Maplelabs scalable-field-device-cloud-native
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This document describes a field device diagnostic solution developed by MapleLabs. The solution involves collecting large amounts of diagnostic data from thousands of field devices, ingesting and parsing the data using microservices running on a Kubernetes cluster in AWS, and storing the structured data in Elasticsearch for analysis and detection of faults. Some key challenges addressed are scalability to handle bursty data loads, security of the data as it moves through the pipeline, debuggability of the microservices, and reducing storage costs through tiering to S3.
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Maplelabs scalable-field-device-cloud-native
- 1. Scalable Field Device Diagnostic Platform. A c a s e s t u d y b y M a p l e L a b s Page 1 of 12
- 2. Agenda • MapleLabs overview • MapleLabs solution portfolio • Field device diagnostic solution: Challenges • Telemetry and diagnostics application spectrum • Solution deployment - Kubernetes Cluster in AWS • Solution details • Q & A • Thank you Page 2 of 12
- 3. Page 3 of 12 MapleLabs overview Founded in 2014, HQ in Sunnyvale with development center in Bengaluru, India Team size of 140 engineers in US & India offices Company specialized in solutions & services to streamline CloudOps
- 4. Page 4 of 12 • SnappyFlow: Application Performance Monitoring • 3 Tier VM & Containerized workloads running on Hybrid Clouds • Monitoring & optimization of analytics pipelines on VMs or Kubernetes environments • Hadoop Performance & Capacity Management • Field data diagnostic solutions • Sizing solutions for storage and hyperconverged environments • Variety of performance test tools MapleLabs solution portfolio
- 5. Page 5 of 12 Field device diagnostic solution: Challenges Proactive monitoring of field deployed devices such as storage clusters at thousands of customer environment. Challenges • Large size of per diagnostics data • Large amount of data from thousands of devices • Storage requirement: preserve data for quick access and longer term • Burst of data Solution requirement • Provisioning applications infrastructure • Monitoring infrastructure to identify bottleneck Field Data Diagnostics
- 6. Page 6 of 12 Telemetry and diagnostics application spectrum Significant commonality across these use cases • Data collectors and forwarders • Streaming Data Ingest at back-end • Data management– storage, organization, compression & meta data • Visualization & Analysis • Alerts & Notifications Where are the differences? • Scale of ingest, storage and data access • Nature of analysis & reporting • Differences in scale can lead to • Increase in scale of ingest or storage • Different storage architectures No of active endpoints / hr Payloadsize/message Application monitoring* Data center monitoring* Field sensors*** Browser end point monitoring* Field server health & usage analysis** * ** *** 100s to 1000s of VMs and containers streaming few 10s of KBs to MBs of logs and metrics every minute Approx. 10’s of thousands of servers that transmit 1 bundle a day of a few GBs Millions of small sensors streaming few bytes to KBs of data every minute
- 7. Page 7 of 12 Solution details NGINX ELB Signature Analytics Ingest (NodeJS) UI/Report (NodeJS) KafkaEFS ELKS3 Solution provision & monitor AWS Kubernetes
- 8. Page 8 of 12 Solution deployment - Kubernetes cluster in AWS ASW ELB Shared storage EFS Kafka Node JS Kibana Node JS Encryption & SJ Upload Kafka Kafka Data parsing service ELK S3 buckets Niginx Niginx Niginx Query from ELK Protect the tar Data with random password Store protected Data to S3, send details to ELK using Kafka and delete the Data from EFS Move Data to EFS Send Data Details to Kafka Consume the details from Kafka Store to ELK Read the file from EFS and parsed the required data Send details to Kafka for data parsing Extract Data to EFS Send details to Kafka for encryption services Extract Query from ELK Upload Data to EBS Kafka Node JS
- 9. Page 9 of 12 Solution details Solution hosted on AWS and running on standard Kubernetes • Daily ingest: • Archive data: • Ingest rate: Key solution goals • Detect faulty elements and trigger support requests • Detect systemic patterns linked to quality of product/versions • Predict failures through signature analysis 2TB of raw data parsed to 75GB structured data and stored in Elasticsearch per day 2TB per day 3000 requests at peak (transferring data of 250MB to 3GB per request)
- 10. Page 10 of 12 Solution details (continued) Key issues addressed Scalability Large point loads due to simultaneous devices each connecting and uploading large bundles at specific time of day. • All stages of pipeline have to be scalable and balanced • Sizing of the individual pods has to be correct or scalability will not be effective by itself Security Ensure only authorized micro-services and authorized users can access data. • Security has to be built into design and not added as after thought or it can lead to significant design changes down the road • Securing data is non trivial and encompasses data tagging, encryption, RBAC, Kerberization etc Debuggability Debugging micro-service applications in Kubernetes environment is not easy. • Ensure monitoring tools are plugged into production environments. Storage cost (#devices x GB per bundle x retention time) leads to very high scale of archive data • Avoid using EFS!! • Tiered S3 is a huge cost saver
- 11. Page 11 of 12 We will be happy to answer your questions.
- 12. Page 12 of 12 Thank You!