This submit was cowritten with Steven Aerts and Reza Radmehr from Airties.
Airties is a wi-fi networking firm that gives AI-driven options for enhancing dwelling connectivity. Based in 2004, Airties makes a speciality of creating software program and {hardware} for wi-fi dwelling networking, together with Wi-Fi mesh methods, extenders, and routers. The flagship software program as a service (SaaS) product, Airties Residence, is an AI-driven platform designed to automate buyer expertise administration for dwelling connectivity, providing proactive buyer care, community optimization, and real-time insights. By utilizing AWS managed providers, Airties can concentrate on their core mission: enhancing dwelling Wi-Fi experiences via automated optimization and proactive concern decision. This consists of minimizing community downtime, enabling quicker diagnostic capabilities for troubleshooting, and enhancing general Wi-Fi high quality. The answer has demonstrated important impression in decreasing each the frequency of assist desk calls and common name period, resulting in improved buyer satisfaction and diminished operational prices for Airties whereas delivering enhanced service high quality to their prospects and the end-users.
In 2023, Airties initiated a strategic migration from Apache Kafka operating on Amazon Elastic Compute Cloud (Amazon EC2) to Amazon Kinesis Information Streams. Previous to this migration, Airties operated a number of fixed-size Kafka clusters, every deployed in a single Availability Zone to reduce cross-AZ site visitors prices. Though this structure served its function, it required fixed monitoring and handbook scaling to deal with various information hundreds. The transition to Kinesis Information Streams marked a major step of their cloud optimization journey, enabling true serverless operations with computerized scaling capabilities. This migration resulted in substantial infrastructure value discount whereas enhancing system reliability, eliminating the necessity for handbook cluster administration and capability planning.
This submit explores the methods the Airties staff employed throughout this transformation, the challenges they overcame, and the way they achieved a extra environment friendly, scalable, and maintenance-free streaming infrastructure.
Kafka use circumstances for Airties workloads
Airties repeatedly ingests information from tens of tens of millions of entry factors (resembling modems and routers) utilizing AWS IoT Core. Earlier than the transition, these messages had been queued and saved inside a number of siloed Kafka clusters, with every cluster deployed in a separate Availability Zone to reduce cross-AZ site visitors prices. This fragmented structure created a number of operational challenges. The segmented information storage required advanced extract, remodel, and cargo (ETL) processes to consolidate info throughout clusters, growing the time to derive significant insights. The info collected serves a number of important functions—from real-time monitoring and reactive troubleshooting to predictive upkeep and historic evaluation. Nevertheless, the siloed nature of the information storage made it significantly difficult to carry out cross-cluster analytics and delayed the flexibility to determine network-wide patterns and developments.
The info processing structure at Airties served two distinct use circumstances. The primary was a conventional streaming sample with a batch reader processing information in bulk for analytical functions. The second use case used Kafka as a queryable information retailer—a sample that, although unconventional, has grow to be more and more frequent in large-scale information architectures.
For this second use case, Airties wanted to supply fast entry to historic gadget information when troubleshooting buyer points or analyzing particular community occasions. This was applied by sustaining a mapping of knowledge factors to their Kafka offsets in a database. When buyer help or analytics groups wanted to retrieve particular historic information, they might shortly find and fetch the precise information from high-retention Kafka subjects utilizing these saved offsets. This method eradicated the necessity for a separate database system whereas sustaining quick entry to historic information.
To deal with the huge scale of operations, this answer was horizontally scaled throughout dozens of Kafka clusters, with every cluster answerable for managing roughly 25 TB of information.
The next diagram illustrates the earlier Kafka-based structure.
Challenges with the Kafka-based structure
At Airties, managing and scaling Kafka clusters has introduced a number of challenges, hindering the group from specializing in delivering enterprise worth successfully:
- Operational overhead: Sustaining and monitoring Kafka clusters requires important handbook effort and operational overhead at Airties. Duties resembling managing cluster upgrades, dealing with {hardware} failures and rotation, and conducting load testing always demand engineering consideration. These operational duties take away from the staff’s potential to focus on core enterprise features and value-adding actions inside the firm.
- Scaling complexities : The method of scaling Kafka clusters entails a number of handbook steps that create operational burden for the cloud staff. These embrace configuring new brokers, rebalancing partitions throughout nodes, and offering correct information distribution—all whereas sustaining system stability. As information quantity and throughput necessities fluctuate, scaling usually entails including or eradicating complete Kafka clusters, which is a fancy and time-consuming course of for the Airties staff.
- Proper-sizing cluster capability: The static nature of Kafka clusters created a “one-size-fits-none” scenario for Airties. For big-scale deployments with excessive information volumes and throughput necessities, including new clusters required important handbook work, together with capability planning, dealer configuration, and partition rebalancing, making it inefficient for dealing with dynamic scaling wants. Conversely, for smaller deployments, the usual cluster dimension was outsized, resulting in useful resource waste and pointless prices.
How the brand new structure addresses these challenges
The Airties staff wanted to discover a scalable, high-performance, and cost-effective answer for real-time information processing that will permit seamless scaling with growing information volumes. Information sturdiness was a important requirement, as a result of dropping gadget telemetry information would create everlasting gaps in buyer analytics and historic troubleshooting capabilities. Though non permanent delays in information entry could possibly be tolerated, the lack of any gadget information level was unacceptable for sustaining service high quality and buyer help effectiveness.
To deal with these challenges, Airties applied two totally different approaches for various situations.
The first use case was real-time information streaming with Kinesis Information Streams. Airties changed Kafka with Kinesis Information Streams to deal with the continual ingestion and processing of telemetry information from tens of tens of millions of endpoints. This shift provided important benefits:
- Auto-scaling capabilities : Kinesis Information Streams could be scaled via easy API calls, assuaging the necessity for advanced configurations and handbook interventions.
- Stream isolation : Every stream operates independently, that means scaling operations on one stream haven’t any impression on others. This alleviated the dangers related to cluster-wide adjustments of their earlier Kafka setup.
- Dynamic shard administration : In contrast to Kafka, the place altering the variety of partitions requires creating a brand new matter, Kinesis Information Streams permits including or eradicating shards dynamically with out dropping message ordering inside a partition.
- Software Auto Scaling: Airties applied AWS Software Auto Scaling with Kinesis Information Streams, permitting the system to mechanically regulate the variety of shards based mostly on precise utilization patterns and throughput necessities.
These options empowered Airties to effectively handle assets, optimizing prices during times of decrease exercise whereas seamlessly scaling as much as deal with peak hundreds.
For offering on-demand entry to historic gadget information, Airties applied a decoupled structure that separates streaming, storage, and information entry issues. This method changed the earlier answer the place historic information was saved instantly in Kafka subjects. The brand new structure consists of a number of key elements working collectively:
- Information assortment and processing : The structure begins with a shopper utility that processes information from Kinesis Information Streams. This utility implements analyzing the information, as making it obtainable for detailed historic evaluation. The results of the information evaluation is written to Amazon Information Firehose, which buffers the information, writing it often to Amazon Easy Storage Service (Amazon S3), the place it could later be picked up by Amazon EMR. This path is optimized for environment friendly storage and bulk studying from Amazon S3 by Amazon EMR. For uncooked information storage, a number of uncooked information samples are batched collectively in bulk recordsdata, that are saved in a separate Amazon S3 path. This path is optimized for storage effectivity and fetching uncooked information utilizing Amazon S3 vary queries.
- Indexing and metadata administration: To allow quick information retrieval, the structure implements a complicated indexing system. For every document within the uploaded bulk recordsdata, two essential items of data are recorded in an Amazon DynamoDB desk: the Amazon S3 location (bucket and key) the place the majority file was written, and the sequence variety of the corresponding information document within the Kinesis Information Streams queue. This indexing technique offers low-latency entry to particular information factors, environment friendly querying capabilities for each real-time and historic information, computerized scaling to deal with growing information volumes, and excessive availability for metadata lookups.
- Advert-hoc information retrieval: When particular historic information must be accessed, the system follows an environment friendly retrieval course of. First, the applying queries the DynamoDB desk utilizing the related identifiers. The question returns the precise Amazon S3 location and offset the place the required information is saved. The appliance then fetches the precise information instantly from Amazon S3 utilizing vary queries. This method allows fast entry to historic information factors, minimal information switch prices by retrieving solely wanted information, environment friendly troubleshooting and evaluation workflows, and diminished latency for buyer help operations.
This decoupled structure makes use of the strengths of every AWS service: Amazon Kinesis Information Streams offers scalable and dependable real-time information streaming, whereas Amazon S3 delivers sturdy and cost-effective object storage for uncooked information, and Amazon DynamoDB allows quick and versatile storage of metadata and indexing. By separating streaming from storage and using every service for its particular strengths, Airties created a less expensive and scalable answer for ad-hoc information entry wants, aligning every part with its optimum AWS service. The brand new structure not solely improved information entry efficiency but in addition considerably diminished operational complexity. As a substitute of managing Kafka subjects for historic information storage, Airties now advantages from absolutely managed AWS providers that mechanically deal with scaling, sturdiness, and availability. This method has confirmed significantly invaluable for buyer help situations, the place fast entry to historic gadget information is essential for resolving points effectively.
Resolution overview
Airties’s new structure entails a number of important elements, together with environment friendly information ingestion, indexing with AWS Lambda features, optimized information aggregation and processing, and complete monitoring and administration practices utilizing Amazon CloudWatch. The next diagram illustrates this structure.
The brand new structure consists of the next key phases:
- Information assortment and storage: The info journey begins with Kinesis Information Streams, which ingests real-time information from tens of millions of entry factors. This streaming information is then processed by a shopper utility that batches the information into bulk recordsdata (also referred to as briefcase recordsdata) for environment friendly storage in Amazon S3. This method of streaming, batching, after which storing minimizes write operations and reduces general prices, whereas offering information sturdiness via built-in replication in Amazon S3. When the information is in Amazon S3, it’s available for each fast processing and long-term evaluation. The processing pipeline continues with aggregators that learn information from Amazon S3, course of it, and retailer aggregated outcomes again in Amazon S3. By integrating AWS Glue for ETL operations and Amazon Athena for SQL-based querying, Airties can course of massive volumes of knowledge effectively and generate insights shortly and cost-effectively.
- Information aggregation and bulk file creation: The aggregators play an important function within the preliminary information processing. They combination the incoming information based mostly on predefined standards and create bulk recordsdata. This aggregation course of reduces the amount of knowledge that must be processed in subsequent steps, optimizing the general information processing workflow. The aggregators then write these bulk recordsdata on to Amazon S3.
- Indexing: Upon profitable add of a bulk file to Amazon S3 by the aggregators, the aggregator will write an index entry for the majority file an Amazon DynamoDB desk. This indexing mechanism permits for environment friendly retrieval of knowledge based mostly on gadget IDs and timestamps, facilitating fast entry to related information utilizing S3 vary queries on the majority recordsdata.
- Additional processing and evaluation: The majority recordsdata saved in Amazon S3 at the moment are in a format optimized for querying and evaluation. These recordsdata could be additional processed utilizing AWS Glue and analyzed utilizing Athena, permitting for advanced queries and in-depth information exploration with out the necessity for extra information transformation steps.
- Monitoring and administration: To take care of the reliability and efficiency of the Kafka-less structure, complete monitoring and administration practices had been applied. CloudWatch offers real-time monitoring of system efficiency and useful resource utilization, permitting for proactive administration of potential points. Moreover, automated alerts and notifications ensure that anomalies are promptly addressed.
Outcomes and advantages
The transition to this new structure yielded important advantages for Airties:
- Scalability and efficiency: The brand new structure empowers Airties to scale seamlessly with growing information volumes. The flexibility to independently scale reader and author operations has diminished efficiency impacts throughout high-demand durations. This can be a important enchancment over the earlier Kafka-based system, the place scaling typically required advanced reconfigurations and will have an effect on your entire cluster. With Kinesis Information Streams, Airties can now deal with peak hundreds effortlessly whereas optimizing useful resource utilization throughout quieter durations.
- Reliability and fault tolerance: By utilizing AWS managed providers, Airties has considerably diminished system latency and improved general uptime. The automated information replication and restoration processes of Kinesis Information Streams present enhanced information sturdiness, a important requirement for Airties’s operations. The improved excessive availability implies that Airties can now supply extra dependable providers to their prospects, minimizing disruptions and enhancing the general high quality of their dwelling connectivity options.
- Operational effectivity: The brand new structure has dramatically diminished the necessity for handbook intervention in capability administration. This shift has freed up invaluable engineering assets, permitting the staff to concentrate on delivering enterprise worth relatively than managing infrastructure. The simplified operational mannequin has elevated the staff’s productiveness, empowering them to innovate quicker and reply extra shortly to buyer wants. The discount in operational overhead has additionally led to quicker deployment cycles and extra frequent characteristic releases, enhancing Airties’s competitiveness available in the market.
- Environmental impression and sustainability: The transition to a serverless structure demonstrated important environmental advantages, reaching a exceptional 40% discount in power consumption. This substantial lower in power utilization was achieved by eliminating the necessity for always operating EC2 cases and utilizing extra environment friendly, managed AWS providers. This enchancment in power effectivity aligns with Airties’s dedication to environmental sustainability and establishes them as an environmentally accountable chief within the tech business.
- Price optimization: The monetary advantages of transitioning to a Kafka-less structure are clearly demonstrated via complete AWS Price Explorer information. As proven within the following diagram, the overall value breakdown throughout all related providers from January to July consists of EC2 cases, DynamoDB, different Amazon EC2 prices, Kinesis Information Streams, Amazon S3, and Amazon Information Firehose. Essentially the most notable change was a 33% discount in complete month-to-month infrastructure prices (in comparison with January baseline), primarily achieved via important lower in Amazon EC2 associated prices because the migration progressed, elimination of devoted Kafka infrastructure, and environment friendly use of the AWS pay-as-you-go mannequin. Though new prices had been launched for managed providers (DynamoDB, Kinesis Information Streams, Amazon Information Firehose, Amazon S3), the general month-to-month AWS prices maintained a transparent downward development. With these value financial savings, Airties can supply extra aggressive pricing to their prospects. The diagram under exhibits month-to-month value breakdown in the course of the transition.
Conclusion
The transition to this new structure with Kinesis Information Streams has marked a major milestone in Airties’s journey in direction of operational excellence and sustainability. These initiatives haven’t solely enhanced system efficiency and scalability, however have additionally resulted in substantial value financial savings (33%) and power effectivity (40%). By utilizing superior applied sciences and progressive options on AWS, the Airties staff continues to set the benchmark for environment friendly, dependable, and sustainable operations, whereas paving the way in which for a sustainable future. To be able to discover how one can modernize your streaming structure with AWS, see the Kinesis Information Streams documentation and watch this re:invent session on serverless information streaming with Kinesis Information Streams and AWS Lambda.
In regards to the Authors
Steven Aerts is a principal software program engineer at Airties, the place his staff is answerable for ingesting, processing, and analyzing the information of tens of tens of millions of properties to enhance their Wi-Fi expertise. He was a speaker at conferences like Devoxx and AWS Summit Dubai, and is an open supply contributor.
Reza Radmehr is a Sr. Chief of Cloud Infrastructure and Operations at Airties, the place he leads AWS infrastructure design, DevOps and SRE automation, and FinOps practices. He focuses on constructing scalable, cost-efficient, and dependable methods, driving operational excellence via sensible, data-driven cloud methods. He’s captivated with mixing monetary perception with technical innovation to enhance efficiency and effectivity at scale.
Ramazan Ginkaya is a Sr. Technical Account Supervisor at AWS with over 17 years of expertise in IT, telecommunications, and cloud computing. He’s a passionate problem-solver, offering technical steerage to AWS prospects to assist them obtain operational excellence and maximize the worth of cloud computing.