04 Mar 2012
tl;dr: There’s no good reason why strong consistency should cost double what eventual consistency costs. Strong consistency in DynamoDB shouldn’t cost Amazon anywhere near double and wouldn’t cost you double if you ran your own data store. While the benefit to your application may not be worth this high price, hiding consistency statistics encourages you to overpay.
Amazon recently released an open beta for DynamoDB, a hosted, “fully managed NoSQL database service.” DynamoDB automatically handles database scaling, configuration, and replica maintenance under a pay-for-throughput model. Interestingly, DynamoDB costs twice as much for consistent reads compared to eventually consistent reads. This means that if you want to be guaranteed to read the data you last wrote, you need to pay double what you could be paying otherwise.
Now, I can’t come up with any good technical explanation for why consistent reads cost 2x (and this is what I’m handsomely compensated to do all day). As far as I can tell, this is purely a business decision that’s bad for users:
- The cost of strong consistency to Amazon is low, if not zero. To you? 2x.
- If you were to run your own distributed database, you wouldn’t incur this cost (although you’d have to factor in hardware and ops costs).
- Offering a “consistent write” option instead would save you money and latency.
- If Amazon provided SLAs so users knew how well eventual consistency worked, users could make more informed decisions about their app requirements and DynamoDB. However, Amazon probably wouldn’t be able to charge so much for strong consistency.
I’d love if someone (read: from Amazon) would tell me why I’m wrong.
The cost of consistency for Amazon is low, if not zero. To you? 2x.
Regardless of the replication model Amazon chose, I don’t think it’s possible that it’s costing them 2x to perform consistent reads compared to eventually consistent reads:
Dynamo-style replication. If Amazon decided to stick to their original Dynamo architecture, then all reads and writes are sent to all replicas in the system. When you perform an eventually consistent read, it means that DynamoDB gives you an answer when the first replica (or first few replicas) reply. This cuts down on latency: after all, waiting for the first of three replicas to reply is faster than waiting for a single replica to respond! However, all of the replicas will eventually respond to your request, whether you wait for them or not.
This means that eventually consistent reads in DynamoDB would use the same number of messages, amount of bandwidth, and processing power as strongly consistent reads. It shouldn’t cost Amazon any more to perform consistent reads, but it costs you double.
Possible explanations: Maybe Amazon didn’t adopt the Dynamo design’s “send-to-all” semantics. This would save bandwidth but cause a significant latency hit. However, Amazon might save in terms of messages and I/O without compromising latency if they chose not to send read requests to remote sites (e.g., across availability zones) for eventually consistent reads . Another possibility is that, because eventual consistency is faster, transient effects like queuing are reduced, which helps with their back-end provisioning. Regardless, none of these possibilities necessitate a 2x price increase. Note that the Dynamo model covers most variants of quorum replication plus anti-entropy. edit: As I mentioned, it’s possible that weak consistency saves an extra I/O per read. However, I’m still unconvinced that this leads to a 2x TCO increase.
Cost to Amazon: probably zero
Master-slave replication. If Amazon has chosen a master-slave replication model in DynamoDB, eventually consistent reads could go to a slave, reducing load on the master. However, this would mean Amazon is performing some kind of monetary-cost-based load-balancing, which seems strange and hard to enforce. Even if this is the case, is doubling the price really the right setting for proper load-balancing at all times and for all data? Is the 2x cost bump really necessary? I’m not convinced.
Cost to Amazon: increased master load. 2x load? Probably not, given proper load balancing.
Read vs. Write Cost
Amazon decided to place this extra charge on the read path. Instead, they could have offered a “consistent write” option, where all subsequent reads would return the data you wrote. This would slow down consistent writes (and speed up reads). I’d wager that a vast majority of DynamoDB operations are reads, so this “consistent write” option would decrease revenue compared to the current “consistent read” option. So, compared to a consistent write option, you’re currently getting charged more, and the majority of your DynamoDB operations are slower.
FUD Helps Sell Warranties
Amazon is vague about eventual consistency in DynamoDB. Amazon says that:
Consistency across all copies of data is usually reached within a second. Repeating a[n eventually consistent] read after a short time should return the updated data.
What does “usually” mean? What’s “a short time”? These are imprecise metrics, and they certaintly aren’t guarantees. This may be intentional.
Best Buy tries to sell you an in-house extended warranty when you buy a new gadget. Most of the time, you don’t buy the warranty because you make a judgment that the chance of failure isn’t worth the price of the warranty. With DynamoDB, you have no idea what the likelihood of inconsistency is, except that “a second” is “usually” long enough. What if you don’t want to wait? What about the best case? Or the worst? Amazon could release a distribution for these delays so you could make an informed decision, but they don’t. Why not?
It’s not for technical reasons. Amazon has to know this distribution. I’ve spent the last six months thinking about how to provide SLAs for consistency. Our new techniques show that you can make predictions with arbitrary precision simply by measuring message delays. Even without our work, I’d bet all my chips on the fact that Amazon has tested the hell out of their service and know exactly how eventual eventual consistency is in DynamoDB. After all, if the window of inconsistency was prohibitively long, it’s unlikely that Amazon would offer eventual consistency as an option in the first place.
With more information, most customers probably wouldn’t pay 2x
So why doesn’t Amazon release this data to customers? There are several business reasons that disincentivize them from doing so, but the basic idea is that it’s not clear that strong consistency delivers a 2x value to the customer in most cases. However, without the data, customers can’t make this call for themselves without a lot of benchmarking. And profiling doesn’t buy you any guarantees like an SLA.
If users knew what “usually” really meant, they could make intelligent decisions about pricing. How many people would pay double for strong consistency if they’d only have to wait a few tens of milliseconds on average for consistent read, or, conversely, that data would be at most a few tens of milliseconds stale? What if 99.9% of reads were fresh within 100ms? 200ms? 500ms? 1000ms? Without the distribution, it’s hard to judge whether eventual consistency works for a given app.
Related: if your probability of consistent reads is sufficiently high (say, 99.999%) after normal client-side round-trip times (which are often long), do you really care about strong consistency? If you did, you could also intentionally block your readers until they would read consistent data with a high enough probability.
If you have “stickiness” and your requests are always sent to a given DynamoDB instance (which you can’t enforce but is a likely design choice!), then you may never read inconsistent data, even under eventual consistency. Even if this happens only some of the time, you’ll see less stale data. This is due to what are known as session guarantees.
It’s not specified how much slower a consistent read is compared to an eventually consistent read, so it’s possible that the wait time until (a high probability of) consistency plus the latency of an eventually consistent read is actually lower than the latency of a strongly consistent read.
If you store timestamps with your data, you can guarantee you don’t read old data (another session guarantee) and, if you’re dissatisfied, you can repeat your eventually consistent read. You can calculate the expected cost of reading consistent data across multiple eventually consistent reads—if you have this data.
I don’t think many people would pay 2x for their reads if they were provided with this data or some consistency SLA. Maybe some total rockstars are okay with this vagueness, but it sure seems hard to design a reliable service without any consistency guarantees from your backend. By only giving users an extremely conservative upper bound on the eventuality of reads (say, if your clients switch between data centers between reads and writes and DynamoDB experiences extremely high message delays), Amazon may be scaring the average (prudent) user into paying more than they probably should.
Strong consistency in DynamoDB probably doesn’t cost Amazon much (if anything) but it costs you twice as much as eventual consistency. It’s highly likely that Amazon has quantitative metrics for eventual consistency in DynamoDB, but keeping those numbers private makes you more likely to pay extra for guaranteed consistency. Without those numbers, it’s much harder for you to reason about your application’s behavior under average-case eventual consistency.
Sometimes you absolutely need strong consistency. Pay in those cases. However, especially for web data, eventual is often good enough. The current problem with DynamoDB is that, because customers don’t have access to quantitative metrics about their window of inconsistency, it’s easy for Amazon to set prices irrationally high.
Disclaimer: I (perhaps obviously) have no privileged knowledge of Amazon’s AWS architecture (or any other parts of Amazon, for that matter). I just happen to spend a lot of time thinking about and working on cool distributed systems problems.
- I Loved Graduate School (01 Jan 2016)
- NSF Graduate Research Fellowship: N=1 Materials for Systems Research (03 Sep 2015)
- Worst-Case Distributed Systems Design (03 Feb 2015)
- When Does Consistency Require Coordination? (12 Nov 2014)
- Data Integrity and Problems of Scope (20 Oct 2014)
- Linearizability versus Serializability (24 Sep 2014)
- MSR Silicon Valley Systems Projects I Have Loved (19 Sep 2014)
- Understanding Weak Isolation Is a Serious Problem (16 Sep 2014)
- Bridging the Gap: Opportunities in Coordination-Avoiding Databases (22 Apr 2014)
- Without Conflicts, Serializability Is Free (14 Apr 2014)