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Understanding Windows Azure Tables comparing with Relational SQL Tables (on-premise/SQL Azure)

Table of Contents

  • Background
  • A comparison of Relational SQL Table and Azure Storage Table
  • Azure Storage Table Concept
  • Understanding Entity
  • Understanding Properties by example
  • Partitions
  • Key things to remember


Windows Azure Storage is a storage option provide by Windows Azure. Using this technology will bring all the benefits of Windows Azure such as High Availability, Scalability, Security, etc. Azure tables were specially designed to store non-relational data that needs some structure, but highly scalable. All the values of Azure tables are stored as Key-Value pairs which makes it a highly scalable storage option.

When someone starts to learn about windows azure table storage, the first thing that will come to his mind will be “Why do we need this when we have Sophisticated Relational SQL Server Tables?”

Therefore, the best way to understand about Windows Azure Tables will be, start thinking about a relational SQL table and learn the differences.

A comparison of Relational SQL Table and Azure Storage Table

Relation SQL table Azure Storage table
Can have Relationships between tables There are no relationships between tables. Follows NoSQL Concepts
Has Rows of values Considered as Entities
Strictly defined Columns & enforces data integrity There is no Concept of Columns, instead values are stored as key-value pairs
Each row has fixed amount of columns Each entity can have Zero or more properties
Each data/value will have a data type which corresponds to the column definition Each data/value are independent & can have different data types
No build-in System columns 3 System properties for each entity (PartitionKey, RowKey, Timestamp)
Merging data from multiple sources that has different formats – may need to be converted programmatically Have the option of saving data in a different formats.Example:Source 1: 01/01/2001Source 2: “2001” (only the year as a String)
Ability to Query – TSQL Limited Query options
Can apply indexes to any column Can’t do that. Is only available for PartitionKey & RowKey built-in
Geographically redundant price (as of 2014-Mar-09) – $9.99 Upto 1GB per Month Cheaper – $0.095 per GB per Month
When comparing only on-premise SQL More Availability
SQL Azure Max Capacity (as of 2014-Mar-09) – 150 GB 200TB

Azure Storage Table Concept

In an Azure storage table a collection of Properties (key-values pairs) will form an Entity. Those entities will be meaningfully grouped in to partitions using PartitionKeys. Ultimately, the collection of entities that were grouped in to partitions will form the Azure Storage Table.

Understanding Entity

An entity has 3 system properties:

  1. PartitionKey – means of partitioning entities for load balancing with the rule of thumb “Data that need to be Queried together, must be kept together”
  2. RowKey – This is a key that helps to uniquely identify an entity that belongs to a partition. So, RowKey together with PartitionKey will make an Entity unique in a given azure table.
  3. TimeStamp – When you first create an entity, the Date & Time of creation will be recorded in this property. And later if you do any change to that entity, the value will get updated with the last updated date & time.

Those system properties are built-in indexed and it is recommended to use these properties as identity columns so it is efficient. Non-system values cannot be indexed.

The paritionKey together with RowKey will form unique entities:

Understanding Properties by example

Example 1: No fixed Schema for storing values

In a relation SQL table all those values that is entered under column “Age” will be bound to the limitations defined in the column definition/design such as Data type, max length, unique key, etc.

Now let’s try to store those values in an Azure Storage Table. Now, those values are not bound to a particular column. (There is no concept of columns)

Each and every values are stored as key-value pairs. Unlike in relational SQL tables, those values can be of different data types as well.

This design has enable some new scenarios that were not possible before.

For example, let’s say we store date values in relational SQL table we have to store them in a standard format. But in SQL Azure table, we can store the date as string for some entities if needed.

Example 2:

There is a requirement to create a simple consolidated search engine of products that are referred from two different ecommerce stores. Store 1 stores Product Expiry Dates as DateTime data type (1900-01-01 00:00:00). On the other hand, Store 2 stores only Year and Month, but as Strings (“1900/June”). If we are doing this using relational SQL, we will have to assume a default date for Store 2 (may be logically incorrect according to the product type) and programmatically convert each and every date stored either as a separate column or do it when querying. But, we can push products from both the stores to Azure tables without any of these effort. (Store 1 values stored as DateTime, Store 2 values stored as String)

Moreover, if we take a particular entity, it can have Zero or more properties.

Example 3

In the above example,

  • First entity will have Zero properties
  • Third entity will have 3 properties (“First”,”Last”,”Birthdate”)
  • Second entity will have 4 properties (“First”,”Last”,”Birthdate”,”Sport”); the property “Sport” only belongs to the second entity and will not be applicable for other entities unless other entities have explicitly defined a property with the same name.
  • PartitionKey together with RowKey will form unique entity in Azure storage table. Although first & second entities have the RowKey “001” since they have different paritionKeys they will be unique inside the table.

Properties supports following data types:

  • Binary
  • Bool
  • DateTime
  • Double
  • GUID
  • Int32
  • Int64
  • String


Azure storage tables are partitioned by the system property called “PartitionKey”.

Entities that were partitioned with the same ParitionKey will belong to the same load balancing group. On other words, anything within the same partition will live in the same server (same storage machine), so they can be queried/accessed faster.

So, as a rule of thumb when we design for Azure Storage tables we need to remember that “Data that need to be queried together, must be kept together in the same partition”.

Since, we cannot index non-system properties, the key idea of azure table is that design it in a way so that we make use of the concept of partitioning for load balancing. So, the art of choosing how to partition entities will depend on the requirements, transactions, and mainly on what kind of Queries that we are going to use on the azure table.

Following are some examples of how developers have used azure tables and how they have paritioned according to different requirements:

In this part of the table, we can we the entities have been partitioned using their document name.

The different versions of the same document are stored using different RowKeys.

In this case, all the versions of a particular document will be stored together. So, retrieving the versions of the same document will be very fast.

As you can see ParitionKey or RowKey doesn’t have to be a GUID or Integer they can be any data type. In the following example, Name is considered as the RowKey.

Following is a good example that describes how independent that values are stored for each entity as properties.

Key things to remember

In summary, when we design for azure storage tables with the concept of partitions, following are some key concerns:

  • First of all, know what you cannot do with Azure storage tables. It is not a replacement for relational SQL tables. In different cases, it can reside independently or it can work together with relational SQL tables to form a system. The latter case is the obvious.
  • Know the transactions; it is important to know what are the entities that are going to be updated together.
  • Know your Queries. It is important to know what are the entities that are going to be queried/accessed together.
  • Therefore, depending on the above concerns, we have to partition data.
  • Sometimes, if we design our tables to server some key queries, it may not possibly cater some other queries, which we will have to look for workarounds. In Relational SQL we have really good guides and best practices. In contrast, there is less direct guidance on designing such NoSQL based tables. So, it’s more of an Art!
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Does Azure tables internally use relational SQL to store its values?

Storage Emulator which comes with the Azure SDK, will simulate the Azure Cloud Storage environment in the local Development environment. And it uses sqlExpress/localdb with NTFS files systems to simulate this environment. It creates this resources the first time when we run a Cloud Project using visual studio.


But this is only for the simulation by the Storage Emulator.

Note: In earlier azure versions after we start the storage emulator some temporary databases will be created in the sqlExpress. With new versions of SQL tools sqlExpress doesn’t run as service anymore. I couldn’t see the databases using Visual Studio Server Explorer. But, i found those temporary database files (mdf & log) in the user profile folder (C:\User\UserName\).


The actual Azure storage will store data in a completely different way without using SQL Server architecture.


this is a very basic view of the internal architecture. So, we have Front Ends which will authenticate & authorize incoming requests and then will route those requests to partition layer. Partition servers in the partition layer will manage all the partitions in the server.

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Error SQL71564 – When Exporting your Database to Azure (bacpac)

When you use the Export Data-tier feature to create a backup of you database in order to upload it to windows azure you might get this error

Error SQL71564 –

The database principal owns a schema in the database, and cannot be dropped

Solution: to drop the user.

Sql Script to remove the schema from the user:


you can specify the required schema instead of db_owner



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SQL Azure migration: Built-in function ‘TEXTPTR’ is not supported in this version of SQL Server.

Not only ‘TEXTPTR’  also ‘READTEXT’ is not supported in SQL Azure.

You will find this issue when you try to execute an old version of AspState script in sql azure.

You will wonder why sql azure does not support most of the new features. it’s because the sql azure was built without the deprecated features of SQL SERVER 2008 R2.

executing the new version will solve the issue.

you can get the new version here:


SQL Azure Migration Wizard (SQLAzureMW) is a cool tool that will help us analyze the issues on SQL AZURE migrations.

you can download it from here:


Sql azure blindly adding bulk clustered indexes

In a previous post i mentioned when migrating to SQL Azure you have to create clustered indexes for each of the table that doesn’t have a clustered index (heap tables).

the error you will get is:

Tables without a clustered index are not supported in this version of SQL Server. Please create a clustered index and try again.

Lets say you have a huge database with huge amount of heap tables. And you need to do a blind test by adding a clustered index for the first column of each table.

Step 1:

Check the heap tables you have:

— List all heap tables
SELECT + ‘.’ + AS TableName
FROM sys.tables AS TBL
INNER JOIN sys.schemas AS SCH
ON TBL.schema_id = SCH.schema_id
INNER JOIN sys.indexes AS IDX
ON TBL.object_id = IDX.object_id
AND IDX.type = 0 — = Heap
ORDER BY TableName

Step 2:

declare @SQL nvarchar(max)
SELECT ‘; CREATE CLUSTERED INDEX NewPK_’ + + ‘ ON ‘ + ‘[‘’]’ + ‘ (‘+(
sELECT top 1 ‘[‘+column_name+’]’ FROM INFORMATION_SCHEMA.Columns where TABLE_NAME =
)+ ‘)’

— List all heap tables
FROM sys.tables AS TBL
INNER JOIN sys.schemas AS SCH
ON TBL.schema_id = SCH.schema_id
INNER JOIN sys.indexes AS IDX
ON TBL.object_id = IDX.object_id
AND IDX.type = 0 — = Heap

FOR XML PATH(”)),1,2,”)
SET @SQL = ‘ ‘ + @SQL
PRINT @SQL — execute until this statement to see only the script to be executed.

EXECUTE (@SQL) — execute until this statement to actually execute the script

NOTE: this is only good for a blind test. but if you are going to move forward. you need to consider the tables case by case and create the clustered indexes for the suitable columns or else it will bring huge performance issues.

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