EM12c: Capacity Planning with OEM Metrics
Description
!1 Today’s Presentation Capacity Planning with Enterprise Manager’s Metrics
!2 Maaz Anjum • Marietta, Georgia • Solutions Architect • EM12c • Golden Gate • Engineered Systems • Member of IOUG, GOUG, RMOUG RAC SIG, BIG DATA SIG EM SIG • Using Oracle products since 2001 Blog: maazanjum.com Email: maaz.anjum@biascorp.com Twitter: @maaz_anjum About Me
!3 ! • Overview • Background • Capacity Planning • Understanding EM Metrics • Using EM Metrics • Lessons • Conclusion Agenda
!4 What is EM12c? ! Did you know it… • Is Integrated with MOS • Can be used for Database and Middleware Provisioning • Can Monitor Engineered Systems – Exadata, Exalogic, Big Data Appliance • Can be used for Compliance tracking • Has a Chargeback and Consolidation Planner feature (spoiler alert!) • Can Manage the Cloud! • Is free to use!! Overview
!5 Systems
!6 Engineered Systems Exadata Exalogic ODA
!7 ! • Overview • Background • Capacity Planning • Engineered Systems • Understanding EM Metrics • Using EM Metrics • Lessons • Conclusion Agenda
!8 The Question • Acmes executive management team had a decision at hand of whether to expand their Exadata footprint going into a key business cycle. • In order to support their procurement decision they tasked the database management team with identify current capacity and resource utilization within the Exadata environment. • Being new to Exadata and a former mainframe shop, they looked to BIAS to help create reports and metrics from which to base this and future capacity planning decisions. Background
!9 ! • Overview • Background • Capacity Planning • Understanding EM Metrics • Using EM Metrics • Lessons • Conclusion Agenda
!10 Capacity Planning Capacity Planning Current Utilization Determine Sustainability Develop Strategy/ Purchase Implement Strategy Estimate Future Growth
!11 Capacity Planning • Determine your capacity planning goals • Collect data and metrics that map to those goals • Plot that data and make forecasts • Use those forecasts to enhance the capacity
!12 Resource Utilization • Is there monitoring enabled for all resources? • Does the monitoring tool store the collected data? • Is the data accessible? • Can reports be run against the data?
!13 • With so many metrics to chose from which ones were relevant? ! ! ! ! • Which target types? ! ! ! • How should the data be represented? Resource Utilization • CPU Utilization • Memory • Storage • IO • Cluster • Host • Database • BI Publisher is a free add-on to EM12c • Reports leverage EM12c Repository • Excel • Good old excel!
!14 ! • Overview • Background • Capacity Planning • Understanding EM Metrics • Using EM Metrics • Lessons • Conclusion Agenda
!15 Understand the“metrics” Metrics Data‘r
!16 Data in Enterprise Manager ! • EM12c Collects Metrics on intervals defined within a targets monitoring setup. • Data is collected via the Management Agents and stored in an Oracle Database Repository • Collected Data can be access via the OMS Console • Metrics are collected a raw data points • Then, aggregated over hourly, and daily • The more data points, the more accurate the aggregation Understand the“metrics”
!17 Understand the“metrics” em_metric_values_daily EM Repository • em_metric_value em_metric_values_hourly
!18 • Metric Tables – em_metric_values – em_metric_values_daily – em_metric_values_hourly • Metric Views – mgmt$metric_current – mgmt$metric_daily – mgmt$metric_hourly • or – gc$metric_values – gc$metric_values_daily – gc$metric_values_hourly Understand the“metrics” New in EM12c
!19 • Default retention for Repository Metric Tables – As per“12c Cloud Control Repository: How to Modify the Default Retention and Purging Policies for Metric Data? (Doc ID 1405036.1)” Understand the“metrics”
!20 • For better aggregation over longer time periods, increase the hourly aggregated data retention. Understand the“metrics” SQL> select table_name, partitions_retained from em_int_partitioned_tables where table_name in ('EM_METRIC_VALUES','EM_METRIC_VALUES_HOURLY','EM_METRIC_VALUES_DAILY'); ! TABLE_NAME PARTITIONS_RETAINED ———————————————————————————————————————————————————— EM_METRIC_VALUES 7 EM_METRIC_VALUES_HOURLY 32 EM_METRIC_VALUES_DAILY 12 SQL> execute gc_interval_partition_mgr.set_retention('SYSMAN', 'EM_METRIC_VALUES_HOURLY', 396); SQL> select table_name, partitions_retained from em_int_partitioned_tables where table_name in ('EM_METRIC_VALUES','EM_METRIC_VALUES_HOURLY','EM_METRIC_VALUES_DAILY'); ! TABLE_NAME PARTITIONS_RETAINED ———————————————————————————————————————————————————— EM_METRIC_VALUES 7 EM_METRIC_VALUES_HOURLY 396 EM_METRIC_VALUES_DAILY 12
!21 Understand the“metrics” ADF Business Components for Java Agent Application Deployment Automatic Storage Management Beacon CSA Collector Cluster Cluster ASM Cluster Database Clustered Application Deployment Database Instance Database System EM Servers System EM Service EMC CLARiiON System Email Driver Forms Generic Service Group Host Identity Management Internet Directory Listener Metadata Repository OC4J OMS Console OMS Platform OMS and Repository Oracle Access Management Cluster Oracle Access Management Server Oracle Application Server Oracle Database Exadata Storage Server System Oracle Database Machine Oracle Engineered System Cisco Switch Oracle Engineered System Healthchecks Oracle Engineered System ILOM Server Oracle Engineered System PDU Oracle Exadata Storage Server Oracle Exadata Storage Server Grid Oracle Fusion Middleware Farm Oracle HTTP Server Oracle High Availability Service Oracle Home Oracle Infiniband Network Oracle Infiniband Switch Oracle Internet Directory Oracle Management Service Oracle Reports Server Oracle SOA Infra Cluster Oracle Service Bus Oracle WebLogic Cluster Oracle WebLogic Domain Oracle WebLogic Server SOA Composite SOA Infrastructure SOA Partition Single Sign-On Single Sign-On Server User Messaging Service Web Cache • Data is collected per Target Type
!22 Where are the“metrics”
!23 Where are the“metrics” • For“any”target, navigate to its home page • Open the“Target Type”drop down • Go to Monitoring • Then“All Metrics”
!24 Where are the“metrics”
!25 Where are the“metrics” Metric Column Metric Groups “Target” Type
!26 Where are the“metrics” CPU Time (sec) Oracle Database Tablespaces DB file sequential read (%) Wait Time (sec) Average Active Sessions Full Index Scans (per second) Open Cursors (per second) Size Free Wait Bottlenecks Throughput
!27 Where are the“metrics” Exadata Metrics • Aggregated Exadata CellDisk Metric • Aggregated Exadata Capacity Metric • Aggregated Exadata Diskgroup Capacity Metric • Aggregated Exadata FlashDisk and HardDisk Metric • Cell Generated Alert • Exadata Cell Metric • Exadata CellDisk Metric • CellSrv Status Metric • Exadata Capacity Metric • Cell Configuration • Cell Configuration Patches • CELL CellDisk Configuration • CELL Flash Cache Cell Disks Configuration • CELL Flash Cache Configuration • CELL Grid Disk Clients Configuration • CELL Grid Disk Configuration • IORM Category Plan • CELL IORM Configuration • Exadata Inter-database Plan • CELL LUN Configuration • CELL LUN Physical Disks Configuration • Exadata Performance Metrics • CELL Physical Disk Configuration • CELL Physical Disk Luns Configuration • Exadata Flash Cache Metric • HCA Configuration • HCA Port Connections and Configuration • HCA Port Configuration Monitor • HCA_PortConnConfigHelper • HCA_PortConnections • HCA Port Errors • HCA Port State • HCA Port State (For Alerts) • Host Interconnect Statistics • Exadata IORM Consumer Group Metric • Exadata IORM DB Metric • IORM Plan Status Metric • Exadata CellDisk Load Imbalance • Response • Top CPU
!28 • Two ways to categorize • By“System or Cluster” ! ! ! ! ! ! ! • By“Line of Business” Categorize the“metrics” Host Host Host Cluster
!29 Categorize the“metrics” Exadata A Cluster B Exadata C Cluster D Cluster Host Server 1 Server 2 Server 3 Server 4 Server 1 Server 2 Server 3 Server 4 Server 1 Server 2 Server 3 Server 4 Server 1 Server 2 Server 3 Server 4
!30 • What about this“Line of Business”business? • Basically a Portfolio hierarchy • Loosely based on LDAP OU Configuration • Line of Business • Department • Application • Database/Database Service • Mapping Data was provided by Acme personnel Categorize the“metrics”
!31 Categorize the“metrics” Finance Marketing Sales Procurem ent Server 1 Server 2 Server 3 Server 4 EBS Pre-Sales Pro SalesForce ProcuPro MarketMax LOB Department Application Host Database Instance DB A: Inst 1 DB A: Inst 2 DB B: Inst 1 DB B: Inst 2 DB C: Inst 1 DB D: Inst 1 Accounts Payable Account Receivable Sales Procurem ent Marketing
!32 ! • Overview • Background • Capacity Planning • Understanding EM Metrics • Using EM Metrics • Lessons • Conclusion Agenda
!33 • After lengthy discussions with Acme’s Architects, four metrics identified in two categories • Host • CPU Utilization % • Memory Utilization • Storage Usage ! • Database • Database CPU Time • To measure the CPU Utilization at a database level for each line of business. ! • This presentation will focus on CPU Utilization only. The right“metrics”
!34 • We used gc$metric_values in the sysman schema • Columns of interest • Target Type • Metric Group Label • Metric Name • Description • Has to be enabled for every target • Requires Lifecycle Management Pack access. • It is used for Chargeback! The right“metrics”
!35 • Great. I know where the data is, but what does it look like? • Quite raw! col entity_type format a15 heading "Target Type" col entity_name format a25 heading "Target Name" col metric_group_label format a7 heading "Metric|Group|Label" col metric_column_name format a7 heading "Metric|Column|Name" col value format 99.99 heading "Value" ! select a.entity_type ,a.entity_name ,a.metric_group_label ,a.metric_column_name ,a.collection_time ,a.value from sysman.gc$metric_values a where entity_type = 'host' and a.metric_column_name = 'cpuUtil' and a.entity_name like 'shade%' order by collection_time; ! Metric Metric Group Column Target Type Target Name Label Name COLLECTION_TIME Value -‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐ -‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐ -‐-‐-‐-‐-‐-‐-‐ -‐-‐-‐-‐-‐-‐-‐ -‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐ -‐-‐-‐-‐-‐-‐ host blue.color.com Load cpuUtil 10-‐FEB-‐14 12.00.08 AM 7.93 host purple.color.com Load cpuUtil 10-‐FEB-‐14 12.02.26 AM 4.53 host red.color.com Load cpuUtil 10-‐FEB-‐14 12.03.58 AM 4.72 host green.color.com Load cpuUtil 10-‐FEB-‐14 12.04.08 AM 12.03 host blue.color.com Load cpuUtil 10-‐FEB-‐14 12.05.08 AM 20.81 host purple.color.com Load cpuUtil 10-‐FEB-‐14 12.07.26 AM 11.75 host red.color.com Load cpuUtil 10-‐FEB-‐14 12.08.58 AM 10.65 host green.color.com Load cpuUtil 10-‐FEB-‐14 12.09.08 AM 18.24 host blue.color.com Load cpuUtil 10-‐FEB-‐14 12.10.08 AM 20.76 host purple.color.com Load cpuUtil 10-‐FEB-‐14 12.12.26 AM 9.87 host red.color.com Load cpuUtil 10-‐FEB-‐14 12.13.58 AM 7.77 host green.color.com Load cpuUtil 10-‐FEB-‐14 12.14.08 AM 11.99 host blue.color.com Load cpuUtil 10-‐FEB-‐14 12.15.08 AM 14.35 host purple.color.com Load cpuUtil 10-‐FEB-‐14 12.17.26 AM 8.47 host red.color.com Load cpuUtil 10-‐FEB-‐14 12.18.58 AM 19.19 host green.color.com Load cpuUtil 10-‐FEB-‐14 12.19.08 AM 29.20 host blue.color.com Load cpuUtil 10-‐FEB-‐14 12.20.08 AM 43.13 host purple.color.com Load cpuUtil 10-‐FEB-‐14 12.22.26 AM 51.01 The right“metrics”
!36 Molding the data – Create a Base View from the metrics above mentioned above – Create Categorical views on top of the base view to further refine the data – Categorical Views leverage PIVOT and WITH clause Time Slice Per Target Time Slice Per Business Unit Base View per Metric The right“metrics”
!37 • Base Views • As mentioned in the table above, the metric_column_name value is the key. • Depending on the metric, simply change the value, and apply the transformation • Would contain data for a specific target type, for example host, database instance etc • Is a de-normalized data set The right“metrics”
!38 The right“metrics” col entity_type format a4 heading "Entity|Type" col host_name format a20 heading "Host|Name" col database_machine format a4 heading "DB|Machine" col metric_column_label format a19 heading "Metric|Column|Label" col metric_column_name format a8 heading "Metric|Column|Name" col metric_group_label format a6 heading "Metric|Group|Label" col year_quarter format a8 heading "Year|Quarter" col year_month format a8 heading "Year|Month" col year_month_day format a22 heading "Year|Month|Day" col avg_value format 990.00 heading "Per|Month|Max|CPU|Util%" col max_value format 990.00 heading "Per|Month|Avg|CPU|Util%" ! -‐-‐create or replace view v_cpuutil_base as with base as ( select entity_type ,substr(entity_name, 1, 4) as database_machine ,entity_name AS host_name ,metric_column_label ,metric_column_name ,metric_group_label ,collection_time ,to_char(collection_time,'yyyy') || '-‐Q' || to_char(collection_time,'q') as year_quarter ,extract(year from collection_time) ||'-‐' || ltrim(to_char(extract(month from collection_time),'09')) as year_month ,collection_time as year_month_day ,round(avg_value,2) as avg_value ,max_value from sysman.gc$metric_values_hourly where entity_type = 'host' and metric_column_name = 'cpuUtil' and metric_group_label = ‘Load') select * from base where database_machine = 'shade'; entity_type = 'host' and metric_column_name in ('usedLogicalMemoryPct','logicMemfreePct') and metric_group_label = ‘Load'; • Base Views
!39 Metric Metric Metric Enti DB Host Column Column Group Year Year Month CPU CPU Type Mach Name Label Name Label COLLECTION_TIME Quarter Month Day Util% Util% -‐-‐-‐-‐ -‐-‐-‐-‐ -‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐ -‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐ -‐-‐-‐-‐-‐-‐-‐-‐ -‐-‐-‐-‐-‐-‐ -‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐ -‐-‐-‐-‐-‐-‐-‐-‐ -‐-‐-‐-‐-‐-‐-‐-‐ -‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐ -‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐ -‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐ host shade blue.color.com CPU Utilization (%) cpuUtil Load 29-‐MAR-‐13 12.00.00 AM 2013-‐Q1 2013-‐03 29-‐MAR-‐13 12.00.00 AM 0.34 1.08 host shade blue.color.com CPU Utilization (%) cpuUtil Load 29-‐MAR-‐13 01.00.00 AM 2013-‐Q1 2013-‐03 29-‐MAR-‐13 01.00.00 AM 0.26 0.27 host shade blue.color.com CPU Utilization (%) cpuUtil Load 29-‐MAR-‐13 02.00.00 AM 2013-‐Q1 2013-‐03 29-‐MAR-‐13 02.00.00 AM 0.26 0.32 host shade blue.color.com CPU Utilization (%) cpuUtil Load 29-‐MAR-‐13 03.00.00 AM 2013-‐Q1 2013-‐03 29-‐MAR-‐13 03.00.00 AM 0.26 0.30 host shade blue.color.com CPU Utilization (%) cpuUtil Load 29-‐MAR-‐13 04.00.00 AM 2013-‐Q1 2013-‐03 29-‐MAR-‐13 04.00.00 AM 0.26 0.27 host shade blue.color.com CPU Utilization (%) cpuUtil Load 29-‐MAR-‐13 05.00.00 AM 2013-‐Q1 2013-‐03 29-‐MAR-‐13 05.00.00 AM 0.32 0.60 host shade blue.color.com CPU Utilization (%) cpuUtil Load 29-‐MAR-‐13 06.00.00 AM 2013-‐Q1 2013-‐03 29-‐MAR-‐13 06.00.00 AM 0.28 0.34 host shade blue.color.com CPU Utilization (%) cpuUtil Load 29-‐MAR-‐13 07.00.00 AM 2013-‐Q1 2013-‐03 29-‐MAR-‐13 07.00.00 AM 0.27 0.34 host shade blue.color.com CPU Utilization (%) cpuUtil Load 29-‐MAR-‐13 08.00.00 AM 2013-‐Q1 2013-‐03 29-‐MAR-‐13 08.00.00 AM 0.33 0.66 host shade blue.color.com CPU Utilization (%) cpuUtil Load 29-‐MAR-‐13 09.00.00 AM 2013-‐Q1 2013-‐03 29-‐MAR-‐13 09.00.00 AM 0.37 0.65 host shade blue.color.com CPU Utilization (%) cpuUtil Load 29-‐MAR-‐13 10.00.00 AM 2013-‐Q1 2013-‐03 29-‐MAR-‐13 10.00.00 AM 0.28 0.31 host shade blue.color.com CPU Utilization (%) cpuUtil Load 29-‐MAR-‐13 11.00.00 AM 2013-‐Q1 2013-‐03 29-‐MAR-‐13 11.00.00 AM 0.29 0.36 host shade blue.color.com CPU Utilization (%) cpuUtil Load 29-‐MAR-‐13 12.00.00 PM 2013-‐Q1 2013-‐03 29-‐MAR-‐13 12.00.00 PM 0.30 0.39 host shade blue.color.com CPU Utilization (%) cpuUtil Load 29-‐MAR-‐13 01.00.00 PM 2013-‐Q1 2013-‐03 29-‐MAR-‐13 01.00.00 PM 0.30 0.65 host shade blue.color.com CPU Utilization (%) cpuUtil Load 29-‐MAR-‐13 02.00.00 PM 2013-‐Q1 2013-‐03 29-‐MAR-‐13 02.00.00 PM 0.36 0.57 host shade blue.color.com CPU Utilization (%) cpuUtil Load 29-‐MAR-‐13 03.00.00 PM 2013-‐Q1 2013-‐03 29-‐MAR-‐13 03.00.00 PM 0.37 0.56 host shade blue.color.com CPU Utilization (%) cpuUtil Load 29-‐MAR-‐13 04.00.00 PM 2013-‐Q1 2013-‐03 29-‐MAR-‐13 04.00.00 PM 0.30 0.37 host shade blue.color.com CPU Utilization (%) cpuUtil Load 29-‐MAR-‐13 05.00.00 PM 2013-‐Q1 2013-‐03 29-‐MAR-‐13 05.00.00 PM 0.39 1.56 host shade blue.color.com CPU Utilization (%) cpuUtil Load 29-‐MAR-‐13 06.00.00 PM 2013-‐Q1 2013-‐03 29-‐MAR-‐13 06.00.00 PM 0.27 0.30 host shade blue.color.com CPU Utilization (%) cpuUtil Load 29-‐MAR-‐13 07.00.00 PM 2013-‐Q1 2013-‐03 29-‐MAR-‐13 07.00.00 PM 0.27 0.29 host shade blue.color.com CPU Utilization (%) cpuUtil Load 29-‐MAR-‐13 08.00.00 PM 2013-‐Q1 2013-‐03 29-‐MAR-‐13 08.00.00 PM 0.28 0.34 host shade blue.color.com CPU Utilization (%) cpuUtil Load 29-‐MAR-‐13 09.00.00 PM 2013-‐Q1 2013-‐03 29-‐MAR-‐13 09.00.00 PM 0.28 0.31 host shade blue.color.com CPU Utilization (%) cpuUtil Load 29-‐MAR-‐13 10.00.00 PM 2013-‐Q1 2013-‐03 29-‐MAR-‐13 10.00.00 PM 0.27 0.28 The right“metrics”
!40 The right“metrics” Metric Metric Metric Year Max Avg Enti DB Host Column Column Group Year Year Month Free Mem Free Mem Type Mach Name Label Name Label COLLECTION_TIME Quarter Month Day Util% Util% -‐-‐-‐-‐ -‐-‐-‐-‐ -‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐ -‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐ -‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐ -‐-‐-‐-‐-‐-‐ -‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐ -‐-‐-‐-‐-‐-‐-‐-‐ -‐-‐-‐-‐-‐-‐-‐-‐ -‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐ -‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐ -‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐ host shade blue.color.com Logical Free Memory (%) logicMemfreePct Load 29-‐MAR-‐13 12.00.00 AM 2013-‐Q1 2013-‐03 29-‐MAR-‐13 12.00.00 AM 12.88 14.00 host shade blue.color.com Logical Free Memory (%) logicMemfreePct Load 29-‐MAR-‐13 01.00.00 AM 2013-‐Q1 2013-‐03 29-‐MAR-‐13 01.00.00 AM 13.03 13.65 host shade blue.color.com Logical Free Memory (%) logicMemfreePct Load 29-‐MAR-‐13 02.00.00 AM 2013-‐Q1 2013-‐03 29-‐MAR-‐13 02.00.00 AM 13.63 14.01 host shade blue.color.com Logical Free Memory (%) logicMemfreePct Load 29-‐MAR-‐13 03.00.00 AM 2013-‐Q1 2013-‐03 29-‐MAR-‐13 03.00.00 AM 13.14 13.99 host shade blue.color.com Logical Free Memory (%) logicMemfreePct Load 29-‐MAR-‐13 04.00.00 AM 2013-‐Q1 2013-‐03 29-‐MAR-‐13 04.00.00 AM 13.43 13.66 host shade blue.color.com Logical Free Memory (%) logicMemfreePct Load 29-‐MAR-‐13 05.00.00 AM 2013-‐Q1 2013-‐03 29-‐MAR-‐13 05.00.00 AM 13.54 13.65 host shade blue.color.com Logical Free Memory (%) logicMemfreePct Load 29-‐MAR-‐13 06.00.00 AM 2013-‐Q1 2013-‐03 29-‐MAR-‐13 06.00.00 AM 13.40 13.66 host shade blue.color.com Logical Free Memory (%) logicMemfreePct Load 29-‐MAR-‐13 07.00.00 AM 2013-‐Q1 2013-‐03 29-‐MAR-‐13 07.00.00 AM 13.83 13.92 host shade blue.color.com Logical Free Memory (%) logicMemfreePct Load 29-‐MAR-‐13 08.00.00 AM 2013-‐Q1 2013-‐03 29-‐MAR-‐13 08.00.00 AM 13.80 13.91 host shade blue.color.com Logical Free Memory (%) logicMemfreePct Load 29-‐MAR-‐13 09.00.00 AM 2013-‐Q1 2013-‐03 29-‐MAR-‐13 09.00.00 AM 13.37 13.67 host shade blue.color.com Logical Free Memory (%) logicMemfreePct Load 29-‐MAR-‐13 10.00.00 AM 2013-‐Q1 2013-‐03 29-‐MAR-‐13 10.00.00 AM 11.86 13.03 host shade blue.color.com Logical Free Memory (%) logicMemfreePct Load 29-‐MAR-‐13 11.00.00 AM 2013-‐Q1 2013-‐03 29-‐MAR-‐13 11.00.00 AM 12.92 13.21 host shade blue.color.com Logical Free Memory (%) logicMemfreePct Load 29-‐MAR-‐13 12.00.00 PM 2013-‐Q1 2013-‐03 29-‐MAR-‐13 12.00.00 PM 13.00 13.57 host shade blue.color.com Logical Free Memory (%) logicMemfreePct Load 29-‐MAR-‐13 01.00.00 PM 2013-‐Q1 2013-‐03 29-‐MAR-‐13 01.00.00 PM 13.40 13.66 host shade blue.color.com Logical Free Memory (%) logicMemfreePct Load 29-‐MAR-‐13 02.00.00 PM 2013-‐Q1 2013-‐03 29-‐MAR-‐13 02.00.00 PM 13.28 13.70 host shade blue.color.com Logical Free Memory (%) logicMemfreePct Load 29-‐MAR-‐13 03.00.00 PM 2013-‐Q1 2013-‐03 29-‐MAR-‐13 03.00.00 PM 7.73 13.64 host shade blue.color.com Logical Free Memory (%) logicMemfreePct Load 29-‐MAR-‐13 04.00.00 PM 2013-‐Q1 2013-‐03 29-‐MAR-‐13 04.00.00 PM 7.77 8.01 host shade blue.color.com Logical Free Memory (%) logicMemfreePct Load 29-‐MAR-‐13 05.00.00 PM 2013-‐Q1 2013-‐03 29-‐MAR-‐13 05.00.00 PM 7.99 8.09 host shade blue.color.com Logical Free Memory (%) logicMemfreePct Load 29-‐MAR-‐13 06.00.00 PM 2013-‐Q1 2013-‐03 29-‐MAR-‐13 06.00.00 PM 7.86 8.18 host shade blue.color.com Logical Free Memory (%) logicMemfreePct Load 29-‐MAR-‐13 07.00.00 PM 2013-‐Q1 2013-‐03 29-‐MAR-‐13 07.00.00 PM 8.06 8.21 host shade blue.color.com Logical Free Memory (%) logicMemfreePct Load 29-‐MAR-‐13 08.00.00 PM 2013-‐Q1 2013-‐03 29-‐MAR-‐13 08.00.00 PM 10.22 13.32 host shade blue.color.com Logical Free Memory (%) logicMemfreePct Load 29-‐MAR-‐13 09.00.00 PM 2013-‐Q1 2013-‐03 29-‐MAR-‐13 09.00.00 PM 9.28 14.15 host shade blue.color.com Logical Free Memory (%) logicMemfreePct Load 29-‐MAR-‐13 10.00.00 PM 2013-‐Q1 2013-‐03 29-‐MAR-‐13 10.00.00 PM 13.39 13.81
!41 The right“metrics” • Cluster Views • Built using the“base”view, for example v_cpuutil_base • Use analytical functions for maximum, average, and 95th percentile col cluster_name format a4 heading "DB|Machine" col metric_column_label format a24 heading "Metric|Column|Label" col metric_column_name format a15 heading "Metric|Column|Name" col metric_group_label format a6 heading "Metric|Group|Label" col year_quarter format a8 heading "Year|Quarter" col per_q_dbm_max_cpuutil_pct format 990.00 heading "Max Per Quarter|CPU|Util%" col per_q_dbm_avg_cpuutil_pct format 990.00 heading "Avg Per Quarter|CPU|Util%" col per_q_dbm_max_95th_pct format 990.00 heading "95th Per Quarter|CPU|Util%” ! -‐-‐create or replace view v_cpuutil_cluster_per_quarter select distinct cluster_name ,metric_column_label ,metric_group_label ,year_quarter ,round(max(max_value) over (partition by cluster_name, year_quarter), 2) as per_q_dbm_max_cpuutil_pct ,round(percentile_cont(0.05) within group (order by max_value desc) over (partition by cluster_name, year_quarter), 2) as per_q_dbm_max_95th_pct ,round(avg(avg_value) over (partition by cluster_name, year_quarter), 2) as per_q_dbm_avg_cpuutil_pct from v_cpuutil_base where cluster_name = 'shade' order by year_quarter; Change these selected columns for quarter, month, day etc for cluster vs hosts views
!42 The right“metrics” Metric Metric Max Per Quarter 95th Per Quarter Avg Per Quarter DB Column Group Year CPU CPU CPU Mach Label Label Quarter Util% Util% Util% -‐-‐-‐-‐ -‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐ -‐-‐-‐-‐-‐-‐ -‐-‐-‐-‐-‐-‐-‐-‐ -‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐ -‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐ -‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐ shade CPU Utilization (%) Load 2013-‐Q1 95.32 90.05 24.17 shade CPU Utilization (%) Load 2013-‐Q2 99.89 84.24 22.42 shade CPU Utilization (%) Load 2013-‐Q3 99.83 96.89 32.45 shade CPU Utilization (%) Load 2013-‐Q4 99.83 87.13 31.27 shade CPU Utilization (%) Load 2014-‐Q1 99.04 81.48 30.54 • Cluster/Database Machine CPU Utilization Per Quarter • Cluster/Database Machine CPU Utilization Per Month Metric Metric Max Per Quarter 95th Per Quarter Avg Per Quarter DB Column Group Year CPU CPU CPU Mach Label Label Month Util% Util% Util% -‐-‐-‐-‐ -‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐ -‐-‐-‐-‐-‐-‐ -‐-‐-‐-‐-‐-‐-‐-‐ -‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐ -‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐ -‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐ shade CPU Utilization (%) Load 2013-‐03 95.32 90.05 24.17 shade CPU Utilization (%) Load 2013-‐04 99.89 85.97 19.33 shade CPU Utilization (%) Load 2013-‐05 99.02 80.99 22.39 shade CPU Utilization (%) Load 2013-‐06 99.65 84.51 25.53 shade CPU Utilization (%) Load 2013-‐07 99.83 98.07 32.84 shade CPU Utilization (%) Load 2013-‐08 99.82 97.25 31.95 shade CPU Utilization (%) Load 2013-‐09 99.67 87.84 32.57 shade CPU Utilization (%) Load 2013-‐10 99.83 91.13 35.64 shade CPU Utilization (%) Load 2013-‐11 96.15 85.08 29.54 shade CPU Utilization (%) Load 2013-‐12 94.68 74.73 28.38 shade CPU Utilization (%) Load 2014-‐01 94.46 79.88 28.31 shade CPU Utilization (%) Load 2014-‐02 99.04 83.22 31.94 shade CPU Utilization (%) Load 2014-‐03 97.66 80.45 32.89
!43 • But what about the Portfolio/Line of Business Views ! • Remember this from earlier? ! ! • We created customized mapping between the Database/ Database Services and their portfolio structure. • For example, Finance -> Accounts Payable -> AP_APP (Host/Database) -> RAC_SVC_AP_APP (RAC Service). • Why do I mention Database and Database Service • Database; To map storage to an Application • Database Service; To map db cpu time to an Application. How does an application connect to the database? The right“metrics”
!44 • But what about the Portfolio/Line of Business CREATE TABLE portfolio { line_of_business NOT NULL VARCHAR2(4000) ,department NOT NULL VARCHAR2(256) ,application NOT NULL VARCHAR2(256) ,host_name NOT NULL VARHCAR2(256) ,database_name NOT NULL VARCHAR2(256) ,service_name NOT NULL VARCHAR2(256) }; SQL> SELECT * FROM portfolio; line_of_business department application host_name database_name service_name -‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐ -‐-‐-‐-‐-‐-‐-‐-‐-‐-‐ -‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐ -‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐ -‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐ -‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐ Supply Chain Transformation Pricing blue.color.com RACDB03 RACDB03_PRC_01 Supply Chain Transformation Consolidation blue.color.com RACDB03 RACDB03_CSL_01 Supply Chain Transformation Transformation blue.col
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