For this next article in the series, were going to be looking at Microsoft’ Azure’s Table Storage service.
Before getting into the specific details of Azure Tables, let’s take a quick stroll through comparing Azure Tables and our popular relation database. There is a high probability that you have a familiarity with Relational Databases (RDBMS) and think along those terms when it comes to storing and structuring data in a database.  However, Azure Table Storage is not a relational database, but a NoSQL database, so there is a completely different approach that needs to be taken when we set out to host our data in Azure’s Table Storage. When we think about relational databases, we think about single table schemas, table relationships with foreign keys and constraints, stored procedures and columns and rows. Since Azure Tables are not a perfect fit for every time we need to persist data, let’s look at some of the major differences between Azure Tables and relational databases to help draw that defining line.
If you have had anything to do with developing an application with a database backend, you probably have seen how the requirements generally start off with having some data that needs to be persisted. With Azure Tables, the most important question that you have to answer before you’re ready to persist data to Table Storage is, what are you going to do with the data? Even though every partition will be served by a Partition Server (that can be responsible for multiple partitions), it is when partitions under heavy load can be designated its own Partition Server.  It is this distribution of load across partitions that allow your Azure Table Storage to be highly scalable. So let’s stop for a second and think about this; if you design your table with a single partition key. Unfortunately, partition servers also create a boundary that will directly affect performance.  Therefore, in contrast to the all-in-one partition approach, creating unique partitions for every entity is a pattern that that will cost you the ability to perform batch operations (discussed later), incur performance penalties for insert throughput as well as when queries cross partition boundaries. Finally, sorting is not something that is controlled after the data has been persisted.  Data in your table will be sorted in ascending order first by the partition key, then sorted ascending by the row key. Row keys also provide a second applied ascending sort order after the applied ascending sort order of the partition key.  Therefore, depending on your circumstances, further thought might be required on how you want data to be sorted when retrieved.
Therefore, the decision you need to make is how will the data be queried, what are the common queries that you expect to be made?
The most important question you need to ask before using or designing your Azure Tables, how will the data be queried.


The table itself is associated with a specific Azure Storage Account.  Therefore, if we want to perform crud and query operations on specific table in our storage account, roughly, we will be required to instantiate objects that represent our storage account, a specific table client object within our storage account and finally, a reference to the table. Third, through the Table Client object, obtain an object that references a table within your storage account. As mentioned earlier, creating the storage credentials object, you need to provide it your storage account name and either the primary or secondary base64 key.  This is all information we covered in the article on blob storage, but you can obtain this information from your Azure Portal by selecting “Manage Access Keys” under “Storage”, where it will list your storage accounts you have created. As you might have noticed, were using a combination of the model#, size and gender for the Row key.  Depending on what common queries you determine will be used for data retrieval from your table, this key can have significant importance on available unique keys within a partition and sort order. This is just a note that the previous DynamicTableEntity wasn’t required to make changes to an existing entity.  But you’re applications entity POCO’s might change, while you want to retain existing property information, or possibly your application has a split persistent model that that needs to merge the data in an entity. And we can see how it has completely altered the structure and data for the existing table entity. Developers on the Microsoft Azure Platform should become familiar with how Table Storage works and how it differs from the relational databases they are used to. The next 2 lines of code get the signed message that is to be used for the Authorization HTTP header. Starting with the next section we’ll go over creating tables and persisting data.  But, this brings us to the most important point when dealing with Azure Tables, design. Therefore, the table is designed to house that data.  Its only afterwards that thought is put into how that information needs to be retrieved and utilized.  Sound familiar? Such as in the case of a table that stores store product information, but you decide to make your partition key “products” and all entities fall under this single partition.  How can Azure partition your data so that it can automatically scale out your table for efficient performance?
Therefore, if sort is of importance, you will need to determine the ways partition key’s and row keys are defined.  A good example is how 11 would come before 2 unless padded with 0’s. Based on that answer, you need to determine how the data can be grouped into partitions.  The following is a list of guidelines that is not exhaustive, but can help making table design decisions easier.
For the creation of tables, I would advice you create the tables ahead of time when you can.


This HTTP response can provide more insight to the results of a table operation.  Going along in this article you’ll see that I capture the returned result just for completion, but it isn’t always necessary.
This post is just an intro how to start, covering the base cases and most popular Node.js modules for Azure Tables. Knowing how table storage works will help you determine if it is a good fit for your particular requirements. This is one of the positive characteristics of NoSQL databases that allow us to focus solely on the data.  Of course, those same characteristics are what provide a relational database its strengths. Table storage is a NoSQL key-attribute data store, which allows for rapid development and fast access to large quantities of data.
Azure virtual machines and cloud services can share file data across application components via mounted shares, and on-premise applications can access file data in a share via the File service REST API. The service is a NoSQL datastore which accepts authenticated calls from inside and outside the Azure cloud. Entities must define the following three system properties as part of the property collection: PartitionKey – The PartitionKey property stores string values that identify the partition that an entity belongs to. Modifications to timestamps are ignored because the table service maintains the value for this property during all inserts and update operations.
Partitions are always served from one partition server and each partition server can serve one or more partitions.



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