It is a visitor submit coauthored with LaunchDarkly.
The LaunchDarkly function administration platform equips software program groups to proactively scale back the danger of transport unhealthy software program and AI purposes whereas accelerating their launch velocity. On this submit, we discover how LaunchDarkly scaled the interior 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 in the course of the migration, the technical answer we applied, the trade-offs we made, and classes we discovered alongside the best way.
The problem
LaunchDarkly has a mission to allow high-velocity groups to launch, monitor, and optimize software program in manufacturing. The centralized knowledge group is liable for monitoring how LaunchDarkly is progressing towards that mission. Moreover, this group is accountable for almost all of the corporate’s inner knowledge wants, which embrace ingesting, warehousing, and reporting on the corporate’s knowledge. Among the massive datasets we handle embrace product utilization, buyer engagement, income, and advertising knowledge.
As the corporate grew, our knowledge quantity elevated, and the complexity and use instances of our workloads expanded exponentially. Whereas utilizing different vendor-managed Airflow-based options, our knowledge analytics group confronted new challenges on time to combine and onboard new AWS companies, knowledge locality, and a non-centralized orchestration and monitoring answer throughout totally different engineering groups inside the group.
Resolution overview
LaunchDarkly has a protracted historical past of utilizing AWS companies to resolve enterprise use instances, akin to scaling our ingestion from 1 TB to 100 TB per day with Amazon Kinesis Information Streams. Equally, migrating to Amazon MWAA helped us scale and optimize our inner extract, rework, and cargo (ETL) pipelines. We used present monitoring and infrastructure as code (IaC) implementations and finally 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.
- Elevate 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.
- Minimize 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 couple of have been executed shortly—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 every week.
Once we began slicing 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 atmosphere for our workload necessities on the Airflow employee disk assigned to the duty. By trial and error in our migration try, we discovered that this tradition operator was basically depending on the conduct and isolation of Airflow’s Kubernetes executors used within the unique cloud supplier platform. Once we started to run our DAGs concurrently on Amazon MWAA (which makes use of Celery Executor employees that behave otherwise), we bumped into a couple of transient points the place the conduct of that customized operator may have an effect on different operating DAGs.
Presently, we took a step again and evaluated options for selling isolation between our operating duties, finally touchdown on Fargate for ECS duties that could possibly be began from Amazon MWAA. We had initially deliberate to maneuver our duties to their very own remoted system relatively than having them run straight in Airflow’s Python runtime atmosphere. Because of the circumstances, we determined to advance this requirement, remodeling our rehosting venture right into a refactoring migration.
We selected Amazon ECS on Fargate for its ease of use, present Airflow integrations (ECSRunTaskOperator), low price, 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 information analytics group’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 process didn’t considerably impression general efficiency.
Our first Amazon ECS implementation concerned a single container that downloads our venture from an artifacts repository on Amazon S3, and runs the command handed to the ECS process. 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 information analytics group may create new DAGs simply by specifying the instructions they have been already acquainted with.
The next diagram illustrates the DAG and process deployment flows.
When our preliminary Amazon ECS implementation was full, we have been in a position to minimize all of our present DAGs over to Amazon MWAA with out the prior concurrency points, as a result of every process ran in its personal remoted Amazon ECS process on Fargate.
Inside a couple of months, we proceeded to Step 4 to improve our Amazon MWAA occasion to Airflow 2. This was a serious model improve (from 1.12 to 2.5.1), which we applied by following the Amazon MWAA Migration Information and subsequently tearing down our legacy assets.
The associated fee improve of including Amazon ECS to our pipelines was minimal. This was as a result of our pipelines run on batch schedules, and due to this fact aren’t energetic always, and Amazon ECS on Fargate solely costs 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 knowledge take a look at failures alongside present LaunchDarkly monitoring.
Scaling the answer past inner analytics
In the course of the preliminary implementation for the information analytics group, we created an Amazon MWAA Terraform module, which enabled us to shortly spin up extra Amazon MWAA environments and share our work with different engineering groups. This allowed the usage of Airflow and Amazon MWAA to energy batch pipelines inside the LaunchDarkly product itself in a few months shortly after the information analytics group accomplished the preliminary migration.
The quite a few AWS service integrations supported by Airflow, the built-in Amazon supplier package deal, 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 easy, 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 knowledge pipelines at scale with Amazon MWAA and Amazon ECS, we’ve gained beneficial insights and classes which have formed the success of our implementation. One of many key classes discovered was the significance of isolation. In the course of the preliminary migration to Amazon MWAA, we encountered points with our customized Airflow operator that relied on the particular conduct of the Kubernetes executors used within the unique cloud supplier platform. This highlighted the necessity for remoted process execution to take care of 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 knowledge take a look at failures, enhancing the general reliability and transparency of our knowledge pipelines.
With the earlier Airflow implementation, we have been operating roughly 100 Airflow duties per day throughout one group and two companies (Amazon ECS and Snowflake). As of the time of penning this submit, we’ve scaled our implementation to a few groups, 4 companies, and execution of over 14,000 Airflow duties per day. Amazon MWAA has turn into a essential part of our batch processing pipelines, growing the pace of onboarding new groups, companies, and pipelines to our knowledge platform from weeks to days.
Trying forward, we plan to proceed iterating on this answer to increase our use of Amazon MWAA to extra AWS companies akin to AWS Lambda and Amazon Easy Queue Service (Amazon SQS), and additional automate our knowledge workflows to assist even better scalability as our firm grows.
Conclusion
Efficient knowledge orchestration is crucial for organizations to assemble and unify knowledge from numerous sources right into a centralized, usable format for evaluation. By automating this course of throughout groups and companies, companies can rework fragmented knowledge into beneficial insights to drive higher decision-making. LaunchDarkly has achieved this by utilizing managed companies like Amazon MWAA and adopting greatest practices akin to process 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 knowledge 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 fitting instruments and strategy, you’ll be able to rework your knowledge operations, drive innovation, and keep forward of rising knowledge 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 knowledge science, Asena has been working within the tech trade for over a decade. Her expertise spans numerous sectors, together with SaaS and logistics, and he or she has spent a good portion of her profession as a Information Platform Engineer, designing and managing large-scale knowledge methods. Asena is keen about utilizing know-how to simplify and optimize workflows, making an actual distinction in the best way groups function.
Dean Verhey is a Information Platform Engineer at LaunchDarkly based mostly in Seattle. He’s labored all throughout knowledge 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 knowledge engineering for quite a lot of firms, together with procurement SaaS, journey startups, and hearth/EMS information administration. When he’s not working, you’ll be able to typically 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 youngsters in video video games and popular culture.