Top all-time · #901
doordash.engineering
Company blog · doordash.engineeringprogrammingsystems
doordash.engineering is the company blog at doordash.engineering, writing about programming and systems. Since 2019, 71 different Hacker News readers have linked it 103 times, most often “Migrating from Python to Kotlin for Our Back End Services”. No feed was found, so read it on the site itself.
Too few sampled comments (13) to say how often HN recommends it.?
Why HN links it
HN readers link DoorDash's engineering blog for backend and infrastructure write-ups, such as LZ4 compression in Redis, the Python-to-Kotlin migration, switchback testing and microservice failures.
Summarised by Claude from a sample of 13 HN comments that link here. Each comment is marked recommending, critical or neutral (citing without judging); how this works.
Most-linked posts?
- Migrating from Python to Kotlin for Our Back End Services9 people · 9 mentions · first linked 2021-05-05
- Using Fault Injection Testing to Improve DoorDash Reliability4 people · 5 mentions · first linked 2022-04-26
- Building a Marketing Engineering Platform Using Next.js, Cloudflare, Contentful4 people · 4 mentions · first linked 2022-02-22
- Eliminating Task Processing Outages by Replacing RabbitMQ with Apache Kafka4 people · 4 mentions · first linked 2020-09-04
- Building a Gigascale ML Feature Store with Redis4 people · 4 mentions · first linked 2020-11-19
- Why Burnout Is Hard to Detect3 people · 3 mentions · first linked 2021-09-02
- Using ML and Optimization to Solve DoorDash’s Dispatch Problem3 people · 3 mentions · first linked 2021-08-18
- Tips for Building High-Quality Django Apps at Scale – DoorDash Engineering Blog3 people · 3 mentions · first linked 2022-01-19
- DoorDash Quickly Spins Up Multiple Image Recognition Use Cases2 people · 2 mentions · first linked 2021-11-04
- Examining Problematic Memory in C/C++ Applications with BPF, Perf, and Memcheck2 people · 2 mentions · first linked 2021-04-15
What Hacker News says?
I've personally used LZ4 on production a scale that really proves how using a compression like LZ4 is more efficient than uncompressed data https://doordash.engineering/2019/01/02/speeding-up-redis-wi...
Comment linking /2019/01/02/speeding-up-redis-with-compression/
on “LZ4 – Extremely fast compression”Submitted Why Apple’s new M1 chips are essential for rapid iOS development as a story · 119 points
HN discussionDoorDash | Remote Contract-to-Hire | https://get.doordash.com We're looking for a highly-motivated junior or mid-career front-end engineer with experience in React, Next.js, Typescript -- key differentiators are a quantitative and qualitative approach to improving UX, a11y, i18n and documentation -- seeking Engineers who ask questions, unravel the thread, and take a holistic, long-term approach to solving problems.…
Comment linking /2022/02/22/building-a-marketing-engineering-platform-using-next-js-cloudflare-and-contentful
on “Ask HN: Who is hiring? (October 2022)”Submitted Eliminating Task Processing Outages by Replacing RabbitMQ with Apache Kafka as a story · 98 points
HN discussionI'm working on the infrastructure of a startup (Whova). Our backend is fully in Django, so our background tasks are run with celery since that's the main tool for that in the python community and a lot of legacy code is built around that. We process millions of tasks per day, for various things: cpu-bound logic, io (email/push notifications, 3rd part api calls, ...), scheduled tasks, ... These tasks are split across…
Comment linking /2020/09/03/eliminating-task-processing-outages-with-kafka/
on “Celery 5.0”Submitted Using Fault Injection Testing to Improve DoorDash Reliability as a story · 47 points
HN discussion> I’ve been spending the last few months reimplementing Java backend code in Python. Lots of people are doing the opposite once they hit scale. DoorDash went from Python to Kotlin/JVM for their backend: https://doordash.engineering/2021/05/04/migrating-from-pytho...
Comment linking /2021/05/04/migrating-from-python-to-kotlin-for-our-backend-services/
on “Proposal to change default annotation processing policy in JDK 23”Submitted Sibyl – Prediction Service at DoorDash (2020) as a story · 27 points
HN discussionI think what you really want to do is randomize A or B within each 30m (or 5m) interval. This is basically switchback testing. Door dash has a nice write-up here: https://doordash.engineering/2018/02/13/switchback-tests-and...
Comment linking /2018/02/13/switchback-tests-and-randomized-experimentation-under-network-effects-at-doordash/
on “Show HN: Birdy – Twitter Profile A/B Testing”Submitted Why Burnout Is Hard to Detect as a story · 18 points
HN discussionThis tutorial addresses the practice: https://doordash.engineering/2017/05/15/tips-for-building-hi... There's also a long, uh, spirited discussion about it here with some decent arguments going both directions: https://forum.djangoproject.com/t/why-do-we-need-apps/827/19
Comment linking /2017/05/15/tips-for-building-high-quality-django-apps-at-scale/
on “Customize Django Admin Interface”Submitted Tips for Building High-Quality Django Apps at Scale – DoorDash Engineering Blog as a story · 17 points
HN discussionCorrect, we are super early so there is no self-serve yet. The primary usecase for this is serving features for ML inference (since eventual consistency is ok and sacrificing write latency for reads is a fair tradeoff). Right now, this is done by using a traditional client-server DB at the moment (Redis/DynamoDB/etc) - or if you're a big tech company that cares about latency you can implement this on your own ( http…
Comment linking /2022/05/03/how-we-applied-client-side-caching/
on “IKV: embedded key-value store, 100x faster than Redis”Submitted From Monolith to Microservices: Reducing the Migration's Pain Points as a story · 15 points
HN discussionThis is an area our team has been working on for the last couple of years — applying control systems principles to prevent metastable failures, overloads and so on. Blog by DoorDash team - https://doordash.engineering/2023/03/14/failure-mitigation-f... Project - https://github.com/fluxninja/aperture
Comment linking /2023/03/14/failure-mitigation-for-microservices-an-intro-to-aperture/
on “An experiment in elastically scaling a thread pool using a PID controller”Submitted Migrating from Python to Kotlin for Our Back End Services as a story · 14 points
HN discussionLately, I've been enjoying a lot of posts in this blog right here: https://eugeneyan.com/writing My company just hired some new VPs from Amazon, and the writings in that blog convey a bit of the Amazon WOW. It's pretty insightful stuff. Also, many posts from Julia Evans :D More concretely, here are a few blog posts I go back to occasionally: https://doordash.engineering/2019/02/20/experiment-rigor-for... https://www…
Comment linking /2019/02/20/experiment-rigor-for-switchback-experiment-analysis/
on “Ask HN: What are your favorite technical blog posts?”Submitted How I use Backdoor Adjustment to measure effects when AB test is impossible as a story · 13 points
HN discussionDoorDash's engineering blog has a much more indepth look at their architecture: https://doordash.engineering/2023/02/07/how-we-scaled-new-ve...
Comment linking /2023/02/07/how-we-scaled-new-verticals-fulfillment-backend-with-cockroachdb/
on “DoorDash manages high-availability CockroachDB clusters at scale”Submitted Improving Development Velocity with Generic, Server-Driven UI Components as a story · 12 points
HN discussionNeither the iOS app nor the web app use Kotlin at DoorDash. Kotlin was picked for other reasons (consistency with the Android app wasn't mentioned): https://doordash.engineering/2021/05/04/migrating-from-pytho...
Comment linking /2021/05/04/migrating-from-python-to-kotlin-for-our-backend-services/
on “Elixir at Ramp”Submitted Leveraging the Pipeline Design Pattern to Modularize Recommendation Services as a story · 10 points
HN discussionLike many organizations, DoorDash moved to a microservice architecture from a monolith in the last couple of years. Along with the benefits of better business isolation and increased development agility, the move to microservices also brings new kinds of failures that are difficult to handle. I have been working on reliability projects at DoorDash for the last year, and witnessed firsthand how fires arise and are pu…
Comment linking /2023/03/14/failure-mitigation-for-microservices-an-intro-to-aperture/
on a deleted threadSubmitted Building a Marketing Engineering Platform Using Next.js, Cloudflare, Contentful as a story · 9 points
HN discussionUtilizing image data opens up a number of opportunities for building new image recognition use cases. The challenge is often processing the image data and spinning up new models quickly. I wanted to share this blog post I wrote about how DoorDash built a solution to process image data with deep neural networks in order to quickly spin up new image recognition use cases. Check it out here https://doordash.engineering…
Comment linking /2021/11/03/how-doordash-quickly-spins-up-multiple-image-recognition-use-cases/
on “DoorDash Quickly Spins Up Multiple Image Recognition Use Cases”Submitted Building Faster Indexing with Apache Kafka and Elasticsearch as a story · 8 points
HN discussionSubmitted Managing Supply and Demand Balance Through Machine Learning as a story · 8 points
HN discussionSubmitted Fast Feedback Loop for Kubernetes Product Development in a Prod Environment as a story · 7 points
HN discussionSubmitted Japan Launch – Overcoming Localization Challenges as a story · 7 points
HN discussionSubmitted DoorDash: Migrating from Python to Kotlin for Our Back End Services as a story · 7 points
HN discussion
