Helm uses gRPC. When gRPC made their 1.4.0 release they made an important change that wasn't obvious from the release notes. Prior to 1.4.0 there was one function of MaxMsgSize. In, and following, the 1.4.0 release this function was deprecated and there were two new functions of MaxRecvMsgSize and MaxSendMsgSize. Where MaxMsgSize had previously set the size on both send and receive it was now just set the receive value. This was a change in behavior.

Some people program after work. I think this is cool, but I don’t! I don’t really feel like I have the energy to do it.

It is of course possible to build systems that use semantic versioning without semantic import versioning, but only by giving up either partial code upgrades or import uniqueness.

We will be taking away some control and expressiveness, just as we took away makefiles, in service of simplifying the system and eliminating complexity for users

We received a word from Digital Ocean, OVH, Linode and Amazon that they tackled the memcached problem, their networks should not be a vector in future attacks. Hurray!

The other problem is that it introduces a lot of performance overhead (for the same reason that strace does: it works using ptrace)! If I run the same program with and without ltrace, the ltraced version will be way slower.

There are many truly excellent products sold on the high-touch model, but to a first approximation, engineering and product are generally considered less important in high-touch SaaS businesses than the sales engine is.

You will find that the sales model for SaaS defines much more about a product (and company) than other distinctions, like whether a company sells to customers (B2C) or businesses (B2B), whether it is bootstrapped or riding the VC rocket ship trajectory, or what technology stack it is built on.

The point of my article is not to point you in any direction, even though it seems that way. I merely point out the main controversial points of AMP, and how and why it works the way it does. In particular, why it’s so fast. Which is not because of AMP. Not at all because of AMP.

Having executable documentation for a package guarantees that the information will not go out of date as the API changes.

Stripe, for example, does a really good job of this by providing test mode tokens.

additional mocks to account for all of these cases previously unaccounted for

As a result, you cannot do this without also changing your codebase such that errors are less noticed by (& less impactful on) your users.

Yet when it comes to testing (or worse, when developing) microservices, most organizations seem to be quite attached to an antediluvian model of testing all components in unison.

I’ve also started asking “hey, do you have a document listing your recent successes that I can look through to make sure I haven’t forgetten anything?“. Some people keep one, and it’s really helpful when they do!

the last one being the only one that is in active development

I’m working on other problems

Distributions can be based on a deep fork of Kubernetes

if the child process segfaults, ignore it and keep going

Basically I worked on porting https://github.com/iovisor/gobpf (a go BCC library) to Rust.

Because the branching factor is typically in the thousands

Along with the languages normal strengths – incredible runtime speed, an amazing concurrency story, a great batteries-included standard library, and the fastest edit-compile-debug loop in the business – Go’s stability and ease of deployment is going to make it a tremendous addition to the Lambda platform

It’s rare to write software and not have it come back to haunt you in a few year’s time as it needs to be fixed and upgraded.

These holes tell us something else about Lambda: new runtimes are non-trivial to create, run, and/or maintain

Instrumentation with statsd

S3 snapshots of indexes

worthy of its own post

In the previous scenario, the backend selected to handle client A will be handling approximately 3000x less load then the backend selected to handle client B! This is a large problem and generally defeats the purpose of load balancing in the first place.

The wider arc of Go’s pragmas is they are used inside the standard library to gain a foothold to implement the runtime, including the garbage collector, in Go itself. Pragmas allowed the runtime developers to extend, the language just enough to meet the requirements of the problem. You’ll find pragmas used, sparingly, inside the standard library, although you’ll never find them listed in godoc.

and any associated material, as this material is generated. The approach may be summed up by the slogan 'no insider information'. It is the logical extreme of transparent approaches to research and explicitly includes the making available of failed, less significant, and otherwise unpublished experiments; so called 'dark data'.[1] The practice of open notebook science, although

long with all raw and processed data, and any associated material, as this material is generated. The approach may be summed up by the slogan 'no insider information'. It is the logical extreme of transparent approaches to research and explicitly includes the making available of failed, less significant, and otherwise unpublished experiments; so called 'dark data'.[1] The practice of open notebook science, although not the norm in the academic community, has gained significant recent attention in the research[2][3] and general[1][4] media as part of a general trend towards more open approaches in research practice and publishing. Open notebook science can therefore be described as part of a wider open science movement that includes the advocacy and adoption of open

ng with all raw and processed data, and any associated material, as this material is generated. The approach may be summed up by the slogan 'no insider information'. It is the logical extreme of transparent approaches to research and explicitly includes the making available of failed, less significant, and otherwise unpublished experiments; so called 'dark data'.[1] The practice of open notebook science, although not the norm in the academic community, has gained significant recent attention in the research[2][3] and general[1][4] media as part of a general trend towards more open approaches in research practice an

s, we use the abstract as a guide as to which information needs to be coded. In the Archival Project, we only code results mentioned in the abstract. This doesn't mean, however, that you only need to read the abstract. What it means is that you should identify the topics mentioned there, and look for them in the rest of the article.

developed a specific coding scheme for volunteers to follow. As a supplement to that scheme, the following tutorial can be used to gain more facility with identifying article structure and und