SlideShare a Scribd company logo
Container native
Monitoring Rohit Jnagal
Anushree Narasimha
Native container monitoring
Overview
● Monitoring for containers
● Monitoring in a distributed system
● cAdvisor
Application Metrics
● In cAdvisor
● Plumbing through a cluster
Future Work
● Heapster/Kubedash
● Templates
Outline
Monitoring in three parts
Collection
● collecting data and metrics
● making it discoverable
● plumbing it to higher-level systems
Processing
● Ingesting, aggregation
● Analytics
Managing
● Actions based on signals
● Alerts. Pagers!
Monitoring in three parts
Collection
● collecting data and metrics
● making it discoverable
● plumbing it to higher-level systems
Processing
● Ingesting, aggregation
● Analytics
Managing
● Actions based on signals
● Alerts. Pagers!
Collection in servers/VMs
● Node agent
● Knows the binary to monitor
● Monitoring logic plugged into agent
Moving to containers
● Same node agent - understands multiple
applications.
● Applications can push data to agent.
● Sidecars
● Applications publish data. Pulled off by off-
host agents.
Near-instant data
● schedulers in cluster management tools
● load balancers
● Alerting systems
Slower feedback loops
● Autoscaling / Autoupdaters
● CPI2
Historical Data
● Offline analysis.
consumers
Monitoring in distributed
environment
• Hosts are invisible
• Containers can hop around
• Monitoring needs to track
and correlate multiple
containers
In Action!
cAdvisor
Analyzes resource usage and performance
characteristics of running containers
Native Docker support, work with LXC and any
other container format
Knows containers deeply and monitors their
performance
github.com/google/cadvisor
Running cAdvisor
docker run
--volume=/:/rootfs:ro
--volume=/var/run:/var/run:rw
--volume=/sys:/sys:ro
--volume=/var/lib/docker/:/var/lib/docker:ro
--publish=8080:8080
--detach=true
--name=cadvisor
google/cadvisor:latest
API
Node and container spec
http://host:8080/api/v2.0/machine
http://host:8080/api/v2.0/spec/redis?type=docker
Hierarchical Container stats:
http://host:8080/api/v2.0/stats/nginx?type=docker
http://host:8080/api/v2.0/stats?recursive=true
Others:
/summary, /events, /storage, /attributes, /ps
Storage
Backends
Coming soon ...
Application Metrics
Every container is packaged with its monitoring data.
Monitoring moves with the container.
Use container composability to stack up metrics from all layers.
Use metadata in image or runtime (Docker labels!) to configure
monitoring
Configuring containers
FROM redis
ADD redis_config.json /var/cadvisor/redis_config.json
LABEL io.cadvisor.metric.redis="/var/cadvisor/redis_config.json"
In cAdvisor
read Labels “io.cadvisor.metric.*”
read /rootfs/proc/<pid>/root/<config path>
Monitoring Configuration
Holds metadata about metrics
● Endpoint (Location to collect metrics from)
● Name of metric
● Type (Counter, Gauge, ...)
● Data Type (int, float)
● Units (kbps, seconds, count)
● Polling Frequency
● Regexps (Regular expressions to be used to collect a metric)
Sample Configurations
{
"endpoint" : "http://localhost:8000/nginx_status",
"metrics_config" : [
{ "name" : "activeConnections",
"metric_type" : "gauge",
"units" : "number of active connections",
"data_type" : "int",
"polling_frequency" : 10,
"regex" : "Active connections: ([0-9]+)"
},
{ "name" : "reading",
"metric_type" : "gauge",
"units" : "number of reading connections",
"data_type" : "int",
"polling_frequency" : 10,
"regex" : "Reading: ([0-9]+) .*"
}
]
}
Get all prometheus metrics:
{
"endpoint" : "http://localhost:9100/metrics",
}
Get selected prometheus metrics:
{
"endpoint" : "http://localhost:8000/metrics",
"metrics_config" : [
{ "scheduler_binding_latency",
"scheduler_e2e_scheduling_latency",
"scheduling_algorithm_latency"
}
]
}
App Metrics in action
Endpoint for custom metrics:
http://localhost:8080/api/v2.0/appmetrics/containerName
Application metrics being collected can be discovered from the spec:
http://localhost:8080/api/v2.0/spec/containerName
Regular stats api also reports application metrics:
http://localhost:8080/api/v2.0/stats/containerName
API
Kubernetes
Open-source Container orchestration from Google
Declarative pattern for managing containers
Physical hosts abstracted out as resources
Inspired and informed by Borg
Kubernetes UI powered by cAdvisor
github.com/kubernetes/kubernetes
App Metrics for Kubernetes
All kubernetes components run in containers
All system services on node runs in containers
All kubernetes nodes run cAdvisor (built into kubelet)
All kubernetes components expose prometheus metrics
App-metrics for kubernetes components!
Heapster
github.com/kubernetes/heapster
Heapster
Kubedash
github.com/kubernetes/kubedash
Templates
Add templates for applications that have stable stats API
LABEL io.cadvisor.metric.type=redis
Infer monitoring information
● Lookup ports through docker inspection
● hit known endpoints (e.g.: /metrics for prometheus)
● overrides through config
Tags
Adding tags to specific metrics
Convey metric intent to processors
eg. Autoscalers
Ongoing work
Endpoints
Storage drivers
Standard config syntax
Automagic
Plumbing through heapster/kubedash/kubernetes/...
Thank you!
Rohit Jnagal jnagal@google
Anushree Narasimha anushree.bnp@gmail
cAdvisor github.com/google/cadvisor
kubernetes kubernetes.io
irc #google-containers
Ad

More Related Content

What's hot (19)

Lessons Learned From PayPal: Implementing Back-Pressure With Akka Streams And...
Lessons Learned From PayPal: Implementing Back-Pressure With Akka Streams And...Lessons Learned From PayPal: Implementing Back-Pressure With Akka Streams And...
Lessons Learned From PayPal: Implementing Back-Pressure With Akka Streams And...
Lightbend
 
Zoo keeper in the wild
Zoo keeper in the wildZoo keeper in the wild
Zoo keeper in the wild
datamantra
 
Python and cassandra
Python and cassandraPython and cassandra
Python and cassandra
Jon Haddad
 
Streaming and Messaging
Streaming and MessagingStreaming and Messaging
Streaming and Messaging
Xin Wang
 
Real-Time Distributed and Reactive Systems with Apache Kafka and Apache Accumulo
Real-Time Distributed and Reactive Systems with Apache Kafka and Apache AccumuloReal-Time Distributed and Reactive Systems with Apache Kafka and Apache Accumulo
Real-Time Distributed and Reactive Systems with Apache Kafka and Apache Accumulo
Joe Stein
 
Deploying and managing SolrCloud in the cloud using the Solr Scale Toolkit
Deploying and managing SolrCloud in the cloud using the Solr Scale ToolkitDeploying and managing SolrCloud in the cloud using the Solr Scale Toolkit
Deploying and managing SolrCloud in the cloud using the Solr Scale Toolkit
thelabdude
 
Kafka Summit NYC 2017 - Running Hundreds of Kafka Clusters with 5 People
Kafka Summit NYC 2017 - Running Hundreds of Kafka Clusters with 5 PeopleKafka Summit NYC 2017 - Running Hundreds of Kafka Clusters with 5 People
Kafka Summit NYC 2017 - Running Hundreds of Kafka Clusters with 5 People
confluent
 
Monitoring Docker at Scale - Docker San Francisco Meetup - August 11, 2015
Monitoring Docker at Scale - Docker San Francisco Meetup - August 11, 2015Monitoring Docker at Scale - Docker San Francisco Meetup - August 11, 2015
Monitoring Docker at Scale - Docker San Francisco Meetup - August 11, 2015
Datadog
 
How to manage large amounts of data with akka streams
How to manage large amounts of data with akka streamsHow to manage large amounts of data with akka streams
How to manage large amounts of data with akka streams
Igor Mielientiev
 
Back-Pressure in Action: Handling High-Burst Workloads with Akka Streams & Kafka
Back-Pressure in Action: Handling High-Burst Workloads with Akka Streams & KafkaBack-Pressure in Action: Handling High-Burst Workloads with Akka Streams & Kafka
Back-Pressure in Action: Handling High-Burst Workloads with Akka Streams & Kafka
Akara Sucharitakul
 
Kafka Summit SF 2017 - Exactly-once Stream Processing with Kafka Streams
Kafka Summit SF 2017 - Exactly-once Stream Processing with Kafka StreamsKafka Summit SF 2017 - Exactly-once Stream Processing with Kafka Streams
Kafka Summit SF 2017 - Exactly-once Stream Processing with Kafka Streams
confluent
 
KSQL: Streaming SQL for Kafka
KSQL: Streaming SQL for KafkaKSQL: Streaming SQL for Kafka
KSQL: Streaming SQL for Kafka
confluent
 
Service Stampede: Surviving a Thousand Services
Service Stampede: Surviving a Thousand ServicesService Stampede: Surviving a Thousand Services
Service Stampede: Surviving a Thousand Services
Anil Gursel
 
Scylla Summit 2018: Introducing ValuStor, A Memcached Alternative Made to Run...
Scylla Summit 2018: Introducing ValuStor, A Memcached Alternative Made to Run...Scylla Summit 2018: Introducing ValuStor, A Memcached Alternative Made to Run...
Scylla Summit 2018: Introducing ValuStor, A Memcached Alternative Made to Run...
ScyllaDB
 
Fact-Based Monitoring - PuppetConf 2014
Fact-Based Monitoring - PuppetConf 2014Fact-Based Monitoring - PuppetConf 2014
Fact-Based Monitoring - PuppetConf 2014
Puppet
 
Apache zookeeper seminar_trinh_viet_dung_03_2016
Apache zookeeper seminar_trinh_viet_dung_03_2016Apache zookeeper seminar_trinh_viet_dung_03_2016
Apache zookeeper seminar_trinh_viet_dung_03_2016
Viet-Dung TRINH
 
Asynchronous Orchestration DSL on squbs
Asynchronous Orchestration DSL on squbsAsynchronous Orchestration DSL on squbs
Asynchronous Orchestration DSL on squbs
Anil Gursel
 
Advanced Operations
Advanced OperationsAdvanced Operations
Advanced Operations
DataStax Academy
 
Monitoring and Scaling Redis at DataDog - Ilan Rabinovitch, DataDog
 Monitoring and Scaling Redis at DataDog - Ilan Rabinovitch, DataDog Monitoring and Scaling Redis at DataDog - Ilan Rabinovitch, DataDog
Monitoring and Scaling Redis at DataDog - Ilan Rabinovitch, DataDog
Redis Labs
 
Lessons Learned From PayPal: Implementing Back-Pressure With Akka Streams And...
Lessons Learned From PayPal: Implementing Back-Pressure With Akka Streams And...Lessons Learned From PayPal: Implementing Back-Pressure With Akka Streams And...
Lessons Learned From PayPal: Implementing Back-Pressure With Akka Streams And...
Lightbend
 
Zoo keeper in the wild
Zoo keeper in the wildZoo keeper in the wild
Zoo keeper in the wild
datamantra
 
Python and cassandra
Python and cassandraPython and cassandra
Python and cassandra
Jon Haddad
 
Streaming and Messaging
Streaming and MessagingStreaming and Messaging
Streaming and Messaging
Xin Wang
 
Real-Time Distributed and Reactive Systems with Apache Kafka and Apache Accumulo
Real-Time Distributed and Reactive Systems with Apache Kafka and Apache AccumuloReal-Time Distributed and Reactive Systems with Apache Kafka and Apache Accumulo
Real-Time Distributed and Reactive Systems with Apache Kafka and Apache Accumulo
Joe Stein
 
Deploying and managing SolrCloud in the cloud using the Solr Scale Toolkit
Deploying and managing SolrCloud in the cloud using the Solr Scale ToolkitDeploying and managing SolrCloud in the cloud using the Solr Scale Toolkit
Deploying and managing SolrCloud in the cloud using the Solr Scale Toolkit
thelabdude
 
Kafka Summit NYC 2017 - Running Hundreds of Kafka Clusters with 5 People
Kafka Summit NYC 2017 - Running Hundreds of Kafka Clusters with 5 PeopleKafka Summit NYC 2017 - Running Hundreds of Kafka Clusters with 5 People
Kafka Summit NYC 2017 - Running Hundreds of Kafka Clusters with 5 People
confluent
 
Monitoring Docker at Scale - Docker San Francisco Meetup - August 11, 2015
Monitoring Docker at Scale - Docker San Francisco Meetup - August 11, 2015Monitoring Docker at Scale - Docker San Francisco Meetup - August 11, 2015
Monitoring Docker at Scale - Docker San Francisco Meetup - August 11, 2015
Datadog
 
How to manage large amounts of data with akka streams
How to manage large amounts of data with akka streamsHow to manage large amounts of data with akka streams
How to manage large amounts of data with akka streams
Igor Mielientiev
 
Back-Pressure in Action: Handling High-Burst Workloads with Akka Streams & Kafka
Back-Pressure in Action: Handling High-Burst Workloads with Akka Streams & KafkaBack-Pressure in Action: Handling High-Burst Workloads with Akka Streams & Kafka
Back-Pressure in Action: Handling High-Burst Workloads with Akka Streams & Kafka
Akara Sucharitakul
 
Kafka Summit SF 2017 - Exactly-once Stream Processing with Kafka Streams
Kafka Summit SF 2017 - Exactly-once Stream Processing with Kafka StreamsKafka Summit SF 2017 - Exactly-once Stream Processing with Kafka Streams
Kafka Summit SF 2017 - Exactly-once Stream Processing with Kafka Streams
confluent
 
KSQL: Streaming SQL for Kafka
KSQL: Streaming SQL for KafkaKSQL: Streaming SQL for Kafka
KSQL: Streaming SQL for Kafka
confluent
 
Service Stampede: Surviving a Thousand Services
Service Stampede: Surviving a Thousand ServicesService Stampede: Surviving a Thousand Services
Service Stampede: Surviving a Thousand Services
Anil Gursel
 
Scylla Summit 2018: Introducing ValuStor, A Memcached Alternative Made to Run...
Scylla Summit 2018: Introducing ValuStor, A Memcached Alternative Made to Run...Scylla Summit 2018: Introducing ValuStor, A Memcached Alternative Made to Run...
Scylla Summit 2018: Introducing ValuStor, A Memcached Alternative Made to Run...
ScyllaDB
 
Fact-Based Monitoring - PuppetConf 2014
Fact-Based Monitoring - PuppetConf 2014Fact-Based Monitoring - PuppetConf 2014
Fact-Based Monitoring - PuppetConf 2014
Puppet
 
Apache zookeeper seminar_trinh_viet_dung_03_2016
Apache zookeeper seminar_trinh_viet_dung_03_2016Apache zookeeper seminar_trinh_viet_dung_03_2016
Apache zookeeper seminar_trinh_viet_dung_03_2016
Viet-Dung TRINH
 
Asynchronous Orchestration DSL on squbs
Asynchronous Orchestration DSL on squbsAsynchronous Orchestration DSL on squbs
Asynchronous Orchestration DSL on squbs
Anil Gursel
 
Monitoring and Scaling Redis at DataDog - Ilan Rabinovitch, DataDog
 Monitoring and Scaling Redis at DataDog - Ilan Rabinovitch, DataDog Monitoring and Scaling Redis at DataDog - Ilan Rabinovitch, DataDog
Monitoring and Scaling Redis at DataDog - Ilan Rabinovitch, DataDog
Redis Labs
 

Viewers also liked (20)

Performance Monitoring: Understanding Your Scylla Cluster
Performance Monitoring: Understanding Your Scylla ClusterPerformance Monitoring: Understanding Your Scylla Cluster
Performance Monitoring: Understanding Your Scylla Cluster
ScyllaDB
 
Monitoring Kubernetes with Prometheus
Monitoring Kubernetes with PrometheusMonitoring Kubernetes with Prometheus
Monitoring Kubernetes with Prometheus
Tobias Schmidt
 
CloudCamp Chicago - Big Data & Cloud May 2015 - All Slides
CloudCamp Chicago - Big Data & Cloud May 2015 - All SlidesCloudCamp Chicago - Big Data & Cloud May 2015 - All Slides
CloudCamp Chicago - Big Data & Cloud May 2015 - All Slides
CloudCamp Chicago
 
Monitoring in a scalable world
Monitoring in a scalable worldMonitoring in a scalable world
Monitoring in a scalable world
TechExeter
 
Skynet project: Monitor, analyze, scale, and maintain a system in the Cloud
Skynet project: Monitor, analyze, scale, and maintain a system in the CloudSkynet project: Monitor, analyze, scale, and maintain a system in the Cloud
Skynet project: Monitor, analyze, scale, and maintain a system in the Cloud
Sylvain Kalache
 
Lifting the Blinds: Monitoring Windows Server 2012
Lifting the Blinds: Monitoring Windows Server 2012Lifting the Blinds: Monitoring Windows Server 2012
Lifting the Blinds: Monitoring Windows Server 2012
Datadog
 
Monitoring kubernetes across data center and cloud
Monitoring kubernetes across data center and cloudMonitoring kubernetes across data center and cloud
Monitoring kubernetes across data center and cloud
Datadog
 
Data Logging and Telemetry
Data Logging and TelemetryData Logging and Telemetry
Data Logging and Telemetry
Francesco Meschia
 
Prometheus and Docker (Docker Galway, November 2015)
Prometheus and Docker (Docker Galway, November 2015)Prometheus and Docker (Docker Galway, November 2015)
Prometheus and Docker (Docker Galway, November 2015)
Brian Brazil
 
Deep-Dive to Application Insights
Deep-Dive to Application Insights Deep-Dive to Application Insights
Deep-Dive to Application Insights
Gunnar Peipman
 
Intro to open source telemetry linux con 2016
Intro to open source telemetry   linux con 2016Intro to open source telemetry   linux con 2016
Intro to open source telemetry linux con 2016
Matthew Broberg
 
How to Monitor Application Performance in a Container-Based World
How to Monitor Application Performance in a Container-Based WorldHow to Monitor Application Performance in a Container-Based World
How to Monitor Application Performance in a Container-Based World
Ken Owens
 
Sysdig Monitorama Slides
Sysdig Monitorama SlidesSysdig Monitorama Slides
Sysdig Monitorama Slides
Loris Degioanni
 
Volta: Logging, Metrics, and Monitoring as a Service
Volta: Logging, Metrics, and Monitoring as a ServiceVolta: Logging, Metrics, and Monitoring as a Service
Volta: Logging, Metrics, and Monitoring as a Service
LN Renganarayana
 
Kubernetes and Prometheus
Kubernetes and PrometheusKubernetes and Prometheus
Kubernetes and Prometheus
Weaveworks
 
Container orchestration
Container orchestrationContainer orchestration
Container orchestration
Timo Derstappen
 
Netflix: From Clouds to Roots
Netflix: From Clouds to RootsNetflix: From Clouds to Roots
Netflix: From Clouds to Roots
Brendan Gregg
 
AWS Re:Invent - High Availability Architecture at Netflix
AWS Re:Invent - High Availability Architecture at NetflixAWS Re:Invent - High Availability Architecture at Netflix
AWS Re:Invent - High Availability Architecture at Netflix
Adrian Cockcroft
 
Container Orchestration Wars
Container Orchestration WarsContainer Orchestration Wars
Container Orchestration Wars
Karl Isenberg
 
Monitoring Kubernetes with Prometheus (Kubernetes Ireland, 2016)
Monitoring Kubernetes with Prometheus (Kubernetes Ireland, 2016)Monitoring Kubernetes with Prometheus (Kubernetes Ireland, 2016)
Monitoring Kubernetes with Prometheus (Kubernetes Ireland, 2016)
Brian Brazil
 
Performance Monitoring: Understanding Your Scylla Cluster
Performance Monitoring: Understanding Your Scylla ClusterPerformance Monitoring: Understanding Your Scylla Cluster
Performance Monitoring: Understanding Your Scylla Cluster
ScyllaDB
 
Monitoring Kubernetes with Prometheus
Monitoring Kubernetes with PrometheusMonitoring Kubernetes with Prometheus
Monitoring Kubernetes with Prometheus
Tobias Schmidt
 
CloudCamp Chicago - Big Data & Cloud May 2015 - All Slides
CloudCamp Chicago - Big Data & Cloud May 2015 - All SlidesCloudCamp Chicago - Big Data & Cloud May 2015 - All Slides
CloudCamp Chicago - Big Data & Cloud May 2015 - All Slides
CloudCamp Chicago
 
Monitoring in a scalable world
Monitoring in a scalable worldMonitoring in a scalable world
Monitoring in a scalable world
TechExeter
 
Skynet project: Monitor, analyze, scale, and maintain a system in the Cloud
Skynet project: Monitor, analyze, scale, and maintain a system in the CloudSkynet project: Monitor, analyze, scale, and maintain a system in the Cloud
Skynet project: Monitor, analyze, scale, and maintain a system in the Cloud
Sylvain Kalache
 
Lifting the Blinds: Monitoring Windows Server 2012
Lifting the Blinds: Monitoring Windows Server 2012Lifting the Blinds: Monitoring Windows Server 2012
Lifting the Blinds: Monitoring Windows Server 2012
Datadog
 
Monitoring kubernetes across data center and cloud
Monitoring kubernetes across data center and cloudMonitoring kubernetes across data center and cloud
Monitoring kubernetes across data center and cloud
Datadog
 
Prometheus and Docker (Docker Galway, November 2015)
Prometheus and Docker (Docker Galway, November 2015)Prometheus and Docker (Docker Galway, November 2015)
Prometheus and Docker (Docker Galway, November 2015)
Brian Brazil
 
Deep-Dive to Application Insights
Deep-Dive to Application Insights Deep-Dive to Application Insights
Deep-Dive to Application Insights
Gunnar Peipman
 
Intro to open source telemetry linux con 2016
Intro to open source telemetry   linux con 2016Intro to open source telemetry   linux con 2016
Intro to open source telemetry linux con 2016
Matthew Broberg
 
How to Monitor Application Performance in a Container-Based World
How to Monitor Application Performance in a Container-Based WorldHow to Monitor Application Performance in a Container-Based World
How to Monitor Application Performance in a Container-Based World
Ken Owens
 
Sysdig Monitorama Slides
Sysdig Monitorama SlidesSysdig Monitorama Slides
Sysdig Monitorama Slides
Loris Degioanni
 
Volta: Logging, Metrics, and Monitoring as a Service
Volta: Logging, Metrics, and Monitoring as a ServiceVolta: Logging, Metrics, and Monitoring as a Service
Volta: Logging, Metrics, and Monitoring as a Service
LN Renganarayana
 
Kubernetes and Prometheus
Kubernetes and PrometheusKubernetes and Prometheus
Kubernetes and Prometheus
Weaveworks
 
Netflix: From Clouds to Roots
Netflix: From Clouds to RootsNetflix: From Clouds to Roots
Netflix: From Clouds to Roots
Brendan Gregg
 
AWS Re:Invent - High Availability Architecture at Netflix
AWS Re:Invent - High Availability Architecture at NetflixAWS Re:Invent - High Availability Architecture at Netflix
AWS Re:Invent - High Availability Architecture at Netflix
Adrian Cockcroft
 
Container Orchestration Wars
Container Orchestration WarsContainer Orchestration Wars
Container Orchestration Wars
Karl Isenberg
 
Monitoring Kubernetes with Prometheus (Kubernetes Ireland, 2016)
Monitoring Kubernetes with Prometheus (Kubernetes Ireland, 2016)Monitoring Kubernetes with Prometheus (Kubernetes Ireland, 2016)
Monitoring Kubernetes with Prometheus (Kubernetes Ireland, 2016)
Brian Brazil
 
Ad

Similar to Native container monitoring (20)

Building a data pipeline to ingest data into Hadoop in minutes using Streamse...
Building a data pipeline to ingest data into Hadoop in minutes using Streamse...Building a data pipeline to ingest data into Hadoop in minutes using Streamse...
Building a data pipeline to ingest data into Hadoop in minutes using Streamse...
Guglielmo Iozzia
 
Webinar Monitoring in era of cloud computing
Webinar Monitoring in era of cloud computingWebinar Monitoring in era of cloud computing
Webinar Monitoring in era of cloud computing
CREATE-NET
 
Fabric - Realtime stream processing framework
Fabric - Realtime stream processing frameworkFabric - Realtime stream processing framework
Fabric - Realtime stream processing framework
Shashank Gautam
 
Intelligent Monitoring
Intelligent MonitoringIntelligent Monitoring
Intelligent Monitoring
Intelie
 
MongoDB for Time Series Data
MongoDB for Time Series DataMongoDB for Time Series Data
MongoDB for Time Series Data
MongoDB
 
Analytics&IoT
Analytics&IoTAnalytics&IoT
Analytics&IoT
Selvaraj Kesavan
 
DNA: an overview
DNA: an overviewDNA: an overview
DNA: an overview
Cisco DevNet
 
[DSC Europe 23] Pramod Immaneni - Real-time analytics at IoT scale
[DSC Europe 23] Pramod Immaneni - Real-time analytics at IoT scale[DSC Europe 23] Pramod Immaneni - Real-time analytics at IoT scale
[DSC Europe 23] Pramod Immaneni - Real-time analytics at IoT scale
DataScienceConferenc1
 
Microsoft Azure Big Data Analytics
Microsoft Azure Big Data AnalyticsMicrosoft Azure Big Data Analytics
Microsoft Azure Big Data Analytics
Mark Kromer
 
MongoDB for Time Series Data: Setting the Stage for Sensor Management
MongoDB for Time Series Data: Setting the Stage for Sensor ManagementMongoDB for Time Series Data: Setting the Stage for Sensor Management
MongoDB for Time Series Data: Setting the Stage for Sensor Management
MongoDB
 
[WSO2Con EU 2018] Patterns for Building Streaming Apps
[WSO2Con EU 2018] Patterns for Building Streaming Apps[WSO2Con EU 2018] Patterns for Building Streaming Apps
[WSO2Con EU 2018] Patterns for Building Streaming Apps
WSO2
 
Apache Big Data EU 2016: Next Gen Big Data Analytics with Apache Apex
Apache Big Data EU 2016: Next Gen Big Data Analytics with Apache ApexApache Big Data EU 2016: Next Gen Big Data Analytics with Apache Apex
Apache Big Data EU 2016: Next Gen Big Data Analytics with Apache Apex
Apache Apex
 
Analytics in Your Enterprise
Analytics in Your EnterpriseAnalytics in Your Enterprise
Analytics in Your Enterprise
WSO2
 
Streaming Analytics and Internet of Things - Geesara Prathap
Streaming Analytics and Internet of Things - Geesara PrathapStreaming Analytics and Internet of Things - Geesara Prathap
Streaming Analytics and Internet of Things - Geesara Prathap
WithTheBest
 
Log aggregation and analysis
Log aggregation and analysisLog aggregation and analysis
Log aggregation and analysis
Dhaval Mehta
 
WSO2Con USA 2015: Patterns for Deploying Analytics in the Real World
WSO2Con USA 2015: Patterns for Deploying Analytics in the Real WorldWSO2Con USA 2015: Patterns for Deploying Analytics in the Real World
WSO2Con USA 2015: Patterns for Deploying Analytics in the Real World
WSO2
 
Azure Monitoring Overview
Azure Monitoring OverviewAzure Monitoring Overview
Azure Monitoring Overview
gjuljo
 
Real time streaming analytics
Real time streaming analyticsReal time streaming analytics
Real time streaming analytics
Anirudh
 
Understanding Business APIs through statistics
Understanding Business APIs through statisticsUnderstanding Business APIs through statistics
Understanding Business APIs through statistics
WSO2
 
[WSO2Con EU 2018] The Rise of Streaming SQL
[WSO2Con EU 2018] The Rise of Streaming SQL[WSO2Con EU 2018] The Rise of Streaming SQL
[WSO2Con EU 2018] The Rise of Streaming SQL
WSO2
 
Building a data pipeline to ingest data into Hadoop in minutes using Streamse...
Building a data pipeline to ingest data into Hadoop in minutes using Streamse...Building a data pipeline to ingest data into Hadoop in minutes using Streamse...
Building a data pipeline to ingest data into Hadoop in minutes using Streamse...
Guglielmo Iozzia
 
Webinar Monitoring in era of cloud computing
Webinar Monitoring in era of cloud computingWebinar Monitoring in era of cloud computing
Webinar Monitoring in era of cloud computing
CREATE-NET
 
Fabric - Realtime stream processing framework
Fabric - Realtime stream processing frameworkFabric - Realtime stream processing framework
Fabric - Realtime stream processing framework
Shashank Gautam
 
Intelligent Monitoring
Intelligent MonitoringIntelligent Monitoring
Intelligent Monitoring
Intelie
 
MongoDB for Time Series Data
MongoDB for Time Series DataMongoDB for Time Series Data
MongoDB for Time Series Data
MongoDB
 
[DSC Europe 23] Pramod Immaneni - Real-time analytics at IoT scale
[DSC Europe 23] Pramod Immaneni - Real-time analytics at IoT scale[DSC Europe 23] Pramod Immaneni - Real-time analytics at IoT scale
[DSC Europe 23] Pramod Immaneni - Real-time analytics at IoT scale
DataScienceConferenc1
 
Microsoft Azure Big Data Analytics
Microsoft Azure Big Data AnalyticsMicrosoft Azure Big Data Analytics
Microsoft Azure Big Data Analytics
Mark Kromer
 
MongoDB for Time Series Data: Setting the Stage for Sensor Management
MongoDB for Time Series Data: Setting the Stage for Sensor ManagementMongoDB for Time Series Data: Setting the Stage for Sensor Management
MongoDB for Time Series Data: Setting the Stage for Sensor Management
MongoDB
 
[WSO2Con EU 2018] Patterns for Building Streaming Apps
[WSO2Con EU 2018] Patterns for Building Streaming Apps[WSO2Con EU 2018] Patterns for Building Streaming Apps
[WSO2Con EU 2018] Patterns for Building Streaming Apps
WSO2
 
Apache Big Data EU 2016: Next Gen Big Data Analytics with Apache Apex
Apache Big Data EU 2016: Next Gen Big Data Analytics with Apache ApexApache Big Data EU 2016: Next Gen Big Data Analytics with Apache Apex
Apache Big Data EU 2016: Next Gen Big Data Analytics with Apache Apex
Apache Apex
 
Analytics in Your Enterprise
Analytics in Your EnterpriseAnalytics in Your Enterprise
Analytics in Your Enterprise
WSO2
 
Streaming Analytics and Internet of Things - Geesara Prathap
Streaming Analytics and Internet of Things - Geesara PrathapStreaming Analytics and Internet of Things - Geesara Prathap
Streaming Analytics and Internet of Things - Geesara Prathap
WithTheBest
 
Log aggregation and analysis
Log aggregation and analysisLog aggregation and analysis
Log aggregation and analysis
Dhaval Mehta
 
WSO2Con USA 2015: Patterns for Deploying Analytics in the Real World
WSO2Con USA 2015: Patterns for Deploying Analytics in the Real WorldWSO2Con USA 2015: Patterns for Deploying Analytics in the Real World
WSO2Con USA 2015: Patterns for Deploying Analytics in the Real World
WSO2
 
Azure Monitoring Overview
Azure Monitoring OverviewAzure Monitoring Overview
Azure Monitoring Overview
gjuljo
 
Real time streaming analytics
Real time streaming analyticsReal time streaming analytics
Real time streaming analytics
Anirudh
 
Understanding Business APIs through statistics
Understanding Business APIs through statisticsUnderstanding Business APIs through statistics
Understanding Business APIs through statistics
WSO2
 
[WSO2Con EU 2018] The Rise of Streaming SQL
[WSO2Con EU 2018] The Rise of Streaming SQL[WSO2Con EU 2018] The Rise of Streaming SQL
[WSO2Con EU 2018] The Rise of Streaming SQL
WSO2
 
Ad

More from Rohit Jnagal (7)

Task migration using CRIU
Task migration using CRIUTask migration using CRIU
Task migration using CRIU
Rohit Jnagal
 
Memory Bandwidth QoS
Memory Bandwidth QoSMemory Bandwidth QoS
Memory Bandwidth QoS
Rohit Jnagal
 
Cat @ scale
Cat @ scaleCat @ scale
Cat @ scale
Rohit Jnagal
 
Kubernetes intro public - kubernetes meetup 4-21-2015
Kubernetes intro   public - kubernetes meetup 4-21-2015Kubernetes intro   public - kubernetes meetup 4-21-2015
Kubernetes intro public - kubernetes meetup 4-21-2015
Rohit Jnagal
 
Docker n co
Docker n coDocker n co
Docker n co
Rohit Jnagal
 
Docker Overview
Docker OverviewDocker Overview
Docker Overview
Rohit Jnagal
 
Docker internals
Docker internalsDocker internals
Docker internals
Rohit Jnagal
 
Task migration using CRIU
Task migration using CRIUTask migration using CRIU
Task migration using CRIU
Rohit Jnagal
 
Memory Bandwidth QoS
Memory Bandwidth QoSMemory Bandwidth QoS
Memory Bandwidth QoS
Rohit Jnagal
 
Kubernetes intro public - kubernetes meetup 4-21-2015
Kubernetes intro   public - kubernetes meetup 4-21-2015Kubernetes intro   public - kubernetes meetup 4-21-2015
Kubernetes intro public - kubernetes meetup 4-21-2015
Rohit Jnagal
 

Recently uploaded (20)

HCL Nomad Web – Best Practices und Verwaltung von Multiuser-Umgebungen
HCL Nomad Web – Best Practices und Verwaltung von Multiuser-UmgebungenHCL Nomad Web – Best Practices und Verwaltung von Multiuser-Umgebungen
HCL Nomad Web – Best Practices und Verwaltung von Multiuser-Umgebungen
panagenda
 
Rusty Waters: Elevating Lakehouses Beyond Spark
Rusty Waters: Elevating Lakehouses Beyond SparkRusty Waters: Elevating Lakehouses Beyond Spark
Rusty Waters: Elevating Lakehouses Beyond Spark
carlyakerly1
 
Massive Power Outage Hits Spain, Portugal, and France: Causes, Impact, and On...
Massive Power Outage Hits Spain, Portugal, and France: Causes, Impact, and On...Massive Power Outage Hits Spain, Portugal, and France: Causes, Impact, and On...
Massive Power Outage Hits Spain, Portugal, and France: Causes, Impact, and On...
Aqusag Technologies
 
DevOpsDays Atlanta 2025 - Building 10x Development Organizations.pptx
DevOpsDays Atlanta 2025 - Building 10x Development Organizations.pptxDevOpsDays Atlanta 2025 - Building 10x Development Organizations.pptx
DevOpsDays Atlanta 2025 - Building 10x Development Organizations.pptx
Justin Reock
 
Cybersecurity Identity and Access Solutions using Azure AD
Cybersecurity Identity and Access Solutions using Azure ADCybersecurity Identity and Access Solutions using Azure AD
Cybersecurity Identity and Access Solutions using Azure AD
VICTOR MAESTRE RAMIREZ
 
SAP Modernization: Maximizing the Value of Your SAP S/4HANA Migration.pdf
SAP Modernization: Maximizing the Value of Your SAP S/4HANA Migration.pdfSAP Modernization: Maximizing the Value of Your SAP S/4HANA Migration.pdf
SAP Modernization: Maximizing the Value of Your SAP S/4HANA Migration.pdf
Precisely
 
Linux Support for SMARC: How Toradex Empowers Embedded Developers
Linux Support for SMARC: How Toradex Empowers Embedded DevelopersLinux Support for SMARC: How Toradex Empowers Embedded Developers
Linux Support for SMARC: How Toradex Empowers Embedded Developers
Toradex
 
IEDM 2024 Tutorial2_Advances in CMOS Technologies and Future Directions for C...
IEDM 2024 Tutorial2_Advances in CMOS Technologies and Future Directions for C...IEDM 2024 Tutorial2_Advances in CMOS Technologies and Future Directions for C...
IEDM 2024 Tutorial2_Advances in CMOS Technologies and Future Directions for C...
organizerofv
 
Electronic_Mail_Attacks-1-35.pdf by xploit
Electronic_Mail_Attacks-1-35.pdf by xploitElectronic_Mail_Attacks-1-35.pdf by xploit
Electronic_Mail_Attacks-1-35.pdf by xploit
niftliyevhuseyn
 
Semantic Cultivators : The Critical Future Role to Enable AI
Semantic Cultivators : The Critical Future Role to Enable AISemantic Cultivators : The Critical Future Role to Enable AI
Semantic Cultivators : The Critical Future Role to Enable AI
artmondano
 
TrsLabs - Fintech Product & Business Consulting
TrsLabs - Fintech Product & Business ConsultingTrsLabs - Fintech Product & Business Consulting
TrsLabs - Fintech Product & Business Consulting
Trs Labs
 
AI and Data Privacy in 2025: Global Trends
AI and Data Privacy in 2025: Global TrendsAI and Data Privacy in 2025: Global Trends
AI and Data Privacy in 2025: Global Trends
InData Labs
 
Heap, Types of Heap, Insertion and Deletion
Heap, Types of Heap, Insertion and DeletionHeap, Types of Heap, Insertion and Deletion
Heap, Types of Heap, Insertion and Deletion
Jaydeep Kale
 
#StandardsGoals for 2025: Standards & certification roundup - Tech Forum 2025
#StandardsGoals for 2025: Standards & certification roundup - Tech Forum 2025#StandardsGoals for 2025: Standards & certification roundup - Tech Forum 2025
#StandardsGoals for 2025: Standards & certification roundup - Tech Forum 2025
BookNet Canada
 
AI Changes Everything – Talk at Cardiff Metropolitan University, 29th April 2...
AI Changes Everything – Talk at Cardiff Metropolitan University, 29th April 2...AI Changes Everything – Talk at Cardiff Metropolitan University, 29th April 2...
AI Changes Everything – Talk at Cardiff Metropolitan University, 29th April 2...
Alan Dix
 
Designing Low-Latency Systems with Rust and ScyllaDB: An Architectural Deep Dive
Designing Low-Latency Systems with Rust and ScyllaDB: An Architectural Deep DiveDesigning Low-Latency Systems with Rust and ScyllaDB: An Architectural Deep Dive
Designing Low-Latency Systems with Rust and ScyllaDB: An Architectural Deep Dive
ScyllaDB
 
Role of Data Annotation Services in AI-Powered Manufacturing
Role of Data Annotation Services in AI-Powered ManufacturingRole of Data Annotation Services in AI-Powered Manufacturing
Role of Data Annotation Services in AI-Powered Manufacturing
Andrew Leo
 
Generative Artificial Intelligence (GenAI) in Business
Generative Artificial Intelligence (GenAI) in BusinessGenerative Artificial Intelligence (GenAI) in Business
Generative Artificial Intelligence (GenAI) in Business
Dr. Tathagat Varma
 
Cyber Awareness overview for 2025 month of security
Cyber Awareness overview for 2025 month of securityCyber Awareness overview for 2025 month of security
Cyber Awareness overview for 2025 month of security
riccardosl1
 
Splunk Security Update | Public Sector Summit Germany 2025
Splunk Security Update | Public Sector Summit Germany 2025Splunk Security Update | Public Sector Summit Germany 2025
Splunk Security Update | Public Sector Summit Germany 2025
Splunk
 
HCL Nomad Web – Best Practices und Verwaltung von Multiuser-Umgebungen
HCL Nomad Web – Best Practices und Verwaltung von Multiuser-UmgebungenHCL Nomad Web – Best Practices und Verwaltung von Multiuser-Umgebungen
HCL Nomad Web – Best Practices und Verwaltung von Multiuser-Umgebungen
panagenda
 
Rusty Waters: Elevating Lakehouses Beyond Spark
Rusty Waters: Elevating Lakehouses Beyond SparkRusty Waters: Elevating Lakehouses Beyond Spark
Rusty Waters: Elevating Lakehouses Beyond Spark
carlyakerly1
 
Massive Power Outage Hits Spain, Portugal, and France: Causes, Impact, and On...
Massive Power Outage Hits Spain, Portugal, and France: Causes, Impact, and On...Massive Power Outage Hits Spain, Portugal, and France: Causes, Impact, and On...
Massive Power Outage Hits Spain, Portugal, and France: Causes, Impact, and On...
Aqusag Technologies
 
DevOpsDays Atlanta 2025 - Building 10x Development Organizations.pptx
DevOpsDays Atlanta 2025 - Building 10x Development Organizations.pptxDevOpsDays Atlanta 2025 - Building 10x Development Organizations.pptx
DevOpsDays Atlanta 2025 - Building 10x Development Organizations.pptx
Justin Reock
 
Cybersecurity Identity and Access Solutions using Azure AD
Cybersecurity Identity and Access Solutions using Azure ADCybersecurity Identity and Access Solutions using Azure AD
Cybersecurity Identity and Access Solutions using Azure AD
VICTOR MAESTRE RAMIREZ
 
SAP Modernization: Maximizing the Value of Your SAP S/4HANA Migration.pdf
SAP Modernization: Maximizing the Value of Your SAP S/4HANA Migration.pdfSAP Modernization: Maximizing the Value of Your SAP S/4HANA Migration.pdf
SAP Modernization: Maximizing the Value of Your SAP S/4HANA Migration.pdf
Precisely
 
Linux Support for SMARC: How Toradex Empowers Embedded Developers
Linux Support for SMARC: How Toradex Empowers Embedded DevelopersLinux Support for SMARC: How Toradex Empowers Embedded Developers
Linux Support for SMARC: How Toradex Empowers Embedded Developers
Toradex
 
IEDM 2024 Tutorial2_Advances in CMOS Technologies and Future Directions for C...
IEDM 2024 Tutorial2_Advances in CMOS Technologies and Future Directions for C...IEDM 2024 Tutorial2_Advances in CMOS Technologies and Future Directions for C...
IEDM 2024 Tutorial2_Advances in CMOS Technologies and Future Directions for C...
organizerofv
 
Electronic_Mail_Attacks-1-35.pdf by xploit
Electronic_Mail_Attacks-1-35.pdf by xploitElectronic_Mail_Attacks-1-35.pdf by xploit
Electronic_Mail_Attacks-1-35.pdf by xploit
niftliyevhuseyn
 
Semantic Cultivators : The Critical Future Role to Enable AI
Semantic Cultivators : The Critical Future Role to Enable AISemantic Cultivators : The Critical Future Role to Enable AI
Semantic Cultivators : The Critical Future Role to Enable AI
artmondano
 
TrsLabs - Fintech Product & Business Consulting
TrsLabs - Fintech Product & Business ConsultingTrsLabs - Fintech Product & Business Consulting
TrsLabs - Fintech Product & Business Consulting
Trs Labs
 
AI and Data Privacy in 2025: Global Trends
AI and Data Privacy in 2025: Global TrendsAI and Data Privacy in 2025: Global Trends
AI and Data Privacy in 2025: Global Trends
InData Labs
 
Heap, Types of Heap, Insertion and Deletion
Heap, Types of Heap, Insertion and DeletionHeap, Types of Heap, Insertion and Deletion
Heap, Types of Heap, Insertion and Deletion
Jaydeep Kale
 
#StandardsGoals for 2025: Standards & certification roundup - Tech Forum 2025
#StandardsGoals for 2025: Standards & certification roundup - Tech Forum 2025#StandardsGoals for 2025: Standards & certification roundup - Tech Forum 2025
#StandardsGoals for 2025: Standards & certification roundup - Tech Forum 2025
BookNet Canada
 
AI Changes Everything – Talk at Cardiff Metropolitan University, 29th April 2...
AI Changes Everything – Talk at Cardiff Metropolitan University, 29th April 2...AI Changes Everything – Talk at Cardiff Metropolitan University, 29th April 2...
AI Changes Everything – Talk at Cardiff Metropolitan University, 29th April 2...
Alan Dix
 
Designing Low-Latency Systems with Rust and ScyllaDB: An Architectural Deep Dive
Designing Low-Latency Systems with Rust and ScyllaDB: An Architectural Deep DiveDesigning Low-Latency Systems with Rust and ScyllaDB: An Architectural Deep Dive
Designing Low-Latency Systems with Rust and ScyllaDB: An Architectural Deep Dive
ScyllaDB
 
Role of Data Annotation Services in AI-Powered Manufacturing
Role of Data Annotation Services in AI-Powered ManufacturingRole of Data Annotation Services in AI-Powered Manufacturing
Role of Data Annotation Services in AI-Powered Manufacturing
Andrew Leo
 
Generative Artificial Intelligence (GenAI) in Business
Generative Artificial Intelligence (GenAI) in BusinessGenerative Artificial Intelligence (GenAI) in Business
Generative Artificial Intelligence (GenAI) in Business
Dr. Tathagat Varma
 
Cyber Awareness overview for 2025 month of security
Cyber Awareness overview for 2025 month of securityCyber Awareness overview for 2025 month of security
Cyber Awareness overview for 2025 month of security
riccardosl1
 
Splunk Security Update | Public Sector Summit Germany 2025
Splunk Security Update | Public Sector Summit Germany 2025Splunk Security Update | Public Sector Summit Germany 2025
Splunk Security Update | Public Sector Summit Germany 2025
Splunk
 

Native container monitoring

  • 1. Container native Monitoring Rohit Jnagal Anushree Narasimha
  • 3. Overview ● Monitoring for containers ● Monitoring in a distributed system ● cAdvisor Application Metrics ● In cAdvisor ● Plumbing through a cluster Future Work ● Heapster/Kubedash ● Templates Outline
  • 4. Monitoring in three parts Collection ● collecting data and metrics ● making it discoverable ● plumbing it to higher-level systems Processing ● Ingesting, aggregation ● Analytics Managing ● Actions based on signals ● Alerts. Pagers!
  • 5. Monitoring in three parts Collection ● collecting data and metrics ● making it discoverable ● plumbing it to higher-level systems Processing ● Ingesting, aggregation ● Analytics Managing ● Actions based on signals ● Alerts. Pagers!
  • 6. Collection in servers/VMs ● Node agent ● Knows the binary to monitor ● Monitoring logic plugged into agent Moving to containers ● Same node agent - understands multiple applications. ● Applications can push data to agent. ● Sidecars ● Applications publish data. Pulled off by off- host agents.
  • 7. Near-instant data ● schedulers in cluster management tools ● load balancers ● Alerting systems Slower feedback loops ● Autoscaling / Autoupdaters ● CPI2 Historical Data ● Offline analysis. consumers
  • 8. Monitoring in distributed environment • Hosts are invisible • Containers can hop around • Monitoring needs to track and correlate multiple containers
  • 10. cAdvisor Analyzes resource usage and performance characteristics of running containers Native Docker support, work with LXC and any other container format Knows containers deeply and monitors their performance github.com/google/cadvisor
  • 12. API Node and container spec http://host:8080/api/v2.0/machine http://host:8080/api/v2.0/spec/redis?type=docker Hierarchical Container stats: http://host:8080/api/v2.0/stats/nginx?type=docker http://host:8080/api/v2.0/stats?recursive=true Others: /summary, /events, /storage, /attributes, /ps
  • 14. Application Metrics Every container is packaged with its monitoring data. Monitoring moves with the container. Use container composability to stack up metrics from all layers. Use metadata in image or runtime (Docker labels!) to configure monitoring
  • 15. Configuring containers FROM redis ADD redis_config.json /var/cadvisor/redis_config.json LABEL io.cadvisor.metric.redis="/var/cadvisor/redis_config.json" In cAdvisor read Labels “io.cadvisor.metric.*” read /rootfs/proc/<pid>/root/<config path>
  • 16. Monitoring Configuration Holds metadata about metrics ● Endpoint (Location to collect metrics from) ● Name of metric ● Type (Counter, Gauge, ...) ● Data Type (int, float) ● Units (kbps, seconds, count) ● Polling Frequency ● Regexps (Regular expressions to be used to collect a metric)
  • 17. Sample Configurations { "endpoint" : "http://localhost:8000/nginx_status", "metrics_config" : [ { "name" : "activeConnections", "metric_type" : "gauge", "units" : "number of active connections", "data_type" : "int", "polling_frequency" : 10, "regex" : "Active connections: ([0-9]+)" }, { "name" : "reading", "metric_type" : "gauge", "units" : "number of reading connections", "data_type" : "int", "polling_frequency" : 10, "regex" : "Reading: ([0-9]+) .*" } ] } Get all prometheus metrics: { "endpoint" : "http://localhost:9100/metrics", } Get selected prometheus metrics: { "endpoint" : "http://localhost:8000/metrics", "metrics_config" : [ { "scheduler_binding_latency", "scheduler_e2e_scheduling_latency", "scheduling_algorithm_latency" } ] }
  • 18. App Metrics in action
  • 19. Endpoint for custom metrics: http://localhost:8080/api/v2.0/appmetrics/containerName Application metrics being collected can be discovered from the spec: http://localhost:8080/api/v2.0/spec/containerName Regular stats api also reports application metrics: http://localhost:8080/api/v2.0/stats/containerName API
  • 20. Kubernetes Open-source Container orchestration from Google Declarative pattern for managing containers Physical hosts abstracted out as resources Inspired and informed by Borg Kubernetes UI powered by cAdvisor github.com/kubernetes/kubernetes
  • 21. App Metrics for Kubernetes All kubernetes components run in containers All system services on node runs in containers All kubernetes nodes run cAdvisor (built into kubelet) All kubernetes components expose prometheus metrics App-metrics for kubernetes components!
  • 24. Templates Add templates for applications that have stable stats API LABEL io.cadvisor.metric.type=redis Infer monitoring information ● Lookup ports through docker inspection ● hit known endpoints (e.g.: /metrics for prometheus) ● overrides through config
  • 25. Tags Adding tags to specific metrics Convey metric intent to processors eg. Autoscalers
  • 26. Ongoing work Endpoints Storage drivers Standard config syntax Automagic Plumbing through heapster/kubedash/kubernetes/...
  • 27. Thank you! Rohit Jnagal jnagal@google Anushree Narasimha anushree.bnp@gmail cAdvisor github.com/google/cadvisor kubernetes kubernetes.io irc #google-containers