arcgis for server performance and …maps.uky.edu/esri-uc/esri_uc_2k12/files/398.pdftechnical...
TRANSCRIPT
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Technical Workshops |
Esri International User Conference San Diego, California
ArcGIS for Server Performance and Scalability—Optimizing GIS Services
Andrea Rosso (Esri), Craig Williams (Esri), David Cordes (Esri), Eric Miller (Esri)
July 26, 2012
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Craig Williams
Optimizing map services
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Overview
• Types of map services - What’s new at 10.1?
• Factors of map service performance - Data access - Rendering speed - Image size/compression
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Map services
• 9.3.1 – 10.0 - MXD based map services - MSD based map services (optimized map service)
• Use the optimized map service for best quality and performance
- Analyzer workflow guides you through potential problems
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Map services at 10.1
• One unified map service - An updated optimized map service - Supports additional capabilities, data types, layers,
renderers
• New extension capabilities:
- Network Analysis - Geoprocessing*
Application
ArcGIS Server
Map Server
http
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Mapping capabilities
• Added data source support
- XY events - Linear referencing
events
• Added feature layer renderer support
- Dot density - Charts - Geostats
• Added support for
layers - Dimensions - Schematics - Network - Network Analysis - Tin - Terrain - Tracking
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Dynamic Layers: The Concept
• New behavior with the map service that allows for per-request changes to the map
• Optional capability of map services • May allow you to reduce the total number of services
you need
• Allows for: - Updating renderers and symbols - Removing and reordering layers - Changing layer data sources - Adding new layers from registered data sources
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Dynamic Layers: Use Cases
• Simple updates to the map service - Remove layers or reorder layers
• Thematic mapping - Updates to renderers
• Adding content from a data library - Find data from registered workspaces
- Including query layers - Add to the map on a per-request basis
Application
10.1 Map Service
REST
Workspaces
http json
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Factors of map service performance
• Data access • Rendering • Image compression / size
• Consider all of these when
creating a map service
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Data access
• Local data will draw faster than remote data • Spatial index
- Do you have one? (e.g. XY Events) - Is it sized correctly? - Universal features, slow all draws
• Attribute indexing - Not always needed
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Data access case study : X Y Events
• Often used in cases where data comes from external systems
• A draw typically requires a complete row scan
• Alternative - Use a native spatial type in your database
- -Query layers - Insert features via SQL in external systems - Features will be indexed and draw much faster
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Data access troubleshooting
• Publishing analyzers indicate lack of a spatial index etc.
• Evaluate index efficiency - The number of features returned for each draw query - Large index grid sizes lead to too many features being
drawn
• Evaluate I/O performance if using remote data
• Unnecessary attribute indexes
- May confuse query plans in some databases - Don’t index fields just because they exist in a def query
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Rendering speed
• Optimized map services were introduced at 9.3.1 to resolve performance bottlenecks at this stage
• Remaining areas to be concerned with: • Complex effects (e.g. geometric effects in
representations) • Inline annotation (aka “Bloated” annotation) • Anti-aliasing performance
- Higher levels use more RAM and are slower - Text anti-aliasing has a negligible effect in most cases
• Layer transparency
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Rendering speed: transparency
• Layer transparency is applied to a layer as a whole - Involves a full layer blend
• Alternative: Use color transparency - Capability of optimized map services - Enabled via an option from analyzer warning 10009
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Image compression / size
• Smaller images are faster to download
• Image formats have limitations - e.g. limited color palettes, lossy compression
• Image compression itself has a performance penalty - Use the preview window to evaluate performance of image
type
• Evaluate size and performance in a test service in network conditions
- Balance size vs. quality when choosing the image type based on your needs
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Image compression / size (con’t)
• Image type used for cached services affects: - Download size - Storage size of the cache
- Portability
• For caching vector data
- Consider new PNG image type at 10.1 - Chooses the correct PNG type (8, 24, 32) for each tile based
on content - Low content areas use less storage.
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Andrea Rosso
ArcGIS Online
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• Very Large Scale deployment • Multi-tenant
ArcGIS Online as a Case Study
Basemaps Portal Hosted Tiles Hosted Features
Geocoding
ArcGIS Online
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ArcGIS Online as a Case Study
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• The Face of ArcGIS Online - Must perform
• Javascript API Front End
- Dojo - REST Client
- ArcGIS Server APIs - Portal API - WMS - …
www.arcgis.com Web Site
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• Builds - Coalesce (build) Javascript Files into fewer files to reduce
downloads
• Compression - Make sure Web Server Gzip compression is enabled
• CDN + Versioning - Edge cache static files in a CDN (Akamai, CloudFront, etc.)
for faster performance around the world - Improvements of 200%+ in page load times - Version resources to enable you to update information
periodically. - /cdn/1916/js/dojo/dojo/dojo.js
Optimizing the Network
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ArcGIS Online Basemaps
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• Capability: Publish Map Services for use in all ArcGIS Clients
• Capacity: Support > 1,000 Concurrent Users
• Availability: Provide 24x7, 99.9% Availability
• Performance: Sub-second Response on Server & High-speed access by User
• Flexibility: Rapidly Deploy New and Updated Maps
Initial Basemap System Requirements
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ArcGIS Online Basemap Architecture
Users Web / ArcGIS
Servers
Data Appliances
Akamai
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ArcGIS Online Basemaps over time
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ArcGIS Online Basemap Architecture
Users Web / ArcGIS
Servers
Data Appliances
Akamai
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Eric Miller (Esri)
ArcGIS Server 10.1 Performance Improvements
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• ArcGIS Server is now fully 64 bit - No memory limit per service instance - Contributes to improved performance
• DCOM-free framework - Number of service instances no longer OS limited - Enables more scalability options and better performance not
limited by firewall considerations
• Moved most WebService handler processing to core
• More efficient core services
Source of 10.1 Performance Improvements
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• All services have improved performance
- Windows - Faster transaction times and greater throughput
- Linux
- Much faster transaction times and much greater throughput
All this despite security being enabled by default
• All services and map caching scale linearly
Performance/Scalability improvements
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Map Service Performance
57 37
28 33 27 27 12
65
40
73 62
40 43 44
0102030405060708090
100
Thro
ughp
ut %
D
Increasing Map Complexity ------------------>>>>
% Throughput Change: 10.1 vs. 10.0 SP2 (Map Service export)
Windows Linux
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Map Service Performance
93
3
48
133
70
31
0
20
40
60
80
100
120
140
Portland_Identify Portland_SpatialQuery Portland_AttributeQuery
Thro
ughp
ut %
D
Map Service Operation
% Throughput Change: 10.1 vs. 10.0 SP2 (Map Service Identify/Query)
Windows Linux
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Map Service Scalability (Windows)
0
100000
200000
300000
400000
500000
600000
1 2 3 4 5 6 7 8
Thro
ughp
ut (T
r/Hr)
Number of GIS Servers in Site
Map Service: Export
CORINE_exportStreetMap_export
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Geocode Service Performance
32 23 45 38 29
209
0
50
100
150
200
250
Thro
ughp
ut %
D
Geocode Service Operation
% Throughput Change: 10.1 vs. 10.0 SP2 (Geocode Service FindAddress/ReverseGeocode)
Windows Linux
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Geocode Service Scalability (Windows)
01000000200000030000004000000500000060000007000000
1 2 3 4 5 6Thro
ughp
ut (T
r/Hr)
Number of GIS Servers in Site
Geocode Service: Find Address Candidates (FAC), Reverse Geocode (RGC)
NAComposite_FACNAComposite_RGC
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Feature Service Performance
41 25 50
17
63
13
67
133 167
0 38
17 0
20406080
100120140160180
Thro
ughp
ut %
D
Feature Service Operation
% Throughput Change: 10.1 vs. 10.0 SP4: Feature Service Edits/Queries
PostGres (PG), Versioned (V), Nonversioned (NV), Archiving (A)
Windows Linux
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Geoprocessing Service Performance (Windows)
130
27 36 50
135
25
161
020406080
100120140160180
Thro
ughp
ut %
D
Geoprocessing Model
% Throughput Change: 10.1 vs. 10.0 SP2 (Geoprocessing Service)
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Geoprocessing Service Scalability (Windows)
0
20000
40000
60000
80000
100000
120000
1 2 3 4 5 6 7 8
Thro
ughp
ut (T
r/Hr)
Number of GIS Servers in Site
PopulationByZip Model
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Image Service Performance
59 40
69 31 49 60
92
35 58
125 92
49
118
4
69 97 111
75 45
182
020406080
100120140160180200
Thro
ughp
ut %
D
Data Source and Method
% Throughput Change: 10.1 vs. 10.0 SP2 (ImageService export/query)
Windows Linux
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Image Service Scalability (Windows)
0
100000
200000
300000
400000
500000
1 2 3 4 5 6 7 8
Thro
ughp
ut (T
r/Hr)
Number of GIS Servers in Site
MDSurdexTIFF: export
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WMS Service Performance
106
21 18 19 36
19 35 41
59
101 100
28 43
11
88
27 10
24 45
98
020406080
100120
Thro
ughp
ut %
D
Data Source and Service Type
% Throughput Change: 10.1 vs. 10.0 SP2 (WMS getMap from MapService and Image Service)
Windows Linux
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WMS Service Scalability (Windows)
0
100000
200000
300000
400000
500000
1 2 3 4 5 6 7 8
Thro
ughp
ut (T
r/H4)
Number of GIS Servers in Site
WMS Service: getMap
CORINE_MS_getMapStreetMap_MS_getMapMDSurdexTIFF_IS_getMap
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Geometry Service Performance
104 89 129 94 89 75 88 67 54 75
140
38 15
126
720
539
257
606
282
3
275
62
252
548
283
584 670
0100200300400500600700800
Thro
ughp
ut %
D
Method Name
10.1 % Change in Throughput vs. 10.0 SP2 (Geometry Service)
Windows Linux
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Cache Size and Completion Time (Windows)
66 58
196 176
114 112 100 93
0
50
100
150
200
250
Cac
he C
reat
ion
Tim
e (m
inut
es)
Map Name and Version
Creation Time (Windows): 1 node, 8 service instances
0
10000
20000
30000
40000
50000
60000
70000
80000
Cac
he S
ize
(MB
ytes
)
Cache Size (Windows)
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Cache Creation Scalability
0
5
10
15
20
25
30
35
1 2 3 4 5 6 7 8
Bun
dles
/Min
ute
Number of GIS Servers in Site
Windows/Linux: Cache Tile Generation Rate (Bundles/Minute)
Corine (Windows: Local)
Corine (Linux: Local)
Streetmap_UT_CO(Windows: Local)Streetmap_UT_CO(Linux: Local)
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David Cordes (Esri)
ArcGIS Server 10.1 Managing Performance
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• Managing Service Instances • Managing Machines using Clustering
Managing Performance at 10.1
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• 10.0: min/max instance settings are total for all machines • 10.1: min/max instance settings are per-machine
• Simplifies sizing – removes the guess work • Scales as your hardware scales
Managing Service Instances at 10.1
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On each machine - Start min # instances when service starts - Start another instance if
- All existing instances busy - Less than max # of instances
- Stop an instance if - Idle time for instance > idle time out - More than the min # of instances
Managing Service Instances at 10.1
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Managing Service Instances at 10.1
Max # Instances
Min # Instances
• Typical use: cyclic and gradual • Usually min instance at least 1. • Hardware or license constrained?
- Max instances less than natural - Idle time out may be less (default 30 min, but maybe 5,10, or
15)
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Managing Service Instances at 10.1
• Two other common use patterns - Blip - Constant
• Blip - Service is pre-dominantly not used. - Used for short periods of time by few people. - Min/Max is usually 0/1. - Idle time-out depends on business needs.
- Constant - Service usage does not vary much day-to-day. Usage often
tied to a resource – workers, trucks, etc. - Min/Max are usually set to be equal. Variability is not
desired.
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Managing Machines through Clustering at 10.1
• Cluster is a set of ArcGIS Server machines. • Each service is assigned to a cluster. • Example:
- Cluster default has Sample service - Machine1 - Machine2
- Cluster 2 - Machine3 - Machine4
When you publish a service S1 to cluster 2, the service will
run on Machine3 and Machine4
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Managing Machines through Clustering at 10.1
• You can move machine between clusters on the fly.
• Example continued – move machine2 from default cluster to cluster 2.
- Cluster default has sample and publishing services - Machine1 - Machine2
- Cluster 2 has service S1 - Machine2 - Machine3 - Machine4
- When you publish a service S1 to cluster 2, the service will run on Machine3 and Machine4
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Managing Machines through Clustering at 10.1
• You can move services between clusters on the fly (requires service to be stopped and restarted).
• Example continued – move sampl e service from default cluster to cluster 2
- Cluster default has publishing service - Machine1 - Machine2
- Cluster 2 has S1 and sample services. - Machine2 - Machine3 - Machine4
- When you publish a service S1 to cluster 2, the service will run on Machine3 and Machine4
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Managing Machines through Clustering at 10.1
• Performance implications: - More important services can have dedicated resources. - Heavy services (caching or geoprocessing) can be isolated
from lighter services (map) - You can rebalance load as needed. - Important: All machines in a cluster should have identical
hardware.
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Further Reading & Sessions
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Performance Related UC Sessions
Session Time & Place Short URL ArcGIS for Server Performance and Scalability—Testing and Monitoring Tools
Wed 3:40 20 min Room 03
http://bit.ly/NMRH5o
ArcGIS for Server Performance and Scalability—Testing Methodologies
Thur 1:30 75 min Room 32B
http://bit.ly/NrSVo6
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Performance Related Reading
Resource URL Enterprise GIS Resource Center
http://resources.arcgis.com (Enterprise GIS Community)
Map Services http://bit.ly/LJsJnC
Geoprocessing Services http://bit.ly/NrUGSb
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Evaluation & Questions
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• Your feedback is extremely important • Please fill out evaluations online • http://Esri.com/ucsessionsurveys
- Offering ID (Wednesday): 672 - Offering ID (Thursday): 1938
Evaluation