In the previous post, I discussed how I can (fairly naively) solve the phone book problem. In this post, I want to take this a bit further. Whenever a developer hears the terms sorted or ordered, I expect them to think about tree. Indeed, trees are the very first thing that I would expect to pop to mind.Assuming that they aren’t versed with persistent data structures, they are likely going to look at in memory trees and map the idea directly to a file. I decided to take the same approach. For algorithms, I find Python to be the best language for me to grok. Mostly because it looks so much like pseudo code. Searching for AVLTree Python got me to this implementation. One issue with AVLTrees is their code size. Even in Python, the code is about 200 lines of code. Here is the structure of a node, which we’ll need to store on the disk.You can see the full code here. It is about twice as long as the previous implementation (around 300 lines of code). I’m not going to cover that in depth, mostly because this is an AVL tree, with the only funny thing here is that I’m using that I’m writing the nodes to the file, not holding them in memory.For example, I have to have some way to find the root node. I do that by writing its position to the end of the file after each write. That means that there are some limits to what I’m doing, but nothing too bad.I don’t have a way to recover disk space, and updates to the data will use new space, not the old one. That is because we have to take into account that the size of the data may change. This implementation is also going to be quite wasteful in terms of the disk seeks, given that it is an AVL Tree with a branching factor of 2. One of the reasons that binary search trees aren’t really used with persistent data structures is that the cost of seeking to another location in the file is enormous. B+Tree solves the problem by having a much higher branching factor. A proper B+Tree, however, is probably going to take about 1,500 lines of code to implement, I think. So if you want to read that code, go ahead and peek into Voron .
Asynchronous methods have existed for a few years in the .NET ecosystem. Many methods know how to handle Task. For instance, Task.Run has overloads to handle tasks correctly:Task Task.Run(Func<Task> action)Task<TResult> Task.Run<TResult>(Func<Task<TResult>> action)C#co
A couple of weeks ago I asked you to rate an interview question that we had sent to candidates. The idea is to build a persistent phone book, with the idea that we care about the amount of I/O traffic that is used more than anything else.The scenario we presented to the candidates was:
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public class Phonebook
{
public class Entry
{
public string Name;
public string Number;
public string Type;
}
private string filename; // cannot have any other fields
public Phonebook(string filename) { _filename = filename; }
public void InsertOrUpdate(Entry entry); // cannot re-write the whole file
public Entry GetByName(string name); // should be faster than O(N)
public IEnumerable<Entry> IterateOrderedByName(string afterName = null); // should stream results, not sort in memory
}
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PhoneBook.cs
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The rules are that you can’t maintain any state in the class itself and that the code should be prepared to handled a lot of records. Of particular interest is the IterateOrderedByName() call, which allows you to do an ordered iteration (by name) from a given name. That pretty much forces us to store the data in a sorted format. Note that we don’t explicitly state that in the requirements for the task, we expect the candidates to understand that this is a requirement given the requirements for the operations. The most naïve option to complete this challenge is to write a CSV file. Each entry in its own line and you are done. However, that wouldn’t allow us to meet the other requirements. In other words, reading the whole file to memory, adding the item, sorting the whole thing and writing it back again is a no go. As a note, this is a task that we give to Junior developers. We expect zero to very little experience. After going over dozens such answers, I can tell you that this task does its primary job, which is to filter people out. That is an explicit goal, by the way. We have had over 200 applicants to this position and the time it takes to go through the that many CVs, interviews, etc is horrendous. I found that this question filters enough people to make it far more manageable. And given the answers I got to my previous post, this is absolutely the kind of task that I would consider a junior developer suitable for. I remember solving similar problems in high school.I like this problem because it is a good way to see if a person is able to do several things at once:Can take a non trivial problem and break it to manageable pieces. Can figure out several pitfalls along the way and handle them.Can recognize what the impact of the various requirements are for the solution.Can apply knowledge from one field to another. In this case, in memory data structures vs. persistent files.Understand that data and representations are different things.I have to admit that I was quite surprised by the results. Pretty much no one chose to use binary format, they all went to the textual format. This makes the problem harder. Also, there were a number of bytes vs. chars errors (that isn’t an issue for a junior position, though). My first attempt is going to be a bit brute force. Instead of trying to implement any fancy data structures, I’m going to simply write the data out to the file in an append only manner. At the end of the file, however, I’ll keep a sorted array of the positions of the items in the file. Here is what this looks like:To do a search in this format, I can use the sorted positions at the end of the file. Whenever I add a new record, I add it at the end of the records (overwriting the sorted positions sections and then writing the new sorted positions at the end). The overhead per write is the size of the sorted array, basically. The bigger the file, the more we’ll spend writing to the array. For example, assuming each record is around 64 bytes, when we get to 10 million records, we’ll have data size of 610MB, but the metadata we’ll have will be around 38 MB (and we’ll need to write all of it each time we modify a record).The advantages, however, is that the code is simple and there are several ways that we can optimize the code without too much trouble. Here is what this looks like in code, there are some tests attached that you can run and the entire code runs at roughly 150 lines of code.That isn’t a great solution, I’ll admit, but it is a straightforward one. It seems like it would be the natural consequence of moving from appending each record to the file to having a sorted access. There are some nice things there, nevertheless. You can “batch” several inserts together and close them by calling Flush() once, instead on each record, for example.Given that the biggest issue here is the size of the sorted positions, we can seek to reduce it in several ways:We can compress the data on write, you can see the changes required for that here.We can create multiple sorted positions and merge them occasionally. For that matter, we can store the sorted positions in another file entirely, which will simplify things like supporting efficient in order inserts.Things that a junior developer might not do here? I’m using BinaryReader and BinaryWriter. That simplify things for me, since I can ignore the cost of textual parsing. That said, I’m not using them in any interesting way and the concepts translates 1:1 to textual interface, with a bit more work.Can you do better than this implementation?
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A couple of days, a fire started in OVH’s datacenter. You can read more about this here:
They use slightly different terminology, but translating that to the AWS terminology, an entire "region” is down, with SGB1-4 being “availability zones” in the region.
For reference, there are some videos from the location that make me very sad. This is what it looked like under active fire:
I’m going to assume that this is a total loss of everything that was in there.
RavenDB Cloud isn’t offering any services in any OVH data centers, but this is a good time to go over the Disaster Recovery Plan for RavenDB and RavenDB Cloud. It is worth noting that the entire data center has been hit, with the equivalent to an entire AWS region going down.
I’m not sure that this is a fair comparison, it doesn’t looks like that SBG 1-4 are exactly the same thing as AWS availability, but it is close enough to draw parallels.
So far, at least, there have been no cases where Amazon has lost an entire region. There were occasions were a whole availability zone was down, but never a complete region. The way Amazon is handling Availability Zones seems to most paranoid, with each availability zone distanced “many kilometers” from each other in the same region. Contrast that with the four SGB that all went down. For Azure, on the other hand, they explicitly call out the fact that availability zones may not provide sufficient cover for DR scenarios. Google Cloud Platform also provides no information on the matter. For that matter, we also have direct criticism on the topic from AWS.
Yesterday, on the other hand, Oracle Cloud had a DNS configuration error that took effectively the entire cloud down. The good news is that this is just inability to access the cloud, not actual loss of a region, as was the case on OVH. However, when doing Disaster Recovery Planning, having the the entire cloud drop off the face of the earth is also something that you have to consider.
With that background out of the way, let’s consider the impact of losing two availability zones inside AWS, losing a entire region in Azure or GCP or even losing an entire cloud platform. What would be the impact on a RavenDB cluster running in that scenario?
RavenDB is designed to be resilient. Using RavenDB Cloud, we make sure that each of the nodes in the cluster is running on a separate availability zone. If we lose two zones in a region, there is still a single RavenDB instance that can operate. Note that in this case, we won’t have a quorum. That means that certain operations won’t be available (you won’t be able to create new databases, for example) but read and write operations will work normally and your application will fail over silently to the remaining server. When the remaining servers recover, RavenDB will update them with all the missing data that was modified while they were down.
The situation with OVH is actually worse than that. In this case, a datacenter is gone. In other words, these nodes aren’t coming back. RavenDB will allow you to perform emergency operations to remove the missing nodes and rebuilt the cluster from the single surviving node.
What about the scenario where the entire region is gone? In this case, if there are no more servers for RavenDB to run on, it is going to be down. That is the time to enact the Disaster Recovery Plan. In this case, it means deploying a new RavenDB Cluster to a new region and restoring from backup.
RavenDB Cloud ensures full daily backups as well as hourly incremental backups for all databases, so the amount of data loss will be minimal. That said, where are the backups located?
By default, RavenDB stores the backups in S3, in the same region as the cluster itself. Amazon S3 has the best durability guarantees in the market. This is beyond just the number of nines that they provide in terms of data durability. A standard S3 object is going to be residing in three separate availability zones. As mentioned, for AWS, we have guarantees about distance between those availability zones that we haven’t seen from other cloud providers. For that reason, when your cluster reside in AWS, we’ll store the backups on S3 in the same region. For Azure and GCP, on the other hand, we’ll also use AWS S3 for backup storage. For a whole host of reasons, we select a nearby region. So a cluster on Azure US East would store its backups on AWS S3 on US-East-1, for example. And a cluster on Azure in the Netherlands will store its backups on AWS S3 on the Frankfurt region. In addition to all other safeguards, the backups are encrypted, naturally.
The cloud has been around for 15 years already (amazing, I know) and so far, AWS has had a great history with not suffering catastrophic failures like the one that OVH has run into. Then again, until last week, you could say the same about OVH, but with 20+ years of operations. Part of the Disaster Recovery Process is knowing what risks are you willing to accept. And the purpose of this post is to expand on what we do and how we plan to react to such scenarios, be they ever so unlikely.
RavenDB actually has a number of features that are applicable for handling these sorts of scenarios. They aren’t enabled in the cloud by default, but they are important to discuss for users who need to have business continuity.
Multi-region or Multi-cloud clusters are available. You can setup RavenDB across multiple disparate location in several manners, but the end result is that you can ensure that you have no single point of failure, while still using RavenDB to its fullest potential. This is commonly used in large applications that are naturally geo distributed, but it also serve as a way to not put all your eggs in a single basket.
In addition to the default backup strategy (same AWS region on AWS or nearby AWS region for Azure or GCP), you can setup backups to additional regions.
One of the key aspects of business continuity is the issue of the speed in which you can go back to normal operations. If you are running a large database, just the time to restore from backup can be a significant amount of time. If you have a database (or databases) whose backup are in the hundreds of GB range, just the time it takes to get the backups can be measures in many hours, leaving aside the restore costs.
For that reason, RavenDB also support the notion of an offsite observer. That can be a single isolated node or a whole cluster. We can take advantage of the fact that the observer is not in active use and under provision it, in that case, when we need to switch over, we can quickly allocate additional resources to it to ramp it up to production grade. For example, let’s assume that we have a cluster running in Azure Northern Europe region, composed of 3 nodes with 8 cores each. We also have setup an observer cluster in Azure Norway East region. Instead of having to allocate a cluster of 3 nodes with 8 cores each, we can allocate a much smaller size, say 2 cores only (paying less than a third of the original cluster cost as a premium). In the case of disaster, we can respond quickly and within a matter of minutes, the Norway East cluster will be expanded so each of the nodes will have 8 cores and can handle full production traffic.
Naturally, RavenDB is likely to be only a part of your strategy. There is a lot more to ensuring that your users won’t notice that something is happening while your datacenter is on fire, but at least as it relates to your database, you know that you are in good hands.
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