We are using com.azure.cosmos.spark:azure-cosmos-spark library from Databricks to bulk write into CosmosDB Containers.
Currently throughput's are set on container (5 containers) level (ex: 10000 RUs). Sometime couple of write operations on a given container throttle's as RUs consumed are 100%, but after re-tries does finish. Load into the containers are in Parallel.
What if we change throughput to database level (ex: 50000 RUs - equally distributed among the containers) and execute write process in sequence. Will container into which data is being written will have access to 50000 RUs or 10000 RUs?
Azure Cosmos containers that are configured in autopilot mode have the following benefits:
Simple – There is no need to invest time in manually scaling throughput or writing code to automatically scale throughput
Reliable – Autopilot scaling is fully managed by Microsoft. There is no disruption to client connections, applications, or impact to SLA’s.
No rate-limiting of operations – Rate limiting (throttled requests) will not happen if throughput consumed is within the max throughput chosen for autopilot mode.
By default, the distribution will be done according to the RU's when we are using Container level throughput.
If we need to manually provide assignment, it can be done to handle total 50K RU's, but it is not cost-effective and also not a great approach to follow.
Autopilot mode pricing:
- Single-region write accounts- Cost for provisioned RU’s is 50% higher
- Multi-region write accounts- Cost for provisioned RU’s is identical to cost of manually provisioned throughput RU’s
Related
We are using a dynamodb with on-demand capacity mode. We are suddenly seeing few of our requests getting write throttled. During the same time frame there is sudden spike in Write Capacity Units (WCU).
I have checked the incoming traffic/ write request count and it is pretty much the same.
Does this mean the WCU increase is due to increase in item size?
How can I verify the size of my request?
Since our dynamodb is already provisioned in on-demand mode, why is it unable to auto scale and handle the requests given that traffic is pretty much the same.
It's possible
You can return the consumed capacity for each write operation which will allow you to understand which items are consuming more capacity. ReturnConsumedCapacity
On-demand mode tables can throttle for 3 reasons:
You exceed twice your previous peak in 30 mins
You exceed partition hard limit of 1000 WCU / 3000 RCU (hot partition)
You have GSI backoressure where you're GSI has a hot partition
You can enable CloudWatch Contributor Insights to understand if you have a hot key(s).
When you are doing your RU calculations for Cosmos DB, do you need to be calculating the max values of reads, inserts, updates and deletes or the average number per second?
Reason why I ask is because the average documents read (in current mongo db) is around 5500 but the maximum number of documents read (in on second) over my sampling period was 965880.
I have looked through all of Microsoft's documentation on Costing Cosmos DB and there is no clear guidance on whether the figure for RU throughput is average or max
As you said there's no MS document on 'average or max' for setting throughput, in my opinion, both average and max are meaningful, but we also need to look at the most common situation, for example, there's always around 5800 per second, and also usually 4500 per second, the min is 3000 and the max is 9000. 1 RU means '1KB doc read', if we set the max number as the throughput, it's expensive and waste, if we set the average, maybe the system usually 'in debt' as the answer said. That's why I say we also need to consider the 'most common' situation.
By the way, MS provides a web based tool for helping estimate the request unit requirements for typical operations. If admin also don't know the real situation about the database, I think this doc may help, in short for the doc, that says, if you're building a new application or a small application, you can start at the minimum RU/s to avoid over-provisioning in the beginning. After running the application for some time, maybe you can use azure monitor to determine if your traffic pattern is suitable.
To avoid throttling you need to provide the MAX throughput you need in RUs. Also, it depends how frequently you hit the max RUs. There are basically 3 ways to provision RUs- Provisioned throughput, Autoscale & Serverless(Preview).
If you provision standard (manual) RU/s at the entry point of 400 RU/s, you won't be able to consume above 400 RU/s, unless you manually change the throughput. You'll be billed for 400 RU/s at the standard (manual) provisioned throughput rate, per hour.
If you provision autoscale throughput at the entry point of max RU/s of 4000 RU/s, the resource will scale between 400 to 4000 RU/s. Since the autoscale throughput billing rate per RU/s is 1.5x of the standard (manual) rate, for hours where the system has scaled down to the minimum of 400 RU/s, your bill will be higher than if you provisioned 400 RU/s manually. However, with autoscale, at any time, if your application traffic spikes, you can consume up to 4000 RU/s with no user action required. In general, you should weigh the benefit of being able to consume up to the max RU/s at any time with the 1.5x rate of autoscale.
For small applications with a low expected traffic, you can also consider the serverless capacity mode, which bills you as you use.
Use the Azure Cosmos DB capacity calculator to estimate your throughput requirements.
Should definitely go through this and related pages of documentation- https://learn.microsoft.com/en-us/azure/cosmos-db/request-units
My Cosmos DB is using Shared Throughput across several containers. I have manually scaled up my Cosmos DB to 70,000 RU/s and I am currently running a large number of requests.
Looking in azure I can see that a portion of my requests are being throttled (returning 429).
To give an idea of numbers around 25k requests return 200 and around 5k requests return 429.
When I follow the warning in the azure portal that says my collection is exceeding provisioned throughput it shows the average throughput is 6.78k RU/s.
I don't understand why when I have 70,000 RU/s that my requests are being throttled when the average throughput is supposedly only 6,780 RU/s.
No other containers are being read or written to, all these requests are made against just one container.
As all these requests are to run a stored procedure they all have a Partition key supplied.
The most likely reason is you have a hot partition that is reaching its allocated throughput before the other partitions are.
For a horizontally scalable database, throughput is allocated across physical partitions (computers) and data is partitioned using a partition key that basically acts as an address to route it to a specific computer to be stored.
Assume I have a collection with three partitions 1, 2, 3 and 30K RU/s. Each one of those will get 10K RU/s allocated to it. If I then run an operation that does a ton of operations on partition 2 and consumes all of it's 10K I'm going to get rate limited (429) even I don't touch partition 1 or 3.
To avoid this you need to pick a partition key that BOTH distributes data as evenly as possible during writes and ideally can also be used to answer queries within one or a small number (bounded) number of partitions, trying to avoid "fan out" queries where queries have to hit every partition.
Now for small collections that only reside on a single physical partition none of this matters because your data is all on a single physical partition. However, as the collection grows larger this causes issues which will prevent the database from scaling fully.
You can learn more here
I am experiencing an issue with my EC2 instance. I am scraping different websites using R programming and it works fine but after some hours, my EC2 instance is freezing.
After raising a ticket to AWS support, they noticed that this was caused by the rise of the "VolumeQueueLength" which then was decreasing the BurstBalance credits from 100 to 0.
See below when I tried around June 19th:
Would you know what is causing this VolumeQueueLength to go up?
Thanks a ton!
From I/O Characteristics and Monitoring - Amazon Elastic Compute Cloud:
If your I/O latency is higher than you require, check VolumeQueueLength to make sure your application is not trying to drive more IOPS than you have provisioned. If your application requires a greater number of IOPS than your volume can provide, you should consider using a larger gp2 volume with a higher base performance level or an io1 volume with more provisioned IOPS to achieve faster latencies.
For more information about Amazon EBS I/O characteristics, see the Amazon EBS: Designing for Performance re:Invent presentation on this topic.
This is basically saying that the IO allocated to an Amazon EBS 'General Purpose' volume is proportional to its size, so a larger volume might solve your IO problems. Alternatively, you could consider moving to a Provisioned IOPS volume (which is faster, but more expensive).
Your application seems to be using more IO than has been allocated for the volume.
Amazon DynamoDB allows the customer to provision the throughput of reads and writes independently. I have read the Amazon Dynamo paper about the system that preceded DynamoDB and read about how Cassandra and Riak implemented these ideas.
I understand how it is possible to increase the throughput of these systems by adding nodes to the cluster which then divides the hash keyspace of tables across more nodes, thereby allowing greater throughput as long as access is relatively random across hash keys. But in systems like Cassandra and Riak this adds throughput to both reads and writes at the same time.
How is DynamoDB architected differently that they are able to scale reads and write independently? Or are they not and Amazon is just charging for them independently even though they essentially have to allocate enough nodes to cover the greater of the two?
You are correct that adding nodes to a cluster should increase the amount of available throughput but that would be on a cluster basis, not a table basis. The DynamoDB cluster is a shared resource across many tables across many accounts. It's like an EC2 node: you are paying for a virtual machine but that virtual machine is hosted on a real machine that is shared among several EC2 virtual machines and depending on the instance type, you get a certain amount of memory, CPU, network IO, etc.
What you are paying for when you pay for throughput is IO and they can be throttled independently. Paying for more throughput does not cause Amazon to partition your table on more nodes. The only thing that cause a table to be partitioned more is if the size of your table grows to the point where more partitions are needed to store the data for your table. The maximum size of the partition, from what I have gathered talking to DynamoDB engineers, is based on the size of the SSDs of the nodes in the cluster.
The trick with provisioned throughput is that it is divided among the partitions. So if you have a hot partition, you could get throttling and ProvisionedThroughputExceededExceptions even if your total requests aren't exceeding the total read or write throughput. This is contrary to what your question ask. You would expect that if your table is divided among more partitions/nodes, you'd get more throughput but in reality it is the opposite unless you scale your throughput with the size of your table.