I have a DynamoDB table that has provisioned read way above the consumed read, the utilization percentage is 70%.
I'm still getting throttling on the table and I couldn't figure out why. Once thing I'm suspecting is hot partition, but I'm unable to verify.
In the case of hot partitioning, does it throttle only the read to the hot partition, or to reads to all partitions?
A single partition can serve 1000WCU and 3000RCU. If you consume capacity on a single partition exceeding that you will be throttled.
If you have a number of keys sharing the same partition then throttling will be for all keys sharing that partition. DynamoDB does have Adaptive Capacity feature that can isolate hot keys but would sometimes have to have some throttling before that happens.
2 things you can try:
Enable CloudWatch contributor insights to understand which keys are throttling allowing you to make necessary changes.
Scale the table up to a large WCU manually, once complete re-enable your autoscaling as before, this will give you more partitions in the backend which may reduce your throttling by separating keys.
Related
Our table has bursty writes, expected once a week. We have auto-scaling enabled, with provisioned capacity as 5 WCU's, with 70% target utilization. This suffices for our off-peak (non-bursty) traffic. However, during the bursty writes, the WCU's reach around 1.5-2k, which leads to a lot of throttled writed and ultimately failures to write as well.
1) Is the auto-scaling suitable for such an use-case?
2) If yes, what should our initial provisioned capacity be?
This answer will tell you why auto-scaling is not working for you:
https://stackoverflow.com/a/53005089/4985580
This answer will tell you how you can configure your SDK to retry operations over a much longer period (and therefore stop your operation failures furing peak requests).
What should be done when the provisioned throughput is exceeded?
Ultimately you should probably move your tables to on-demand.
For tables using on-demand mode, DynamoDB instantly accommodates
customers’ workloads as they ramp up or down to any previously
observed traffic level. If the level of traffic hits a new peak,
DynamoDB adapts rapidly to accommodate the workload.
No, auto-scaling is not suitable for your needs. It takes a few minutes to scale up and it does that by increasing a fixed percentage of your current capacity at each time. There's also a limited number of times it scales up or down per day, so you can't get from 5 to 2,000 in a matter of minutes. You may not even get that in a matter of hours.
I'd suggest to try on demand mode, or manually setting capacity to 2,000 some time before you actually need it (it doesn't really scale instantly).
I strongly advise to read the ENTIRE dynamo documentation with regards to best practices for primary key, GSI, data architecture. Depending on the size of your table (lager that 10 Gb), the 2,000 units may get spread across partitions and you could potentially still have throttled requests.
I read the following announcement with great interest.
https://aws.amazon.com/about-aws/whats-new/2018/11/announcing-amazon-dynamodb-on-demand/
The new "on-demand" feature really helps with capacity planning. Reading the documentation, I can't really see if they do some "magic" to resolve the problem of hot partitions, and partition key distribution.
Is partition key design just as important if you provision a table "on-demand"?
Yes, partition key design is just as important. That aspect has not changed.
Since you mentioned adaptive capacity in a comment, one thing to make sure is clear. Once it is on for a table, it is on and DynamoDB is monitoring your table.
There are two features at play here:
* On-demand capacity mode
* Adaptive capacity
On-demand capacity mode allows you to pay per each request to DynamoDB instead of provisioning a particular amount of RCUs/WCUs (this is called provisioned capacity). The benefit is that you only pay for what you use (and not for what you provision), but the downside is that if you receive a constant flow of requests, you would end up paying more if you provisioned the right amount of RCUs/WCUs. The on-demand capacity mode is the best suit for spiky traffic, while the provisioned mode is better for applications with a constant, predictable stream of requests
Adaptive capacity is a different feature, and it can work with either on-demand or provisioned capacity modes. It allows to "borrow" unused capacity from other partitions if one of your partitions receive a higher share of requests. It used to take some time to enable adaptive capacity, but as for now, adaptive capacity is enabled immediately.
Even with adaptive capacity, a good key design is still important. It only helps with cases when it is hard to achieve a balanced distribution of requests among shards. A single partition in DynamoDB can only handle up to 3K RCUs and 1K WCUs. So if a single partition receives more than that even with adaptive capacity requests will be throttled. So you have to design your keys to avoid this scenario.
As of 5/2019 the answer to this question has changed. I'd like to preface my answer by saying I have not validated this against a production workload. Also my answer assumes you, like the OP, are using on-demand pricing.
First a general understanding of how DynamoDB (DDB) adaptive capacity works can be gleamed by reading this. Adaptive capacity is on by default. In short, when a hot partition exceeds its throughput capacity, DDB "moves the rudder" and instantly increases throughput capacity on the partition.
Before 5/2019 you'd 300 seconds of instant burst capacity, then you'd be throttled until adaptive capacity fully "kicked in" (5-30 minutes).
On 5/23/2019 AWS announced that adaptive capacity is now instant. This means no more 5-30 minute wait.
Does this mean if you use DDB on-demand pricing, your hot partition problems go away? Yes and no.
Yes, in that you should not get throttled.
No, in that your bank account will take the hit. In order to not get throttled, DDB will scale up on-demand (now instantly). You will pay for the RCUs and WCUs needed to handle the throughput.
So the moral is, you still have to think about hot partitions and design your application to avoid them as much as possible. You won't be paying for it in downtime/unhappy customers, you'll be paying for it out of profits.
#Glenn First of all thankyou for the great question, after some research i have reached to the conclution that hot partition problem is still important but only for 5-30 minutes as soon as dynamo db will detect you are having hot partitions it will use the mechanism like adaptive capacity and automatic resharding, dynamo db has improved a lot since its launch and now AWS handles hot partitions by something called automatic resharding, i think automatic resharding works in both on demand and provision model but i could not find any proof for that i will update the answer as soon as i find it for reference you can watch this keynote.
AWS reinvent 2018 keynote
I have a DynamoDB table configured with auto-scaling enabled. I also have a Lambda function that is writing to this table. I've started seeing throttling exceptions in the lambda and the table also reports lots of throttling events, but the consumed write capacity is only at around 70% of what is allocated. Strangely, none of the GSIs report any throttling activity at all.
I've considered that there might be a hot key problem, but the partition key for this table is a UUID (stored as a string) and most of the traffic on this table is adding new entries, with only a small portion of the traffic updating existing data.
What else can I look at to try troubleshooting this issue?
I'm using AWS Java SDK in Apache Spark job to populate DynamoDB table with data extracted from S3. Spark job just writes data using single PutItems with very intense flow (three m3.xlarge nodes used only to write) and without any retry policy.
DynamoDB docs state that AWS SDK has backoff policy, but eventually if rate is too high ProvisionedThroughputExceededException can be raised. My spark job worked for three days and was constrained only by DynamoDB thoughput (equal 500 units) so I expect rate was extremely high and queue was extremely long, however I didn't have any signs of thrown exceptions or lost data.
So, my question is - when it is possible to get an exception when writing to DynamoDB with very high rate.
You can also get throughput exception if you have a hot partition. Because throughput is divided between partitions, each partition has a lower limit than total provisioned throughput, so if you write to the same partition often, you can hit limit even if you are not using full provisioned throughput.
Another thing to consider is that DynamoDB does accumulate unused throughput and use it to burst throughput available for short duration if you go above your limit briefly.
Edit: DynamoDB now has new adaptive capacity feature which somewhat solves the problem of hot partitions by redistributing total throughput unequally.
I am new to cloud computing, but had a question if a mechanism as what I am about to describe exists or is possible to create?
Dynamodb has provisioned throughput (eg. 100 writes/second). Of course, in real world application real life throughput is very dynamic and will almost never be your provisioned amount of 100 writes/second. I was thinking what would be great would be some type of queue for dynamodb. For example, my dynamodb during peak hours may receive 500 write requests per second (5 times what I have allocated) and would return errors. Is it there some queue I can put in between the client and database, so the client requests go to the queue, the client gets acknowledged their request has been dealt with, then the queue spits out the request to the dynamodb at a rate of 100/ writes per second exactly, so that way there are no error returned and I don't need to raise the through put which will raise my costs?
Putting AWS SQS is front of DynamoDB would solve this problem for you, and is not an uncommon design pattern. SQS is already well suited to scale as big as it needs to, and ingest a large amount of messages with unpredictable flow patterns.
You could either put all the messages into SQS first, or use SQS as an overflow buffer when you exceed the design thoughput on your DynamoDB database.
One or more worker instances can than read messages from the SQS queue and put them into DynamoDB at exactly the the pace you decide.
If the order of the messages coming in is extremely important, Kinesis is another option for you to ingest the incoming messages and then insert them into DynamoDB, in the same order they arrived, at a pace you define.
IMO, SQS will be easier to work with, but Kineses will give you more flexibility if your needs are more complicated.
This cannot be accomplished using DynamoDB alone. DynamoDB is designed for uniform, scalable, predictable workloads. If you want to put a queue in front of DynamoDB you have do that yourself.
DynamoDB does have a little tolerance for burst capacity, but that is not for sustained use. You should read the best practices section Consider Workload Uniformity When Adjusting Provisioned Throughput, but here are a few, what I think are important, paragraphs with a few things emphasized by me:
For applications that are designed for use with uniform workloads, DynamoDB's partition allocation activity is not noticeable. A temporary non-uniformity in a workload can generally be absorbed by the bursting allowance, as described in Use Burst Capacity Sparingly. However, if your application must accommodate non-uniform workloads on a regular basis, you should design your table with DynamoDB's partitioning behavior in mind (see Understand Partition Behavior), and be mindful when increasing and decreasing provisioned throughput on that table.
If you reduce the amount of provisioned throughput for your table, DynamoDB will not decrease the number of partitions . Suppose that you created a table with a much larger amount of provisioned throughput than your application actually needed, and then decreased the provisioned throughput later. In this scenario, the provisioned throughput per partition would be less than it would have been if you had initially created the table with less throughput.
There are tools that help with auto-scaling DynamoDB, such as sebdah/dynamic-dynamodb which may be worth looking into.
One update for those seeing this recently, for having burst capacity DynamoDB launched on 2018 the On Demand capacity mode.
You don't need to decide on the capacity beforehand, it will scale read and write capacity following the demand.
See:
https://aws.amazon.com/blogs/aws/amazon-dynamodb-on-demand-no-capacity-planning-and-pay-per-request-pricing/