This can be a visitor publish coauthored with LaunchDarkly.
The LaunchDarkly characteristic administration platform equips software program groups to proactively scale back the danger of delivery dangerous software program and AI purposes whereas accelerating their launch velocity. On this publish, we discover how LaunchDarkly scaled the inner analytics platform as much as 14,000 duties per day, with minimal improve in prices, after migrating from one other vendor-managed Apache Airflow answer to AWS, utilizing Amazon Managed Workflows for Apache Airflow (Amazon MWAA) and Amazon Elastic Container Service (Amazon ECS). We stroll you thru the problems we bumped into throughout the migration, the technical answer we carried out, the trade-offs we made, and classes we realized alongside the way in which.
The problem
LaunchDarkly has a mission to allow high-velocity groups to launch, monitor, and optimize software program in manufacturing. The centralized information workforce is accountable for monitoring how LaunchDarkly is progressing towards that mission. Moreover, this workforce is accountable for almost all of the corporate’s inner information wants, which embrace ingesting, warehousing, and reporting on the corporate’s information. A few of the massive datasets we handle embrace product utilization, buyer engagement, income, and advertising and marketing information.
As the corporate grew, our information quantity elevated, and the complexity and use circumstances of our workloads expanded exponentially. Whereas utilizing different vendor-managed Airflow-based options, our information analytics workforce confronted new challenges on time to combine and onboard new AWS companies, information locality, and a non-centralized orchestration and monitoring answer throughout totally different engineering groups throughout the group.
Answer overview
LaunchDarkly has an extended historical past of utilizing AWS companies to unravel enterprise use circumstances, akin to scaling our ingestion from 1 TB to 100 TB per day with Amazon Kinesis Knowledge Streams. Equally, migrating to Amazon MWAA helped us scale and optimize our inner extract, rework, and cargo (ETL) pipelines. We used current monitoring and infrastructure as code (IaC) implementations and ultimately prolonged Amazon MWAA to different groups, establishing it as a centralized batch processing answer orchestrating a number of AWS companies.
The answer for our transformation jobs embrace the next elements:
Our unique plan for the Amazon MWAA migration was:
- Create a brand new Amazon MWAA occasion utilizing Terraform following LaunchDarkly service requirements.
- Carry and shift (or rehost) our code base from Airflow 1.12 to Airflow 2.5.1 on the unique cloud supplier to the identical model on Amazon MWAA.
- Lower over all Directed Acyclic Graph (DAG) runs to AWS.
- Improve to Airflow 2.
- With the flexibleness and ease of integration inside AWS ecosystem, iteratively make enhancements round containerization, logging, and steady deployment.
Steps 1 and a pair of have been executed rapidly—we used the Terraform AWS supplier and the present LaunchDarkly Terraform infrastructure to construct a reusable Amazon MWAA module initially at Airflow model 1.12. We had an Amazon MWAA occasion and the supporting items (CloudWatch and artifacts S3 bucket) operating on AWS inside per week.
After we began chopping over DAGs to Amazon MWAA in Step 3, we bumped into some points. On the time of migration, our Airflow code base was centered round a customized operator implementation that created a Python digital surroundings for our workload necessities on the Airflow employee disk assigned to the duty. By trial and error in our migration try, we realized that this practice operator was basically depending on the habits and isolation of Airflow’s Kubernetes executors used within the unique cloud supplier platform. After we started to run our DAGs concurrently on Amazon MWAA (which makes use of Celery Executor employees that behave otherwise), we bumped into a number of transient points the place the habits of that customized operator may have an effect on different operating DAGs.
Right now, we took a step again and evaluated options for selling isolation between our operating duties, ultimately touchdown on Fargate for ECS duties that may very well be began from Amazon MWAA. We had initially deliberate to maneuver our duties to their very own remoted system fairly than having them run instantly in Airflow’s Python runtime surroundings. Because of the circumstances, we determined to advance this requirement, remodeling our rehosting undertaking right into a refactoring migration.
We selected Amazon ECS on Fargate for its ease of use, current Airflow integrations (ECSRunTaskOperator), low value, and decrease administration overhead in comparison with a Kubernetes-based answer akin to Amazon Elastic Kubernetes Service (Amazon EKS). Though an answer utilizing Amazon EKS would enhance the duty provisioning time even additional, the Amazon ECS answer met the latency necessities of the info analytics workforce’s batch pipelines. This was acceptable as a result of these queries run for a number of minutes on a periodic foundation, so a pair extra minutes for spinning up every ECS job didn’t considerably impression total efficiency.
Our first Amazon ECS implementation concerned a single container that downloads our undertaking from an artifacts repository on Amazon S3, and runs the command handed to the ECS job. We set off these duties utilizing the ECSRunTaskOperator in a DAG in Amazon MWAA, and created a wrapper across the built-in Amazon ECS operator, so analysts and engineers on the info analytics workforce may create new DAGs simply by specifying the instructions they have been already accustomed to.
The next diagram illustrates the DAG and job deployment flows.
When our preliminary Amazon ECS implementation was full, we have been in a position to lower all of our current DAGs over to Amazon MWAA with out the prior concurrency points, as a result of every job ran in its personal remoted Amazon ECS job on Fargate.
Inside a number of months, we proceeded to Step 4 to improve our Amazon MWAA occasion to Airflow 2. This was a significant model improve (from 1.12 to 2.5.1), which we carried out by following the Amazon MWAA Migration Information and subsequently tearing down our legacy assets.
The price improve of including Amazon ECS to our pipelines was minimal. This was as a result of our pipelines run on batch schedules, and subsequently aren’t energetic always, and Amazon ECS on Fargate solely prices for vCPU and reminiscence assets requested to finish the duties.
As part of Step 5 for steady evaluation and enhancements, we enhanced our Amazon ECS implementation to push logs and metrics to Datadog and CloudWatch. We may monitor for errors and mannequin efficiency, and catch information check failures alongside current LaunchDarkly monitoring.
Scaling the answer past inner analytics
Throughout the preliminary implementation for the info analytics workforce, we created an Amazon MWAA Terraform module, which enabled us to rapidly spin up extra Amazon MWAA environments and share our work with different engineering groups. This allowed using Airflow and Amazon MWAA to energy batch pipelines throughout the LaunchDarkly product itself in a few months shortly after the info analytics workforce accomplished the preliminary migration.
The quite a few AWS service integrations supported by Airflow, the built-in Amazon supplier bundle, and Amazon MWAA allowed us to increase our utilization throughout groups to make use of Amazon MWAA as a generic orchestrator for distributed pipelines throughout companies like Amazon Athena, Amazon Relational Database Service (Amazon RDS), and AWS Glue. Since adopting the service, onboarding a brand new AWS service to Amazon MWAA has been simple, sometimes involving the identification of the present Airflow Operator or Hook to make use of, after which connecting the 2 companies with AWS Identification and Entry Administration (IAM).
Classes and outcomes
Via our journey of orchestrating information pipelines at scale with Amazon MWAA and Amazon ECS, we’ve gained priceless insights and classes which have formed the success of our implementation. One of many key classes realized was the significance of isolation. Throughout the preliminary migration to Amazon MWAA, we encountered points with our customized Airflow operator that relied on the precise habits of the Kubernetes executors used within the unique cloud supplier platform. This highlighted the necessity for remoted job execution to keep up the reliability and scalability of our pipelines.
As we scaled our implementation, we additionally acknowledged the significance of monitoring and observability. We enhanced our monitoring and observability by integrating with instruments like Datadog and CloudWatch, so we may higher monitor errors and mannequin efficiency and catch information check failures, enhancing the general reliability and transparency of our information pipelines.
With the earlier Airflow implementation, we have been operating roughly 100 Airflow duties per day throughout one workforce and two companies (Amazon ECS and Snowflake). As of the time of penning this publish, we’ve scaled our implementation to a few groups, 4 companies, and execution of over 14,000 Airflow duties per day. Amazon MWAA has change into a crucial part of our batch processing pipelines, rising the pace of onboarding new groups, companies, and pipelines to our information platform from weeks to days.
Wanting forward, we plan to proceed iterating on this answer to increase our use of Amazon MWAA to further AWS companies akin to AWS Lambda and Amazon Easy Queue Service (Amazon SQS), and additional automate our information workflows to help even higher scalability as our firm grows.
Conclusion
Efficient information orchestration is crucial for organizations to collect and unify information from numerous sources right into a centralized, usable format for evaluation. By automating this course of throughout groups and companies, companies can rework fragmented information into priceless insights to drive higher decision-making. LaunchDarkly has achieved this through the use of managed companies like Amazon MWAA and adopting finest practices akin to job isolation and observability, enabling the corporate to speed up innovation, mitigate dangers, and shorten the time-to-value of its product choices.
In case your group is planning to modernize its information pipelines orchestration, begin assessing your present workflow administration setup, exploring the capabilities of Amazon MWAA, and contemplating how containerization may benefit your workflows. With the best instruments and method, you’ll be able to rework your information operations, drive innovation, and keep forward of rising information processing calls for.
Concerning the Authors
Asena Uyar is a Software program Engineer at LaunchDarkly, specializing in constructing impactful experimentation merchandise that empower groups to make higher selections. With a background in arithmetic, industrial engineering, and information science, Asena has been working within the tech business for over a decade. Her expertise spans varied sectors, together with SaaS and logistics, and she or he has spent a good portion of her profession as a Knowledge Platform Engineer, designing and managing large-scale information techniques. Asena is obsessed with utilizing expertise to simplify and optimize workflows, making an actual distinction in the way in which groups function.
Dean Verhey is a Knowledge Platform Engineer at LaunchDarkly based mostly in Seattle. He’s labored all throughout information at LaunchDarkly, starting from inner batch reporting stacks to streaming pipelines powering product options like experimentation and flag utilization charts. Previous to LaunchDarkly, he labored in information engineering for a wide range of firms, together with procurement SaaS, journey startups, and hearth/EMS data administration. When he’s not working, you’ll be able to usually discover him within the mountains snowboarding.
Daniel Lopes is a Options Architect for ISVs at AWS. His focus is on enabling ISVs to design and construct their merchandise in alignment with their enterprise targets with all benefits AWS companies can present them. His areas of curiosity are event-driven architectures, serverless computing, and generative AI. Exterior work, Daniel mentors his children in video video games and popular culture.