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.
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We all know that if the workload running on a logical partition consumes more than the throughput that was allocated to the underlying physical partition, it's possible that operations become rate-limited. This occurs when the workload is not evenly distributed on the several (logical) partitions, that is usually when a wrong partition key is selected for the document/container at creation time.
This performance problem is referred to as "hot partition".
But what if we provision the database as 'serverless', which is the mode where throughput is dynamically allocated, (not provisioned).
Does the issue still persist? If yes, why?
Serverless isn't terribly helpful here and in workloads with sustained requests would be prohibitively expensive.
If you have persistent hot partitions, you may want to look into this feature here which is in preview. https://learn.microsoft.com/azure/cosmos-db/nosql/distribute-throughput-across-partitions
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
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
In cosmos Db hope are RUs increased for writes? To my understanding, the replicas are only for reads but only one node is used for writes. What is the model for writes distribution in cosmos db when the load is high?
In Cosmos DB, data is stored by it's partition key. In high write volume scenarios a key to success is ensuring your partition key has high enough cardinality to give you enough throughput to meet your performance needs for a given amount of RU/s.
Beyond scalability in a single geographic region though, using multi-master is what provides geographic scale for writes or lower latency.
I am seeing some throttles on my updates on DynamoDB table. I know that throttle work on per second basis, that peaks above provisioned capacity can be sometimes absorbed, but not guaranteed. I know that one is supposed to evenly distribute the load, which I have not done.
BUT please look at the 1 minute average graphs from metrics; attached. The utilized capacity is way below the provisioned capacity. Where are these throttles coming from? Because all writes went to a particular shard?
There are no batch writes. The workload distribution is something that cannot, easily, control.
DynamoDB is built on the assumption that to get the full potential out of your provisioned throughput your reads and writes must be uniformly distributed over space (hash/range keys) and time (not all coming in at the exact same second).
Based on the allocated throughput on your graphs you are still most likely at one shard, but it is possible that there are two or more shards if you have previously raised the throughput above the current level and lowered it down to what it is at now. While this is something to be mindful of, it likely is not what is causing this throttling behavior directly. If you have a lot of data in your table, over 10 GB then you definitely will have multiple shards. This would mean you likely have a lot of cold data in your table and that may be causing this issue, but that seems less likely.
The most likely issue is that you have some hot keys. Specifically, you have one or just a few records that are receiving a very high number of read or write requests and this is resulting in throttling. Essentially DynamoDB can support massive IOPS for both writes and reads, but you can't apply all of those IOPS to just a few records, they need to be distributed among all of the records uniformly in an ideal situation.
Since the number of throttles you were showing is in the order of magnitude of 10s to 100s it may not be something to worry about. As long as you are using the official AWS SDK it will automatically take care of retries with exponential backoff to retry requests several times before completely giving up.
While it is difficult in many circumstances to control the distribution of reads and writes to a table, it may be worth taking another look at your hash/range key design to make sure it is really optimal for your pattern of reads and writes to the table. Also, for reads you may employ caching through Memcached or Redis, even if the cache expired in just a few minutes or a few seconds to help reduce the impact of hot keys. For writes you would need to look at the logic in the application to make sure there are not any unnecessary writes being performed that could be causing this issue.
One last point related to batch writes: A batch operation in DynamoDB does not reduce the consumed amount of read or writes the different child requests consume, it simply reduces the overhead of making multiple HTTP requests. While batch requests generally help with throughput, they are not useful at reducing the likelihood of throttling in DynamoDB.