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Use Case: Monitoring /
Observability
Balaji Palani - Sr. Director of Product Management,
InfluxData
Brian Gilmore - Director of IoT and Emerging
Technologies, InfluxData
Connect Learn Build
Hear from and meet developers
from the InfluxDB Community
Be inspired by use cases from
our partners and InfluxDB engineers
Learn best practices that will
help you build great experiences
for your projects
In this fireside chat, Balaji and Brian discuss the
evolution of the monitoring and observability industry,
the role that InfluxDB plays and a look at how one
customer is using InfluxDB in their solution.
Balaji Palani
Sr. Director of Product
Management, InfluxData
Balaji Palani is the Senior
Director of Product
Management focused on
InfluxDB Cloud. He is
passionate about building
powerful cloud products
that help Developers
achieve the fastest time to
awesome.
Use Case: Monitoring /
Observability
Brian Gilmore
Director of IoT and Emerging
Technologies, InfluxData
Brian Gilmore is Director
of IoT and Emerging
Technology at InfluxData.
He has worked with
organizations to drive the
unification of industrial
and enterprise IoT with
machine learning, cloud,
and other technologies
Agenda
1. Evolution of monitoring
2. How customers are using InfluxDB for monitoring
3. Customer implementations
Use Case Categories
IoT Monitoring Developer Tools Real-time Analytics
Industrial
Enterprise
Consumer
DevOps
Networks
Security
Cloud
Applications
APIs
Gaming
Renewable
Energy
FinTech
Crypto
Metrics, Events, and Traces
Usually derived through sampling, usually numeric, and
typically regular in period.
Usually emitted, on-event or on-exception. Can be either
numeric or strings. Irregular period by nature.
Bundled and uniquely labeled collections of related
metrics and events related to a specific transaction or
interaction. Irregular period and explicit duration.
Metrics
Events
Traces
How They Relate
Metrics (one/sec):
Events (varied):
Traces (bundled):
12s
3s
8.26s
Condition 1
Condition 2
Condition 3
Condition 4
Trace ID: 86ef2836-58fe-4a2e-ba69-3de4596072fd
duration 37 seconds
6s
InfluxData Reference Architecture
Data Sources
Application
Workflows
Infrastructure
Insights
Telegraf
Client Libraries
HTTP
Syslog
Kubernetes
Apache Kafka
Python
Arduino
Node.js
JavaScript
Go
Data Systems
Mobile apps
Web apps
Cloud Services
Devices
Sensors
Databases
Networks
Message Queues
APIs
IoT Platforms
CRMs
InfluxDB Platform
IoT
Actions
InfluxDB
Purpose-Built Time Series Database
Visualization, Query & Task Engine
Collect
Downsample
Trigger
Alert
Transform
…
300+ Plugins
14+ Languages
…
Native Ecosystems
JMeter
NiFi
AWS Kinesis
Azure Event Hubs
GCP PubSub
Java
.NET/C#
PHP
Ruby
Vector
Fluentd
Scrapers
Native Collectors
Reference Architecture Detail
DevOps Monitoring with InfluxData
Data Sources Integrations
Applications/Services
• Web Application Infrastructure
• Containers
• Message Brokers
• Databases
• K8S
• Custom microservices
• Network Instrumentation
• Customer facing web applications
• Customer facing mobile apps
Devices/Infrastructure
• Mobile Apps
• Web Apps
• APIs
• Message Queues
• Ecosystem Devices
(Connectivity, coordination
ie Alexa)
Telegraf
• AMQP
• Active MQ
• Amazon Cloudwatch
• Amazon Kinesis
• Kafka
• Docker
• Azure Event Hub
• ExecD
• GCP Pub/Sub
• HTTP
• Jenkins
• Kernel
• Kubernetes
• MQTT Consumer
• NATS Consumer
• NGINX
• OpenTelemetry
• Redis
Client Libraries
• Java
• .NET/C#
14+ Languages
300+ Plugins
• Product quality
• Process optimization
• Code integration
• Testing and load balancing
• Deployment optimization
• Error handling
• Monitoring Analytics
• Alerting Frameworks
• Go
• JavaScript
Applications & Use Cases
InfluxDB
Purpose-Built Time Series Database
Visualization, Query & Task Engine
Collect
Transform
Downsample
Trigger Workflows
Alert
SLO PLATFORM
Translate Observability Data into Clear Action
Roadmap Decisions
Justifying Tech Investments
& Cloud Spend
On Call Alerts &
Interruptions
OBSERVABILITY
DATA SOURCES
Normalized Service Level
Objectives (SLOs) across
multiple data sources:
● Quantified Customer
Expectations
● Tech Debt & Service
Delivery Risks
● Define Clear Trade-offs in
Service Management
Deployment
Optimization
Reference Architecture
DevOps Monitoring with InfluxData
Error
Handling
Sources
CI/CD Pipelines
Cloud Services
Containers
Kubernetes (K8S)
Mobile apps
Web apps
Networking
System Stats (CPU,
Memory)
Microservices
InfluxDB
Purpose-Built Time Series Database
Visualization, Query & Task Engine
Telegraf
Client Libraries
• HTTP
• Syslog
• Kubernetes
• Apache Kafka
• AWS Kinesis
• Azure Event Hubs
• GCP PubSub
300+ Plugins
• Java
• Go
• .NET/C#
• JavaScript
• Node.js
• Python
• Arduino
• PHP
• Ruby
12+ Languages
Data Types
APIs
Application
performance
Metrics
Infrastructure
metrics
Business
transaction metrics
Service adoption
metrics
Collect
Transform
Downsample
Trigger Workflows
Alert
• Product quality
• Process optimization
• Code integration
• Testing and load
balancing
• Deployment
optimization
• Error handling
• Monitoring Analytics
• Alerting Frameworks
Applications &
Use Cases
Nobl9 Architecture - Black Box View
Error
Budgets
Web App
API
InfluxDB
PM & Business
Stakeholders
YAML
GUI
A
l
e
r
t
P
r
i
o
r
i
t
i
z
e
Raw SLIs
SLO Config
Ops/SREs &
Application Leads
Govern
Align
New Relic Prometheus
Datadog
Calculations
Customer Platforms and Services
App
CI/CD Web Services
Data
GitOps
SLO Based
Events &
Alerts
Graphs
Reports
Review &
Align
Review &
Align
Calculation of SLO time series
Nobl9 Calculation Architecture
● Rearchitected into custom code and Kafka
○ FIFO calculation approach
○ Maintains state, uses object storage as a
backing store
○ Scales horizontally
Processing of telemetry data (SLIs) and math
API Tier
Catalog
Ingestor
Kafka
Ingestor
Ingester
Querier
Querier
Querier (SQL)
Compacter
Compacter
Compacter
Object Store
InfluxQL
Flux
Queries
Writes
Powered by IOx
T H A N K Y O U

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Gilmore, Palani [InfluxData] | Use Case: Monitoring / Observability | InfluxDays 2022

  • 1.
  • 2. Use Case: Monitoring / Observability Balaji Palani - Sr. Director of Product Management, InfluxData Brian Gilmore - Director of IoT and Emerging Technologies, InfluxData
  • 3. Connect Learn Build Hear from and meet developers from the InfluxDB Community Be inspired by use cases from our partners and InfluxDB engineers Learn best practices that will help you build great experiences for your projects
  • 4. In this fireside chat, Balaji and Brian discuss the evolution of the monitoring and observability industry, the role that InfluxDB plays and a look at how one customer is using InfluxDB in their solution. Balaji Palani Sr. Director of Product Management, InfluxData Balaji Palani is the Senior Director of Product Management focused on InfluxDB Cloud. He is passionate about building powerful cloud products that help Developers achieve the fastest time to awesome. Use Case: Monitoring / Observability Brian Gilmore Director of IoT and Emerging Technologies, InfluxData Brian Gilmore is Director of IoT and Emerging Technology at InfluxData. He has worked with organizations to drive the unification of industrial and enterprise IoT with machine learning, cloud, and other technologies
  • 5. Agenda 1. Evolution of monitoring 2. How customers are using InfluxDB for monitoring 3. Customer implementations
  • 6. Use Case Categories IoT Monitoring Developer Tools Real-time Analytics Industrial Enterprise Consumer DevOps Networks Security Cloud Applications APIs Gaming Renewable Energy FinTech Crypto
  • 7. Metrics, Events, and Traces Usually derived through sampling, usually numeric, and typically regular in period. Usually emitted, on-event or on-exception. Can be either numeric or strings. Irregular period by nature. Bundled and uniquely labeled collections of related metrics and events related to a specific transaction or interaction. Irregular period and explicit duration. Metrics Events Traces
  • 8. How They Relate Metrics (one/sec): Events (varied): Traces (bundled): 12s 3s 8.26s Condition 1 Condition 2 Condition 3 Condition 4 Trace ID: 86ef2836-58fe-4a2e-ba69-3de4596072fd duration 37 seconds 6s
  • 9. InfluxData Reference Architecture Data Sources Application Workflows Infrastructure Insights Telegraf Client Libraries HTTP Syslog Kubernetes Apache Kafka Python Arduino Node.js JavaScript Go Data Systems Mobile apps Web apps Cloud Services Devices Sensors Databases Networks Message Queues APIs IoT Platforms CRMs InfluxDB Platform IoT Actions InfluxDB Purpose-Built Time Series Database Visualization, Query & Task Engine Collect Downsample Trigger Alert Transform … 300+ Plugins 14+ Languages … Native Ecosystems JMeter NiFi AWS Kinesis Azure Event Hubs GCP PubSub Java .NET/C# PHP Ruby Vector Fluentd Scrapers Native Collectors
  • 10. Reference Architecture Detail DevOps Monitoring with InfluxData Data Sources Integrations Applications/Services • Web Application Infrastructure • Containers • Message Brokers • Databases • K8S • Custom microservices • Network Instrumentation • Customer facing web applications • Customer facing mobile apps Devices/Infrastructure • Mobile Apps • Web Apps • APIs • Message Queues • Ecosystem Devices (Connectivity, coordination ie Alexa) Telegraf • AMQP • Active MQ • Amazon Cloudwatch • Amazon Kinesis • Kafka • Docker • Azure Event Hub • ExecD • GCP Pub/Sub • HTTP • Jenkins • Kernel • Kubernetes • MQTT Consumer • NATS Consumer • NGINX • OpenTelemetry • Redis Client Libraries • Java • .NET/C# 14+ Languages 300+ Plugins • Product quality • Process optimization • Code integration • Testing and load balancing • Deployment optimization • Error handling • Monitoring Analytics • Alerting Frameworks • Go • JavaScript Applications & Use Cases InfluxDB Purpose-Built Time Series Database Visualization, Query & Task Engine Collect Transform Downsample Trigger Workflows Alert
  • 11.
  • 12. SLO PLATFORM Translate Observability Data into Clear Action Roadmap Decisions Justifying Tech Investments & Cloud Spend On Call Alerts & Interruptions OBSERVABILITY DATA SOURCES Normalized Service Level Objectives (SLOs) across multiple data sources: ● Quantified Customer Expectations ● Tech Debt & Service Delivery Risks ● Define Clear Trade-offs in Service Management
  • 13. Deployment Optimization Reference Architecture DevOps Monitoring with InfluxData Error Handling Sources CI/CD Pipelines Cloud Services Containers Kubernetes (K8S) Mobile apps Web apps Networking System Stats (CPU, Memory) Microservices InfluxDB Purpose-Built Time Series Database Visualization, Query & Task Engine Telegraf Client Libraries • HTTP • Syslog • Kubernetes • Apache Kafka • AWS Kinesis • Azure Event Hubs • GCP PubSub 300+ Plugins • Java • Go • .NET/C# • JavaScript • Node.js • Python • Arduino • PHP • Ruby 12+ Languages Data Types APIs Application performance Metrics Infrastructure metrics Business transaction metrics Service adoption metrics Collect Transform Downsample Trigger Workflows Alert • Product quality • Process optimization • Code integration • Testing and load balancing • Deployment optimization • Error handling • Monitoring Analytics • Alerting Frameworks Applications & Use Cases
  • 14.
  • 15. Nobl9 Architecture - Black Box View Error Budgets Web App API InfluxDB PM & Business Stakeholders YAML GUI A l e r t P r i o r i t i z e Raw SLIs SLO Config Ops/SREs & Application Leads Govern Align New Relic Prometheus Datadog Calculations Customer Platforms and Services App CI/CD Web Services Data GitOps SLO Based Events & Alerts Graphs Reports Review & Align Review & Align
  • 16. Calculation of SLO time series Nobl9 Calculation Architecture ● Rearchitected into custom code and Kafka ○ FIFO calculation approach ○ Maintains state, uses object storage as a backing store ○ Scales horizontally Processing of telemetry data (SLIs) and math
  • 18. T H A N K Y O U