1 <Insert Picture Here> Oracle Enterprise Manager 12 c : ASH in 3D Deba Chatterjee and John Beresniewicz, Oracle Anton Topurov, CERN
2 Agenda The DB Time Data Cube What is DB Time? ASH fundamentals Populating the cube Studying Database Performance with ASH Analytics Exploring the performance dimensions over time Multi-dimensional analysis within a time period ASH Analytics in Action Case Studies Demo 2 Copyright 2011, Oracle and/or its affiliates. All rights reserved.
3 What is DB Time? What is ASH? How are ASH and DB Time related? 3 Copyright 2011, Oracle and/or its affiliates. All rights reserved.
4 4 Copyright 2011, Oracle and/or its affiliates. All rights reserved. DB Time Overview
5 Database Time (DB Time) Total time spent inside database calls by active foreground sessions Includes CPU time, IO time and nonidle wait time Fundamental measure of Oracle performance throughput Database Time is total time spent by user processes either actively working or actively waiting in a database call. 5 Copyright 2011, Oracle and/or its affiliates. All rights reserved.
6 Active Sessions Active In a database call Contributing to DB Time Average Activity of a Session Active Time Elapsed Time Average Active Sessions Total of average activity across all sessions Total DB Time Elapsed Time 6 Copyright 2011, Oracle and/or its affiliates. All rights reserved.
7 Average Active Sessions How many fully active sessions required to generate observed DB Time? Fundamental database performance metric Proportional to load on the database Responds directly to performance problems Time-normalized DB Time and thus comparable Across systems Across time periods 7 Copyright 2011, Oracle and/or its affiliates. All rights reserved.
8 <Insert Picture Here> ASH: The DB Time Data Cube 8 Copyright 2011, Oracle and/or its affiliates. All rights reserved.
9 Active Session History Rolling buffer Statistics ASH V$SESSION SGA MMON MMNL Recent history V$ACTIVE_SESSION_HISTORY AWR snapshots ASH captures ACTIVE SESSION information every second 9 Copyright 2011, Oracle and/or its affiliates. All rights reserved.
10 Active Session History (ASH) All Active sessions captured every second Foregrounds and backgrounds are sampled Active foregrounds contribute to DB Time In-memory: V$ACTIVE_SESSION_HISTORY Sampling interval = 1 second On-disk: DBA_HIST_ACTIVE_SESS_HISTORY Sampling interval = 10 seconds ASH is a system-wide record of database activity A FACT table with multiple dimensions that help diagnose performance issues 10 Copyright 2011, Oracle and/or its affiliates. All rights reserved.
11 ASH and DB Time Active sessions contribute to DB Time ASH samples active sessions ASH Math = estimate DB Time by counting ASH samples COUNT of ASH Samples = Total DB Time in seconds for that time interval Group by over 70+ performance dimensions 11 Copyright 2011, Oracle and/or its affiliates. All rights reserved.
12 Dimensions in ASH Logically groups them under 5 categories Enables DB Time analysis over many dimensions SQL Session Attributes ASH PL/SQL Session Identifiers Resource Consumption 12 Copyright 2011, Oracle and/or its affiliates. All rights reserved.
13 Drilling into Logical Dimensions SQL SQL PL/SQL Resources Identifiers Attributes SQL ID PL/SQL Wait Class Instance Cons. Group Session Attributes ASH PL/SQL Plan Hash Top Level PL/SQL Wait Event Services Module Operation Object User Session Action Session Identifiers Resource Usage OpCode Blocking Session Parallel Process Client Program Trans. ID Session Type 13 Copyright 2011, Oracle and/or its affiliates. All rights reserved.
14 Analytic Operations Performed on Data Cubes Operation Rollup Drilldown Slice Dice Pivot Description Performs aggregation on a data cube mainly by dimension reduction Is the reverse of rollup. Navigates from less detailed data to more detailed data on a dimension hierarchy Performs a selection on one dimension of the data cube resulting in a sub cube Defines a sub-cube by performing a selection of two or more dimension Visualization operation that rotates the data axes to provide alternate presentation 14 Copyright 2011, Oracle and/or its affiliates. All rights reserved.
15 MODULE Key Analytics Operations DICE Dice on Service and Module E.g. SELECT COUNT(*)FROM ASH WHERE SERVICE = :FOO AND MODULE = :BAR Rollup on Module: SELECT COUNT(*), MODULE FROM ASH GROUP BY ROLLUP(MODULE) ROLLUP ASH DB Time Cube Slice by Service SELECT COUNT(*) FROM ASH WHERE Service = :FOO SLICE SERVICE Drilldown on MODULE SELECT COUNT(*), ACTION FROM ASH WHERE MODULE = :BAR GROUP BY ACTION DRILLDOWN 15 Copyright 2011, Oracle and/or its affiliates. All rights reserved.
16 <Insert Picture Here> ASH Analytics 16 Copyright 2011, Oracle and/or its affiliates. All rights reserved.
17 Top Activity Page Predecessor to ASH Analytics Key Facts Entirely sourced from ASH Multi-dimensional Use case: problem detection, isolation, triage Method: skew analysis Dimensions visible Time Wait Class SQL Sessions Services Modules 17 Copyright 2011, Oracle and/or its affiliates. All rights reserved.
18 Top Activity Page...Where could we improve? Flexibility Can t switch dimensions on area chart Top left list is fixed to Top SQL; right table only has few dimensions No offline analysis Utilizing the full value Some key dimensions omitted Fixed width slider 5 min real-time, 30 min historical Visualization Visualization limited to time and one other dimension Drilldown always sends you to a new page 18 Copyright 2011, Oracle and/or its affiliates. All rights reserved.
19 ASH Analytics Flexible Time Picker Flexible Activity Chart Flexible Top Chart Flexible Top Chart 19 Copyright 2011, Oracle and/or its affiliates. All rights reserved.
20 Rollup the ASH Cube Rolled up ASH data in 3 broad wait class categories Green for CPU Blue for I/O Orange for OTHER Waits The default view shows 1 hour of ASH data 5 minute default slider width. 20 Copyright 2011, Oracle and/or its affiliates. All rights reserved.
21 Drilldown Drilldown on the wait class dimension for the selected time period All consumed Wait classes displayed. Analysis pane that allows to slice and dice the ASH data 21 Copyright 2011, Oracle and/or its affiliates. All rights reserved.
22 Case Study # 1: Slow response time due to high I/O Wait Class Add Filter Wait Event Sliced the data on User I/O Drilling down to Top SQL using the Wait Event dimension Top SQL 22 Copyright 2011, Oracle and/or its affiliates. All rights reserved.
23 Case Study # 2: What is eating my DB Time? Service Add Filter Module Slicing the data on services Drilldown using the dimension hierarchy module and Top SQL Top SQL 23 Copyright 2011, Oracle and/or its affiliates. All rights reserved.
24 Enterprise Manager at CERN Anton Topurov IT Department
25 The largest particle physics lab in the world Annual budget in M CHF (1300 M USD) External funding for experiments People 2415 Staff 730 Fellows and associates 200 Students 9133 Users 2000 External Firm Twenty Member States Austria, Belgium, Bulgaria, Czech Republic, Denmark, Finland, France, Germany, Greece, Italy, Hungary, Netherlands, Norway, Poland,Portugal, Slovakia, Spain, Sweden, Switzerland, United Kingdom Eight Observer States European Commission, USA, Russian Federation, India, Israel, Japan, Turkey, UNESCO 25
26 The largest particle accelerators 17 miles (27km) long tunnel Thousands of superconducting magnets Ultra vacuum: 10x emptier than on the Moon Coldest place in the Universe: -271 C
28 Enterprise Manager Cloud Control at CERN Beta testing at Oracle HQ and at CERN Several tracks and functionalities Performance tuning of specific importance for our DBAs for the developers 28
29 Case Study: Slow backup times on VMs Wait Class Add Filter Wait Event Sliced the data on RMAN I/O Drilling down to corresponding wait event and histograms Slow network speed due to MTU 1500 on OVM Histogram 29
30 Advantages of EM CC 12 for performance tuning Interactiveness Lots of dimensions to choose from Flexibility Fine-grained sliders Active Reports Can involve developers without access to EM system 30
31 Multidimensional Visual Analytics: Treemaps Space-efficient visualization of hierarchical (tree) structure Branches are rectangles, sub-branches are nested rectangles Scales well to hundreds or even thousands of elements ASH dimensions provide many possible hierarchies 31 Copyright 2011, Oracle and/or its affiliates. All rights reserved.
32 ASH Analytics: Removing the time dimension Wait Class Wait Event Default Treemap View displays the Wait class Wait Event hierarchy for the selected time period Size of each rectangle corresponds to the number of samples collected for each wait event 32 Copyright 2011, Oracle and/or its affiliates. All rights reserved.
33 Treemap: Multi-Dimensional View Module Service SQL ID Action ASH Wait Class Wait Event Object SQL ID Session Several pre-defined 2D & 3D hierarchies to view the data cube Ability to create custom hierarchy in advanced mode 33 Copyright 2011, Oracle and/or its affiliates. All rights reserved.
34 Case Study # 4: Analyzing the impact of the Enterprise Manager Agent Informative picture of where the measured time is being spent by metric SQL collection on this instance 34 Copyright 2011, Oracle and/or its affiliates. All rights reserved.
35 Advantages of ASH Analytics SQL ID Wait Class Wait Event Changeable dimensions everywhere Many new dimensions added (e.g. SQL type, client machine, etc) User Session ASH Instance Filters Stay on same page while adding context Action Service Treemap view Effective top-list visualization Module Better time picker Fully changeable slider can select any time period Active Report Will replace ASH Report eventually 35 Copyright 2011, Oracle and/or its affiliates. All rights reserved.
36 Copyright 2011, Oracle and/or its affiliates. All rights reserved. DEMO
37 Copyright 2011, Oracle and/or its affiliates. All rights reserved. Questions
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