SlideShare ist ein Scribd-Unternehmen logo
1 von 23
Downloaden Sie, um offline zu lesen
LOG AGGREGATION
To better manage your Red Hat footprint
Miguel Pérez Colino
Strategic Design Team - ISBU
2017-05-03
@mmmmmmpc
Agenda
Managing your Red Hat footprint with Log Aggregation
● The Situation
● The Challenge
● The Solution
THE SITUATION
Cloud Deployments
They do really scale ...
https://www.cncf.io/blog/2016/08/23/deploying-1000-nodes-of-openshift-on-the-cncf-cluster-part-1/
● Higher scalability
● More workloads per physical
machine (multi-tenant)
● Network and Storage also
Software Defined
● Containers and Microservices
providing more granularity
Cloud Deployments
Act as one single thing …
… and need to be managed and operated as one
Source: https://commons.wikimedia.org/wiki/File:Auklet_flock_Shumagins_1986.jpg
THE CHALLENGE
Data (What)
Data + Information flow in Log Aggregation
ProcessIngest StoreCollect Query ViewGenerate
Derived from: http://www.dataintensive.info/
Personas (Who)
That can use Log Aggregation
Log Aggregation
Monitoring
Provides Events,
Consumes Logs
Cloud Ops
Root Cause
Analysis
Developer
App Analysis &
Debug
Security Engineer
Sec Analysis, Audits
User /
Marketing
Access to stats
Service
DesignerIT Manager
Access to
aggregated data,
i.e. SLA, usage
Personas (Motivation)
That need Log Aggregation
Cloud Ops (Apps)
“I want to proactively know
about active or potential
degradation of service”
Cloud Ops (OpenStack)
“User reports that their VM
request failed and returned
error”
Developer (OpenShift)
“My recent commit resulted in
Jenkins test failure”
“Application (multi-tiered)
launched from CloudForms
returns error”
Cloud Suite User
Situational Awareness (Why)
Or the need of it!
Source: https://en.wikipedia.org/wiki/Situation_awareness
THE SOLUTION
Architecture
Proposed General Architecture
Real Time
Analytics
and
Response
Host
Bus
N N N
Archive
Data
Store
General
Visualization
M
C
M
C
Storage
Legend
M
C
N
Message
Client
Normalizer
C
C
C Collector
Slide Credit: Tushar Katarki [@tkatarki]
Implementation
Introduction to EFK
KibanaElasticSearchLog Source Fluentd
User Interface for:
● Search
● Graph
● Dashboard
Index and store
data and metadata
making search
fast and reliable
● Parsing
● Filtering
● Enriching
● Deleting
● Output
Buffering
● TCP/UDP
● HTTP
● File: Text
● Stdout: CSV,
JSON,
MessagePack
● syslog/journal
Slide Credit: Tushar Katarki [@tkatarki]
Current Status
Being delivered and supported
OpenShift Container
Platform 3.5
● Full EFK stack provided
as containers
OpenStack Platform 10
● Fluentd as log collector
Red Hat Virtualization
● Coming Soon!
Log files
Journal Fluentd
Kuberentes
Services
Syslog
Master Nodes
Elastic
search
Kibana
...
Application Nodes
Log files
Journal Fluentd
App inside
container
Syslog
Infra Nodes
Elastic
search
Kibana
host logs
App inside
container
Elastic
search
Curator
Multi-Tenant
Access
Diagram Credit: Tushar Katarki [@tkatarki]
BEYOND ...
Common Data Model
To ensure integration and interoperability
What Is It?
● A Data Model for Logs (and other data) to identify
and tag data (i.e. log fields)
Why?
● Alignment/Correlation with different RH products
● Improved maintainability of Data
● Better presentation/data consumption
● Enables 3rd party ecosystem
● Facilitates deep learning analysis of data
Ingestion pipeline
Consumption pipeline
Indexing and Storage
Common Data Model
Example ...
Data extracted:
● Container name
● Pod name
● Namespace name
● Docker container ID
K8S data queried:
● Pod UID
● Pod labels
● Pod host
● Namespace UID.
All merged into output log in JSON Format
Images Credit: Anton Sherkhonov [@peatz]
CDM
A → 1
B → 2
C → 3
User Experience
Prototyping and validating dashboards for users
Slide Credits: Peter Portante & Vince Conzola
Exploring different approaches
Prototyping with alternative toolsets with partners
Slide Credits: Luca Rosellini (Keedio)
ACTION!
How are you doing it?
Please, provide your feedback ...
http://bit.ly/log-aggregation
THANK YOU
plus.google.com/+RedHat
linkedin.com/company/red-hat
youtube.com/user/RedHatVideos
facebook.com/redhatinc
twitter.com/RedHatNews
Manage Red Hat Footprint with Log Aggregation

Weitere ähnliche Inhalte

Was ist angesagt?

Lightweight Collection and Storage of Software Repository Data with DataRover
Lightweight Collection and Storage of  Software Repository Data with DataRoverLightweight Collection and Storage of  Software Repository Data with DataRover
Lightweight Collection and Storage of Software Repository Data with DataRoverChristoph Matthies
 
MongoDB IoT City Tour LONDON: Managing the Database Complexity, by Arthur Vie...
MongoDB IoT City Tour LONDON: Managing the Database Complexity, by Arthur Vie...MongoDB IoT City Tour LONDON: Managing the Database Complexity, by Arthur Vie...
MongoDB IoT City Tour LONDON: Managing the Database Complexity, by Arthur Vie...MongoDB
 
Introducing MagnetoDB, a key-value storage sevice for OpenStack
Introducing MagnetoDB, a key-value storage sevice for OpenStackIntroducing MagnetoDB, a key-value storage sevice for OpenStack
Introducing MagnetoDB, a key-value storage sevice for OpenStackMirantis
 
Monitoring your shiny new docker environment
Monitoring your shiny new docker environmentMonitoring your shiny new docker environment
Monitoring your shiny new docker environmentSamuel Vandamme
 
Intro elasticsearch taswarbhatti
Intro elasticsearch taswarbhattiIntro elasticsearch taswarbhatti
Intro elasticsearch taswarbhattiTaswar Bhatti
 
IOT Paris Seminar 2015 - Storage Challenges in IOT
IOT Paris Seminar 2015 - Storage Challenges in IOTIOT Paris Seminar 2015 - Storage Challenges in IOT
IOT Paris Seminar 2015 - Storage Challenges in IOTMongoDB
 
Approaches for duplicating Kubernetes Storage with Gluster
Approaches for duplicating Kubernetes Storage with GlusterApproaches for duplicating Kubernetes Storage with Gluster
Approaches for duplicating Kubernetes Storage with Glustermountpoint.io
 
GIS on Rails by Oleksandr Kychun
GIS on Rails by Oleksandr Kychun GIS on Rails by Oleksandr Kychun
GIS on Rails by Oleksandr Kychun Pivorak MeetUp
 
BDE_SC4_WS3_6_Luigi Selmi - Pilot SC4
BDE_SC4_WS3_6_Luigi Selmi - Pilot SC4BDE_SC4_WS3_6_Luigi Selmi - Pilot SC4
BDE_SC4_WS3_6_Luigi Selmi - Pilot SC4BigData_Europe
 
Iceberg: a fast table format for S3
Iceberg: a fast table format for S3Iceberg: a fast table format for S3
Iceberg: a fast table format for S3DataWorks Summit
 
ScienceBase and CINERGI - thoughts
ScienceBase and CINERGI - thoughtsScienceBase and CINERGI - thoughts
ScienceBase and CINERGI - thoughtsSky Bristol
 
Mobicents Summit 2012 - Alexandre Mendonca - Mobicents jDiameter
Mobicents Summit 2012 - Alexandre Mendonca - Mobicents jDiameterMobicents Summit 2012 - Alexandre Mendonca - Mobicents jDiameter
Mobicents Summit 2012 - Alexandre Mendonca - Mobicents jDiametertelestax
 
Big data @ Hootsuite analtyics
Big data @ Hootsuite analtyicsBig data @ Hootsuite analtyics
Big data @ Hootsuite analtyicsClaudiu Coman
 
OpenNebulaConf2017EU: Enabling Dev and Infra teams by Lodewijk De Schuyter,De...
OpenNebulaConf2017EU: Enabling Dev and Infra teams by Lodewijk De Schuyter,De...OpenNebulaConf2017EU: Enabling Dev and Infra teams by Lodewijk De Schuyter,De...
OpenNebulaConf2017EU: Enabling Dev and Infra teams by Lodewijk De Schuyter,De...OpenNebula Project
 
Semantic Technology In Oracle Database 12c
Semantic Technology In Oracle Database 12cSemantic Technology In Oracle Database 12c
Semantic Technology In Oracle Database 12cMartin Toshev
 
The Dark Side Of Go -- Go runtime related problems in TiDB in production
The Dark Side Of Go -- Go runtime related problems in TiDB  in productionThe Dark Side Of Go -- Go runtime related problems in TiDB  in production
The Dark Side Of Go -- Go runtime related problems in TiDB in productionPingCAP
 

Was ist angesagt? (20)

Lightweight Collection and Storage of Software Repository Data with DataRover
Lightweight Collection and Storage of  Software Repository Data with DataRoverLightweight Collection and Storage of  Software Repository Data with DataRover
Lightweight Collection and Storage of Software Repository Data with DataRover
 
Workshop introduction-to-rxjs
Workshop introduction-to-rxjsWorkshop introduction-to-rxjs
Workshop introduction-to-rxjs
 
MongoDB IoT City Tour LONDON: Managing the Database Complexity, by Arthur Vie...
MongoDB IoT City Tour LONDON: Managing the Database Complexity, by Arthur Vie...MongoDB IoT City Tour LONDON: Managing the Database Complexity, by Arthur Vie...
MongoDB IoT City Tour LONDON: Managing the Database Complexity, by Arthur Vie...
 
Geobricks Framework
Geobricks FrameworkGeobricks Framework
Geobricks Framework
 
Introducing MagnetoDB, a key-value storage sevice for OpenStack
Introducing MagnetoDB, a key-value storage sevice for OpenStackIntroducing MagnetoDB, a key-value storage sevice for OpenStack
Introducing MagnetoDB, a key-value storage sevice for OpenStack
 
Monitoring your shiny new docker environment
Monitoring your shiny new docker environmentMonitoring your shiny new docker environment
Monitoring your shiny new docker environment
 
Intro elasticsearch taswarbhatti
Intro elasticsearch taswarbhattiIntro elasticsearch taswarbhatti
Intro elasticsearch taswarbhatti
 
IOT Paris Seminar 2015 - Storage Challenges in IOT
IOT Paris Seminar 2015 - Storage Challenges in IOTIOT Paris Seminar 2015 - Storage Challenges in IOT
IOT Paris Seminar 2015 - Storage Challenges in IOT
 
Approaches for duplicating Kubernetes Storage with Gluster
Approaches for duplicating Kubernetes Storage with GlusterApproaches for duplicating Kubernetes Storage with Gluster
Approaches for duplicating Kubernetes Storage with Gluster
 
GIS on Rails by Oleksandr Kychun
GIS on Rails by Oleksandr Kychun GIS on Rails by Oleksandr Kychun
GIS on Rails by Oleksandr Kychun
 
BDE_SC4_WS3_6_Luigi Selmi - Pilot SC4
BDE_SC4_WS3_6_Luigi Selmi - Pilot SC4BDE_SC4_WS3_6_Luigi Selmi - Pilot SC4
BDE_SC4_WS3_6_Luigi Selmi - Pilot SC4
 
Iceberg: a fast table format for S3
Iceberg: a fast table format for S3Iceberg: a fast table format for S3
Iceberg: a fast table format for S3
 
ScienceBase and CINERGI - thoughts
ScienceBase and CINERGI - thoughtsScienceBase and CINERGI - thoughts
ScienceBase and CINERGI - thoughts
 
DBpedia Viewer - LDOW 2014
DBpedia Viewer - LDOW 2014DBpedia Viewer - LDOW 2014
DBpedia Viewer - LDOW 2014
 
Mobicents Summit 2012 - Alexandre Mendonca - Mobicents jDiameter
Mobicents Summit 2012 - Alexandre Mendonca - Mobicents jDiameterMobicents Summit 2012 - Alexandre Mendonca - Mobicents jDiameter
Mobicents Summit 2012 - Alexandre Mendonca - Mobicents jDiameter
 
Big data @ Hootsuite analtyics
Big data @ Hootsuite analtyicsBig data @ Hootsuite analtyics
Big data @ Hootsuite analtyics
 
OpenNebulaConf2017EU: Enabling Dev and Infra teams by Lodewijk De Schuyter,De...
OpenNebulaConf2017EU: Enabling Dev and Infra teams by Lodewijk De Schuyter,De...OpenNebulaConf2017EU: Enabling Dev and Infra teams by Lodewijk De Schuyter,De...
OpenNebulaConf2017EU: Enabling Dev and Infra teams by Lodewijk De Schuyter,De...
 
Semantic Technology In Oracle Database 12c
Semantic Technology In Oracle Database 12cSemantic Technology In Oracle Database 12c
Semantic Technology In Oracle Database 12c
 
Netflix Data Benchmark @ HPTS 2017
Netflix Data Benchmark @ HPTS 2017Netflix Data Benchmark @ HPTS 2017
Netflix Data Benchmark @ HPTS 2017
 
The Dark Side Of Go -- Go runtime related problems in TiDB in production
The Dark Side Of Go -- Go runtime related problems in TiDB  in productionThe Dark Side Of Go -- Go runtime related problems in TiDB  in production
The Dark Side Of Go -- Go runtime related problems in TiDB in production
 

Ähnlich wie Manage Red Hat Footprint with Log Aggregation

Bandwidth: Use Cases for Elastic Cloud on Kubernetes
Bandwidth: Use Cases for Elastic Cloud on Kubernetes Bandwidth: Use Cases for Elastic Cloud on Kubernetes
Bandwidth: Use Cases for Elastic Cloud on Kubernetes Elasticsearch
 
Event Driven Microservices
Event Driven MicroservicesEvent Driven Microservices
Event Driven MicroservicesFabrizio Fortino
 
Day 13 - Creating Data Processing Services | Train the Trainers Program
Day 13 - Creating Data Processing Services | Train the Trainers ProgramDay 13 - Creating Data Processing Services | Train the Trainers Program
Day 13 - Creating Data Processing Services | Train the Trainers ProgramFIWARE
 
Flink Forward San Francisco 2019: Massive Scale Data Processing at Netflix us...
Flink Forward San Francisco 2019: Massive Scale Data Processing at Netflix us...Flink Forward San Francisco 2019: Massive Scale Data Processing at Netflix us...
Flink Forward San Francisco 2019: Massive Scale Data Processing at Netflix us...Flink Forward
 
ArtigofinalpublicadoASTESJ_060139.pdf
ArtigofinalpublicadoASTESJ_060139.pdfArtigofinalpublicadoASTESJ_060139.pdf
ArtigofinalpublicadoASTESJ_060139.pdfMeftahMehdawi
 
Session 8 - Creating Data Processing Services | Train the Trainers Program
Session 8 - Creating Data Processing Services | Train the Trainers ProgramSession 8 - Creating Data Processing Services | Train the Trainers Program
Session 8 - Creating Data Processing Services | Train the Trainers ProgramFIWARE
 
Solving enterprise challenges through scale out storage & big compute final
Solving enterprise challenges through scale out storage & big compute finalSolving enterprise challenges through scale out storage & big compute final
Solving enterprise challenges through scale out storage & big compute finalAvere Systems
 
Considerations for Abstracting Complexities of a Real-Time ML Platform, Zhenz...
Considerations for Abstracting Complexities of a Real-Time ML Platform, Zhenz...Considerations for Abstracting Complexities of a Real-Time ML Platform, Zhenz...
Considerations for Abstracting Complexities of a Real-Time ML Platform, Zhenz...HostedbyConfluent
 
The Enterprise Guide to Building a Data Mesh - Introducing SpecMesh
The Enterprise Guide to Building a Data Mesh - Introducing SpecMeshThe Enterprise Guide to Building a Data Mesh - Introducing SpecMesh
The Enterprise Guide to Building a Data Mesh - Introducing SpecMeshIanFurlong4
 
Combinação de logs, métricas e rastreamentos para observabilidade unificada
Combinação de logs, métricas e rastreamentos para observabilidade unificadaCombinação de logs, métricas e rastreamentos para observabilidade unificada
Combinação de logs, métricas e rastreamentos para observabilidade unificadaElasticsearch
 
Why Wait? Realtime Ingestion With Chen Qin and Heng Zhang | Current 2022
Why Wait? Realtime Ingestion With Chen Qin and Heng Zhang | Current 2022Why Wait? Realtime Ingestion With Chen Qin and Heng Zhang | Current 2022
Why Wait? Realtime Ingestion With Chen Qin and Heng Zhang | Current 2022HostedbyConfluent
 
Managing 100s of PetaBytes of data in Cloud
Managing 100s of PetaBytes of data in CloudManaging 100s of PetaBytes of data in Cloud
Managing 100s of PetaBytes of data in Cloudlohitvijayarenu
 
Red Hat Enterprise Linux: Open, hyperconverged infrastructure
Red Hat Enterprise Linux: Open, hyperconverged infrastructureRed Hat Enterprise Linux: Open, hyperconverged infrastructure
Red Hat Enterprise Linux: Open, hyperconverged infrastructureRed_Hat_Storage
 
Story of migrating event pipeline from batch to streaming
Story of migrating event pipeline from batch to streamingStory of migrating event pipeline from batch to streaming
Story of migrating event pipeline from batch to streaminglohitvijayarenu
 
Enterprise guide to building a Data Mesh
Enterprise guide to building a Data MeshEnterprise guide to building a Data Mesh
Enterprise guide to building a Data MeshSion Smith
 
Data Mesh Part 4 Monolith to Mesh
Data Mesh Part 4 Monolith to MeshData Mesh Part 4 Monolith to Mesh
Data Mesh Part 4 Monolith to MeshJeffrey T. Pollock
 
Logging, indicateurs et APM : le trio gagnant pour des opérations réussies
Logging, indicateurs et APM : le trio gagnant pour des opérations réussiesLogging, indicateurs et APM : le trio gagnant pour des opérations réussies
Logging, indicateurs et APM : le trio gagnant pour des opérations réussiesElasticsearch
 
Red Hat Storage: Emerging Use Cases
Red Hat Storage: Emerging Use CasesRed Hat Storage: Emerging Use Cases
Red Hat Storage: Emerging Use CasesRed_Hat_Storage
 
OGF Introductory Overview - OGF 44 at EGI Conference 2015
OGF Introductory Overview - OGF 44 at EGI Conference 2015OGF Introductory Overview - OGF 44 at EGI Conference 2015
OGF Introductory Overview - OGF 44 at EGI Conference 2015Alan Sill
 
Cncf storage-final-filip
Cncf storage-final-filipCncf storage-final-filip
Cncf storage-final-filipJuraj Hantak
 

Ähnlich wie Manage Red Hat Footprint with Log Aggregation (20)

Bandwidth: Use Cases for Elastic Cloud on Kubernetes
Bandwidth: Use Cases for Elastic Cloud on Kubernetes Bandwidth: Use Cases for Elastic Cloud on Kubernetes
Bandwidth: Use Cases for Elastic Cloud on Kubernetes
 
Event Driven Microservices
Event Driven MicroservicesEvent Driven Microservices
Event Driven Microservices
 
Day 13 - Creating Data Processing Services | Train the Trainers Program
Day 13 - Creating Data Processing Services | Train the Trainers ProgramDay 13 - Creating Data Processing Services | Train the Trainers Program
Day 13 - Creating Data Processing Services | Train the Trainers Program
 
Flink Forward San Francisco 2019: Massive Scale Data Processing at Netflix us...
Flink Forward San Francisco 2019: Massive Scale Data Processing at Netflix us...Flink Forward San Francisco 2019: Massive Scale Data Processing at Netflix us...
Flink Forward San Francisco 2019: Massive Scale Data Processing at Netflix us...
 
ArtigofinalpublicadoASTESJ_060139.pdf
ArtigofinalpublicadoASTESJ_060139.pdfArtigofinalpublicadoASTESJ_060139.pdf
ArtigofinalpublicadoASTESJ_060139.pdf
 
Session 8 - Creating Data Processing Services | Train the Trainers Program
Session 8 - Creating Data Processing Services | Train the Trainers ProgramSession 8 - Creating Data Processing Services | Train the Trainers Program
Session 8 - Creating Data Processing Services | Train the Trainers Program
 
Solving enterprise challenges through scale out storage & big compute final
Solving enterprise challenges through scale out storage & big compute finalSolving enterprise challenges through scale out storage & big compute final
Solving enterprise challenges through scale out storage & big compute final
 
Considerations for Abstracting Complexities of a Real-Time ML Platform, Zhenz...
Considerations for Abstracting Complexities of a Real-Time ML Platform, Zhenz...Considerations for Abstracting Complexities of a Real-Time ML Platform, Zhenz...
Considerations for Abstracting Complexities of a Real-Time ML Platform, Zhenz...
 
The Enterprise Guide to Building a Data Mesh - Introducing SpecMesh
The Enterprise Guide to Building a Data Mesh - Introducing SpecMeshThe Enterprise Guide to Building a Data Mesh - Introducing SpecMesh
The Enterprise Guide to Building a Data Mesh - Introducing SpecMesh
 
Combinação de logs, métricas e rastreamentos para observabilidade unificada
Combinação de logs, métricas e rastreamentos para observabilidade unificadaCombinação de logs, métricas e rastreamentos para observabilidade unificada
Combinação de logs, métricas e rastreamentos para observabilidade unificada
 
Why Wait? Realtime Ingestion With Chen Qin and Heng Zhang | Current 2022
Why Wait? Realtime Ingestion With Chen Qin and Heng Zhang | Current 2022Why Wait? Realtime Ingestion With Chen Qin and Heng Zhang | Current 2022
Why Wait? Realtime Ingestion With Chen Qin and Heng Zhang | Current 2022
 
Managing 100s of PetaBytes of data in Cloud
Managing 100s of PetaBytes of data in CloudManaging 100s of PetaBytes of data in Cloud
Managing 100s of PetaBytes of data in Cloud
 
Red Hat Enterprise Linux: Open, hyperconverged infrastructure
Red Hat Enterprise Linux: Open, hyperconverged infrastructureRed Hat Enterprise Linux: Open, hyperconverged infrastructure
Red Hat Enterprise Linux: Open, hyperconverged infrastructure
 
Story of migrating event pipeline from batch to streaming
Story of migrating event pipeline from batch to streamingStory of migrating event pipeline from batch to streaming
Story of migrating event pipeline from batch to streaming
 
Enterprise guide to building a Data Mesh
Enterprise guide to building a Data MeshEnterprise guide to building a Data Mesh
Enterprise guide to building a Data Mesh
 
Data Mesh Part 4 Monolith to Mesh
Data Mesh Part 4 Monolith to MeshData Mesh Part 4 Monolith to Mesh
Data Mesh Part 4 Monolith to Mesh
 
Logging, indicateurs et APM : le trio gagnant pour des opérations réussies
Logging, indicateurs et APM : le trio gagnant pour des opérations réussiesLogging, indicateurs et APM : le trio gagnant pour des opérations réussies
Logging, indicateurs et APM : le trio gagnant pour des opérations réussies
 
Red Hat Storage: Emerging Use Cases
Red Hat Storage: Emerging Use CasesRed Hat Storage: Emerging Use Cases
Red Hat Storage: Emerging Use Cases
 
OGF Introductory Overview - OGF 44 at EGI Conference 2015
OGF Introductory Overview - OGF 44 at EGI Conference 2015OGF Introductory Overview - OGF 44 at EGI Conference 2015
OGF Introductory Overview - OGF 44 at EGI Conference 2015
 
Cncf storage-final-filip
Cncf storage-final-filipCncf storage-final-filip
Cncf storage-final-filip
 

Kürzlich hochgeladen

How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.Curtis Poe
 
A Framework for Development in the AI Age
A Framework for Development in the AI AgeA Framework for Development in the AI Age
A Framework for Development in the AI AgeCprime
 
How to Effectively Monitor SD-WAN and SASE Environments with ThousandEyes
How to Effectively Monitor SD-WAN and SASE Environments with ThousandEyesHow to Effectively Monitor SD-WAN and SASE Environments with ThousandEyes
How to Effectively Monitor SD-WAN and SASE Environments with ThousandEyesThousandEyes
 
Why device, WIFI, and ISP insights are crucial to supporting remote Microsoft...
Why device, WIFI, and ISP insights are crucial to supporting remote Microsoft...Why device, WIFI, and ISP insights are crucial to supporting remote Microsoft...
Why device, WIFI, and ISP insights are crucial to supporting remote Microsoft...panagenda
 
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024BookNet Canada
 
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptxThe Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptxLoriGlavin3
 
Genislab builds better products and faster go-to-market with Lean project man...
Genislab builds better products and faster go-to-market with Lean project man...Genislab builds better products and faster go-to-market with Lean project man...
Genislab builds better products and faster go-to-market with Lean project man...Farhan Tariq
 
Testing tools and AI - ideas what to try with some tool examples
Testing tools and AI - ideas what to try with some tool examplesTesting tools and AI - ideas what to try with some tool examples
Testing tools and AI - ideas what to try with some tool examplesKari Kakkonen
 
[Webinar] SpiraTest - Setting New Standards in Quality Assurance
[Webinar] SpiraTest - Setting New Standards in Quality Assurance[Webinar] SpiraTest - Setting New Standards in Quality Assurance
[Webinar] SpiraTest - Setting New Standards in Quality AssuranceInflectra
 
Data governance with Unity Catalog Presentation
Data governance with Unity Catalog PresentationData governance with Unity Catalog Presentation
Data governance with Unity Catalog PresentationKnoldus Inc.
 
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptxUse of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptxLoriGlavin3
 
UiPath Community: Communication Mining from Zero to Hero
UiPath Community: Communication Mining from Zero to HeroUiPath Community: Communication Mining from Zero to Hero
UiPath Community: Communication Mining from Zero to HeroUiPathCommunity
 
From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .Alan Dix
 
Time Series Foundation Models - current state and future directions
Time Series Foundation Models - current state and future directionsTime Series Foundation Models - current state and future directions
Time Series Foundation Models - current state and future directionsNathaniel Shimoni
 
TrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data PrivacyTrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data PrivacyTrustArc
 
So einfach geht modernes Roaming fuer Notes und Nomad.pdf
So einfach geht modernes Roaming fuer Notes und Nomad.pdfSo einfach geht modernes Roaming fuer Notes und Nomad.pdf
So einfach geht modernes Roaming fuer Notes und Nomad.pdfpanagenda
 
Enhancing User Experience - Exploring the Latest Features of Tallyman Axis Lo...
Enhancing User Experience - Exploring the Latest Features of Tallyman Axis Lo...Enhancing User Experience - Exploring the Latest Features of Tallyman Axis Lo...
Enhancing User Experience - Exploring the Latest Features of Tallyman Axis Lo...Scott Andery
 
Digital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptxDigital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptxLoriGlavin3
 
The Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and ConsThe Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and ConsPixlogix Infotech
 
Manual 508 Accessibility Compliance Audit
Manual 508 Accessibility Compliance AuditManual 508 Accessibility Compliance Audit
Manual 508 Accessibility Compliance AuditSkynet Technologies
 

Kürzlich hochgeladen (20)

How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.
 
A Framework for Development in the AI Age
A Framework for Development in the AI AgeA Framework for Development in the AI Age
A Framework for Development in the AI Age
 
How to Effectively Monitor SD-WAN and SASE Environments with ThousandEyes
How to Effectively Monitor SD-WAN and SASE Environments with ThousandEyesHow to Effectively Monitor SD-WAN and SASE Environments with ThousandEyes
How to Effectively Monitor SD-WAN and SASE Environments with ThousandEyes
 
Why device, WIFI, and ISP insights are crucial to supporting remote Microsoft...
Why device, WIFI, and ISP insights are crucial to supporting remote Microsoft...Why device, WIFI, and ISP insights are crucial to supporting remote Microsoft...
Why device, WIFI, and ISP insights are crucial to supporting remote Microsoft...
 
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
 
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptxThe Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
 
Genislab builds better products and faster go-to-market with Lean project man...
Genislab builds better products and faster go-to-market with Lean project man...Genislab builds better products and faster go-to-market with Lean project man...
Genislab builds better products and faster go-to-market with Lean project man...
 
Testing tools and AI - ideas what to try with some tool examples
Testing tools and AI - ideas what to try with some tool examplesTesting tools and AI - ideas what to try with some tool examples
Testing tools and AI - ideas what to try with some tool examples
 
[Webinar] SpiraTest - Setting New Standards in Quality Assurance
[Webinar] SpiraTest - Setting New Standards in Quality Assurance[Webinar] SpiraTest - Setting New Standards in Quality Assurance
[Webinar] SpiraTest - Setting New Standards in Quality Assurance
 
Data governance with Unity Catalog Presentation
Data governance with Unity Catalog PresentationData governance with Unity Catalog Presentation
Data governance with Unity Catalog Presentation
 
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptxUse of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
 
UiPath Community: Communication Mining from Zero to Hero
UiPath Community: Communication Mining from Zero to HeroUiPath Community: Communication Mining from Zero to Hero
UiPath Community: Communication Mining from Zero to Hero
 
From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .
 
Time Series Foundation Models - current state and future directions
Time Series Foundation Models - current state and future directionsTime Series Foundation Models - current state and future directions
Time Series Foundation Models - current state and future directions
 
TrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data PrivacyTrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data Privacy
 
So einfach geht modernes Roaming fuer Notes und Nomad.pdf
So einfach geht modernes Roaming fuer Notes und Nomad.pdfSo einfach geht modernes Roaming fuer Notes und Nomad.pdf
So einfach geht modernes Roaming fuer Notes und Nomad.pdf
 
Enhancing User Experience - Exploring the Latest Features of Tallyman Axis Lo...
Enhancing User Experience - Exploring the Latest Features of Tallyman Axis Lo...Enhancing User Experience - Exploring the Latest Features of Tallyman Axis Lo...
Enhancing User Experience - Exploring the Latest Features of Tallyman Axis Lo...
 
Digital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptxDigital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptx
 
The Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and ConsThe Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and Cons
 
Manual 508 Accessibility Compliance Audit
Manual 508 Accessibility Compliance AuditManual 508 Accessibility Compliance Audit
Manual 508 Accessibility Compliance Audit
 

Manage Red Hat Footprint with Log Aggregation

  • 1. LOG AGGREGATION To better manage your Red Hat footprint Miguel Pérez Colino Strategic Design Team - ISBU 2017-05-03 @mmmmmmpc
  • 2. Agenda Managing your Red Hat footprint with Log Aggregation ● The Situation ● The Challenge ● The Solution
  • 4. Cloud Deployments They do really scale ... https://www.cncf.io/blog/2016/08/23/deploying-1000-nodes-of-openshift-on-the-cncf-cluster-part-1/ ● Higher scalability ● More workloads per physical machine (multi-tenant) ● Network and Storage also Software Defined ● Containers and Microservices providing more granularity
  • 5. Cloud Deployments Act as one single thing … … and need to be managed and operated as one Source: https://commons.wikimedia.org/wiki/File:Auklet_flock_Shumagins_1986.jpg
  • 7. Data (What) Data + Information flow in Log Aggregation ProcessIngest StoreCollect Query ViewGenerate Derived from: http://www.dataintensive.info/
  • 8. Personas (Who) That can use Log Aggregation Log Aggregation Monitoring Provides Events, Consumes Logs Cloud Ops Root Cause Analysis Developer App Analysis & Debug Security Engineer Sec Analysis, Audits User / Marketing Access to stats Service DesignerIT Manager Access to aggregated data, i.e. SLA, usage
  • 9. Personas (Motivation) That need Log Aggregation Cloud Ops (Apps) “I want to proactively know about active or potential degradation of service” Cloud Ops (OpenStack) “User reports that their VM request failed and returned error” Developer (OpenShift) “My recent commit resulted in Jenkins test failure” “Application (multi-tiered) launched from CloudForms returns error” Cloud Suite User
  • 10. Situational Awareness (Why) Or the need of it! Source: https://en.wikipedia.org/wiki/Situation_awareness
  • 12. Architecture Proposed General Architecture Real Time Analytics and Response Host Bus N N N Archive Data Store General Visualization M C M C Storage Legend M C N Message Client Normalizer C C C Collector Slide Credit: Tushar Katarki [@tkatarki]
  • 13. Implementation Introduction to EFK KibanaElasticSearchLog Source Fluentd User Interface for: ● Search ● Graph ● Dashboard Index and store data and metadata making search fast and reliable ● Parsing ● Filtering ● Enriching ● Deleting ● Output Buffering ● TCP/UDP ● HTTP ● File: Text ● Stdout: CSV, JSON, MessagePack ● syslog/journal Slide Credit: Tushar Katarki [@tkatarki]
  • 14. Current Status Being delivered and supported OpenShift Container Platform 3.5 ● Full EFK stack provided as containers OpenStack Platform 10 ● Fluentd as log collector Red Hat Virtualization ● Coming Soon! Log files Journal Fluentd Kuberentes Services Syslog Master Nodes Elastic search Kibana ... Application Nodes Log files Journal Fluentd App inside container Syslog Infra Nodes Elastic search Kibana host logs App inside container Elastic search Curator Multi-Tenant Access Diagram Credit: Tushar Katarki [@tkatarki]
  • 16. Common Data Model To ensure integration and interoperability What Is It? ● A Data Model for Logs (and other data) to identify and tag data (i.e. log fields) Why? ● Alignment/Correlation with different RH products ● Improved maintainability of Data ● Better presentation/data consumption ● Enables 3rd party ecosystem ● Facilitates deep learning analysis of data Ingestion pipeline Consumption pipeline Indexing and Storage
  • 17. Common Data Model Example ... Data extracted: ● Container name ● Pod name ● Namespace name ● Docker container ID K8S data queried: ● Pod UID ● Pod labels ● Pod host ● Namespace UID. All merged into output log in JSON Format Images Credit: Anton Sherkhonov [@peatz] CDM A → 1 B → 2 C → 3
  • 18. User Experience Prototyping and validating dashboards for users Slide Credits: Peter Portante & Vince Conzola
  • 19. Exploring different approaches Prototyping with alternative toolsets with partners Slide Credits: Luca Rosellini (Keedio)
  • 21. How are you doing it? Please, provide your feedback ... http://bit.ly/log-aggregation