Real-Time Read Database Heavy Workloads: Considerations and Best Practices
Explore the challenges associated with real-time read-heavy database workloads and get tips for addressing them Reading and writing are distinctly different beasts. This is true with reading/writing words, reading/writing code, and also when we’re talking about reading/writing data to a database. So, when it comes to optimizing database performance, your read:write ratio really does matter. We recently wrote about performance considerations that are important for write-heavy workloads – covering factors like LSM tree vs B-tree engines, payload size, compression, compaction, and batching. But read-heavy database workloads bring a different set of challenges; for example: Scaling a cache: Many teams try to speed up reads by adding a cache in front of their database, but the cost and complexity can become prohibitive as the workload grows. Competing workloads: Things might work well initially, but as new use cases are added, a single workload can end up bottlenecking all the others. Constant change: As your dataset grows or user behaviors shift, hotspots might surface. In this article, we explore high-level considerations to keep in mind when you have a latency-sensitive read-heavy workload. Then, we’ll introduce a few ScyllaDB capabilities and best practices that are particularly helpful for read-heavy workloads. What Do We Mean by “a Real-Time Read Heavy Workload”? First, let’s clarify what we mean by a “real-time read-heavy” workload. We’re talking about workloads that: Involve a large amount of sustained traffic (e.g., over 50K OPS) Involve more reads than writes Are bound by strict latency SLAs (e.g., single digit millisecond P99 latency) Here are a few examples of how they manifest themselves in the wild: Betting: Everyone betting on a given event is constantly checking individual player, team, and game stats as the match progresses. Social networks: A small subset of people are actually posting new content, while the vast majority of users are typically just browsing through their feeds and timelines. Product Catalogs: As with social media, there’s a lot more browsing than actual updating. Considerations Next, let’s look at key considerations that impact read performance in real-time database systems. The Database’s Read Path To understand how databases like ScyllaDB process read operations, let’s recap its read path. When you submit a read (a SELECT statement), the database first checks for the requested data in memtables, which are in-memory data structures that temporarily hold your recent writes. Additionally, the database checks whether the data is present in the cache. Why is this extra step necessary? Because the memtable may not always hold the latest data. Sometimes data could be written out-of-order, especially if applications consume data from unordered sources. As the protocol allows for clients to manipulate record timestamps to prevent correct ordering, checking both the memtable and the cache is necessary to ensure that the latest write takes gets returned. Then, the database takes one of two actions: If the data is stored on the disk, the database populates the cache to speed up subsequent reads. If the data doesn’t exist on disk, the database notes this absence in the cache – avoiding further unnecessary lookups there. As memtables flush to disk, the data also gets merged with the cache. That way, the cache ends up reflecting the latest on-disk data. Hot vs. Cold Reads Reading from cache is always faster than reading from disk. The more data your database can serve directly from cache, the better its performance (since reading data from memory has a practically unlimited fetch ceiling). But how can you tell whether your reads are going to cache or disk? Monitoring. You can use tools such as the ScyllaDB Monitoring stack to learn all about your cache hits and misses. The fewer cache misses, the better your read latencies. ScyllaDB uses a Least Recently Used (LRU) caching strategy, similar to Redis and Memcached. When the cache gets full, the least-accessed data is evicted to make room for new entries. With this LRU approach, you need to be mindful about your reads. You want to avoid situations where a few “expensive” reads end up evicting important items from your cache. If you don’t optimize cache usage, you might encounter a phenomenon called “cache thrashing.” That’s what happens when you’re continuously evicting and replacing items in your cache, essentially rendering the cache ineffective. For instance, full table scans can create significant cache pressure, particularly when your working set size is larger than your available caching space. During a scan, if a competing workload relies on reading frequently cached data, its read latency will momentarily increase because those items were evicted. To prevent this situation, expensive reads should specify options like ScyllaDB’s BYPASS_CACHE to prevent its results from evicting important items. Paging Paging is another important factor to consider. It’s designed to prevent the database from running out of memory when scanning through large results. Basically, rows get split into pages as defined by your page size, and selecting an appropriate page size is essential for minimizing end-to-end latency. For example, assume you have a quorum read request in a 3-node cluster. Two replicas must respond for the request to be successful. Each replica computes a single page, which then gets reconciled by the coordinator before returning data back to the client. Note that: ScyllaDB latencies are reported per page. If your application latencies are high, but low on the database side, it is an indication that your clients may be often paging. Smaller page sizes increase the number of client-server roundtrips. For example, retrieving 1,000 rows with a page size of 10 requires 100 client-server round trips, impacting latency. Testing various page sizes helps finding the optimal balance. Most drivers default to 5,000 rows per page, which works well in most cases, but you may want to increase from the defaults when scanning through wide rows, or during full scans – at the expense of letting the database work more before receiving a response. Sometimes trial and error is needed to get the page size nicely tuned for your application. Tombstones In Log-Structured Merge-tree (LSM-tree) databases like ScyllaDB, handling tombstones (markers for deleted data) is also important for read performance. Tombstones ensure that deletions are properly propagated across replicas to avoid deleted data from being “resurrected.” They’re critical for maintaining correctness. However, read-heavy workloads with frequent deletions may have to process lots of tombstones to return a single page of live data. This can really impact latency. For example, consider this extreme example. Here, tracing data shows that a simple select query took a whopping 6 seconds to process a single row because it had to go through 10 million tombstones. There are a couple ways to avoid this: tuning compaction strategies, such as the more aggressive LeveledCompactionStrategy, or using ICS Space Amplification Goal, or optimizing your access patterns to scanning through fewer dead rows on every point query. Optimizing Read-Heavy Workloads with ScyllaDB While ScyllaDB’s LSM tree storage engine makes it quite well-suited for write-heavy workloads, our engineers have introduced a variety of features that optimize it for ultra-low latency reads as well. ScyllaDB Cache One of ScyllaDB’s key components for achieving low latency is its unique caching mechanism. Many databases rely on the operating system’s page cache, which can be inefficient and doesn’t provide the level of control needed for predictable low latency. The OS cache lacks workload-specific context, making it difficult to prioritize which items should remain in memory and which can be safely evicted. At ScyllaDB, our engineering team addressed this by implementing our own unified internal cache. When ScyllaDB starts, it locks most of the server’s memory and directly manages it, bypassing the OS cache. Additionally, ScyllaDB’s cache uses a shared-nothing approach, giving each shard/vCPU its own cache, memtable, and SSTable. This eliminates the need for concurrency locks and reduces context switching, further maximizing performance. You can read more about that unified cache in this engineering blog post. SSTable Index Caching Another performance-focused feature of ScyllaDB is its ability to cache SSTable indexes in memory. Since working sets often exceed the memory available, reads sometimes go to disk. However, disk access is costly. By caching SSTable indexes, ScyllaDB reduces disk IO costs by up to 3x. This significantly improves read performance – particularly during cache misses. ScyllaDB’s index caching is demand-driven: entries are cached upon access and evicted on demand. If your workload reads heavily from disk, it’s often helpful to increase the size of this index cache. Workload Prioritization Competing workloads can lead to latency issues, as we mentioned at the beginning of this article. ScyllaDB provides a solution for this: its Workload Prioritization feature, which allows you to assign priority levels to different workloads. This is particularly useful if you have workloads with varying latency requirements, as it lets you prioritize latency-sensitive queries over others. You assign service levels to each workload, then ScyllaDB’s internal scheduler handles query prioritization according to those predefined levels. To learn more, see my recent talk from ScyllaDB Summit. Heat-Weighted Load Balancing (HWLB) Heat-Weighted Load Balancing (HWLB) is a powerful ScyllaDB feature that’s commonly overlooked. HWLB mitigates performance issues that can arise when a replica node restarts with a cold cache, like after a rolling restart for a configuration change or an upgrade. In such cases, other nodes notice that the replica’s cache is cold and gradually start directing requests to the restarted node until its cache eventually warms up. The HWLB algorithm controls how requests are routed to a cold replica. The mathematical formula behind this gradual allocation is shown above – it explains the pacing of requests sent to a node as it warms up. HWLB ensures that nodes with a cold cache do not immediately receive full traffic, in turn preventing abrupt latency spikes. When restarting ScyllaDB replicas, pay attention to the Reciprocal Miss Rate (HWLB) panel within the ScyllaDB Monitoring. Nodes with a higher ratio will serve more reads compared to other nodes. Prepared statements with ScyllaDB’s shard-aware drivers On the client side, using prepared statements is a critical best practice. A prepared statement is a query parsed by ScyllaDB and then saved for later use. Prepared statements allow ScyllaDB to route queries directly to replica nodes and shards that hold the requested data. Without prepared statements, a query may be routed to a node without the required data – resulting in extra round trips. With prepared statements, queries are always routed efficiently, minimizing network overhead and improving response times. Try it out: This ScyllaDB University lesson walks you through prepared statements. High concurrency Perhaps the most important tip here is to remember that ScyllaDB loves concurrency… but only up to a certain point. If you send too few requests to the database, you won’t be able to fully maximize its potential. However, if you have unbounded concurrency – you send too many requests to the database – that excessive concurrency can cause performance degradation. To find the sweet spot, apply this formula: *Concurrency = Throughput × Latency*. For example, if you want to run 200K operations per second with an average latency of 1ms, you would aim for a concurrency level of 200. Using this calculation, adjust your driver settings – setting the number of connections and maximum in-flight requests per connection to meet your target concurrency. If your driver settings yield a concurrency higher than needed, reduce them. If it’s lower, increase them accordingly. Wrapping Up As we’ve discussed, there are a lot of ways you can keep latencies low with read-heavy workloads – even on databases such as ScyllaDB which are also optimized for write-heavy workloads. In fact, ScyllaDB performance is comparable to dedicated caching solutions like Memcached for certain workloads. If you want to learn more, here are some firsthand perspectives from teams who tackled some interesting read-heavy challenges: Discord: With millions of users actively reading and searching chat history, Discord needs ultra-low-latency reads and high throughput to maintain real-time interactions at scale. Epic Games: To support Unreal Engine Cloud, Epic Games needed a high-speed, scalable metadata store that could handle rapid cache invalidation and support metadata storage for game assets. Zeroflucs: To power their sports betting application, ZeroFlucs had to process requests in near real-time, constantly, and in a region local to both the customer and the data. Also, take a look at the following video, where we go into even greater depth on these read-heavy challenges and also walk you through what these workloads look like on ScyllaDB.
easy-cass-stress Joins the Apache Cassandra Project
I’m taking a quick break from my series on Cassandra node density to share some news with the Cassandra community: easy-cass-stress has officially been donated to the Apache Software Foundation and is now part of the Apache Cassandra project ecosystem as cassandra-easy-stress.
Why This Matters
Over the past decade, I’ve worked with countless teams struggling with Cassandra performance testing and benchmarking. The reality is that stress testing distributed systems requires tools that can accurately simulate real-world workloads. Many tools make this difficult by requiring the end user to learn complex configurations and nuance. While consulting at The Last Pickle, I set out to create an easy to use tool that lets people get up and running in just a few minutes
Alan Shimel and Dor Laor on Database Elasticity, ScyllaDB X Cloud
Alan and Dor chat about elasticity, 90% storage utilization, powering feature stores and other AI use cases, ScyllaDB’s upcoming vector search release, and much more ScyllaDB recently announced ScyllaDB X Cloud: a truly elastic database that supports variable/unpredictable workloads with consistent low latency, plus low costs. To explore what’s new and how X Cloud impacts development teams, DevOps luminary Alan Shimel recently connected with ScyllaDB Co-founder and CEO Dor Laor for a TechStrongTV interview. Alan and Dor chatted about database elasticity, 90% storage utilization, powering ML feature stores and other AI use cases, ScyllaDB’s upcoming vector search release, and much more. If you prefer to read rather than watch, here’s a transcript (lightly edited for brevity and clarity) of the core conversation. What is ScyllaDB X Cloud Alan: Dor, you guys recently announced ScyllaDB X Cloud. Tell us about it. Dor: ScyllaDB is available as a self-managed database (ScyllaDB Enterprise) and also as a fully managed database, a service in the cloud (ScyllaDB Cloud). Recently, we released a new version called ScyllaDB X Cloud. What’s special about it? It allows us to be the most elastic database on the market. Why would you need elasticity? At first, teams look for a database that offers performance and the high availability needed for mission-critical use cases. Afterwards, when they start using it, they might scale to a very high extent, and that often costs quite a bit of money. Sometimes your peak level varies and the usage varies. There are cases where a Black Friday or similar event occurs, and then you need to scale your deployments. This is predictable scale. There’s also unpredictable scale: sometimes you have a really good success, traffic ends up surging, and you need to scale the database to service it. In many cases, elasticity is needed daily. Some sites have predictable traffic patterns: it increases when people wake up, traffic subsides for a while, then there’s another peak later. If you provision 100% for the peak load, you’re wasting resources through a lot of the day. However, with traditional databases, keeping your capacity aligned with your actual traffic requires constantly meddling with the number of servers and infrastructure. Many users have petabyte-sized deployments, and moving around petabytes within several hours is an extremely difficult task. X Cloud lets you move data fast. Teams can double or quadruple throughput within minutes. That’s the value of X Cloud. See ScyllaDB scaling in this short demo: Elastic Scaling Alan: You know, in many ways, this whole idea of elasticity feels like déjà vu. I was told this about the cloud in general, right? That was one of the things about being in the cloud: burstable. But in the cloud, it proved to be like a balloon. If you blow up a balloon it stretches out – but then you let the air out, and the balloon is still stretched out. And for so many people, cloud elasticity is just one way. You can always make it bigger, but how many people really do make it smaller? Is this something that’s also true in ScyllaDB? Is it truly that elastic? Dor: Everything you described is absolutely true. Many times, people just keep on adding more and more data. If you just add and add but you don’t delete it, we have different capabilities to help with that. There are normally two reasons why you’d want to scale out and why you’d like to scale in. Number one is storage. So if your storage grows, you will scale out and add resources. If you delete data, it will automatically scale back in again. And our current release has two unique things to address that. First, it has autoscale with storage. The storage automatically scales, and our compute is bundled together with the storage. I’ll tell you a secret: at the end of the day, we run servers, and each server has memory and disk and networking and compute all together. That’s one of the reasons why we have really good performance. We bundle compute and storage. So if the amount of data people store decreases, we automatically decrease the number of servers, and we do it with 90% utilization. So the server, the disk capacity, can go up to 90% of the shared cluster infrastructure. It’s a very high percentage. Previously, we went from 50% to 70%. Now, because we’re very elastic, and we can move data very fast, we can go up to 90%. The scaling is all automated. When you go to 90%, we automatically provision more servers. If you go below 90% (there is some threshold like 85%), then we automatically decrease the number of servers. And we also do it in a very precise way. For example, assume you have three gigantic servers – we support servers up to 256 CPUs. If you run out of space, you could add another gigantic server, but then your utilization will suddenly be very low. So it’s not ideal to add a big server if you just need another 1% or 2% of utilization. X Cloud automatically selects the server size for you, and we mix server sizes. So if you only need an extra 5% along with these very big servers, we’re going to add a small, tiny server with two vCPUs next to those other big servers – and we will keep replacing it automatically for you, without you having to worry about it. That translates to value. The total cost of ownership will be low because we are targeting 90% utilization of this capacity. The other reason why you’d want to scale out is sometimes throughput and CPU consumption. This is even easier because we need to move less storage. We let our customers scale in and out multiple times an hour. ScyllaDB and AI, Vector Search Alan: Got it, that’s great, very in-depth. Thank you very much for that. You know, Dor, all the news today is “AI agentic, AI generative, AI LLMs…” Underlying all of this, though, is data. They’re training on data, storing data, using data…How has this affected ScyllaDB’s business? Dor: It definitely drives our business. We have three main pillars related to AI. Number one, there’s traditional machine learning. ScyllaDB is used for machine learning feature stores, for real-time personalization and customization. For example, Tripadvisor is a customer of ours. They use ScyllaDB for a feature store that helps find the best recommendations, deals, and advice for their users. Number two is for AI use cases that need large, scalable storage underneath. For example, a major vehicle vendor is using ScyllaDB to train their AI model for autonomous self-driving cars. Lots of AI use cases need to store and access massive amounts of data, fast. Third is vector search, which is a core component of RAG and many agentic AI pipelines. ScyllaDB will release a vector search product by the end of the year – right now, it’s in closed beta. Extreme Automation Alan: You heard it here! I just want to make sure we hit the main points of the X Cloud offering. With all of these things that you’ve mentioned already, really what are the results? You’ve improved compression, improved streaming, helping to reduce storage and cloud costs…and network too, right? That’s an important piece of the equation as well. The other thing I want to emphasize for our audience is that all this is offered as a “database as a service,” so you don’t have to worry about your infrastructure. Dor: Absolutely. We have a high amount of automation that acts on behalf of the end user. This isn’t about us doing manual operations on your behalf. The elasticity is all automated and natural, like the cluster is breathing. Users just set the scaling policies and the cluster will take it from that point onward. Everything runs under the hood, including backups. For example, imagine a customer who runs at 89% utilization and a backup brings them temporarily beyond 90% capacity. The second it crosses the 90% trigger, we will automatically provision more servers to the cluster. Once the backup is complete and the image backup snapshot is loaded to S3, then the image will be deleted, the cluster will go back below 90% utilization, and we can automatically decrease the number of servers. Everything is automated. It’s really fascinating to see all of those use cases run under the hood. Engineering Deep Dive: ScyllaDB X Cloud Interview with CTO Avi Kivity Want to learn more? ScyllaDB Co-Founder/CTO Avi Kivity recently discussed the design decisions behind ScyllaDB X Cloud’s elasticity and efficiency. Watch NowAzure fault domains vs availability zones: Achieving zero downtime migrations
The challenges of operating production-ready enterprise systems in the cloud are ensuring applications remain up to date, secure and benefit from the latest features. This can include operating system or application version upgrades, but it is not limited to advancements in cloud provider offerings or the retirement of older ones. Recently, NetApp Instaclustr undertook a migration activity for (almost) all our Azure fault domain customers to availability zones and Basic SKU IP addresses.
Understanding Azure fault domains vs availability zones
“Azure fault domain vs availability zone” reflects a critical distinction in ensuring high availability and fault tolerance. Fault domains offer physical separation within a data center, while availability zones expand on this by distributing workloads across data centers within a region. This enhances resiliency against failures, making availability zones a clear step forward.
The need for migrating from fault domains to availability zones
NetApp Instaclustr has supported Azure as a cloud provider for our Managed open source offerings since 2016. Originally this offering was distributed across fault domains to ensure high availability using “Basic SKU public IP Addresses”, but this solution had some drawbacks when performing particular types of maintenance. Once released by Azure in several regions we extended our Azure support to availability zones which have a number of benefits including more explicit placement of additional resources, and we leveraged “Standard SKU Public IP’s” as part of this deployment.
When we introduced availability zones, we encouraged customers to provision new workloads in them. We also supported migrating workloads to availability zones, but we had not pushed existing deployments to do the migration. This was initially due to the reduced number of regions that supported availability zones.
In early 2024, we were notified that Azure would be retiring support for Basic SKU public IP addresses in September 2025. Notably, no new Basic SKU public IPs would be created after March 1, 2025. For us and our customers, this had the potential to impact cluster availability and stability – as we would be unable to add nodes, and some replacement operations would fail.
Very quickly we identified that we needed to migrate all customer deployments from Basic SKU to Standard SKU public IPs. Unfortunately, this operation involves node-level downtime as we needed to stop each individual virtual machine, detach the IP address, upgrade the IP address to the new SKU, and then reattach and start the instance. For customers who are operating their applications in line with our recommendations, node-level downtime does not have an impact on overall application availability, however it can increase strain on the remaining nodes.
Given that we needed to perform this potentially disruptive maintenance by a specific date, we decided to evaluate the migration of existing customers to Azure availability zones.
Key migration consideration for Cassandra clusters
As with any migration, we were looking at performing this with zero application downtime, minimal additional infrastructure costs, and as safe as possible. For some customers, we also needed to ensure that we do not change the contact IP addresses of the deployment, as this may require application updates from their side. We quickly worked out several ways to achieve this migration, each with its own set of pros and cons.
For our Cassandra customers, our go to method for changing cluster topology is through a data center migration. This is our zero-downtime migration method that we have completed hundreds of times, and have vast experience in executing. The benefit here is that we can be extremely confident of application uptime through the entire operation and be confident in the ability to pause and reverse the migration if issues are encountered. The major drawback to a data center migration is the increased infrastructure cost during the migration period – as you effectively need to have both your source and destination data centers running simultaneously throughout the operation. The other item of note, is that you will need to update your cluster contact points to the new data center.
For clusters running other applications, or customers who are more cost conscious, we evaluated doing a “node by node” migration from Basic SKU IP addresses in fault domains, to Standard SKU IP addresses in availability zones. This does not have any short-term increased infrastructure cost, however the upgrade from Basic SKU public IP to Standard SKU is irreversible, and different types of public IPs cannot coexist within the same fault domain. Additionally, this method comes with reduced rollback abilities. Therefore, we needed to devise a plan to minimize risks for our customers and ensure a seamless migration.
Developing a zero-downtime node-by-node migration strategy
To achieve a zero-downtime “node by node” migration, we explored several options, one of which involved building tooling to migrate the instances in the cloud provider but preserve all existing configurations. The tooling automates the migration process as follows:
- Begin with stopping the first VM in the cluster. For cluster availability, ensure that only 1 VM is stopped at any time.
- Create an OS disk snapshot and verify its success, then do the same for data disks
- Ensure all snapshots are created and generate new disks from snapshots
- Create a new network interface card (NIC) and confirm its status is green
- Create a new VM and attach the disks, confirming that the new VM is up and running
- Update the private IP address and verify the change
- The public IP SKU will then be upgraded, making sure this operation is successful
- The public IP will then be reattached to the VM
- Start the VM
Even though the disks are created from snapshots of the original disks, we encountered several discrepancies in our testing, with settings between the original VM and the new VM. For instance, certain configurations, such as caching policies, did not automatically carry over, requiring manual adjustments to align with our managed standards.
Recognizing these challenges, we decided to extend our existing node replacement mechanism to streamline our migration process. This is done so that a new instance is provisioned with a new OS disk with the same IP and application data. The new node is configured by the Instaclustr Managed Platform to be the same as the original node.
The next challenge: our existing solution is built so that the replaced node was provisioned to be the exact same as the original. However, for this operation we needed the new node to be placed in an availability zone instead of the same fault domain. This required us to extend the replacement operation so that when we triggered the replacement, the new node was placed in the desired availability zone. Once this operation completed, we had a replacement tool that ensured that the new instance was correctly provisioned in the availability zone, with a Standard SKU, and without data loss.
Now that we had two very viable options, we went back to our existing Azure customers to outline the problem space, and the operations that needed to be completed. We worked with all impacted customers on the best migration path for their specific use case or application and worked out the best time to complete the migration. Where possible, we first performed the migration on any test or QA environments before moving onto production environments.
Collaborative customer migration success
Some of our Cassandra customers opted to perform the migration using our data center migration path, however most customers opted for the node-by-node method. We successfully migrated the existing Azure fault domain clusters over to the Availability Zone that we were targeting, with only a very small number of clusters remaining. These clusters are operating in Azure regions which do not yet support availability zones, but we were able to successfully upgrade their public IP from Basic SKUs that are set for retirement to Standard SKUs.
No matter what provider you use, the pace of development in cloud computing can require significant effort to support ongoing maintenance and feature adoption to take advantage of new opportunities. For business-critical applications, being able to migrate to new infrastructure and leverage these opportunities while understanding the limitations and impact they have on other services is essential.
NetApp Instaclustr has a depth of experience in supporting business critical applications in the cloud. You can read more about another large-scale migration we completed The worlds Largest Apache Kafka and Apache Cassandra Migration or head over to our console for a free trial of the Instaclustr Managed Platform.
The post Azure fault domains vs availability zones: Achieving zero downtime migrations appeared first on Instaclustr.
Blowing Up Your DynamoDB Bill
Why real-world DynamoDB usage scenarios often lead to unexpected expenses In my last post on DynamoDB costs, I covered how unpredictable workloads lead to unpredictable costs in DynamoDB. Now let’s go deeper. Once you’ve understood the basics, like the nutty 7.5x inflation of on-demand compared to reserved, or the excessive costs around item size, replication and caching … you’ll realize that DynamoDB costs aren’t just about read/write volume – it’s a lot more nuanced in the real-world. Round ‘em up! A million writes per second at 100 bytes isn’t in the same galaxy as a million writes at 5KB. Why? Because DynamoDB meters out the costs in rounded-up 1KB chunks for writes (and 4KB for reads). Writing a 1.2KB item? You’re billed for 2KB. Reading a 4.5KB item with strong consistency? You get charged for 8KB. You’re not just paying for what you use, you’re paying for rounding up. Remember this character in Superman III taking ½ a cent from each paycheck? It’s the same deal (and yes, $85,789.90 was a lot of money in 1983) … Wasted capacity is unavoidable at scale, but it becomes very real, very fast when you cross that boundary on every single operation. And don’t forget that hard cap of 400KB per item. That’s not a pricing issue directly, but it’s something that has motivated DynamoDB customers to look at alternatives. Our DynamoDB cost calculator lets you model all of this. What it doesn’t account for are some of the real-world landmines – like the fact that a conflict resolved write (such as concurrent updates in multiple regions) still costs you for each attempt, even if only the last write wins. Or when you build your own TTL expiration logic, maybe pulling a bunch of items in a scan, checking timestamps in app code, or issuing deletes. All that data transfer and (replicated) write/delete activity adds up fast … even though you’re trying to “clean up.” We discussed these tricky situations in detail in a recent DynamoDB costs webinar, which you can now watch on-demand. Global tables are a global pain So you want low latency for users worldwide? Global tables are the easiest way to do that. Some might even say that it’s “batteries-included.” But those batteries come with a huge price tag. Every write gets duplicated across additional regions. Write a 3.5KB item and replicate it to 4 regions? Now you’re paying for 4 x 4KB (rounded up, of course). Don’t forget to tack on inter-region network transfer. That’s another hit at premium pricing. And sorry, you cannot reserve those replicated writes either. You’re paying for that speed, several times over, and the bill scales linearly with your regional growth. It gets worse when multiple regions write to the same item concurrently. DynamoDB resolves the conflict (last write wins) but you still pay for every attempt. Losing writes? Still charged. Our cost calculator lets you model all this. We use conservative prices for US-East, but the more exotic the region, the more likely the costs will be higher. As an Australian, I feel your pain. So have a think about that batteries-included global tables replication cost, and please remember, it’s per table! DAX caching with a catch Now do you want even tighter read latency, especially for your latency-sensitive P99? DynamoDB Accelerator (DAX) helps, but it adds overhead, both operational and financial. Clusters need to be sized right, hit ratios tuned, and failover cases handled in your application. Miss the cache, pay for the read. Fail to update the cache, risk stale data. Even after you have tuned it, it’s not free. DAX instances are billed by the hour, at a flat rate, and once again, without reserved instance options like you might be accustomed to. Our DynamoDB cost calculator lets you simulate cache hit ratios, data set sizes, instance types and nodes. It won’t predict cache efficiency, but it will help you catch those cache gotchas. Multi-million-dollar recommendation engine A large streaming service built a global recommendation engine with DynamoDB. Daily batch jobs generate fresh recommendations and write them to a 1PB single table, replicated across 6 regions. They optimized for latency and local writes. The cost? Every write to the base table plus 5 replicated writes. Every user interaction triggered a write (watch history, feedback, preferences). And thanks to that daily refresh cycle, they were rewriting the table – whether or not anything changed. They used provision capacity, scaling up for anticipated traffic spikes, but still struggled with latency. Cache hit rates were too low to make Redis or DAX cost-effective. The result? Base workload alone cost tens of millions per year, and the total doubled after accommodating peaks in traffic spikes and batch load processes. For many teams, that’s more than the revenue of the product itself! So, they turned to ScyllaDB. After they switched to our pricing model based on provisioned capacity (not per-operation billing), ScyllaDB was able to significantly compress their data stored, while also improving network compression between AZs and regions. They had the freedom to do this on any cloud (or even on-premise). They slashed their costs, improved performance, and removed the need to overprovision for spikes. Daily batch jobs run faster and their business continues to scale without their database bill doing the same. Another case of caching to survive An adtech company using DynamoDB ran into cache complexity the hard way. They deployed 48 DAX nodes across 4 regions to hit their P99 latency targets. Each node is tailored to that region’s workload (after a lot of trial and error). Their writes (246 bytes/item) were wasting 75% of the write unit billed. Their analytics workload tanked live traffic during spikes. And perhaps worst of all, auto-scaling triggers just weren’t fast enough, resulting in request throttling and application failures. The total DynamoDB and DAX cost was hundreds of thousands per year. ScyllaDB offered a much simpler solution. Built-in row caching used instance memory at no extra cost with no external caching layer to maintain. They also ran their analytics and OLTP workloads side by side using workload prioritization with no hit to performance. Even better, their TTL-based session expiration was handled automatically without extra read/delete logic. Cost and complexity dropped, and they’re now a happy customer. Watch the DynamoDB costs video If you missed the webinar, be sure to check out the DynamoDB costs video – especially where Guilherme covers all these real-world workloads in detail. Key takeaways: DynamoDB costs are non-linear and shaped by usage patterns, not just throughput. Global tables, item size, conflict resolution, cache warmup and more can turn “reasonable” usage into a 7-figure nightmare. DAX and auto-scaling aren’t magic; they need tuning and still cost significant money to get right. Our DynamoDB cost calculator helps model these hidden costs and compare different setups, even if you’re not using ScyllaDB. And finally, if you’re a team with unpredictable costs and performance using DynamoDB, make the switch to ScyllaDB and enjoy the benefits of predictable pricing, built-in efficiency and more control over your database architecture. If you want to discuss the nuances of your specific use case and get your technical questions answered, chat with us here.How Yieldmo Cut Database Costs and Cloud Dependencies
Rethinking latency-sensitive DynamoDB apps for multicloud, multiregion deployment “The entire process of delivering an ad occurs within 200 to 300 milliseconds. Our database lookups must complete in single-digit milliseconds. With billions of transactions daily, the database has to be fast, scalable, and reliable. If it goes down, our ad-serving infrastructure ceases to function.” – Todd Coleman, technical co-founder and chief architect at Yieldmo Yieldmo’s online advertising business depends on processing hundreds of billions of daily ad requests with subsecond latency responses. The company’s services initially depended on DynamoDB, which the team valued for simplicity and stability. However, DynamoDB costs were becoming unsustainable at scale and the team needed multicloud flexibility as Yieldmo expanded to new regions. An infrastructure choice was threatening to become a business constraint. In a recent talk at Monster SCALE Summit, Todd Coleman, Yieldmo’s technical co-founder and chief architect, shared the technical challenges the company faced and why the team ultimately moved forward with ScyllaDB’s DynamoDB-compatible API. You can watch his complete talk below or keep reading for a recap. Lag = Lost Business Yieldmo is an online advertising platform that connects publishers and advertisers in real time as a page loads. Nearly every ad request triggers a database query that retrieves machine learning insights and device-identity information. These queries enable its ad servers to: Run effective auctions Help partners decide whether to bid Track which ads they’ve already shown to a device so advertisers can manage frequency caps and optimize ad delivery The entire ad pipeline completes in a mere 200 to 300 milliseconds, with most of that time consumed by partners evaluating and placing bids. More specifically: When a user visits a website, an ad request is sent to Yieldmo. Yieldmo’s platform analyzes the request. It solicits potential ads from its partners. It conducts an auction to determine the winning bid. The database lookup must happen before any calls to partners. And these lookups must complete with single-digit millisecond latencies. Coleman explained, “With billions of transactions daily, the database has to be fast, scalable and reliable. If it goes down, our ad-serving infrastructure ceases to function.” DynamoDB Growing Pains Yieldmo’s production infrastructure runs on AWS, so DynamoDB was a logical choice as the team built their app. DynamoDB proved simple and reliable, but two significant challenges emerged. First, DynamoDB was becoming increasingly expensive as the business scaled. Second, the company wanted the option to run ad servers on cloud providers beyond AWS. Coleman shared, “In some regions, for example, the US East Coast, AWS and GCP [Google Cloud Platform] data centers are close enough that latency is minimal. There, it’s no problem to hit our DynamoDB database from an ad server running in GCP. However, when we attempted to launch a GCP-based ad-serving cluster in Amsterdam while accessing DynamoDB in Dublin, the latency was far too high. We quickly realized that if we wanted true multicloud flexibility, we needed a database that could be deployed anywhere.” DynamoDB Alternatives Yieldmo’s team started exploring DynamoDB alternatives that would suit their extremely read-heavy database workloads. Their write operations fall into two categories: A continuous stream of real-time data from their partners, essential for matching Yieldmo’s data with theirs Batch updates driven by machine learning insights derived from their historical data Given this balance of high-frequency reads and structured writes, they were looking for a database that could handle large-scale, low-latency access while efficiently managing concurrent updates without degradation in performance. The team first considered staying with DynamoDB and adding a caching layer. However, they found that caching couldn’t fix the geographic latency issue and cache misses would be even slower with this option. They also explored Aerospike, which offered speed and cross-cloud support. However, they learned that Aerospike’s in-memory indexing would have required a prohibitively large and expensive cluster to handle Yieldmo’s large number of small data objects. Additionally, migrating to Aerospike would have required extensive and time-consuming code changes. Then they discovered ScyllaDB, which also provided speed and cross-cloud support, but with a DynamoDB-compatible API (Alternator) and lower costs. Coleman shared, “ScyllaDB supported cross-cloud deployments, required a manageable number of servers and offered competitive costs. Best of all, its API was DynamoDB-compatible, meaning we could migrate with minimal code changes. In fact, a single engineer implemented the necessary modifications in just a few days.” ScyllaDB evaluation, migration and results To start evaluating how ScyllaDB worked in their environment, the team migrated a subset of ad servers in a single region. This involved migrating multiple terabytes while keeping real-time updates. Process-wise, they had ScyllaDB’s Spark-based migration tool copy historical data, paused ML batch jobs and leveraged their Kafka architecture to replay recent writes into ScyllaDB. Moving a single DynamoDB table with ~28 billion objects (~3.3 TB) took about 10 hours. The next step was to migrate all data across five AWS regions. This phase took about two weeks. After evaluating the performance, Yieldmo promoted ScyllaDB to primary status and eventually stopped writing to DynamoDB in most regions. Reflecting on the migration almost a year later, Coleman summed up, “The biggest benefit is multicloud flexibility, but even without that, the migration was worthwhile. Database costs were cut roughly in half compared with DynamoDB, even with reserved-capacity pricing, and we saw modest latency improvements. ScyllaDB has proven reliable: Their team monitors our clusters, alerts us to issues and advises on scaling. Ongoing maintenance overhead is comparable to DynamoDB, but with greater independence and substantial cost savings.” How ScyllaDB compares to DynamoDBScyllaDB Cloud: Fully-Managed in Your Own Google Cloud Account
You can now run ScyllaDB’s monstrously fast and scalable NoSQL database within your own Google Cloud (GCP) accounts We’re pleased to share that ScyllaDB Cloud is now available with the Bring Your Own (Cloud) Account model on Google Cloud. This means: ScyllaDB runs inside your private Google Cloud account. Your data remains fully under your control and never leaves your Google Cloud account. Your database operations, updates, monitoring, and maintenance are all managed by ScyllaDB Cloud. Existing cloud contracts and loyalty programs can be applied to your ScyllaDB Cloud spend. This is the same deployment model that we’ve offered on AWS for nearly 4 years. The BYOA model is frequently requested by teams who want both: The fully managed ScyllaDB Cloud service with near-zero operations and maintenance. The regionality, governance, and billing benefits that come from running in your private cloud account It’s especially well-suited for highly regulated industries like healthcare and finance with Data privacy, compliance, and data sovereignty guarantees. With BYOA, all ScyllaDB servers, storage, networking, and IP addresses are created in your cloud account. Data never leaves your VPC environment; all database resources remain under your ownership and governance policies. For additional security, ScyllaDB Cloud runs Bring Your Own Key (BYOK), our transparent database-level encryption, encrypting all the data with CMK. If you are the target of a cyberattack, or you have a security breach, you can protect the data immediately by revoking the database key. Under the BYOA model, the infrastructure costs are paid directly to the cloud provider. That means your organization can apply its existing GCP commitments and take advantage of any available discounts, credits, or enterprise agreements (e.g., Committed Use, Sustained Use, Enterprise Agreements(EA)). ScyllaDB Cloud costs are reduced to license and support fees.NOTE: The Bring Your Own (Cloud) Account feature is often addressed as BYOC, spotlighting the “Cloud” aspect. We prefer the term “account” as it more accurately represents our offering, though both concepts are closely related.How ScyllaDB BYOA Works on Google Cloud Once BYOA service is enabled for your GCP project , the ScyllaDB Cloud control plane can use the Google Cloud API to create the necessary resources in your designated GCP project. After the network is configured, ScyllaDB Cloud securely connects to your cluster’s VPC to provision and manage ScyllaDB database clusters. You can configure a VPC peering connection between your application VPC and your ScyllaDB dedicated cluster VPC (as shown on the right side of the diagram). Our wizard will guide you through the configuration process for your GCP project. Using the wizard, you will configure one IAM role with policies to provision the required resources within the GCP project. ScyllaDB Cloud will operate using this role. Configuration To use the Bring Your Own Account feature, you will need to choose one project in your GCP account. This project will be used as a destination to provision your clusters. The specific policies required can be found here. Make sure your Cloud quotas are as per the recommendation. Here’s a short guide on how you can configure your GCP account to work with ScyllaDB Cloud. You will need permissions to a GCP account and a very basic understanding of Terraform. Once you complete the setup, you can use your GCP Project as any other deployment target. In the create new cluster screen, you can select this project next to ScyllaDB Cloud hosted option. In the “Create New Cluster” screen, you will be able to select this project alongside the ScyllaDB Cloud hosted option. You can select a geographical area (Region), the nature of access (private/public), and the expected instance type based on the volume of traffic, ScyllaDB Cloud will create a ScyllaDB cluster for you. From there, you can choose a geographical region, specify the type of access (public or private), and select the appropriate instance type based on your expected traffic volume. ScyllaDB Cloud will then provision and configure a cluster for you accordingly. Next steps ScyllaDB Cloud BYOA is currently live on Google Cloud Platform. If you’re ready to set up your account, you can go to http://cloud.scylladb.com to use our onboarding wizard and our step-by-step documentation. Our team is available to support you — from setup to production. Just ping your existing representative or reach out via forums, Slack, chat, etc.
