From 275758b5e0f094b14d61c2efee9b9b689fe8e543 Mon Sep 17 00:00:00 2001 From: Muhammad Sameer Ali <32276372+msali123@users.noreply.github.com> Date: Tue, 16 Jan 2024 09:55:39 +0000 Subject: [PATCH 1/8] Created new post & added content --- blog/_posts/2024-01-22-konfig.md | 99 +++++++++++++++++++++++++++ blog/assets/images/konfig/header.jpg | Bin 0 -> 2211964 bytes 2 files changed, 99 insertions(+) create mode 100644 blog/_posts/2024-01-22-konfig.md create mode 100644 blog/assets/images/konfig/header.jpg diff --git a/blog/_posts/2024-01-22-konfig.md b/blog/_posts/2024-01-22-konfig.md new file mode 100644 index 000000000..ce1c0b69d --- /dev/null +++ b/blog/_posts/2024-01-22-konfig.md @@ -0,0 +1,99 @@ +--- +title: "How Earthly Solved Our CI Problem" +categories: + - Tutorials +toc: true +author: Adam + +internal-links: + - just an example +excerpt: | + In this guest post, the Konfig team discusses how they solved their complex Continuous Integration challenges with Earthly, sharing insights valuable for any software development team. +--- + +The value of a continuous integration pipeline in software development is clear. It allows engineers to test every code change, ensuring deployments are free from regressions in functionality and performance. At Konfig, a startup creating SDKs in many coding languages from our clients' OpenAPI specs, not having Continuous Integration was a major issue. Code generation software tends to be especially fickle, and many of our code paths are shared across generators for different coding languages, making it easy to introduce bugs. When we made revisions for one customer, it wasn't uncommon for these changes to be incompatible for another customer. We badly needed CI -- and quickly, too. Thus we embarked on (what we thought would be) the treacherous journey of building a CI pipeline that could accommodate our architecture. + +## The Problem: Multiple Languages and Components + +From the get-go, we presumed that building an efficient CI pipeline for Konfig would not be straightforward. None of us had any experience creating a CI pipeline for a multi-service architecture. Our product naturally interacts with many languages, necessitating the deployment of different services tailored to each language. For instance, certain functionalities, like formatting, require environments that support their specific language. . Despite our complicated architecture, we wanted our CI to be speedy and cost-effective. As a startup, we were moving fast, and could not afford for build times to stretch into hours. This was a tricky problem. + +The nature of our system lent itself well to docker compose, where we could bake each service into a docker image and use a compose file to orchestrate their communication. Docker compose proved extremely effective for local testing, as docker layer caching offered a huge benefit: Only components that changed needed to be rebuilt. It wasn't too problematic to translate each of our components into docker images that could be used to run the testing pipeline locally. + +``` +version: "3.8" +services: + konfig-api: + image: konfig-api:latest + ports: + - "8911:8911" + depends_on: + - konfig-db + + konfig-python-formatter: + image: konfig-python-formatter:latest + ports: + - "10000:10000" + + konfig-generator: + image: konfig-generator:latest + ports: + - "8080:8080" + + konfig-db: + image: postgres + restart: always + ports: + - "5432:5432" +``` + +*A simplified version of our Docker compose* + +However, the hard part was imitating this behavior in the cloud in order to create a fast and economical CI pipeline. Of course, we could just blindly port this docker-compose setup to Github actions, but then we would crucially lose the benefit of caching. When developing locally, docker uses your local disk to cache images in layers, allowing you to re-use these layers in subsequent builds if nothing has changed. Because there is no built-in persistent storage across CI builds, two builds running one after the other would each be forced to build all of the images from the ground up. One potential solution to this is to introduce a persistent storage that is pushed to and pulled from before and after each CI build; the problem with this is that our images are large -- in the realm of multiple GB apiece -- and the network cost of uploading/downloading these images would offset any gains from reusing them. + +Thus, to avoid a network bottleneck, we need persistent storage that lives next to the compute instance (much like it does when running docker on your laptop!) To this end, we explored the nascent github actions cache feature, only to unfortunately find that it was limited to 10GB -- not enough space for our various large images. + +So, to recap: In order to achieve CI with our desired speed, efficiency, and tooling, we were looking for a CI that wasn't too dissimilar from docker with a large amount of persistent storage living next to the execution environment. Luckily for us, that last sentence is a perfect characterization of Earthly. + +## The Solution: Earthly + +When Konfig first found Earthly, I'll admit, I immediately liked the "Earth" branding; as a result, I really wanted Earthly to work out as the solution we were searching for. + +After installing, it was easy to get our system ported into the Earthly ecosystem -- the Earthfile doesn't fall too far from the Dockerfile tree, and Earthly's integration with docker compose let me reuse the same compose.yaml file. The target syntax, which lets Earthfiles reference files and images from other targets in any Earthfile, was advantageous, as it let me clean up some duplicated resources between different images. (This is also possible with docker multi-stage builds, but Earthly made it even easier and more intuitive!) + +``` +​test: + FROM earthly/dind:alpine-3.18-docker-23.0.6-r4 + COPY compose/.env . + COPY compose/compose.yaml . + ARG TEST_ARGS + WITH DOCKER \ + --compose compose.yaml \ + --load konfig-python-formatter:latest=../konfig-python-formatter-server-blackd+run-python-formatter \ + --load konfig-generator:latest=../konfig-generator-api+run-generator \ + --load konfig-api:latest=../konfig-dash+run-konfig-api \ + --load konfig-integration-tests:latest=+integration-tests + RUN docker run --network="konfig-network" konfig-integration-tests:latest $TEST_ARGS + END + +``` + +** Very simplified test against docker-compose structure from one of our Earthfile** + +One favorable characteristic that I enjoyed was how I could build and experiment with Earthly locally, much as I would with docker. In fact, its use of docker under the hood made it easy to reuse familiar tools for local development and debugging, like docker desktop. Our Earthly pipeline essentially functions to build the images for each of our components, then use docker compose (integrated into Earthly) to orchestrate them in order to run our tests against them. Earthly builds targets in parallel, only waiting when required by interdependencies between targets, which made the builds quite speedy without requiring us to write any scripts. + +Once everything was working locally, it was beyond simple to get Earthly running in the cloud -- launching an Earthly "satellite" took just a single CLI command. After that, everything stayed the same, except it wasn't running on my machine anymore -- although, the logs streaming back to my terminal in real-time could have fooled me. Satellites have large built-in caches, so the caching worked just as well in the cloud as it was on my laptop. The final step was just integrating the process into Github actions to trigger on pull requests, which once again could not have been easier: just a "Setup Earthly" action, followed by the same CLI command I'd been using to run Earthly locally. + +I can now look back and say my presumptions were wrong, and building an efficient CI for Konfig was not so treacherous once we found Earthly, a tool seemingly built with our exact conundrum in mind. To this day, it never fails to make me happy seeing `*cached* *cached* *cached*` in our github action logs! + + +## Outside Article Checklist + +- [ ] Add in Author page +- [ ] Create header image in Canva +- [ ] Optional: Find ways to break up content with quotes or images +- [ ] Verify look of article locally +- [ ] Would any images look better `wide` or without the `figcaption`? +- [ ] Run mark down linter (`lint`) +- [ ] Add keywords for internal links to front-matter +- [ ] Run `link-opp` and find 1-5 places to incorporate links +- [ ] Add Earthly `CTA` at bottom `{% include_html cta/bottom-cta.html %}` diff --git a/blog/assets/images/konfig/header.jpg b/blog/assets/images/konfig/header.jpg new file mode 100644 index 0000000000000000000000000000000000000000..cc2eef6a649c335d3c1fd34867a230352ff462c5 GIT binary patch literal 2211964 zcmb@td0diP*EfEFq8vIJ6j&K5M;iz$kL83@P@t7rP@v^lnnTW^X->((2BOr=)YMeO zRKzKBO3Q4r%-Nhx8=Z%&97>zsO`UV@`*}a_^VjdURIbftb6tC{y@u~vYx|?;#~V0) 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zpoXVMkJB%{P)UoGxCD%BwTX z?nUd8{cXh))SR~-8DC-v!Tcksox)zk+XmN*>Si(H8Ga*MumACWG*sQ*!8h_Uj4q37;Z{j?|8-0k>b!E1w}p zXOs>fc={OR-|6^PIS#x}s1iFLVStkelfqfKzk?=I^vrO;$)|nk_{?N+R zrt1a;=jsvDjv*JjOMK*4eheb2tc(63&Ajw>Z;>~dgGb#F5ogxSIH!x%P&Hf zppGxqgTZ-nlGzOB<_19c~O=)N-TxQcDUb|O=|&ZemYgqq#iECaK+E?_Ald<;c_TAxa+av*1 zC(Gcz(F@8Bu91>Jo>!MSWQ>I8t0H%`sMjWX`-ZfA}}4=Hz$#;)bo(+7qid o=i<7Ho5TBw!dh6VynASEB5nUx^R*O4$N#uA5{@uEi~n2wKc~{-u>b%7 literal 0 HcmV?d00001 From 52db1258635ab08909008ed4536b5a84d971ada7 Mon Sep 17 00:00:00 2001 From: Muhammad Sameer Ali <32276372+msali123@users.noreply.github.com> Date: Tue, 16 Jan 2024 10:26:41 +0000 Subject: [PATCH 2/8] Resolved lint warnings and added CTA --- .../styles/HouseStyle/tech-terms/general.txt | 2 ++ blog/_posts/2024-01-22-konfig.md | 33 +++++++++---------- 2 files changed, 17 insertions(+), 18 deletions(-) diff --git a/.github/styles/HouseStyle/tech-terms/general.txt b/.github/styles/HouseStyle/tech-terms/general.txt index 2f3df3649..e61d8e2bc 100644 --- a/.github/styles/HouseStyle/tech-terms/general.txt +++ b/.github/styles/HouseStyle/tech-terms/general.txt @@ -606,3 +606,5 @@ Ryzen imagetools multicontainer retrigger + +Konfig \ No newline at end of file diff --git a/blog/_posts/2024-01-22-konfig.md b/blog/_posts/2024-01-22-konfig.md index ce1c0b69d..ede0fd4fe 100644 --- a/blog/_posts/2024-01-22-konfig.md +++ b/blog/_posts/2024-01-22-konfig.md @@ -11,15 +11,15 @@ excerpt: | In this guest post, the Konfig team discusses how they solved their complex Continuous Integration challenges with Earthly, sharing insights valuable for any software development team. --- -The value of a continuous integration pipeline in software development is clear. It allows engineers to test every code change, ensuring deployments are free from regressions in functionality and performance. At Konfig, a startup creating SDKs in many coding languages from our clients' OpenAPI specs, not having Continuous Integration was a major issue. Code generation software tends to be especially fickle, and many of our code paths are shared across generators for different coding languages, making it easy to introduce bugs. When we made revisions for one customer, it wasn't uncommon for these changes to be incompatible for another customer. We badly needed CI -- and quickly, too. Thus we embarked on (what we thought would be) the treacherous journey of building a CI pipeline that could accommodate our architecture. +The value of a continuous integration pipeline in software development is clear. It allows engineers to test every code change, ensuring deployments are free from regressions in functionality and performance. At Konfig, a startup creating SDKs in many coding languages from our clients' OpenAPI specs, not having Continuous Integration was a major issue. Code generation software tends to be especially fickle, and many of our code paths are shared across generators for different coding languages, making it easy to introduce bugs. When we made revisions for one customer, it wasn't uncommon for these changes to be incompatible for another customer. We badly needed CI -- and quickly, too. Thus we embarked on (what we thought would be) the treacherous journey of building a CI pipeline that could accommodate our architecture. -## The Problem: Multiple Languages and Components +## The Problem: Multiple Languages and Components -From the get-go, we presumed that building an efficient CI pipeline for Konfig would not be straightforward. None of us had any experience creating a CI pipeline for a multi-service architecture. Our product naturally interacts with many languages, necessitating the deployment of different services tailored to each language. For instance, certain functionalities, like formatting, require environments that support their specific language. . Despite our complicated architecture, we wanted our CI to be speedy and cost-effective. As a startup, we were moving fast, and could not afford for build times to stretch into hours. This was a tricky problem. +From the get-go, we presumed that building an efficient CI pipeline for Konfig would not be straightforward. None of us had any experience creating a CI pipeline for a multi-service architecture. Our product naturally interacts with many languages, necessitating the deployment of different services tailored to each language. For instance, certain functionalities, like formatting, require environments that support their specific language. . Despite our complicated architecture, we wanted our CI to be speedy and cost-effective. As a startup, we were moving fast, and could not afford for build times to stretch into hours. This was a tricky problem. The nature of our system lent itself well to docker compose, where we could bake each service into a docker image and use a compose file to orchestrate their communication. Docker compose proved extremely effective for local testing, as docker layer caching offered a huge benefit: Only components that changed needed to be rebuilt. It wasn't too problematic to translate each of our components into docker images that could be used to run the testing pipeline locally. -``` +~~~ version: "3.8" services: konfig-api: @@ -44,13 +44,13 @@ services: restart: always ports: - "5432:5432" -``` +~~~ -*A simplified version of our Docker compose* +## *A Simplified Version of our Docker Compose* -However, the hard part was imitating this behavior in the cloud in order to create a fast and economical CI pipeline. Of course, we could just blindly port this docker-compose setup to Github actions, but then we would crucially lose the benefit of caching. When developing locally, docker uses your local disk to cache images in layers, allowing you to re-use these layers in subsequent builds if nothing has changed. Because there is no built-in persistent storage across CI builds, two builds running one after the other would each be forced to build all of the images from the ground up. One potential solution to this is to introduce a persistent storage that is pushed to and pulled from before and after each CI build; the problem with this is that our images are large -- in the realm of multiple GB apiece -- and the network cost of uploading/downloading these images would offset any gains from reusing them. +However, the hard part was imitating this behavior in the cloud in order to create a fast and economical CI pipeline. Of course, we could just blindly port this docker-compose setup to Github actions, but then we would crucially lose the benefit of caching. When developing locally, docker uses your local disk to cache images in layers, allowing you to re-use these layers in subsequent builds if nothing has changed. Because there is no built-in persistent storage across CI builds, two builds running one after the other would each be forced to build all of the images from the ground up. One potential solution to this is to introduce a persistent storage that is pushed to and pulled from before and after each CI build; the problem with this is that our images are large -- in the realm of multiple GB apiece -- and the network cost of uploading/downloading these images would offset any gains from reusing them. -Thus, to avoid a network bottleneck, we need persistent storage that lives next to the compute instance (much like it does when running docker on your laptop!) To this end, we explored the nascent github actions cache feature, only to unfortunately find that it was limited to 10GB -- not enough space for our various large images. +Thus, to avoid a network bottleneck, we need persistent storage that lives next to the compute instance (much like it does when running docker on your laptop!) To this end, we explored the nascent github actions cache feature, only to unfortunately find that it was limited to 10GB -- not enough space for our various large images. So, to recap: In order to achieve CI with our desired speed, efficiency, and tooling, we were looking for a CI that wasn't too dissimilar from docker with a large amount of persistent storage living next to the execution environment. Luckily for us, that last sentence is a perfect characterization of Earthly. @@ -60,7 +60,7 @@ When Konfig first found Earthly, I'll admit, I immediately liked the "Earth" bra After installing, it was easy to get our system ported into the Earthly ecosystem -- the Earthfile doesn't fall too far from the Dockerfile tree, and Earthly's integration with docker compose let me reuse the same compose.yaml file. The target syntax, which lets Earthfiles reference files and images from other targets in any Earthfile, was advantageous, as it let me clean up some duplicated resources between different images. (This is also possible with docker multi-stage builds, but Earthly made it even easier and more intuitive!) -``` +~~~ ​test: FROM earthly/dind:alpine-3.18-docker-23.0.6-r4 COPY compose/.env . @@ -75,25 +75,22 @@ After installing, it was easy to get our system ported into the Earthly ecosyste RUN docker run --network="konfig-network" konfig-integration-tests:latest $TEST_ARGS END -``` +~~~ -** Very simplified test against docker-compose structure from one of our Earthfile** +## **Very Simplified Test Against Docker-Compose Structure from One of our Earthfile** -One favorable characteristic that I enjoyed was how I could build and experiment with Earthly locally, much as I would with docker. In fact, its use of docker under the hood made it easy to reuse familiar tools for local development and debugging, like docker desktop. Our Earthly pipeline essentially functions to build the images for each of our components, then use docker compose (integrated into Earthly) to orchestrate them in order to run our tests against them. Earthly builds targets in parallel, only waiting when required by interdependencies between targets, which made the builds quite speedy without requiring us to write any scripts. +One favorable characteristic that I enjoyed was how I could build and experiment with Earthly locally, much as I would with docker. In fact, its use of docker under the hood made it easy to reuse familiar tools for local development and debugging, like docker desktop. Our Earthly pipeline essentially functions to build the images for each of our components, then use docker compose (integrated into Earthly) to orchestrate them in order to run our tests against them. Earthly builds targets in parallel, only waiting when required by interdependencies between targets, which made the builds quite speedy without requiring us to write any scripts. -Once everything was working locally, it was beyond simple to get Earthly running in the cloud -- launching an Earthly "satellite" took just a single CLI command. After that, everything stayed the same, except it wasn't running on my machine anymore -- although, the logs streaming back to my terminal in real-time could have fooled me. Satellites have large built-in caches, so the caching worked just as well in the cloud as it was on my laptop. The final step was just integrating the process into Github actions to trigger on pull requests, which once again could not have been easier: just a "Setup Earthly" action, followed by the same CLI command I'd been using to run Earthly locally. +Once everything was working locally, it was beyond simple to get Earthly running in the cloud -- launching an Earthly "satellite" took just a single CLI command. After that, everything stayed the same, except it wasn't running on my machine anymore -- although, the logs streaming back to my terminal in real-time could have fooled me. Satellites have large built-in caches, so the caching worked just as well in the cloud as it was on my laptop. The final step was just integrating the process into Github actions to trigger on pull requests, which once again could not have been easier: just a "Setup Earthly" action, followed by the same CLI command I'd been using to run Earthly locally. I can now look back and say my presumptions were wrong, and building an efficient CI for Konfig was not so treacherous once we found Earthly, a tool seemingly built with our exact conundrum in mind. To this day, it never fails to make me happy seeing `*cached* *cached* *cached*` in our github action logs! +{% include_html cta/bottom-cta.html %} ## Outside Article Checklist - [ ] Add in Author page - [ ] Create header image in Canva -- [ ] Optional: Find ways to break up content with quotes or images -- [ ] Verify look of article locally -- [ ] Would any images look better `wide` or without the `figcaption`? -- [ ] Run mark down linter (`lint`) + - [ ] Add keywords for internal links to front-matter - [ ] Run `link-opp` and find 1-5 places to incorporate links -- [ ] Add Earthly `CTA` at bottom `{% include_html cta/bottom-cta.html %}` From fbda30252c354c5c56af8e3bbe99c19905587393 Mon Sep 17 00:00:00 2001 From: Muhammad Sameer Ali <32276372+msali123@users.noreply.github.com> Date: Wed, 17 Jan 2024 08:47:28 +0000 Subject: [PATCH 3/8] Added new author --- blog/_data/authors.yml | 6 +++++- blog/_posts/2024-01-22-konfig.md | 3 +-- blog/assets/images/authors/eddie-chayes.jpeg | Bin 0 -> 100904 bytes 3 files changed, 6 insertions(+), 3 deletions(-) create mode 100644 blog/assets/images/authors/eddie-chayes.jpeg diff --git a/blog/_data/authors.yml b/blog/_data/authors.yml index ef0cbc61f..1875df604 100644 --- a/blog/_data/authors.yml +++ b/blog/_data/authors.yml @@ -556,4 +556,8 @@ Samuel Umoren: Rajkumar Venkatasamy: name : "Rajkumar Venkatasamy" bio : "Rajkumar has nearly sixteen years of experience in the software industry as a developer, data modeler, tester, project lead, product consultant, data architect, ETL specialist, and technical architect. Currently he is a principal architect at an MNC. He has hands-on experience in various technologies, tools, and libraries." - avatar : "/assets/images/authors/rajkumar-venkatasamy.jpg" \ No newline at end of file + avatar : "/assets/images/authors/rajkumar-venkatasamy.jpg" +Eddie Chayes: + name : "Eddie Chayes" + bio : "Eddie is an early career software engineer with a few years of experience working with AI platforms; he’s now a founding engineer at Konfig, a startup that generates SDKs for API companies." + avatar : "/assets/images/authors/eddie-chayes.jpg" \ No newline at end of file diff --git a/blog/_posts/2024-01-22-konfig.md b/blog/_posts/2024-01-22-konfig.md index ede0fd4fe..0178d8f38 100644 --- a/blog/_posts/2024-01-22-konfig.md +++ b/blog/_posts/2024-01-22-konfig.md @@ -3,7 +3,7 @@ title: "How Earthly Solved Our CI Problem" categories: - Tutorials toc: true -author: Adam +author: Eddie Chayes internal-links: - just an example @@ -89,7 +89,6 @@ I can now look back and say my presumptions were wrong, and building an efficien 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-> 190019 bytes 4 files changed, 10 insertions(+), 6 deletions(-) create mode 100644 blog/assets/images/konfig/img1.png create mode 100644 blog/assets/images/konfig/img2.png diff --git a/blog/_data/authors.yml b/blog/_data/authors.yml index 1875df604..276e85e4f 100644 --- a/blog/_data/authors.yml +++ b/blog/_data/authors.yml @@ -560,4 +560,4 @@ Rajkumar Venkatasamy: Eddie Chayes: name : "Eddie Chayes" bio : "Eddie is an early career software engineer with a few years of experience working with AI platforms; he’s now a founding engineer at Konfig, a startup that generates SDKs for API companies." - avatar : "/assets/images/authors/eddie-chayes.jpg" \ No newline at end of file + avatar : "/assets/images/authors/eddie-chayes.jpeg" \ No newline at end of file diff --git a/blog/_posts/2024-01-22-konfig.md b/blog/_posts/2024-01-22-konfig.md index 0178d8f38..02a2b53c3 100644 --- a/blog/_posts/2024-01-22-konfig.md +++ b/blog/_posts/2024-01-22-konfig.md @@ -13,13 +13,15 @@ excerpt: | The value of a continuous integration pipeline in software development is clear. It allows engineers to test every code change, ensuring deployments are free from regressions in functionality and performance. At Konfig, a startup creating SDKs in many coding languages from our clients' OpenAPI specs, not having Continuous Integration was a major issue. Code generation software tends to be especially fickle, and many of our code paths are shared across generators for different coding languages, making it easy to introduce bugs. When we made revisions for one customer, it wasn't uncommon for these changes to be incompatible for another customer. We badly needed CI -- and quickly, too. Thus we embarked on (what we thought would be) the treacherous journey of building a CI pipeline that could accommodate our architecture. +![Img]({{site.images}}{{page.slug}}/img1.png)\ + ## The Problem: Multiple Languages and Components From the get-go, we presumed that building an efficient CI pipeline for Konfig would not be straightforward. None of us had any experience creating a CI pipeline for a multi-service architecture. Our product naturally interacts with many languages, necessitating the deployment of different services tailored to each language. For instance, certain functionalities, like formatting, require environments that support their specific language. . Despite our complicated architecture, we wanted our CI to be speedy and cost-effective. As a startup, we were moving fast, and could not afford for build times to stretch into hours. This was a tricky problem. The nature of our system lent itself well to docker compose, where we could bake each service into a docker image and use a compose file to orchestrate their communication. Docker compose proved extremely effective for local testing, as docker layer caching offered a huge benefit: Only components that changed needed to be rebuilt. It wasn't too problematic to translate each of our components into docker images that could be used to run the testing pipeline locally. -~~~ +~~~{.yaml caption=""} version: "3.8" services: konfig-api: @@ -46,7 +48,7 @@ services: - "5432:5432" ~~~ -## *A Simplified Version of our Docker Compose* +## A Simplified Version of our Docker Compose However, the hard part was imitating this behavior in the cloud in order to create a fast and economical CI pipeline. Of course, we could just blindly port this docker-compose setup to Github actions, but then we would crucially lose the benefit of caching. When developing locally, docker uses your local disk to cache images in layers, allowing you to re-use these layers in subsequent builds if nothing has changed. Because there is no built-in persistent storage across CI builds, two builds running one after the other would each be forced to build all of the images from the ground up. One potential solution to this is to introduce a persistent storage that is pushed to and pulled from before and after each CI build; the problem with this is that our images are large -- in the realm of multiple GB apiece -- and the network cost of uploading/downloading these images would offset any gains from reusing them. @@ -54,13 +56,15 @@ Thus, to avoid a network bottleneck, we need persistent storage that lives next So, to recap: In order to achieve CI with our desired speed, efficiency, and tooling, we were looking for a CI that wasn't too dissimilar from docker with a large amount of persistent storage living next to the execution environment. Luckily for us, that last sentence is a perfect characterization of Earthly. +![Img]({{site.images}}{{page.slug}}/img2.png)\ + ## The Solution: Earthly When Konfig first found Earthly, I'll admit, I immediately liked the "Earth" branding; as a result, I really wanted Earthly to work out as the solution we were searching for. -After installing, it was easy to get our system ported into the Earthly ecosystem -- the Earthfile doesn't fall too far from the Dockerfile tree, and Earthly's integration with docker compose let me reuse the same compose.yaml file. The target syntax, which lets Earthfiles reference files and images from other targets in any Earthfile, was advantageous, as it let me clean up some duplicated resources between different images. (This is also possible with docker multi-stage builds, but Earthly made it even easier and more intuitive!) +After installing, it was easy to get our system ported into the Earthly ecosystem -- the Earthfile doesn't fall too far from the Dockerfile tree, and Earthly's integration with docker compose let me reuse the same `compose.yaml` file. The target syntax, which lets Earthfiles reference files and images from other targets in any Earthfile, was advantageous, as it let me clean up some duplicated resources between different images. (This is also possible with docker multi-stage builds, but Earthly made it even easier and more intuitive!) -~~~ +~~~{.yaml caption="compose.yaml"} ​test: FROM earthly/dind:alpine-3.18-docker-23.0.6-r4 COPY compose/.env . @@ -77,7 +81,7 @@ After installing, it was easy to get our system ported into the Earthly ecosyste ~~~ -## **Very Simplified Test Against Docker-Compose Structure from One of our Earthfile** +## Very Simplified Test Against Docker-Compose Structure from One of our Earthfile One favorable characteristic that I enjoyed was how I could build and experiment with Earthly locally, much as I would with docker. In fact, its use of docker under the hood made it easy to reuse familiar tools for local development and debugging, like docker desktop. Our Earthly pipeline essentially functions to build the images for each of our components, then use docker compose (integrated into Earthly) to orchestrate them in order to run our tests against them. 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bb57f30e7ed1653cc1204e8cb784c86034649084 Mon Sep 17 00:00:00 2001 From: Muhammad Sameer Ali <32276372+msali123@users.noreply.github.com> Date: Wed, 17 Jan 2024 09:19:23 +0000 Subject: [PATCH 5/8] Added header image --- blog/_posts/2024-01-22-konfig.md | 5 ++--- blog/assets/images/konfig/header.jpg | Bin 2211964 -> 301092 bytes blog/assets/images/konfig/img3.png | Bin 0 -> 27836 bytes 3 files changed, 2 insertions(+), 3 deletions(-) create mode 100644 blog/assets/images/konfig/img3.png diff --git a/blog/_posts/2024-01-22-konfig.md b/blog/_posts/2024-01-22-konfig.md index 02a2b53c3..be7efd34d 100644 --- a/blog/_posts/2024-01-22-konfig.md +++ b/blog/_posts/2024-01-22-konfig.md @@ -11,6 +11,8 @@ excerpt: | In this guest post, the Konfig team discusses how they solved their complex Continuous Integration challenges with Earthly, sharing insights valuable for any software development team. --- +![Img]({{site.images}}{{page.slug}}/img3.png)\ + The value of a continuous integration pipeline in software development is clear. It allows engineers to test every code change, ensuring deployments are free from regressions in functionality and performance. At Konfig, a startup creating SDKs in many coding languages from our clients' OpenAPI specs, not having Continuous Integration was a major issue. Code generation software tends to be especially fickle, and many of our code paths are shared across generators for different coding languages, making it easy to introduce bugs. When we made revisions for one customer, it wasn't uncommon for these changes to be incompatible for another customer. We badly needed CI -- and quickly, too. 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zn)wZX{KUwL^KHEVm!g~IA!jj*i?1y-QlDR6MQ33PMF^gWPkes6rzGz@2#b$D1}5^E z>Z$BmJvTSyU2KrRm72a`FT zVepnCpJ_nvlYf`&&VP(L9Yu~~g39z|EeE(2i(TW34Sy$6UGs7Hw^A;MJ4`xcG91X3 zmcM!BQ`?e-JhtGJW0SmZ^%gQbmNg8xpkkx6+}Y6xeLjiiEhV7Ku)hXip@YMNQFDW@ zu*SrmVK0vTq`))OwK7RL{OkUC=5%hNPGys!saeP~4evm@zmpPiP1AK;7_mSFYTxkr zkdncxF?w6wBQ~6I<8kBj-JJq016R__2k(o#Cw;zwqf+%?+v7W=lk~zt4K;{yRu8U z3EBO*^3}e&(TQ3wHpt`)q=yx-W#?tX%M7AWY7t9^eKdQzQc5!oFv zc|U%v~b z&n^_P0K)M~eFHx%k#qa|s9bgg?RrkWP{z(Sx(dR`lKJlhO%D?9&d@M{$K-v}l$ycd z4XC$H);$ycuv|!RJ86@dD>64+1iq_=F{36e8f{KZZgVl82yxF3{Hc9d;`}$==NN;R z!nwbHZC>K$F0TlCxHqBB4qdkvnQNYS&rD5CxZF-2_DSeY_^ps{EpGbBz4Ccjn2O)F zOhjvj<+jkz*uxU^g~v*U#I(YfC#Qbz7R;KNksj_~n1wfFiz>!K2?VWocCu~7(4FGv z0rk+GEYcj=RF{EXrDyAbN~tNm7eU!TMc?3@SO1^7f`s25duQ-NVi>%PJ>Je-J6bfZ z&%?{0#i8{1=X$LRNr#2*&wHY`&mgiCCl@{O36MSH&aAB)kNxjoDS+Yrzx{pU|Mqu}1KX4S*Y^J%w*Ti7ijHqFJyWrMU-yxtuA-%!uXOkE Fe*r(Yz*YbN literal 0 HcmV?d00001 From 688a0c2c2547370374d7b9ad7f51783cd95e835e Mon Sep 17 00:00:00 2001 From: Muhammad Sameer Ali <32276372+msali123@users.noreply.github.com> Date: Wed, 17 Jan 2024 09:20:50 +0000 Subject: [PATCH 6/8] Added keywords for internal links --- blog/_posts/2024-01-22-konfig.md | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/blog/_posts/2024-01-22-konfig.md b/blog/_posts/2024-01-22-konfig.md index be7efd34d..2af7fc326 100644 --- a/blog/_posts/2024-01-22-konfig.md +++ b/blog/_posts/2024-01-22-konfig.md @@ -6,7 +6,10 @@ toc: true author: Eddie Chayes internal-links: - - just an example + - ci problem solved + - how earthly helps with ci problems + - solving ci problems with earthly + - earthly the problem solver excerpt: | In this guest post, the Konfig team discusses how they solved their complex Continuous Integration challenges with Earthly, sharing insights valuable for any software development team. --- @@ -92,7 +95,3 @@ Once everything was working locally, it was beyond simple to get Earthly running I can now look back and say my presumptions were wrong, and building an efficient CI for Konfig was not so treacherous once we found Earthly, a tool seemingly built with our exact conundrum in mind. To this day, it never fails to make me happy seeing `*cached* *cached* *cached*` in our github action logs! {% include_html cta/bottom-cta.html %} - - -- [ ] Add keywords for internal links to front-matter -- [ ] Run `link-opp` and find 1-5 places to incorporate links From bb0fb148121693f60ae3e4d31d74ef5c3ccc2ddd Mon Sep 17 00:00:00 2001 From: Muhammad Sameer Ali <32276372+msali123@users.noreply.github.com> Date: Wed, 17 Jan 2024 15:03:36 +0000 Subject: [PATCH 7/8] Changes made --- blog/_posts/2024-01-22-konfig.md | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/blog/_posts/2024-01-22-konfig.md b/blog/_posts/2024-01-22-konfig.md index 2af7fc326..568507861 100644 --- a/blog/_posts/2024-01-22-konfig.md +++ b/blog/_posts/2024-01-22-konfig.md @@ -14,6 +14,8 @@ excerpt: | In this guest post, the Konfig team discusses how they solved their complex Continuous Integration challenges with Earthly, sharing insights valuable for any software development team. --- +In this guest post, the Konfig team discusses how they solved their complex Continuous Integration challenges with Earthly, sharing insights valuable for any software development team. + ![Img]({{site.images}}{{page.slug}}/img3.png)\ The value of a continuous integration pipeline in software development is clear. It allows engineers to test every code change, ensuring deployments are free from regressions in functionality and performance. At Konfig, a startup creating SDKs in many coding languages from our clients' OpenAPI specs, not having Continuous Integration was a major issue. Code generation software tends to be especially fickle, and many of our code paths are shared across generators for different coding languages, making it easy to introduce bugs. When we made revisions for one customer, it wasn't uncommon for these changes to be incompatible for another customer. We badly needed CI -- and quickly, too. Thus we embarked on (what we thought would be) the treacherous journey of building a CI pipeline that could accommodate our architecture. @@ -26,7 +28,7 @@ From the get-go, we presumed that building an efficient CI pipeline for Konfig w The nature of our system lent itself well to docker compose, where we could bake each service into a docker image and use a compose file to orchestrate their communication. Docker compose proved extremely effective for local testing, as docker layer caching offered a huge benefit: Only components that changed needed to be rebuilt. It wasn't too problematic to translate each of our components into docker images that could be used to run the testing pipeline locally. -~~~{.yaml caption=""} +~~~{.yaml caption="compose.yaml"} version: "3.8" services: konfig-api: @@ -69,7 +71,7 @@ When Konfig first found Earthly, I'll admit, I immediately liked the "Earth" bra After installing, it was easy to get our system ported into the Earthly ecosystem -- the Earthfile doesn't fall too far from the Dockerfile tree, and Earthly's integration with docker compose let me reuse the same `compose.yaml` file. The target syntax, which lets Earthfiles reference files and images from other targets in any Earthfile, was advantageous, as it let me clean up some duplicated resources between different images. (This is also possible with docker multi-stage builds, but Earthly made it even easier and more intuitive!) -~~~{.yaml caption="compose.yaml"} +~~~{.dockerfile caption="Earthfile"} ​test: FROM earthly/dind:alpine-3.18-docker-23.0.6-r4 COPY compose/.env . From 530e7f3e5be689889774eb1b7fde8760f91ad333 Mon Sep 17 00:00:00 2001 From: Muhammad Sameer Ali <32276372+msali123@users.noreply.github.com> Date: Wed, 17 Jan 2024 19:27:19 +0000 Subject: [PATCH 8/8] Changes done --- blog/_posts/2024-01-22-konfig.md | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/blog/_posts/2024-01-22-konfig.md b/blog/_posts/2024-01-22-konfig.md index 568507861..87be49595 100644 --- a/blog/_posts/2024-01-22-konfig.md +++ b/blog/_posts/2024-01-22-konfig.md @@ -14,9 +14,7 @@ excerpt: | In this guest post, the Konfig team discusses how they solved their complex Continuous Integration challenges with Earthly, sharing insights valuable for any software development team. --- -In this guest post, the Konfig team discusses how they solved their complex Continuous Integration challenges with Earthly, sharing insights valuable for any software development team. - -![Img]({{site.images}}{{page.slug}}/img3.png)\ +![*In this guest post, the [Konfig](https://konfigthis.com/) team discusses how they solved their complex Continuous Integration challenges with Earthly, sharing insights valuable for any software development team.*]({{site.images}}{{page.slug}}/img3.png) The value of a continuous integration pipeline in software development is clear. It allows engineers to test every code change, ensuring deployments are free from regressions in functionality and performance. At Konfig, a startup creating SDKs in many coding languages from our clients' OpenAPI specs, not having Continuous Integration was a major issue. Code generation software tends to be especially fickle, and many of our code paths are shared across generators for different coding languages, making it easy to introduce bugs. When we made revisions for one customer, it wasn't uncommon for these changes to be incompatible for another customer. We badly needed CI -- and quickly, too. Thus we embarked on (what we thought would be) the treacherous journey of building a CI pipeline that could accommodate our architecture. @@ -24,7 +22,7 @@ The value of a continuous integration pipeline in software development is clear. ## The Problem: Multiple Languages and Components -From the get-go, we presumed that building an efficient CI pipeline for Konfig would not be straightforward. None of us had any experience creating a CI pipeline for a multi-service architecture. Our product naturally interacts with many languages, necessitating the deployment of different services tailored to each language. For instance, certain functionalities, like formatting, require environments that support their specific language. . Despite our complicated architecture, we wanted our CI to be speedy and cost-effective. As a startup, we were moving fast, and could not afford for build times to stretch into hours. This was a tricky problem. +From the get-go, we presumed that building an efficient CI pipeline for Konfig would not be straightforward. None of us had any experience creating a CI pipeline for a multi-service architecture. Our product naturally interacts with many languages, necessitating the deployment of different services tailored to each language. For instance, certain functionalities, like formatting, require environments that support their specific language. Despite our complicated architecture, we wanted our CI to be speedy and cost-effective. As a startup, we were moving fast, and could not afford for build times to stretch into hours. This was a tricky problem. The nature of our system lent itself well to docker compose, where we could bake each service into a docker image and use a compose file to orchestrate their communication. Docker compose proved extremely effective for local testing, as docker layer caching offered a huge benefit: Only components that changed needed to be rebuilt. It wasn't too problematic to translate each of our components into docker images that could be used to run the testing pipeline locally.

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