table Compression • Cold or historical data • Partitions using Online Archival Compression • Data Warehouses • Table Partitioning • Heavily accessed data • Partitions using Warehouse Compression • Cold or historical data • Partitions using Online Archival Compression
- 25. 25 25 Hybrid Columnar Compression Customer Success Stories • Data Warehouse Customers (Warehouse Compression) • Top Financial Services 1: 11x • Top Financial Services 2: 24x • Top Financial Services 3: 18x • Top Telco 1: 8x • Top Telco 2: 14x • Top Telco 3: 6x • Scientific Data Customer (Archive Compression) • Top R&D customer (with PBs of data): 28x • OLTP Archive Customer (Archive Compression) • SAP R/3 Application, Top Global Retailer: 28x • Oracle E-Business Suite, Oracle Corp.: 23x • Custom Call Center Application, Top Telco: 15x
- 26. 26 Incremental Global Statistics Sales Table May 22nd 2008 May 23rd 2008 May 18th 2008 May 19th 2008 May 20th 2008 May 21st 2008 Sysaux Tablespace 1. Partition level stats are gathered & synopsis created 2. Global stats generated by aggregating partition synopsis
- 27. 27 Incremental Global Statistics Cont‟d Sales Table May 22nd 2008 May 23rd 2008 May 24th 2008 May 18th 2008 May 19th 2008 May 20th 2008 May 21st 2008 Sysaux Tablespace 3. A new partition is added to the table & Data is Loaded 4. Gather partition statistics for new partition 5. Retrieve synopsis for each of the other partitions from Sysaux 6. Global stats generated by aggregating the original partition synopsis with the new one
- 28. 28 How Parallel Execution Works User connects to the database User Background process is spawned When user issues a parallel SQL statement the background process becomes the Query Coordinator QC gets parallel servers from global pool and distributes the work to them Parallel servers - individual sessions that perform work in parallel Allocated from a pool of globally available parallel server processes & assigned to a given operation Parallel servers communicate among themselves & the QC using messages that are passed via memory buffers in the shared pool
- 29. 29 Parallel Servers do majority of the work Monitoring Parallel Execution SELECT c.cust_last_name, s.time_id, s.amount_sold FROM sales s, customers c WHERE s.cust_id = c.cust_id; Query Coordinator
- 30. 30 Oracle Parallel Query Scanning a Table • Data is divided into Granules • Block range or partition • Each Parallel Server assigned one or more Granules • No two Parallel Servers ever contend for the same Granule • Granules assigned so that load is balanced across Parallel Servers • Dynamic Granules chosen by optimizer • Granule decision is visible in . . . execution plan Parallel server # 1 Parallel server # 2 Parallel server # 3
- 31. 31 Identifying Granules of Parallelism During Scans in the Plan
- 32. 32 Producers Consumers Query coordinator P1 P2 P3 P4 Hash join always begins with a scan of the smaller table. In this case that’s is the customer table. The 4 producers scan the customer table and send the resulting rows to the consumers P8 P7 P6 P5 SALES Table CUSTOMERS Table SELECT c.cust_last_name, s.time_id, s.amount_sold FROM sales s, customers c WHERE s.cust_id = c.cust_id; How Parallel Execution Works
- 33. 33 Producers Consumers Query coordinator P1 P2 P3 P4 Once the 4 producers finish scanning the customer table, they start to scan the Sales table and send the resulting rows to the consumers P8 P7 P6 P5 SALES Table CUSTOMERS Table SELECT c.cust_last_name, s.time_id, s.amount_sold FROM sales s, customers c WHERE s.cust_id = c.cust_id; How Parallel Execution Works
- 34. 34 Producers Consumers P1 P2 P3 P4 P8 P7 P6 P5 Once the consumers receive the rows from the sales table they begin to do the join. Once completed they return the results to the QC Query coordinator SALES Table CUSTOMERS Table SELECT c.cust_last_name, s.time_id, s.amount_sold FROM sales s, customers c WHERE s.cust_id = c.cust_id; How Parallel Execution Works
- 35. 35 SELECT c.cust_last_name, s.time_id, s.amount_sold FROM sales s, customers c WHERE s.cust_id = c.cust_id; Query Coordinator Producers CCoonnssuummeerrss Monitoring Parallel Execution
- 36. 36 SQL Monitoring Screens The green arrow indicates which line in the execution plan is currently being worked on Click on parallel tab to get more info on PQ
- 37. 37 SQL Monitoring Screens By clicking on the + tab you can get more detail about what each individual parallel server is doing. You want to check each slave is doing an equal amount of work
- 38. 38 Best Practices for Using Parallel Execution Current Issues • Difficult to determine ideal DOP for each table without manual tuning • One DOP does not fit all queries touching an object • Not enough PX server processes can result in statement running serial • Too many PX server processes can thrash the system • Only uses IO resources Solution • Oracle automatically decides if a statement 1. Executes in parallel or not and what DOP it will use 2. Can execute immediately or will be queued 3. Will take advantage of aggregated cluster memory or not
- 39. 39 Auto Degree of Parallelism Enhancement addressing: • Difficult to determine ideal DOP for each table without manual tuning • One DOP does not fit all queries touching an object SQL statement Statement is hard parsed And optimizer determines the execution plan Statement executes in parallel Actual DOP = MIN(PARALLEL_DEGREE_LIMIT, ideal DOP) Statement executes serially If estimated time less than threshold* Optimizer determines ideal DOP based on all scan operations If estimated time greater than threshold* NOTE: Threshold set in parallel_min_time_threshold (default = 10s)
- 40. 40 SQL statements Statement is parsed and oracle automatically determines DOP If enough parallel servers available execute immediately If not enough parallel servers available queue the statement 64 32 16 128 8 FIFO Queue When the required number of parallel servers become available the first stmt on the queue is dequeued and executed 128 64 32 16 Parallel Statement Queuing Enhancement addressing: • Not enough PX server processes can result in statement running serial • Too many PX server processes can thrash the system NOTE: Parallel_Servers_Target new parameter controls number of active PX processes before statement queuing kicks in
- 41. 41 Efficient Data Loading • Full usage of SQL capabilities directly on the data • Automatic use of parallel capabilities • No need to stage the data again
- 42. 42 Pre-Processing in an External Table • New functionality in 11.1.0.7 and 10.2.0.5 • Allows flat files to be processed automatically during load – Decompression of large file zipped files • Pre-processing doesn‟t support automatic granulation – Need to supply multiple data files - number of files will determine DOP • Need to GRANT READ, EXECUTE privileges directories CREATE TABLE sales_external (…) ORGANIZATION EXTERNAL ( TYPE ORACLE_LOADER DEFAULT DIRECTORY data_dir1 ACCESS PARAMETERS (RECORDS DELIMITED BY NEWLINE PREPROCESSOR exec_dir: ‘zcat' FIELDS TERMINATED BY '|' ) LOCATION (…) );
- 43. 43 Direct Path Load • Data is written directly to database storage using multiple blocks per I/O request using asynchronous writes • A CTAS command always uses direct path but an IAS needs an APPEND hint Insert /*+ APPEND */ into Sales partition(p2) Select * From ext_tab_for_sales_data; • Ensure you do direct path loads in parallel • Specify parallel degree either with hint or on both tables • Enable parallel DML by issuing alter session command ALTER SESSION ENABLE PARALLEL DML;
- 44. 44 Data Loading Best Practices • Never locate the staging data files on the same disks as the RDBMS • DBFS on a Database Machine is an exception • The number of files determine the maximum DOP • Always true when pre-processing is used • Ensure proper space management • Use bigfile ASSM tablespace • Auto allocate extents preferred • Ensure sufficiently large data extents for the target • Set INITIAL and NEXT to 8 MB for non-partitioned tables • Use Parallelism – Manual (DOP) or Auto DOP • More on Data Loading best practices can found on OTN
- 45. 45 Sales Table May 22nd 2008 May 23rd 2008 May 24th 2008 May 18th 2008 May 19th 2008 May 20th 2008 May 21st 2008 DBA 1. Create external table for flat files 2. Use CTAS command to create non-partitioned table TMP_SALES Tmp_ sales Table 4. Alter table Sales exchange partition May_24_2008 with table tmp_sales Sales table now has all the data 3. Create indexes Tmp_ sales Table Partition Exchange Loading 5. Gather Statistics
- 46. 46 Summary Implement the three Ps of Data Warehousing • Power – Balanced hardware configuration • Make sure the system can deliver your SLA • Partitioning – Performance, Manageability, ILM • Make sure partition pruning and partition-wise joins occur • Parallel – Maximize the number of processes working • Make sure the system is not flooded using DOP limits & queuing
- 47. 47 Oracle Exadata Database Machine Additional Resources Exadata Online at www.oracle.com/exadata Exadata Best Practice Webcast Series On Demand Best Practices for Implementing a Data Warehouse on Oracle Exadata and Best Practices for Workload Management of a Data Warehouse on Oracle Exadata http://www.oracle.com/us/dm/sev100056475-wwmk11051130mpp016-1545274.html