Ayende @ Rahien

Oren Eini aka Ayende Rahien CEO of Hibernating Rhinos LTD, which develops RavenDB, a NoSQL Open Source Document Database.

Get in touch with me:


+972 52-548-6969

Posts: 7,198 | Comments: 50,268

Privacy Policy Terms
filter by tags archive
time to read 4 min | 606 words

imageI recently had to discuss the issue on the impact of latency a few times, and I found the coffee cup analogy to be an excellent tool to explain exactly what is going on. Consider the humble coffee cup, without which there would be no code.

It is a pretty simple drink, composed of coffee, water and milk. I’ll ignore coffee snobs and the like for now and focus strictly on the process of making a cup of coffee. I found this recipe:

  • 1 cup milk
  • ½ cup cold brewed coffee
  • 2 sweetener

Mix milk, coffee, and sweetener together in a glass until sweetener is dissolved.

If I was writing this in code, I would probably write something like this:

Simple enough, right? There is just a little bit of details to fill. How are the coffee() or sweetner() methods implemented?

The nice thing about this code is that this is nicely abstracted, the coffee recipe and the code reads almost in the same manner. However, there is an issue with the actual implementation. We have the go_to_store() method, but we know that this is an expensive operation. To avoid making it too often, we calculate the amounts that we need to make 20 cups of coffee and make sure that we set the relevant XYZ_AMOUNT_TO_BUY appropriately.

What do you think will happen on the 21th cup of coffee, however? We run out of coffee, so we’ll go to the store to get some. Once we got it, we can pour the coffee to the cup, but then we need to put the milk in, in which case we’ll discover that we run out. Off to the store we go, and all the way back. And then there is the sweetener that run out, so that is the third trip to the store.

Abstraction, in this case, is actively hurting us. We ignore the fact that ingredients may be missing, and that isn’t something that we can afford to. The cost of going to the store outweigh anything else in the process of making a cup of coffee, and we just did that three times.

In the context of software, of course, we are talking about the issue of making a remote call. For example, sending a separate query to the database for each datum that you need. The cost of the remote call far exceed any other costs you have in the system.

To solve the coffee cup problem, you’ll need to do something like:

Abstraction? What abstraction? There are no abstractions here. We are very clearly focused on the things that need to happen to get it working properly. In fact, a better alternative would be to not check that we have enough for the current cup but to schedule a purchase when we notice that we are low.

That, again, intermix the responsibilities of making the coffee and making sure that we have the ingredients at hand. That is not an actual problem, however. That is something that we are fine with, given the difference in performance that this entails.

In the same manner, when I see people trying to hide (RPC, database calls, etc) behind an abstraction layer, I know that it will almost always end in tears. Because if you have what looks like a cheap function call go to the store for you, the end result is that you have to wait a lot of time for your coffee. Maybe enough to (gasp) not even have coffee.

On that note, I have a cup of coffee to finish…

time to read 2 min | 251 words

I got an interesting question by email and I thought that this is worth a post. The question was whatever RavenDB can handle Pivot tasks. Consider the case where I have orders data, and I want to see a summary product sales on a monthly basis, like so:


This data was produced using the sample data in RavenDB and the following map/reduce index:

That works, but it gives each individual month on its own row. When using Excel, we can Pivot the whole thing so instead of rows, we’ll get columns. For certain types of data, that makes it much easier to work with. For example, let’s say that I want to compare monthly sales data across different products.

The data we see is the same, it is just the way we process and show it that is different. Let’s see how we can do that in RavenDB. We can do that with a secondary aggregation step in the reduce, like so:

The idea is that the reduce step in RavenDB can have its own complex processing, and the result of this process gives us the following output:

If we use JavaScript indexes, we can even manipulate the data to skip the nested values, the code is nastier (likely a product of my skill in JavaScript, I’ll freely admit), but the results are nice.


time to read 3 min | 585 words

imageI recently run into a bit of code that made me go: Stop! Don’t you dare going this way!

The reason that I had such a reaction for the code in question is that I have seen where such code will lead you, and that is not anywhere good. The code in question?

This is a pretty horrible thing to do to your system. Let’s count the ways:

  • Queries are happening fairly deep in your system, which means that you’re now putting this sort of behavior in a place where it is generally invisible for the rest of the code.
  • What happens if the calling code also have something similar? Now we got retries on retries.
  • What happens if the code that you are calling has something similar? Now we got retries on retries on retries.
    • You can absolutely rely on the code you are calling to do retries. If only because that is how TCP behaves. But also because there are usually resiliency measures implemented.
  • What happens if the error actually matters. There is no exception throw in any case, which means that important information is written to the log, which no one ever reads.
  • There is no distinction of the types of errors where retry may help and where it won’t.
  • What is the query has side effects? For example, you may be calling a stored procedure, but multiple times.
  • What happens when you run out of retries? The code will return null, which means that the calling code will like fail with NRE.

What is worst, by the way, is that this piece of code is attempting to fix a very specific issue. Being unable to reach the relevant database. For example, if you are writing a service, you may run into that on reboot, your service may have started before the database, so you need to retry a few times to the let the database to load. A better option would be to specify the load order of the services.

Or maybe there was some network hiccup that you had to deal with? That would sort of work, and probably the one case where this will work. But TCP already does that by resending packets, you are adding this again and it is building up to be a nasty case.

When there is an error, your application is going to sulk, throw strange errors and refuse to tell you what is going on. There are going to be a lot of symptoms that are hard to diagnose and debug.

To quote Release It!:

Connection timeouts vary from one operating system to another, but they’re usually measured in minutes! The calling application’s thread could be blocked waiting for the remote server to respond for ten minutes!

You added a retry on top of that, and then the system just… stops.

Let’s take a look at the usage pattern, shall we?

That will fail pretty badly (and then cause a null reference exception). Let’s say that this is a service code, which is called from a client that uses a similar pattern for “resiliency”.

Question – what do you think will happen the first time that there is an error?  Cascading failures galore.

In general, unknown errors shouldn’t be handled locally, you don’t have a way to do that here. You should raise them up as far as possible. And yes, showing the error to the user is general better than just spinning in place, without giving the user any feedback whatsoever.

time to read 2 min | 234 words

I run into a task that I needed to do in Go, given a PFX file, I needed to get a tls.X509KeyPair from that. However, Go doesn’t have support for PFX. RavenDB makes extensive use of PFX in general, so that made things hard for us. I looked into all sorts of options, but I couldn’t find any way to manage that properly. The nearest find was the pkcs12 package, but that has support for only some DER format, and cannot handle common PFX files. That was a problem.

Luckily, I know how to use OpenSSL, but while there are countless examples on how to use OpenSSL to convert PFX to PEM and the other way around, all of them assume that you are using that from the command line, which isn’t what we want. It took me a bit of time, but I cobbled together a one off code that does the work. The code has a strange shape, I’m aware, because I wrote it to interface with Go, but it does the job.

Now, from Go, I can run the following:

As you can see, most of the code is there to manage error handling. But you can now convert a PFX to PEM and then pass that to X509keyPair easily.

That said, this seems just utterly ridiculous to me. There has got to be a better way to do that, surely.

time to read 5 min | 854 words

I needed to pay one of our suppliers. That supplier happens to be living in Europe, while Hibernating Rhinos is headquartered in Israel. That means that I have to send an international money transfer to get them paid.

So far, that isn’t an issue, this is literally something that we have to do multiple times a week and have been doing for the past decade and a half. This time, however…

We run into a problem, we initiated the payment round as usual and let the suppliers know that the money was transferred. And then I forgot about it, anything from this point on is on the accounting department.

A few days later, we started getting calls, telling us that the money that we sent didn’t arrive. I called the bank and they checked, it appears that some of the transfers that we made hit an internal bank limit. Basically, there are rules in place for how much money you can send out of the country before you have to get the IRS involved. I believe that the issue is about moving money to off shore accounts in order to provide taxes on that, but that isn’t relevant for the story at hand.  What is relevant here is that the bank didn’t process some of the payments in the run (those that hit the limit for the tax block). Some payment did went through, but some didn’t.

The issue with such things isn’t so much the block (I can understand why it is there, although I wish there was some earlier notice). The major issue was that we try to have a good payment schedule for our suppliers, meaning that we’ll pay most invoices within a short amount of time. When something like that happens, it means that we have to wade into the bureaucracy of the (international) tax system. That takes time, and in that time, the suppliers aren’t getting paid. Technically, we are okay, we are usually paying far ahead of the invoice due date, but I strongly dislike this.

We used a different source for the funds and paid all the suppliers immediately, then set to clear the tax hurdles involved in the usual manner in which we are paying. I also paid to expediate the transfer and they all had the money arrive faster than normal. In all, I would estimate that this meant that we had a delay of just a few days over when the money would arrive normally.

But that isn’t where the story ends. For most of the suppliers, the original transfers never happened, because of the tax issue. For two of them, however, the money was gone from our account. One of them also confirmed that they received the money, so I expected the second one to get it as well.

They didn’t. But the money was gone from our account. Talking to the bank, the money was in the bank’s account, waiting for the tax permit to go ahead with that. I asked the bank to cancel the order, then I transferred the money using the alternative way.

Except… that supplier called me, confused. The money appeared in their account twice. Checking with the bank, it was indeed gone from the original and alternative accounts. Well, that wasn’t expected. Luckily, this is a supplier that I’m doing regular business with, we decided that the simplest option is just to consider the extra payment to be credit for future charges. The supplier sent me a(n already marked as) paid invoice, and aside from shaking my head in disbelief, the issue was done.

Except.. that supplier called me, even more confused. Their bank called them, saying that the originating bank has cancelled the transfer, and they need to send the money back.

Que: imageimageimage

The key here was that their bank wanted them to transfer the money back to us. I had a very negative reaction to that, because this pinged all the hallmarks for a common scam: Overpayment Scam.  I asked the supplier to do nothing with that, since if the bank need to transfer the money, they can do it directly.

The fear was that the supplier would send the money back, then the bank will refund the money, resulting in the supplier having no money. I mentioned already: image?

I talked to my bank and cancelled the cancellation, so hopefully things will stabilize now. This is an ongoing event and I don’t know if we hit peak kafkianess.

As for what happens, I suspect that when I asked to cancel the original transfer, the was another logic branch that was followed. Since the money already left my account, they had to record that as a cancellation, but that apparently was sent to the destination bank, along with the money, I guess?

At this point, I don’t even wanna know.

time to read 5 min | 834 words

I wrote a post a couple of weeks ago called: Architecture foresight: Put a queue on that. I got an interesting comment from Mike Tomaras on the post that deserve its own post in reply.

Even though the benefits of an async queue are indisputable, I will respectfully point out that you brush over or ignore the drawbacks.

… redacted, see the real comment for details …

I think we agree that your sync code example is much easier to reason about than your async one. "Well, it is a bit more complex to manage in the user interface", "And you can play games on the front end" hides a lot of complexity in the FE to accommodate async patterns.

Your "At more advanced levels" section presents no benefits really, doing these things in a sync pattern is exactly the same as in async, the complexity is moved to the infrastructure instead of the code.

This is a great discussion, and I agree with Mike that there are additional costs to using the async option compared to the synchronous one. There is a really good reason why pretty much all modern languages has something similar to async/await, after all. And anyone who did any work with Node.js and promises without that knows exactly what are the cost of trying to keep the state of the system through multiple levels of callbacks.

It is important, however, that my recommendation had nothing to do with async directly, although that is the end result. My recommendation had a lot more to do with breaking apart the behavior of the system, so you aren’t expected to give immediate replies to the user.

Consider this: ⏱. When you are processing a user’s request, you have a timer inherent to the operation. That timer can be a real one (how long until the request times out) or it can be a mental one (how long until the user gets bored). That means that you have a very short SLA to run the actual request.

What is the impact of that on your system? You have to provision enough capacity in the system to handle the spikes within the small SLA that you have to work with. That is tough. Let’s assume that you are running a website that accepts comments, and you need to run spam detection on the comment before actually posting that. This seems like a pretty standard scenario, right? It doesn’t require specialized scenarios.

However, the service you use has a rate limit of 10 comments / sec. That is also something that is pretty common and reasonable. How would you handle something like that if you have a post that suddenly gets a lot of comments? Well, you’ll have something that ensure that you don’t pass the limit, but then the user is sitting there, waiting and thinking that the request timed out. On the other hand, if you accept the request and place it into a queue, you can show it in the UI as accepted immediately and then process that at leisure.

Yes, this is more complex than just making the call inline, it requires a higher degree of complexity, but it also ensure that you have proper separation in your system. The front end submit messages to the backend, which will reply when it is done. By having this separation upfront, as part of your overall design, you get options. You can change how you are processing things in the backend quickly. Your front end feel fast (which is usually much more important than being fast, mind you).

As for the rate limits and the SLA? In the case of spam API or similar services, sure, this is obvious. But there are usually a lot of implicit SLAs like that. Your database disk is only able to serve so many writes a second, for example. That isn’t usually surfaced to you as X writes / sec limit, but it is true nevertheless. And a queue will smooth over any such issues easily. With making the request directly, you have to ensure that you have enough capacity to handle spikes, and that is usually far more expensive.

What is more interesting, in my opinion, is that the queue gives you options that you wouldn’t have otherwise. For example, tracing of all operations (great for audits), retries if needed, easy model for scale out, smoothing out of spikes, etc.

You cannot actually put everything into a queue, of course. The typical example is that you’ll want to handle a login page. You cannot really “let the user login immediately and process in the background”. Another example where you don’t want to use asynchronous processing is when you are making a query. There are patterns for async query completions, but they are pretty horrible to work with.

In general, the idea is that whenever the is any operation in the system, you throw that to a queue. Reads and certain key aspects are things that you’ll need to run directly.

time to read 2 min | 395 words

A user called us to ask about how they can manage to move a particular report from a legacy system to RavenDB. They need to be able to ask questions such as the following one:

This is an interesting issue, when you think about it from the point of view of a database engine. The distinct issue means that we have to keep state (all the unique values) while we evaluate the query, which can be expensive. One of the design principles of RavenDB was that we want to make it hard to accidently create expensive queries. Indeed, a query like that isn’t trivial to implement in RavenDB. We need to have a two stage approach for implementing this feature.

First, we’ll introduce a Map/Reduce index, which will aggregate the data on Employee, Company and City. Along the way, it will run the distinct operation on the City, because it will group by it. That gives us a model in which we get the distinct amount for free, and in a highly efficient manner. Here is the index in question:

The interesting thing about this index is that querying it will not give us the right results. We don’t want to get the details based on Employee, Company and City. We want just by Employee and Company. This is where the second stage comes into play. Instead of running a simple query on the index, we’ll use a faceted query. Here is what it will look like:

What this does is to aggregate the results (which were already partially aggregated by the Map/Reduce) and give us the totals. And here are the results:

The end result is that we are able to do most of the work an indexing time, and the query time is left working on already aggregated data. That means that the queries should be much faster and that there is a lot less work for the database to do.

It also isn’t RavenDB’s strong suit. Such queries are typically more inline with OLAP systems, to be honest. If you know what your query patterns looks like, you can use this technique to easily handle such queries, but if there is a wide range of dynamic queries, you may want to use RavenDB as the system of record and then use either SQL ETL or OLAP ETL to push that to a reporting system.

time to read 1 min | 149 words

Implementing a unit of work in Python can be an interesting challenge. Consider the following code:

This is about as simple a code as possible, to associate a tag to an object, right?

However, this code will fail for the following scenario:

You’ll get a lovely: “TypeError: unhashable type: 'Item'” when you try this. This is because data classes in Python has a complicated relationship with __hash__().

An obvious solution to the problem is to use:

However, the id() in Python is not guaranteed to be unique. Consider the following code:

On my machine, running this code gives me:


In other words, the id has been reused. This makes sense, since this is just the pointer to the value. We can fix that by holding on to the object reference, like so:

With this approach, we are able to implement proper reference equality and make sure that we aren’t mixing different values.

time to read 5 min | 945 words

A common question that is raised by customers is how to determine what kind of hardware you need to run RavenDB on. I’m sorry, but the answer is it’s depend, because there are a lot of variables to juggle, in this post, I”m going to try to give some insights about what sort of things you should consider when sizing your instances.

In general, you have three axis that you can work with. CPU, Memory and I/O. In terms of the best bang for the buck, optimizing I/O is usually the way to go and will return the most dividends. This is because most of the time, RavenDB will be bottlenecked on the I/O. This is especially true when you are running on the cloud, where 500 IOPS is a fairly common default (that is basically zilch to a database).

To give more a concrete answer we’ll need more details. Let’s say that you have an application with a database per customer (common for multi tenant scenarios). The structure of the database is the same, but the databases contain data that is separated from each customer. The database has 20 indexes in total, 15 map / full text search as well as 5 for map-reduce / facets operations. There is also an few ETL tasks and a couple of subscriptions for background work.

Let’s breakdown the load on  single server in this mode, shall we?

  • 100 databases (meaning 100 tx merger threads for I/O).
  • 2,000 indexes - 20 indexes x 100 databases (meaning 2,000 indexing threads).

Across the cluster, we also have:

  • 500 ETL tasks – 5 per database x 100
  • 200 subscriptions – you get the drill

The latest items are spread fairly among all the nodes that you have, but the first two are present in all nodes in the cluster.

What does this mean? We have 2,100 threads active at any given point in time? Well, that is where things gets a bit complex. We need to know more than just the raw numbers, we need to understand usage.

How many of those databases are active at any given point in time? In a multi tenant system, it is common to have many customers using the system sporadically, which can allow you to pack a lot more instances into the same hardware resources.

Of more interest, however, is usually the rate of writes. Here we need to ask ourselves what is the write write as well. In general, for reads RavenDB will load all the relevant items into memory and serve directly from there. For writes, given it’s durable nature, RavenDB must hit the disk. And the question now becomes how many database are active at the same time?

This is important, because 10 writes per second to a single database are far better than 10 writes / second across 10 databases. This is because RavenDB is able to batch I/O for a single database, but not across databases. Let’s consider the scenario where we have writes that would impact 5 indexes in the database, what is going to happen when we have 10 writes / sec in a single database?

  • 1 – 5 writes to the disk for the actual documents writes (depends on a lot of factors, and assuming that we are talking about concurrent requests here).
  • 5 – 10 index updates: 1 –2 index updates x 5 relevant indexes (in most cases, we are able to batch indexes even better than documents writes).

Total number of writes to disk: 6 – 15 writes.

However, if we take the same scenario, but now run it across 10 databases, each having a single write? There is no way for us to batch updates, so we’ll have:

  • 10 databases x (1 document writes  + 5 index updates) = 50 writes to disk.

If the number of relevant indexes is high, or if there are more databases involved, it is easy to hit the limits of I/O, especially on the cloud.

I’m actually painting somewhat bleak picture, in most cases you don’t have to worry too much about those details, RavenDB will take care of that for you. However, when you need to consider the sizing, you want to be aware of the possible load that you’ll have. Ironically enough, if you have enough load, RavenDB is able to really optimize things, it is when you have sporadic operations, spread across many locations that we start putting a lot of load on the underlying system.

So far, I was talking about I/O only, but there are other factors as well. Let’s assume that you are running 100 databases with 20 indexes each on a system with 4 cores. How is RavenDB going to split the load across the system?

The first priority is going to be given to processing requests, and then we’ll start on running indexes. That is actually by design, to ensure that we won’t overwhelmed the underlying system by issuing too much work all at once. That means that we’ll round robin the work across all the indexes that want to run, while keeping enough capacity to process user requests. In this case, more cores will allow us higher degree of parallelism, but if you have an unbalanced system (a lot of CPU but slow I/O), you’re going to see stalls because we’ll wait a lot for I/O.

In short, you need to have a fair idea about how your system is going to be used. If you don’t have at least a good guess on the topic, you are probably better off getting more I/O bandwidth than anything else. RavenDB continuously monitor itself and will alert you if there are resource issues. You are then able to shore up anything that is lacking to get the best system performance.

time to read 6 min | 1120 words

I was pointed to the Odin language after my post about the Zig language. On the surface, Odin and Zig are very similar, but they have some fundamental differences in behavior and mindset. I’m basing most of what I’m writing here on admittedly cursory reading of the Odin language docs and this blog post.

Odin has a great point on conditional compilation. The if statements that are evaluated at compile time are hard to distinguish. I like Odin’s when clauses better, but Zig has comptime if as well, which make it easier. The actual problem I have with this model in Zig is that it is easy to get to a situation where you write (new) code that doesn’t get called, but Zig will detect that it is unused and not bother compiling it. When you are actually trying to use it, you’ll hit a lot of compilation errors that you need to fix. This is in contrast to the way I would usually work, which is to almost always have the code in compliable state and leaning hard on the compiler to double check my work.

Beyond that, I have grave disagreements with Ginger, the author of the blog post and the Odin language. I want to pull just a couple of what I think are the most important points from that post:

I have never had a program cause a system to run out of memory in real software (other than artificial stress tests). If you are working in a low-memory environment, you should be extremely aware of its limitations and plan accordingly. If you are a desktop machine and run out of memory, don’t try to recover from the panic, quit the program or even shut-down the computer. As for other machinery, plan accordingly!

This is in relation to automatic heap allocations (which can fail, which will usually kill the process because there is no good way to report it). My reaction to that is “640KB is enough for everything”, right?

To start with, I write databases for a living. I run my code on containers with 128MB when the user uses a database that is 100s of GB in size. Even if running on proper server machines, I almost always have to deal with datasets that are bigger than memory. Running out of memory happens to us pretty much every single time we start the program. And handling this scenario robustly is important to building system software. In this case, planning accordingly in my view is not using a language that can put me in a hole. This is not theoretical, that is real scenario that we have to deal with.

The biggest turnoff for me, however, was this statement on errors:

…my issue with exception-based/exception-like errors is not the syntax but how they encourage error propagation. This encouragement promotes a culture of pass the error up the stack for “someone else” to handle the error. I hate this culture and I do not want to encourage it at the language level. Handle errors there and then and don’t pass them up the stack. You make your mess; you clean it.

I didn’t really know how to answer that at first. There are so many cases where that doesn’t even make sense that it isn’t even funny. Consider a scenario where I need to call a service that would compute some value for me. I’m doing that as gRPC over TCP + SSL. Let me count the number of errors that can happen here, shall we?

  • Bad reaction on the service (run out of memory, for example).
  • Argument passed is not a valid one
  • Invalid SSL certificate
  • Authentication issues
  • TCP firewall issue
  • DNS issue
  • Wrong URL / port

My code, which is calling the service, need to be able to handle any / all of those. And probably quite a few more that I didn’t account for. Trying to build something like that is onerous, fragile and doesn’t really work. For that matter, if I passed the wrong URL for the service, what is the code that is doing the gRPC call supposed to do but bubble the error up? If the DNS is returning an error, or there is a certificate issue, how do you clean it up? The only reasonable thing to do is to give as much context as possible and raise the error to the caller.

When building robust software, bubbling it up so the caller can decide what to do isn’t about passing the back, it is a b best practice. You only need to look at Erlang and how applications with the highest requirements for reliability are structured. They are meant to fail, error handling and recovery is something that happens in dedicated (supervisors) locations, because these places has the right context to make an actual determination.

The killer impact of this, however, is that Zig has explicit notion of errors, while Odin relies on the multiple return values system. We have seen how good that is with Go. In fact, one of the most common issues with Go is the issue with how much manual work it takes to do proper error handling.

But I think that the key issue here is that errors as a first class aspect of the language gives us a very powerful ability, errdefer. This single language feature is the reason I think that Zig is an amazing language. The concept of first class errors combine with errdefer makes building complex structures so much easier.

Consider the following code:

Note that I’m opening a file, mapping it to memory, validating its size and then that it has the right hash. I’m using defer to ensure that I cleanup the file handle, but what about the returned memory, in this case, I want to clean it up if there is an error, but not otherwise.

Consider how you would write this code without errdefer. I would have to add the “close the map” portion to both places where I want to return an error. And what happens if I’m using more than a couple of resources, I may be needing to do something that require a file, network socket, memory, etc. Any of those operations can fail, but I want to clean them up only on failure. Otherwise, I need to return them to my caller. Using errdefer (which relies on the explicit distinction between regular returns and errors) will ensure that I don’t have a problem. Everything works, and the amount of state that I have to keep in my head is greatly reduce.

Consider how you’ll that that in Odin or Go, on the other hand, and you can see how error handling become a big beast. Having explicit language support to assist in that is really nice.


  1. Production postmortem: The Guinness record for page faults & high CPU - 3 hours from now
  2. Atomic reference counting (with Zig code samples) - 3 days from now

There are posts all the way to Sep 20, 2021


  1. Production postmortem (31):
    07 Jan 2021 - The file system limitation
  2. RavenDB 5.2 (2):
    06 Aug 2021 - Simplifying atomic cluster wide transactions
  3. Postmortem (2):
    23 Jul 2021 - Accidentally quadratic indexing output
  4. re (28):
    23 Jun 2021 - The performance regression odyssey
  5. Challenge (58):
    16 Jun 2021 - Detecting livelihood in a distributed cluster
View all series


Main feed Feed Stats
Comments feed   Comments Feed Stats