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Azure CosmosDB Bulk Insert with Batching

I'm using Azure CosmosDB with Azure Function. In function, I'm trying to save around 27 Million records.
The method AddBulk looks as below:
public async Task AddBulkAsync(List<TEntity> documents)
{
try
{
List<Task> concurrentTasks = new();
foreach (var item in documents)
{
concurrentTasks.Add(_container.CreateItemAsync<TEntity>(item, new PartitionKey(Convert.ToString(_PartitionKey?.GetValue(item)))));
}
await Task.WhenAll(concurrentTasks);
}
catch
{
throw;
}
}
I've around 27 Million records categorized by a property called batch. That means each batch holds around 82-85K records.
Currently, I'm creating a list of 27 Million records and sending that list to AddBulkAsync. It is causing a problem in terms of Memory and Throughput. Instead, I'm planning to send the data in batch:
Get distinct batchId from all the docs and create a batch list.
Loop batch list and get documents (i.e around 85K records) for the first batch (in a loop).
Call AddBulk method for documents (i.e around 85K records) of the first batch.
Continue the loop for the next batchId and so on.
Here, I wanted to understand as it calls AddBulk thousands of times and in turn, AddBulk calls await Task.WhenAll(concurrentTasks) those many times internally.
Will it cause a problem? Are there any better approaches available to achieve such a scenario? Please guide.

Some suggestions in scenarios like this.
When doing bulk operations like these, be sure you enable bulkMode = true in the Connection Options for the Cosmos client when instantiating. Bulk Mode works by queuing up divided into groups based upon a partition key range for a physical partition within a container. (Logical key values, when hashed, fall within a range of hashes that are then mapped to the physical partition where they reside). As the queue fills up the Cosmos client will dispatch all the queued-up items for that partition. If it does not fill up, it will dispatch what it has, full or not.
One way you can help with this is to try to batch updates such that the number of items you send are roughly evenly spread across logical partition key values within your container. This will allow the Cosmos client to more fully saturate the available throughput with batches that are as full as possible and sent in parallel. When data is not evenly distributed across logical partition key values, the volume of data ingested will be slower since dispatches will be sent as the queues fill up. It is less efficient and will not fully utilize every ounce of throughput provisioned.
One other thing to suggest is, if at all possible, stream your data rather than batch it. Cosmos DB measures provisioned throughput per second (RU/s). You get only the amount of throughput you've provisioned on a per second basis. Anything over that and you are either rate limited (i.e. 429s) or the speed of operations is slower than it otherwise could be. This is because throughput is evenly spread across all partitions. If you provision 60K RU/s you'll end up with ~6 physical partitions, each of them get 10K RU/s. There is no sharing of throughput. As a result, operations like this are less efficient than they could be.
One effective way to deal with deal with all of this is to amortize your throughput over a longer period of time. Streaming is the perfect way to accomplish this. Streaming data, in general, requires less overall provisioned throughput because you are utilizing throughput over a longer period of time. This allows you to work with a nominal amount of throughput, rather than having to scale up and down again and is just overall more efficient. If streaming from the source is not an option, you can accomplish this with queue-based load leveling by first sending data to a queue, then streaming from there into Cosmos DB.
Either way you go, these docs can help squeeze the most performance out of Azure Functions hosting bulk ingestion workloads into Cosmos DB.
Blog post on Bulk Mode
Bulk support improvements blog post
Advanced Configurations for Azure Function Cosmos DB Triggers
Manage Azure Function Connections This is important because Direct TCP Mode in Cosmos client can create LOTS of connections.

Retrieve 1+ million records from Azure Table Storage

My table storage has approximately 1-2 million records and I have a daily job that needs needs to retrieve all the records that does not have a property A and do some further processing.
It is expected that there are about 1 - 1.5 million records without property A. I understand there are two approaches.
Query all records then filter results after
Do a table scan
Currently, it is using the approach where we query all records and filter in c#. However, the task is running in an Azure Function App. The query to retrieve all the results is sometimes taking over 10 minutes which is the limit for Azure Functions.
I'm trying to understand why retrieve 1 million records is taking so long and how to optimise the query. The existing design of the table is that the partition and row key are identical and is a guid - this leads me to believe that there is one entity per partition.
Looking at Microsoft docs, here are some key Table Storage limits (https://learn.microsoft.com/en-us/azure/storage/common/storage-scalability-targets#azure-table-storage-scale-targets):
Maximum request rate per storage account: 20,000 transactions per second, which assumes a 1-KiB entity size
Target throughput for a single table partition (1 KiB-entities): Up to 2,000 entities per second.
My initial guess is that I should use another partition key to group 2,000 entities per partition to achieve the target throughput of 2,000 per second per partition. Would this mean that 2,000,000 records could in theory be returned in 1 second?
Any thoughts or advice appreciated.

I found this question after blogging on the very topic. I have a project where I am using the Azure Functions Consumption plan and have a big Azure Storage Table (3.5 million records).
Here's my blog post:
https://www.joelverhagen.com/blog/2020/12/distributed-scan-of-azure-tables
I have mentioned a couple of options in this blog post but I think the fastest is distributing the "table scan" work into smaller work items that can be easily completed in the 10-minute limit. I have an implementation linked in the blog post if you want to try it out. It will likely take some adapting to your Azure Function but most of the clever part (finding the partition key ranges) is implemented and tested.
This looks to be essentially what user3603467 is suggesting in his answer.

I see two approaches to retrieve 1+ records in a batch process, where the result must be saved to a single media - like a file.
First) You identity/select all primary id/key of related data. Then you spawn parallel jobs with chunks of these primary id/keys where you read the actual data and process it. each job then report to the single media with the result.
Second) You identity/select (for update) top n of related data, and mark this data with a state of being processed. Use concurrency locking here, that should prevent others from picking that data up if this is done in parallel.
I will go for the first solution if possible, since it is the simplest and cleanest solution. The second solution is best if you use "select for update", i dont know if that is supported on Azure Table Storage.

You'll need to paralise the task. As you don't know the partition keys, run 24 separate queries PK that start and end for each letter of the alaphabet. Write a query where PK > A && PK < B, and > B < C etc. Then join the 24 results in memory. Super easy to do in a single function. In JS just use Promise.all([]).

How to Minimize Data Transfer Out From Azure Query in C# .NET

I have a small table(23 rows, 2 int columns), just a basic user-activity monitor. The first column represents user id. The second column holds a value that should be unique to every user, but I must alert the users if two values are the same. I'm using an Azure Sql database to hold this table, and Linq to Sql in C# to run the query.
The problem: Microsoft will bill me based on data transferred out of their data-centers. I would like have all of my users to be aware of the current state of this table at all times, second by second, and keep data-transfer under 5 GB per month. I'm thinking along the lines of a Linq-To-Sql expression such as
UserActivity.Where(x => x.Val == myVal).Count() > 1;
But this would download the table to the client, which cannot happen. Should I be implementing a Linq solution? Or would SqlDataReader download less metadata from the server? Am I taking the right approach by using a database at all? Gimme thoughts!

If it is data transfer you are worried about you need to do your processing on the server and return only the results. A SQLDataReader solution can return a smaller, already processed set of data to minimise the traffic.

A couple thoughts here:
First, I strongly encourage you to profile the SQL generated by your LINQ-to-SQL queries. There are several tools available for this, here's one at random (I have no particular preference or affiliation):
LINQ Profiler from Devart
Your prior experience with LINQ query inefficiency notwithstanding, the LINQ sample you quote in your question isn't particularly complex so I would expect you could make it or similar work efficiently, given a good feedback mechanism like the tool above or similar.
Second, you don't explicitly mention whether your query client is running in Azure or outside, but I gather from your concern about data egress costs that its running outside Azure. So the data egress costs are going to be query results using the TDS protocol (low-level protocol for SQL Server), which is pretty efficient. Some quick back-of-the-napkin math shows that you should be fine to stay below your monthly 5 GB limit:
23 users
10 hours/day
30 days/month (less if only weekdays)
3600 requests/hour/user
32 bits of raw data per response
= about 95 MB of raw response data per month
Even if you assume 10x overhead of TDS for header metadata, etc. (and if my math is right :-) ) then you've still got plenty of room underneath 5 GB. The point isn't that you should stop thinking about it and assume it's fine... but don't assume it isn't fine, either. In fact, don't assume anything. Test, and measure, and make an informed choice. I suspect you'll find a way to stay well under 5 GB without much trouble, even with LINQ.
One other thought... perhaps you could consider running your query inside Azure, and weigh the cost of that vs. the cost of data egress under the "query running outside Azure" scenario? This could (for example) take the form of a small Azure Web Job that runs the query every second and notifies the 23 users if the count goes above 1.
Azure Web Jobs
In essence, you wouldn't notify them if the condition is false, only when it's true. As for the notification mechanism, there are various cloud-friendly options:
Azure mobile push notifications
SMS messaging
SignalR notifications
The key here is to determine whether its more cost-effective and in line with any bigger-picture technology or business goals to have each user issue the query continuously, or to use some separate process in Azure to notify users asynchronously if the "trigger condition" is met.
Best of luck!

Azure Drive vs Block Blob vs Table

I couldn't decide the best approach to handle the following scenario via Azure storage.
~1500+ CSV files between ~1MB to ~500MB overall ~20GB data
Each file uses exactly same model and each model.toString() is ~50 characters ~400byte
Every business day, during 6 hours period, ~8000+ new rows comes per minute
Based on property value, each row goes to the correct file
Multiple instance writing is not necessary as long as multiple reading is supported even there is few seconds delay for snapshot period is OK.
I would like to use Block Blob but downloading ~400MB single file into the computer, just to add a single line and upload it back doesn't make sense and I couldn't find other way around.
There is a Drive option which uses Page Blob unfortunately it is not supported by SDKv2 and makes me nervous about possible discontinuation of the support
And final one is Table which looks OK other than reading few hundred thousands rows continuesly may become an issue
Basically, I prefer to write files when I retrieve the data immediately. But, if it does worth to give up, I can live with the single update at the end of the day which means ~300-1000 lines per file
What would be best approach to handle this scenario?

Based on your above requirement, Azure Tables are the optimal option. With single Azure Storage account you get the following:
Storage Transactions – Up to 20,000 entities/messages/blobs per second
Single Table Partition – a table partition are all of the entities in a table with the same partition key value, and most tables have many partitions. The throughput target for a single partition is:
Up to 20,000 entities per second
Note, this is for a single partition, and not a single table. Therefore, a table with good partitioning, can process up to a few thousand requests per second (up to the storage account target 20,000).
Tables – use a more finely grained PartitionKey for the table in order to allow us to automatically spread the table partitions across more servers.
About reading "few hundred thousands rows" continuously, your main obstacle is storage level 20,000 transactions/sec however if you design your partition so granular to segment them on hundreds of servers, you could be able to read "hundred of thousands" in minutes.
Source:
Windows Azure Storage Abstractions and their Scalability Targets
Windows Azure’s Flat Network Storage and 2012 Scalability Targets

SQL Design: Big table, thread access serialization

I have one BIG table(90k rows, size cca 60mb) which holds info about free rooms capacities for about 50 hotels. This table has very few updates/inserts per hour.
My application sends async requests to this(and joined tables) at max 30 times per sec.
When i start 30 threads(with default AppPool class at .NET 3.5 C#) at one time(with random valid sql query string), only few(cca 4) are processed asynchronously and other threads waits. Why?
Is it becouse of SQL SERVER 2008 table locking, or becouse of .NET core? Or something else?
If it is a SQL problem, can help if i split this big table into one table per each hotel model?
My goal is to have at least 10 threads servet at a time.

This table is tiny. It's doesn't even qualify as a "medium sized" table. It's trivial.
You can be full table scanning it 30 times per second, you can be copying the whole thing in ram, no server is going to be the slightest bit bothered.
If your data fits in ram, databases are fast. If you don't find this, you're doing something REALLY WRONG. Therefore I also think the problems are all on the client side.

It is more than likely on the .NET side. If it were table locking more threads would be processing, but they would be waiting on their queries to return. If I remember correctly there's a property for thread pools that controls how many actual threads they create at once. If there are more pending threads than that number, then they get in line and wait for running threads to finish. Check that.

Have you tried changing the transaction isolation level?
Even when reading from a table Sql Server will lock the table
try setting the isolation level to read uncommitted and see if that improves the situation,
but be advised that its feasible that you will read 'dirty' data make sure you understand the ramifications if this is the solution
this link explains what it is.
link text

Rather than ask, measure. Each of your SQL queries that is actually submitted by your application will create a request on the server, and the sys.dm_exec_requests DMV shows the state of each request. When the request is blocked the wait_type column shows a non-empty value. You can judge from this whether your requests are blocked are not. If they are blocked you'll also know the reason why they are blocked.