Azure DocumentDB – Preparing, Loading and Querying Data


Recently I have been using Azure DocumentDB to validate several business use cases for a variety of application purposes.

For those SQL DBA’s and others who are new to Azure DocumentDB, its a recent entrant to the NoSQL document database world, and as its a PaaS document database cloud service it has the agility, scalability and availability of the Azure Cloud.

Being a schema-less Azure PaaS “document database” for my use case I wanted to verify…

  • basic costing and performance levels
  • methods to create valid JSON documents from SQL Server
  • methods to load JSON documents into Azure DocumentDB
  • performing basic like-for-like document query comparisons with SQL Server

Some homework reading for those interested…


22 May 2017 [EDIT]

Microsoft have just recently added significant new functionality and also formally renamed Azure DocumentDB as Azure CosmosDBa major evolution of the NoSQL database engine.  The details on the renamed service is here –


And so… let get into the belly of Azure DocumentDB CosmosDB!

Costing and Performance Levels

An Azure CosmosDB Account is essentially just a container into which you create one or more Databases.  Databases, like Accounts, are also just containers into which you create one or more Collections.  Collections are where you place your JSON Documents (ie your actual data), and also stores application logic like Stored Procedures, Triggers and Functions (all of which are written in JavaScript).   See here if you are interested to read more on DocumentDB programming –

From a CosmosDB perspective its the Collections that actually determine your cost and performance.   You have 2 options…

  • You can choose one of the predefined performance levels (S1, S2, S3) each of which allow 10GB document storage.
  • OR… you can set your own performance and storage level – and pay accordingly!

The set performance levels allow you to drive a certain amount of throughput per second in your Collection calculated as “RU‘s”.  Every time you work with JSON documents in Azure DocumentDB, whether it be loading (inserting), updating (replacing) or selecting (querying), then it is calculated as “RU’s” or Request Units.

During the time you have your CosmosDB collection stored in Azure then whether you use zero RUs per second or your Collections maximum RU per second allocation (say 2500 RUs per second for an S3) then you pay that same amount.  So pricing wise its about inline with how Azure SQL Database is priced.

If you push RU throughput to your Collection that exceeds your RU allocation then you will get errors on your client along the lines of “Message: {“Errors”:[“Request rate is large”]}“.  DocumentDB performance itself wont degrade or be throttled as you get close to your RU limit so your application needs to handle such errors in case you exceed it.


How do you work out what CosmosDB will cost?  

Well, 1 RU corresponds to a GET of a 1KB document.  You then need to know the specifics of your workload such as number of documents, avg document size, how the documents are accessed (query patterns), and how often documents are updated.

I suggest loading your data and running your standard queries in the Azure Portal (see below) to see what RU’s your workload generates, and use that in your calculations.

So in my situation I priced up my requirements as follows;

  • Approx 20K documents, with each document on average about 3KB.  Total data size of approx 40MB
  • Approx 85 concurrent SIMPLE queries/sec on the Document Collection.  A typical query returning all the orders/details for a specific customer (id).  Each query uses approx 3 RU’s therefore we need approx 260 RU / Sec.
  • Approx 20 concurrent COMPLEX queries/sec on the Document Collection.  A typical query returning all the orders/details for high value orders.  Each query uses approx 48 RU’s therefore we need approx 960 RU / Sec
  • (In my benchmarking I wasn’t concerned with updates so didn’t calculate this)

TOTAL RU’s / SEC = 1220

Therefore I need an S3 instance (2500 RU’s), or a custom collection serving min 1300 RU’s

Long short, you need to work it out yourself!  See this article on the process to do it –


2016-07-01 [EDIT]

As of about a week ago Microsoft released an awesome sizing estimation tool.  All you need is a sample JSON document and some guesstimates of how you’d query the database and it will provide you an RU size and storage size!



Creating JSON Documents from SQL Server

Azure CosmosDB stores JSON documents, which means that any data you try to put into it needs to be a valid document.  Additionally you need to ensure that the JSON document you create models the business purpose that you need to store for your data queries, while minimising document updates in the future for changes to the underlying data.

How you model your documents will have a direct impact on your database usability, costs and query performance.  I wont go into document modelling in this blog, however if you want to know more then this is an excellent reference –


If you don’t have any JSON data to test Azure CosmosDB, then you can generate some using SQL Server (below).  If you already have some sample JSON data then you can skip this section if you want!

For our purposes,  I just want to get some valid data out of SQL Server into a decent JSON document structure so I can load and query it.  Luckily SQL Server 2016 can natively talk JSON!  Bingo!

This script runs on the AdventureWorks2016 sample database and returns about 10K rows containing 19119 JSON documents about Customer Orders.

You can see some embedded subqueries in the SQL code – this is required if you want to get repeating JSON blocks in your JSON document.  This is a great article that spells out some nice methods to get the right JSON structure for child rows –

SELECT cast(c.CustomerID as varchar(10)) AS id,
 p.FirstName AS [Name.First],
 p.MiddleName AS [Name.Middle],
 p.LastName AS [Name.Last],
 select cast(soh.SalesOrderID as varchar(10)) AS [OrderID],
 convert(varchar(10), soh.OrderDate, 121) AS [OrderDate],
 soh.SalesOrderNumber AS [OrderNumber],
 soh.PurchaseOrderNumber AS [PurchaseOrderNumber],
 soh.TotalDue AS [Amount],
 select cast(sod.SalesOrderDetailID as varchar(10)) AS [SalesOrderDetailID],
 sod.OrderQty AS [OrderQty],
 sod.UnitPrice AS [UnitPrice],
 sod.LineTotal AS [LineTotal],
 REPLACE(REPLACE(REPLACE(pr.Name, '''', ''), '/', ' '), '"', '') AS [ProductName]
 from [Sales].[SalesOrderDetail] as sod
 inner join [Production].[Product] pr
 on sod.ProductID = pr.ProductID
 where soh.SalesOrderID = sod.SalesOrderID
 order by sod.SalesOrderDetailID
 ) as [OrderDetail]
 from [Sales].[SalesOrderHeader] as soh
 where soh.CustomerID = c.CustomerID
 order by soh.SalesOrderID
 ) as [Orders]
FROM [Sales].[Customer] c
 inner join [Person].[Person] p
 on c.PersonID = p.BusinessEntityID
order by c.CustomerID

Running the query on SQL Server 2016 will output a JSON document structure that looks something like this…

 "id": "15447",
 "Name": {
 "First": "Regina",
 "Last": "Madan"
    "Orders": [
    "OrderID": "58744",
    "OrderDate": "2013-10-27",
    "OrderNumber": "SO58744",
    "Amount": 27.614,
    "OrderDetail": [
       "SalesOrderDetailID": "67055",
       "OrderQty": 1,
       "UnitPrice": 24.99,
       "LineTotal": 24.99,
       "ProductName": "LL Mountain Tire"


How can you tell if your JSON is valid?


The Pain of Multiple JSON Rows in SSMS Output

When exporting JSON data to the SSMS GUI from SQL Server there is a little quirk you need to be aware of.  If the JSON output in SSMS returns over multiple rows then you will find that its valid but CosmosDB wont like it because it cannot parse it.  So if exporting data in SSMS it means you need to get all the JSON output rows onto a single line of text.


SSMS JSON EXPORT – if you have LESS than 65K characters — then you can do this by assigning the JSON to a VARCHAR(MAX) variable like below… but any more than 65K and it will wrap lines in SSMS.

select ISJSON(@JSONText)


SSMS JSON EXPORT – if you have MORE than 65K characters — then you can do it by running a PowerShell that extracts the JSON file.  Quick and dirty, but it works!

$DBServer = "MySQLServer"
$DBName = "MyDatabase"
$outputfile = "MyOutputPathAndFile"
$Query = "MySQLtoJSONQueryHere"
$cn = new-object System.Data.SqlClient.SqlConnection("Data Source=$DBServer;Integrated Security=SSPI;Initial Catalog=$DBName")
$cmd = new-object "System.Data.SqlClient.SqlCommand" ($Query , $cn)
$reader = $cmd.ExecuteReader()
while ($reader.Read())
 for ($i = 0; $i -lt $Reader.FieldCount; $i++)
 $FullString = $FullString + $Reader.GetValue($i)
$FullString | Out-File $outputfile


Loading JSON Documents into Azure CosmosDB

Once you have your valid JSON files, there are a number of methods to get that data into an Azure CosmosDB


The CosmosDB Data Migration Tool allows loading from lots of different source systems directly into Azure CosmosDB.  The main reasons I like it…

  • Its very simple to use, but can also do some complex stuff
  • It offers both sequential and batch record loading.  Batch is faster but has fewer options.
  • Performance wise its pretty quick – you can also increase the number of parallel requests (but beware if you hit your RU limit some document loads can fail with {“Errors”:[“Request rate is large”]})
  • You can immediately partition your documents by an id within the document (I wont go into the benefits of partitioning here, maybe later, but if interested here’s the partitioning feature here –
  • Where the document id matches, it can update those existing documents in place
  • You can setup your document indexes within the tool
  • Any import you create with the tool can be saved as a command line activity


I setup an Azure CosmosDB Collection as per below and I ran several different load tests with the following performance results.

Load Particulars…

  • Target Database: Single Partition Azure CosmosDB Collection, 1000 RU’s, S2
  • Source File: 39MB JSON data file containing 19119 documents (each document is approx 3KB)

Load Performance…

Load Type Requests/Sec Time (mm:ss) Failed Documents
Sequential 10 23:05 0
Bulk 50 07:21 0
Sequential 100 08:38 257
Sequential 1000 08:08 311


NOTE that the Data Migration Tool can also extract data directly from SQL Server and into CosmosDB, but it didn’t correctly extract multiple levels of repeating child data so I didn’t benchmark it.  Give it a try yourself as your mileage may vary.




Querying JSON Documents from Azure CosmosDB

Ok so its about time we look at ways to get data out! CosmosDB is NOT a big data store – so no point here storing data unless you are going to actually use it!

The Azure DocumentDB query language is very similar to that of SQL Server T-SQL and so for the SQL DBA’s out there you will note some close similarities – and some stuff that just does not fit well in your relational headspace.


To run queries you have a few options;

  • The Azure Portal.  In the Query Explorer section of your Collection you can type in and run queries against the documents in your collection.  It will output the results along with how many RU’s of your maximum allocation that query used.
  • Programatically.  Naturally this would be your application logic itself!
  • Azure CosmosDB Studio.  This is not a Microsoft tool – however I really like this tool as you can browse your collections and perform many document related functions.  It also shows the query RU’s and elapsed time (the Azure Portal does not show time).  The code is here on GitHub so you can download and compile it –
  • Visual Studio (2013/2015).  Nope, unfortunately Visual Studio Azure Cloud Explorer currently does not support running queries against CosmosDB, hopefully this comes soon!


Lets do some basic query comparisons with SQL Server!

NOTE that I cleared the SQL Server buffer cache etc before each SQL query run to get a baseline performance.


Return Single Document by ID (First Document in Collection)

SQL Server 2016

FROM [Sales].[Customer] c
 inner join [Person].[Person] p
 on c.PersonID = p.BusinessEntityID
 inner join [Sales].[SalesOrderHeader] as soh
 on soh.CustomerID = c.CustomerID
 inner join [Sales].[SalesOrderDetail] as sod
 on soh.SalesOrderID = sod.SalesOrderID
 inner join [Production].[Product] pr
 on sod.ProductID = pr.ProductID
WHERE c.CustomerID = 11000


Azure CosmosDB

FROM CustomerOrders c
WHERE = "11000"

ELAPSED TIME (MS) = 72  (RU = 3)


Return Single Document by ID (Last Document in Collection / Last Row in Database)

SQL Server 2016

FROM [Sales].[Customer] c
 inner join [Person].[Person] p
 on c.PersonID = p.BusinessEntityID
 inner join [Sales].[SalesOrderHeader] as soh
 on soh.CustomerID = c.CustomerID
 inner join [Sales].[SalesOrderDetail] as sod
 on soh.SalesOrderID = sod.SalesOrderID
 inner join [Production].[Product] pr
 on sod.ProductID = pr.ProductID
WHERE c.CustomerID = 18759


Azure CosmosDB

FROM CustomerOrders c
WHERE = "30118"

ELAPSED TIME (MS) = 783  (RU = 9)


Return Single Document By Last Name Order by First Name

SQL Server 2016

FROM [Sales].[Customer] c
 inner join [Person].[Person] p
 on c.PersonID = p.BusinessEntityID
 inner join [Sales].[SalesOrderHeader] as soh
 on soh.CustomerID = c.CustomerID
 inner join [Sales].[SalesOrderDetail] as sod
 on soh.SalesOrderID = sod.SalesOrderID
 inner join [Production].[Product] pr
 on sod.ProductID = pr.ProductID
WHERE p.LastName = 'Yang'
ORDER BY p.FirstName


Azure CosmosDB

FROM CustomerOrders c 
WHERE c.Name.Last="Yang"
ORDER BY c.Name.First

ELAPSED TIME (MS) = 432  (RU = 75)


Return Top 10 Customer Orders

SQL Server 2016

 c.CustomerID AS id,
 soh.SalesOrderID AS [OrderID],
FROM [Sales].[Customer] c
 inner join [Sales].[SalesOrderHeader] as soh
 on soh.CustomerID = c.CustomerID 
 inner join [Sales].[SalesOrderDetail] as sod
 on soh.SalesOrderID = sod.SalesOrderID


Azure CosmosDB

SELECT TOP 10, o.OrderID, o.Amount
FROM CustomerOrders AS c
JOIN o IN c.Orders

ELAPSED TIME (MS) = 72  (RU = 5)


Return All Orders Where Amount > 50000

SQL Server 2016

SELECT soh.SalesOrderID AS [OrderID],
 soh.TotalDue AS [Amount]
FROM [Sales].[SalesOrderHeader] as soh
WHERE soh.TotalDue > 50000


Azure CosmosDB

SELECT c.OrderID, c.Amount
FROM CustomerOrders co
JOIN c IN co.Orders
WHERE c.Amount > 50000

ELAPSED TIME (MS) = 276  (RU = 310)


Return All Customers and Orders Where Quantity Between 20 and 25

SQL Server 2016

SELECT c.CustomerID AS id,
 soh.SalesOrderID AS [OrderID],
 soh.OrderDate AS [OrderDate],
 soh.TotalDue AS [Amount],
 sod.OrderQty AS [OrderQty]
FROM [Sales].[Customer] c
 inner join [Sales].[SalesOrderHeader] as soh
 on soh.CustomerID = c.CustomerID
 inner join [Sales].[SalesOrderDetail] as sod
 on soh.SalesOrderID = sod.SalesOrderID
WHERE sod.OrderQty between 20 and 25


Azure CosmosDB

SELECT, o.OrderID, o.Amount, o.OrderDate, od.OrderQty
FROM CustomerOrders AS c
JOIN o IN c.Orders
JOIN od IN o.OrderDetail
WHERE od.OrderQty between 20 and 25

ELAPSED TIME (MS) = 366  (RU = 121)


Return Just the First Order for Each Customer Where Amount > 5000 and Quantity > 20 and Order By the Customer ID

SQL Server 2016

with CTE as
 SELECT SalesOrderID AS [OrderID],
 OrderDate AS [OrderDate],
 TotalDue AS [Amount],
 FROM [Sales].[SalesOrderHeader]
 WHERE TotalDue > 5000
SELECT cte.[OrderID],
 inner join [Sales].[SalesOrderDetail] as sod
 on cte.[OrderID] = sod.SalesOrderID
WHERE cte.RN = 1
AND sod.OrderQty > 20
order by 1


Azure CosmosDB

SELECT c.OrderID, c.OrderNumber, c.Amount, od.OrderQty
FROM CustomerOrders.Orders[0] AS c
JOIN od IN c.OrderDetail
WHERE od.OrderQty > 20
AND c.Amount > 5000

ELAPSED TIME (MS) = 204  (RU = 198)


What’s Missing in the Query Language?

The DocumentDB query language isn’t fully developed (yet!) so there’s some things that it doesn’t do well, and things you cannot do at all.


Key Summary

So there you have it – a pretty good start into understanding about Azure CosmosDB – creating JSON from a SQL Query, methods for loading JSON documents and methods for querying JSON documents.

For the SQL DBA’s out there hopefully this has also given you a bit of a peek into the world of Azure, JSON, CosmosDB and NoSQL and demystified what they are for, their use cases and how they work.

NoSQL isn’t going to replace any relational databases in the world any time soon – they clearly serve different use cases – however the new future world of the modern DBA will have a requirement to know, manage and operate not just your cornerstone relational database systems but also many other on-prem, hybrid and cloud data stores such as HDInsight/Hadoop,  Data Lake and NoSQL to name a few.

As per usual with all my posts, please test everything yourself as your mileage may vary!

Disclaimer: all content on Mr. Fox SQL blog is subject to the disclaimer found here


2 thoughts on “Azure DocumentDB – Preparing, Loading and Querying Data

  1. Alex Bransky 10 May 2017 / 9:44 AM

    Thanks! Have you tried using the DocumentDB ODBC driver with SSIS? I wish I could use it as a linked server…


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