The Art of Targeted Caching: Render What You Need

Proprietary & Confidential – Do Not Copy or Distribute Proprietary & Confidential – Do Not Copy or Distribute

Presented by: Josh Foley Quorum Business Solutions

October 25, 2011

The Art of Targeted Caching: Render What You Need

Proprietary & Confidential – Do Not Copy or Distribute

Overview

� Case study y Dynamic vs. Blended Dynamic / Cached services

� Cache management best practices y Scales, coordinate systems, projections y Cache tile migration y Multi-layer cache services

� Cache by Feature Class y Target high-traffic areas y Reduce cache generation time y Reduce storage footprint on servers


What Does it Mean to Cache a Map Service?

� A cached service has a set of map images that have been pre-rendered for rapid display

� Created at pre-determined scale levels

� Tiles stored on the ArcGIS server for fast retrieval

1:250,000

1:500,000


Case Study: Un-cached vs. Cached


Map Services

� The following two maps are identical other than the cache status of the Wells, Survey and Base map data

• Agreement – Dynamic • Well – Dynamic • Survey – Dynamic • Base – Dynamic

• Agreement – Dynamic • Well – Cached • Survey – Cached • Base – Cached

Un-cached Cached


Map Services Un-cached Cached


Analysis Un-cached Cached


Cache Management Best Practices


Cache Scale Levels

� Blending cached services requires identical scale levels

� Understanding the data you plan to serve to your users is key in determining the scale levels to cache y If you plan to use ArcGIS Online, you should select the

same tile scales to cache

� If your tile’s scale does not match other sources you plan to consume, there will be a compression / expansion of services which will counteract the benefits of caching


Coordinate Systems and Projections

� Blending map services requires a consistent coordinate system / projection y Alignment issues will occur between data in differing

spatial references y Utilize Esri’s native, on the fly projection for cache map

documents

� Set your cache map spatial reference in the data frame properties of the map document you are caching y This will increase the cache generation time, but your

users will not see an impact in performance as they will be pulling the cached tiles


Cache Tile Migration

� Generating caches can be time consuming and managing multiple environments can require multiple caches of the same data y Create the service on the destination y Configure the cache, but do not create the tiles y Migrate the files from the source cache directory


Esri 10 Cache Tile Management

� Export Map Server Cache y Exports tiles from a map cache to a folder on disk drive

- The tiles can either be imported into other caches or they can be accessed from ArcGIS Desktop as a raster dataset, independent from their parent service

y Before running this tool, create the folder on a disk drive that will hold the exported tiles

y Export Map Server Cache lets you choose a specific area or scale from a given cache map service and convert its storage format, while simultaneously exporting the tiles to a location you define


Compact Caching

� Compact is the new Esri 10 format

� Tiles are grouped together into bundles y One bundle can hold up to 16,384 tiles y Reduces the discrepancy between “Size” and “Size on

Disk”

� Benefits y Data mobility y Disk usage y Cache generation throughput y Scalability


Multi-Layer Cache Services

� Cache services are a stored image, therefore it is not possible to turn off individual layers within a service y Build multiple cache services as illustrated in the case

study y Blend the services together in order to view the data as

a single map with the ability to toggle specific services y Create specific services for Wells and Survey to allow

users the ability to turn on / off desired features without losing the Base Culture data underlay


Cache by Feature Class


Objective

� Problem y Caching the largest scales consumes the majority of processing

time (final scale ~75% of total time) - This often leaves the scales needed most without cached tiles

y Hogs resources!!

� Tips & Tricks for caching optimization y Target high-traffic areas

� Outcome y Reduces map cache processing time y Reduces storage footprint on servers


Control Model

� A control was utilized to benchmark the processing / memory required for caching the entire state of Wyoming

� Base map service y Cache configured from ArcGIS Online scales

y Cache only for Wyoming

� Five map service instances utilized


Optimized Model

� Dissolved, buffered data inputs create a unique polygon y Agreements – 1 mile buffer y Pipelines – ½ mile buffer y Roads – ½ mile buffer

� Base map service y Cache configured from ArcGIS Online scales y Cache by feature class containing the dissolved,

buffered inputs

� Five map service instances utilized


Cache by Feature Class Toolset

� Allows users to define data inputs

� Creates a feature class for use in the cache

� Allows users to manage map service cache


Buffered Inputs Model


Buffered, Dissolved Inputs


Control Model Analysis

� Elapsed time y 21 hours

� Disk space y 5.15 GB


Optimized Model Analysis

� Elapsed time y 5 hours 47 minutes

� Disk usage y 2.46 GB


Control vs. Optimized – Size on Disk

16 - 1:4508.935525 15 - 1:9017.871050 14 - 1:18035.742100 13 - 1:36071.484201 12 - 1:72142.968401 11 - 1:144285.936802 10 - 1:288571.873604 9 - 1:577143.747209 8 - 1:1154287.494417 7 - 1:2308574.988835 6 - 1:4617149.977669 5 - 1:9234299.955339 4 - 1:18468599.910677 3 - 1:36937199.821354 2 - 1:73874399.642709 1 - 1:147748799.285417

ArcGIS Online scales


Cache at Record Level Changes

� Update Map Server Cache by Feature Class y Checks the feature class to see if the “Cached” field

exists - If it does not exist, it will create the column

y Skips the features that have already been marked as having been cached and picks up where it left off

y Can be scheduled to run as often as data changes y Configurable

- Feature class to target, server name, service name, data frame, scales to cache, update mode and anti-aliasing

Source: http://arcscripts.esri.com/details.asp?dbid=15161

http://arcscripts.esri.com/details.asp?dbid=15161

http://arcscripts.esri.com/details.asp?dbid=15161


Conclusions

� Dynamic data caching can be achieved with an optimized model

� Ability to cache at all scales

� Models / Python scripts can be configured to business needs

� Flexible enough to target all map services

� Models / Python scripts may be scheduled

� Everyone gets faster maps!

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