Microsoft SQL Server 2005 Analysis Services

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Microsoft SQL Server Analysis Services provides fast access to data by means of multidimensional data structures and the multidimensional query languag MDX. Analysis Services provides the capability to design, create, and manage multidimensional cubes based on data warehouse tables, and it serves as the foundation for the Microsoft  Business Intelligence strategy.

Microsoft SQL Server 2005 Analysis Services gives the reader insight into the way Analysis Services functions. ...

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Microsoft SQL Server Analysis Services provides fast access to data by means of multidimensional data structures and the multidimensional query languag MDX. Analysis Services provides the capability to design, create, and manage multidimensional cubes based on data warehouse tables, and it serves as the foundation for the Microsoft  Business Intelligence strategy.

Microsoft SQL Server 2005 Analysis Services gives the reader insight into the way Analysis Services functions. It not only explains ways to design and create multidimensional objects, databases, dimensions, and cubes, but also provides invaluable information about the reasons behind design decisions made by the development team. 

Here's what you will find inside:

  • Understand the key concepts of multidimensional modeling
  • Explore the multidimensional object model and its definition language
  • Learn the main concepts of the MDX language and gain an in-depth understanding of advanced MDX concepts
  • Understand the mechanisms of integrating multidimensional and relational databases
  • Learn how to build client applications to access data in Analysis Services
  • Examine server architecture, including main data structures, data processing, and query resolution algorithms
  • Gain a deep understanding of the internal and external protocols for data transfer, including the

    Explore how Analysis Services manages memory

  • Explore the security model, including role-based security, code-access security, and data security
  • Discover how to monitor and manage Analysis Services

All the code for the sample database used in the book can be found at

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Product Details

  • ISBN-13: 9780672327827
  • Publisher: Sams
  • Publication date: 1/1/2007
  • Series: SQL Server Series
  • Pages: 842
  • Product dimensions: 6.94 (w) x 9.06 (h) x 1.94 (d)

Meet the Author

Edward Melomed is one of the original members of the Microsoft SQL Server Analysis Services team. He arrived in Redmond as a part of Microsoft's acquisition of Panorama Software Systems, Inc., which led to the technology that gave rise to Analysis Services 2005. He works as a program manager and plays a major role in the infrastructure design for the Analysis Services engine.

Irina Gorbach is a senior software designer on the Analysis Services team, which she joined soon after its creation nine years ago. During her time at Microsoft, Irina has designed and developed many features, was responsible for client subsystems OLEDB and ADOMD.NET, and was in the original group of architects that designed the

Alexander Berger was one of the first developers to work on OLAP systems at Panorama. After it was acquired by Microsoft, he led the development of Microsoft OLAP Server through all its major releases. He is one of the architects of OLEDB for the OLAP standard and MDX language, and holds more than 30 patents in the area of multidimensional databases.

Py Bateman is a technical writer at Microsoft. She originally hails from Texas, which was considered a separate country on the multinational Analysis Services team.


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Read an Excerpt


It was a pleasure to be asked to write the foreword to this new book, which is remarkable for two reasons:

  • People who have spent five years developing a product are normally more than ready to move on to the next release once the product is finally ready for release. Indeed, long before a new version gets into customers' hands, the developers are normally already working on the next release. So, for the actual developers to spend the considerable time that this book must have taken to write a lengthy, detailed book on it is very rare.
  • In my years as an industry analyst with The OLAP Report, and much earlier as a product manager, I have rarely come across developers who are prepared to provide such chapter and verse information on exactly how a product works. Even under NDA, few software vendors are prepared to volunteer this level of inside information.

But why should this be of interest to anyone who isn't an OLAP server developer? Why should a mere user or even an application developer care about what exactly happens under the hood, any more than ordinary car drivers needs to know the details of exactly how their car's engine management system works?

There are some good reasons why this is relevant. Analysis Services is now by far the most widely used OLAP server, which inevitably means that most of its users are new to OLAP. The OLAP Surveys have consistently found that the main reason for the choice is price and the fact that it is bundled with SQL Server, rather than performance, scalability, ease of use, or functionality.

This is not to say that Analysis Services lacks these capabilities; just thattypical Analysis Services buyers are less concerned about them than are the buyers of other products. But when they come to build applications, they certainly will need to take these factors into account, and this book will help them succeed. Just because Analysis Services is perceived as being a low-cost, bundled product does not mean that it is a small, simple add-on: particularly in the 2005 release, it is an ambitious, complex, sophisticated product. How it works is far from obvious, and how to make the most of it requires more than guesswork.

Many of the new Analysis Services users will have used relational databases previously, and will assume that OLAP databases are similar. They are not, despite the superficial similarities between MDX and SQL. You really need to think multidimensionally, and understand how Analysis Services cubes work.

Even users with experience of other OLAP servers will find that they differ from each other much more than do relational databases. If you start using Analysis Services without understanding the differences and without knowing how Analysis Services really works, you will surely store up problems for the future. Even if you manage to get the right results now, you may well compromise the performance and future maintainability of the application.

The OLAP Surveys have consistently found that if there is one thing that really matters with OLAP, it is a fast query response. Slow performance is the biggest single product-related complaint from OLAP users in general, and Analysis Services users are no different. Slow query performance was also the biggest technical deterrent to wider deployment.

Many people hope that ever improving hardware performance will let them off the hook: If the application is too slow, just rely on the next generation of faster hardware to solve the problem. But results from The OLAP Surveys show that this will not work—the rate of performance complaints has gone up every year, whether actual query performance has improved or not. In an era when everyone expects free sub-second Web searches of billions of documents, books, and newsgroup postings, they are no longer willing to wait five or ten seconds for a simple management report from a modest internal database. It is not enough for an OLAP application to be faster than the spreadsheet or relational application it replaced—it must be as fast as other systems that we all use every day.

The good news is that fast query performance is possible if you take full advantage of the OLAP server's capabilities: The OLAP Survey 6 found that 57% of Analysis Services 2005 users reported that their typical query response was less than five seconds. This was the traditional benchmark target query time, but in the new era of instant Web searches, I think the new target should be reduced to one second. This is a tough target, and will require application developers to really know what they are doing, and to take the time to optimize their systems.

This is where this book comes in. The authors—who have been involved with Analysis Services from its earliest days, long before it was called Analysis Services—have documented, in detail, what really happens inside Analysis Services 2005, right down to the bit structure of data records. Along the way, numerous controllable parameters are described, with helpful information about how they cause memory or other computer resources to be used.

This book is not intended to teach new users how to use Analysis Services 2005; it is for technically competent implementers who want to make the most of Analysis Services by understanding how it really works, as described by those who really know, unlike other books written by external authors who sometimes have to speculate. If you are new to Analysis Services, you probably need to start with a "how do I?" book or course, rather than a "what happens inside?" book like this one.

Nigel Pendse

Editor of The OLAP Report
Author of The OLAP Survey

© Copyright Pearson Education. All rights reserved.

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Table of Contents

Foreword     xxii
Introduction     1
Introduction to Analysis Services
What's New in Analysis Services 2005     5
Modeling Capabilities of Analysis Services 2005     5
Advanced Analytics in Analysis Services 2005     6
New Client-Server Architecture     7
Improvements in Scalability     7
Development and Management Tools     8
Manageability of Analysis Services     8
Sample Project     9
Customer Data     9
Store Data     9
Product and Warehouse Data     9
Time Data     10
Account Data     10
Currency Data     10
Employee Data     10
The Warehouse and Sales Cube     10
The HR Cube     11
The Budget Cube     11
The Sales and Employees Cube     11
Summary     11
Multidimensional Databases     13
The Multidimensional Data Model     15
The Conceptual Data Model     15
The Physical Data Model     15
The Application Data Model     16
Multidimensional Space     16
Describing Multidimensional Space     16
Summary     23
UDM: Linking Relational and Multidimensional Databases     25
Summary     27
Client/Server Architecture and Multidimensional Databases: An Overview     29
Two-Tier Architecture     30
One-Tier Architecture     31
Three-Tier Architecture     32
Four-Tier Architecture     33
Distributed Systems     34
Distributed Storage     34
Thin Client/Thick Client     34
Summary     36
Creating Multidimensional Models
Conceptual Data Model     39
Data Definition Language     39
Objects in DDL     40
Summary     43
Dimensions in the Conceptual Model     45
Dimension Attributes     46
Attribute Properties and Values     48
Relationships Between Attributes     49
Attribute Member Keys     52
Attribute Member Names     55
Relationships Between Attributes     56
Attribute Discretization     58
Parent Attributes     60
Dimension Hierarchies     61
Types of Hierarchies     61
Attribute Hierarchies     64
Parent-Child Hierarchies     66
Summary     67
Cubes and Multidimensional Analysis     69
Cube Dimensions     71
Cube Dimension Attributes     74
Cube Dimension Hierarchies     76
Role-Playing Dimensions     76
The Dimension Cube     77
Perspectives     78
Summary     80
Measures and Multidimensional Analysis     83
Measures in Multidimensional Cubes     83
Sum     86
Max and Min     87
Count     87
Distinct Count     87
Measure Groups     88
Measure Group Dimensions     90
Granularity of a Fact     91
Indirect Dimensions     95
Measure Expressions     103
Linked Measure Groups     106
Summary     106
Multidimensional Models and Business Intelligence Development Studio     109
Creating a Data Source     110
Creating a New Data Source     110
Modifying an Existing Data Source     111
Modifying a DDL File     112
Designing a Data Source View     113
Creating a New Data Source View      114
Modifying a Data Source View     115
Designing a Dimension     117
Creating a Dimension     117
Modifying an Existing Dimension     121
Designing a Cube     125
Creating a Cube     126
Modify a Cube     128
Build a Cube Perspective     134
Defining Cube Translations     134
Configuring and Deploying a Project So That You Can Browse the Cube     136
Configuring a Project     136
Deploying a Project     138
Browsing a Cube     138
Summary     139
Using MDX to Analyze Data
MDX Concepts     143
The Select Statement     144
The Select Clause     144
Defining Coordinates in Multidimensional Space     145
Default Members and the WHERE Clause     148
Query Execution Context     151
Set Algebra and Basic Set Operations     153
Union     153
Intersect     154
Except     155
CrossJoin     155
Extract     156
MDX Functions     157
Functions for Navigating Hierarchies     158
The Function for Filtering Sets      160
Functions for Ordering Data     162
Referencing Objects in MDX and Using Unique Names     163
By Name     163
By Qualified Name     163
By Unique Name     164
Summary     164
Advanced MDX     165
Using Member and Cell Properties in MDX Queries     165
Member Properties     165
Cell Properties     166
Dealing with Nulls     168
Null Members, Null Tuples, and Empty Sets     168
Nulls and Empty Cells     173
Type Conversions Between MDX Objects     177
Strong Relationships     178
Sets in a Where Clause     180
SubSelect and Subcubes     183
Summary     190
Cube-Based MDX Calculations     193
MDX Scripts     195
Calculated Members     196
Defining Calculated Members     196
Assignments     202
Assignment Operator     203
Specifying a Calculation Property     206
Scope Statements     207
Root and Leaves Functions     209
Calculated Cells     211
Named Sets     212
Order of Execution for Cube Calculations     216
The Highest Pass Wins     218
Recursion Resolution     219
Summary     222
Dimension-Based MDX Calculations     225
Unary Operators     226
Custom Member Formulas     229
Semiadditive Measures     231
ByAccount Aggregation Function     234
Order of Execution for Dimension Calculations     237
The Closest Wins     237
Summary     241
Extending MDX with Stored Procedures     243
Creating Stored Procedures     244
Creating Common Language Runtime Assemblies     245
Using Application Domains to Send-Box Common Language Runtime Assemblies     250
Creating COM Assemblies     251
Calling Stored Procedures from MDX     252
Security Model     254
Role-Based Security     254
Code Access Security     254
User-Based Security     255
Server Object Model     257
Operations on Metadata Objects     259
Operations on MDX Objects     261
Using Default Libraries     263
Summary     264
Key Performance Indicators, Actions, and the Drillthrough Statement     265
Key Performance Indicators     266
Defining KPIs     266
Discovering and Querying KPIs     271
Actions     272
Defining Actions     273
Discovering Actions     279
Drillthrough     284
Drillthrough Statement     285
Defining Drillthrough Columns in a Cube     287
Summary     290
Writing Data into Analysis Services     291
Using the Update Cube Statement to Write Data into Cube Cells     292
Updatable and Nonupdatable Cells     297
Lifetime of the Update     297
Enabling Writeback     299
Converting a Writeback Partition to a Regular Partition     301
Other Ways to Perform Writeback     301
Summary     302
Creating a Data Warehouse
Loading Data from a Relational Database     305
Loading Data     305
Data Source Objects     307
Data Source Object Properties     308
Data Source Security     309
Connection Timeouts     311
Summary     311
DSVs and Object Bindings     313
Data Source View     313
Named Queries     315
Named Calculations     316
Object Bindings     316
Column Bindings     317
Table Bindings     318
Query Bindings     319
Summary     320
Multidimensional Models and Relational Database Schemas     321
Relational Schemas for Data Warehouses     321
Building Relational Schemas from the Multidimensional Model     323
Using Wizards to Create Relational Schemas     323
Using Templates to Create Relational Schemas     328
Summary     330
Bringing Data into Analysis Services
The Physical Data Model     333
Internal Components for Storing Data     334
Data Store Structure     334
File Store Structure     334
Bit Store Structure     336
String Store Structure     336
Compressed Store Structure     337
Hash Index of a Store     338
Data Structure of a Dimension     339
Data Structures of the Attributes     339
Attribute Relationships     343
Data Structures of Hierarchies     347
Physical Model of the Cube     351
Defining a Partition Using Data Definition Language      351
Physical Model of the Partition     354
Overview of Cube Data Structures     361
Summary     362
Dimension and Partition Processing     365
Dimension Processing     365
Attribute Processing     365
Hierarchy Processing     371
Building Decoding Tables     372
Building Indexes     372
Schema of Dimension Processing     373
Dimension Processing Options     373
Processing ROLAP Dimensions     376
Processing Parent-Child Dimensions     377
Cube Processing     378
Data Processing     379
Building Aggregations and Indexes     381
Cube Processing Options     383
Progress Reporting and Error Configuration     388
ErrorConfiguration Properties     389
Processing Error Handling     391
Summary     393
Using SQL Server Integration Services to Load Data     395
Using Direct Load ETL     396
Creating an SSIS Dimension-Loading Package     398
Creating an SSIS Partition-Loading Package     402
Summary     404
Aggregation Design and Usage-Based Optimization      405
Designing Aggregations     407
Relational Reporting-Style Dimensions     408
Flexible Versus Rigid Aggregations     410
Aggregation Objects and Aggregation Design Objects     411
The Aggregation Design Algorithm     414
Query Usage Statistics     415
Setting Up a Query Log     416
Monitoring Aggregation Usage     418
Summary     419
Proactive Caching and Real-Time Updates     421
Data Latency and Proactive Caching     422
Timings and Proactive Caching     424
Frequency of Updates     424
Long-Running MOLAP Cache Processing     425
Proactive Caching Scenarios     426
MOLAP Scenario     426
Scheduled MOLAP Scenario     427
Automatic MOLAP Scenario     428
Medium-Latency MOLAP Scenario     428
Low-Latency MOLAP Scenario     428
Real-time HOLAP Scenario     428
Real-time ROLAP Scenario     429
Change Notifications and Object Processing During Proactive Caching     429
Scheduling Processing and Updates     430
Change Notification Types     431
Incremental Updates Versus Full Updates      434
General Considerations for Proactive Caching     434
Monitoring Proactive Caching Activity     435
Summary     436
Building Scalable Analysis Services Applications     437
Approaches to Scalability     437
The Scale-Up Approach     437
The Scale-Out Approach     438
OLAP Farm     439
Data Storage     439
Network Load Balancing     441
Linked Dimensions and Measure Groups     442
Updates to the Source of a Linked Object     443
Linked Dimensions     443
Linked Measure Groups     447
Remote Partitions     450
Processing Remote Partitions     452
Using Business Intelligence Development Studio to Create Linked Dimensions     453
Summary     456
Analysis Server Architecture
Server Architecture and Command Execution     459
Command Execution     459
Session Management     463
Server State Management     465
Executing Commands That Change Analysis Services Objects     465
Creating Objects     466
Editing Objects     467
Deleting Objects     468
Processing Objects     468
Commands That Control Transactions     471
Managing Concurrency     473
Using a Commit Lock for Transaction Synchronization     474
Canceling a Command Execution     476
Batch Command     478
Summary     484
Memory Management     485
Economic Memory Management Model     486
Server Performance and Memory Manager     487
Memory Holders     487
Memory Cleanup     488
Managing Memory of Different Subsystems     490
Cache System Memory Model     491
Managing Memory of File Stores     491
Managing Memory Used by User Sessions     492
Other Memory Holders     492
Memory Allocators     492
Effective Memory Distribution with Memory Governor     494
Model of Attribute and Partition Processing     496
Model of Building Aggregations     499
Model of Building Indexes     500
Summary     500
Architecture of Query Execution-Calculating MDX Expressions     503
Query Execution Stages     503
Parsing an MDX Request     505
Creation of Calculation Scopes      507
Global Scope and Global Scope Cache     509
Session Scope and Session Scope Cache     510
Global and Session Scope Lifetime     510
Building a Virtual Set Operation Tree     513
Optimizing Multidimensional Space by Removing Empty Tuples     514
Calculating Cell Values     515
Calculation Execution Plan Construction     516
Evaluation of Calculation Execution Plan     517
Execution of the Calculation Execution Plan     518
Cache Subsystem     518
Dimension and Measure Group Caches     518
Formula Caches     521
Summary     522
Architecture of Query Execution-Retrieving Data from Storage     523
Query Execution Stages     524
Querying Different Types of Measure Groups     526
Querying Regular Measure Groups     526
Querying ROLAP Partitions     529
Querying Measure Groups with DISTINCT_COUNT Measures     529
Querying Remote Partitions and Linked Measure Groups     532
Querying Measure Groups with Indirect Dimensions     533
Summary     535
Accessing Data in Analysis Services
Client/Server Architecture and Data Access     539
Using TCP/IP for Data Access      539
Using Binary XML and Compression for Data Access     540
Using HTTP for Data Access     542
Offline Access to Data     543
Summary     545
Client Components Shipped with Analysis Services     547
Using XML for Analysis to Build Your Application     547
Using Analysis Services Libraries to Build Your Application     548
Query Management for Applications Written in Native Code     549
Query Management for Applications Written in Managed Code     549
Using DSO and AMO for Administrative Applications     551
Summary     552
XML for Analysis     553
State Management     554
XML/A Methods     557
The Discover Method     557
The Execute Method     561
Handling Errors and Warnings     567
Errors That Result in the Failure of the Whole Method     568
Errors That Occur After Serialization of the Response Has Started     570
Errors That Occur During Cell Calculation     571
Warnings     572
Summary     573
ADOMD.NET     575
Creating an ADOMD.NET Project     575
Writing Analytical Applications      577
ADOMD.NET Connections     578
Working with Metadata Objects     586
Operations on Collections     586
Caching Metadata on the Client     591
Working with a Collection of Members (MemberCollection)     593
Working with Metadata That Is Not Presented in the Form of Objects     600
AdomdCommand     605
Properties     606
Methods     607
Using the CellSet Object to Work with Multidimensional Data     612
Handling Object Symmetry     619
Working with Data in Tabular Format     622
AdomdDataReader     625
Using Visual Studio User Interface Elements to Work with OLAP Data     628
Which Should You Use: AdomdDataReader or CellSet?     630
Using Parameters in MDX Requests     631
Asynchronous Execution and Cancellation of Commands     634
Error Handling     639
AdomdErrorResponseException     640
AdomdUnknownResponseException     642
AdomdConnectionException     642
AdomdCacheExpiredException     642
Summary     644
Analysis Management Objects     647
AMO Object Model     647
Types of AMO Objects      648
Dependent and Referenced Objects     657
Creating a Visual Studio Project That Uses AMO     663
Connecting to the Server     664
Canceling Long-Running Operations     667
AMO Object Loading     671
Working with AMO in Disconnected Mode     672
Using the Scripter Object     673
Using Traces     676
Error Handling     684
OperationException     685
ResponseFormatException     685
ConnectionException     686
OutOfSyncException     687
Summary     688
Security Model for Analysis Services     693
Connection Security     694
TCP/IP Connection Security     695
HTTP Security     696
External Data Access Security     700
Choosing a Service Logon Account     700
Changing a Service Logon Account     701
Security for Running Named Instances (SQL Server Browser)     702
Security for Running on a Failover Cluster     702
Summary     702
Object Security Model for Analysis Services     705
Server Administrator Security     705
Database Roles and the Hierarchy of Permission Objects     707
Permission Objects     710
Managing Database Roles     713
Summary     714
Securing Dimension Data     715
Defining Dimension Security     718
The AllowedSet and DeniedSet Properties     719
The VisualTotals Property     724
Defining Dimension Security Using the User Interface     725
Testing Dimension Security     727
Dynamic Security     729
Dimension Security Architecture     731
Dimension Security, Cell Security, and MDX Scripts     732
Summary     733
Securing Cell Values     735
Defining Cell Security     735
Testing Cell Security     738
Contingent Cell Security     740
Dynamic Security     742
Summary     744
Using Trace to Monitor and Audit Analysis Services     749
Trace Architecture     750
Types of Trace Objects     751
Administrative Trace     751
Session Trace     752
Flight Recorder Trace     752
Creating Trace Command Options     752
SQL Server Profiler     753
Defining a Trace     754
Running a Trace     756
Flight Recorder     759
How Flight Recorder Works     761
Configuring Flight Recorder Behavior     761
Discovering Server State     762
Tracing Processing Activity     764
Reporting the Progress of Dimension Processing     764
Reporting the Progress of Partition Processing     766
Query Execution Time Events     767
Running a Simple Query     767
Changing the Simple Query     769
Running a More Complex Query     770
Changing the Complex Query     771
Changing Your Query Just a Little More     772
Summary     773
Backup and Restore Operations     775
Backing Up Data     775
Planning Your Backup Operation     776
Benefits of Analysis Server 2005 Backup Functionality     777
Using the Backup Database Dialog Box to Back Up Your Database     111
Using a DDL Command to Back Up Your Database     779
Backing Up Related Files     780
Backing Up the Configuration File     781
Backing Up the Query Log Database     781
Backing Up Writeback Tables      781
Backup Strategies     782
Typical Backup Scenario     782
High Availability System Backup Scenario     783
Automating Backup Operations     784
SQL Server Agent     784
SQL Server Integration Services     784
AMO Application     785
Restoring Lost or Damaged Data     785
Using the Restore Database Dialog Box     786
Using the DDL Command to Restore Your Database     787
Using Discover_Locations to Specify Alternative Locations for Partitions     788
MDX Extensions for Browsing Your File System     789
The MDX Extensions     790
Summary     791
Deployment Strategies     793
Using the Deployment Wizard     793
Synchronizing Your Databases     795
Using the Synchronize Database Wizard     797
Using a DDL Command to Synchronize Databases     798
Similarities Between the Synchronization and Restore Commands     799
Synchronization and Remote Partitions     800
Synchronization and Failover Clusters     802
Summary     802
Index     803
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Sort by: Showing all of 2 Customer Reviews
  • Anonymous

    Posted January 25, 2007

    different way of analysing SQL data

    The book teaches a means of analysing massive data sets, that is different from SQL, but which can be applied to SQL databases. Called MDX, it lets you, the analyst, define a hyperspace of several dimensions, where the number of dimensions can be greater than 3. Along each axis, there is a discrete set of values. Unlike engineering or physics analysis, where the spaces often take on continuum values. The authors show how MDX comes with a rich set of built in functions. But you can also easily write your own, that use these, or start from scratch. The Analysis Service version 2005 encompasses MDX, along with a user interface. The MDX syntax borrows deliberately in part from SQL, since as a practical matter, many of its users will come from a SQL background. But for analysis, it is often superior, offering a more flexible and intuitive syntax geared towards analysis. One potential 'problem', which is not mentioned, is that if you get used to the MDX syntax, going back to writing code for a strict SQL application might now seem so constricting. Of course, this is scarcely MDX's fault. The book's chapters are often quite short ('bite-sized') and are easy to follow.

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  • Anonymous

    Posted August 11, 2011

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