SlideShare a Scribd company logo
1 of 88
Download to read offline
Architectural Caching Patterns
for Kubernetes
Rafał Leszko
@RafalLeszko
rafalleszko.com
Hazelcast
About me
● Cloud-Native Team Lead at Hazelcast
● Worked at Google and CERN
● Author of the book "Continuous Delivery
with Docker and Jenkins"
● Trainer and conference speaker
● Live in Kraków, Poland
About Hazelcast
● Distributed Company
● Open Source Software
● 140+ Employees
● Products:
○ Hazelcast IMDG
○ Hazelcast Jet
○ Hazelcast Cloud
@Hazelcast
www.hazelcast.com
● Introduction
● Caching Architectural Patterns
○ Embedded
○ Embedded Distributed
○ Client-Server
○ Cloud
○ Sidecar
○ Reverse Proxy
○ Reverse Proxy Sidecar
● Summary
Agenda
Service 1
Service 2
v1
Service 2
v2
Service 1
Service 4
v1
Service 4
v2
Service 4
v3
Ruby
Microservice World
Microservice World
Service 1
Service 2
v1
Service 2
v2
Service 1
Service 4
v1
Service 4
v2
Service 4
v3
Ruby
cache
cache
cache
cache
Microservice World
Service 1
Service 2
v1
Service 2
v2
Service 1
Service 4
v1
Service 4
v2
Service 4
v3
Ruby
cache
cache
cache
Microservice World
Service 1
Service 2
v1
Service 2
v2
Service 1
Service 4
v1
Service 4
v2
Service 4
v3
Ruby
cache
cache
cache
● Introduction ✔
● Caching Architectural Patterns
○ Embedded
○ Embedded Distributed
○ Client-Server
○ Cloud
○ Sidecar
○ Reverse Proxy
○ Reverse Proxy Sidecar
● Summary
Agenda
1. Embedded
Application
Kubernetes
Service
Cache
Application
Cache
Request
Embedded Cache
Kubernetes Pod
Kubernetes Pod
private ConcurrentHashMap<String, String> cache =
new ConcurrentHashMap<>();
private String processRequest(String request) {
if (cache.contains(request)) {
return cache.get(request);
}
String response = process(request);
cache.put(request, response);
return response;
}
Embedded Cache (Pure Java implementation)
private ConcurrentHashMap<String, String> cache =
new ConcurrentHashMap<>();
private String processRequest(String request) {
if (cache.contains(request)) {
return cache.get(request);
}
String response = process(request);
cache.put(request, response);
return response;
}
Embedded Cache (Pure Java implementation)
● No Eviction Policies
● No Max Size Limit
(OutOfMemoryError)
● No Statistics
● No built-in Cache Loaders
● No Expiration Time
● No Notification Mechanism
Java Collection is not a Cache!
CacheBuilder.newBuilder()
.initialCapacity(300)
.expireAfterAccess(Duration.ofMinutes(10))
.maximumSize(1000)
.build();
Embedded Cache (Java libraries)
Embedded Cache (Java libraries)
CacheBuilder.newBuilder()
.initialCapacity(300)
.expireAfterAccess(Duration.ofMinutes(10))
.maximumSize(1000)
.build();
Caching Application Layer
@Service
public class BookService {
@Cacheable("books")
public String getBookNameByIsbn(String isbn) {
return findBookInSlowSource(isbn);
}
}
Caching Application Layer
@Service
public class BookService {
@Cacheable("books")
public String getBookNameByIsbn(String isbn) {
return findBookInSlowSource(isbn);
}
}
Be Careful, Spring uses ConcurrentHashMap by default!
Embedded Cache
Application
Kubernetes
Service
Cache
Application
Cache
Request
Kubernetes Pod
Kubernetes Pod
1*. Embedded Distributed
Application
Application
Kubernetes
Service
Cache
Cache
Request
Hazelcast
Cluster
Embedded Distributed Cache
Kubernetes Pod
Kubernetes Pod
@Configuration
public class HazelcastConfiguration {
@Bean
CacheManager cacheManager() {
return new HazelcastCacheManager(
Hazelcast.newHazelcastInstance());
}
}
Embedded Distributed Cache (Spring with Hazelcast)
Hazelcast Discovery Plugins
Hazelcast Discovery Plugins
Embedded Distributed Cache
Application
Application
Kubernetes
Service
Cache
Cache
Request
Hazelcast
Cluster
Kubernetes Pod
Kubernetes Pod
Embedded Cache
Pros Cons
● Simple configuration /
deployment
● Low-latency data access
● No separate Ops Team
needed
● Not flexible management
(scaling, backup)
● Limited to JVM-based
applications
● Data collocated with
applications
● Introduction ✔
● Caching Architectural Patterns
○ Embedded ✔
○ Embedded Distributed ✔
○ Client-Server
○ Cloud
○ Sidecar
○ Reverse Proxy
○ Reverse Proxy Sidecar
● Summary
Agenda
2. Client-Server
Application
Kubernetes
Service
Application
Request
Cache Server
StatefulSet
Client-Server Cache
Kubernetes Pod
Kubernetes Pod
Client-Server Cache
Application
Kubernetes
Service
Application
Request
Cache Server
StatefulSet
Kubernetes Pod
Kubernetes Pod
Client-Server Cache
Separate Management:
● backups
● (auto) scaling
● security
Ops Team
Application
Kubernetes
Service
Application
Request
Cache Server
StatefulSet
Kubernetes Pod
Kubernetes Pod
Client-Server Cache
Application
Kubernetes
Service
Application
Request
Cache Server
StatefulSet
Kubernetes Pod
Kubernetes Pod
Client-Server Cache
Application
Kubernetes
Service
Application
Request
Cache Server
StatefulSet
Kubernetes Pod
Kubernetes Pod
Client-Server Cache
Client-Server Cache
Client-Server Cache
Starting Hazelcast Cache Server
$ helm install hazelcast/hazelcast
Client-Server Cache
Hazelcast Client
@Configuration
public class HazelcastClientConfiguration {
@Bean
CacheManager cacheManager() {
ClientConfig clientConfig = new ClientConfig();
clientConfig.getNetworkConfig().getKubernetesConfig()
.setEnabled(true);
return new HazelcastCacheManager(HazelcastClient
.newHazelcastClient(clientConfig));
}
}
Starting Hazelcast Cache Server
$ helm install hazelcast/hazelcast
Client-Server Cache
Separate Management:
● backups
● (auto) scaling
● security
Ops Team
Application
Kubernetes
Service
Application
Request
Cache Server
StatefulSet
Kubernetes Pod
Kubernetes Pod
2*. Cloud
Application
Kubernetes
Service
Application
Request
Cloud (Cache as a Service)
Kubernetes Pod
Kubernetes Pod
Cloud (Cache as a Service)
Management:
● backups
● (auto) scaling
● security
Ops Team
Application
Kubernetes
Service
Application
Request
Kubernetes Pod
Kubernetes Pod
Cloud (Cache as a Service)
Management:
● backups
● (auto) scaling
● security
Ops Team
Application
Kubernetes
Service
Application
Request
Kubernetes Pod
Kubernetes Pod
Cloud (Cache as a Service)
Application
Kubernetes
Service
Application
Request
Kubernetes Pod
Kubernetes Pod
@Configuration
public class HazelcastCloudConfiguration {
@Bean
CacheManager cacheManager() {
ClientConfig clientConfig = new ClientConfig();
clientConfig.getNetworkConfig().getCloudConfig()
.setEnabled(true)
.setDiscoveryToken("KSXFDTi5HXPJGR0wRAjLgKe45tvEEhd");
clientConfig.setGroupConfig(
new GroupConfig("test-cluster", "b2f984b5dd3314"));
return new HazelcastCacheManager(
HazelcastClient.newHazelcastClient(clientConfig));
}
}
Cloud (Cache as a Service)
Client-Server (Cloud) Cache
Pros
● Data separate from
applications
● Separate management
(scaling, backup)
● Programming-language
agnostic
Cons
● Separate Ops effort
● Higher latency
● Server network requires
adjustment (same region,
same VPC)
● Introduction ✔
● Caching Architectural Patterns
○ Embedded ✔
○ Embedded Distributed ✔
○ Client-Server ✔
○ Cloud ✔
○ Sidecar
○ Reverse Proxy
○ Reverse Proxy Sidecar
● Summary
Agenda
3. Sidecar
Kubernetes Service
Request
Hazelcast
Cluster
Kubernetes POD
Application Container
Cache Container
Application Container
Cache Container
Kubernetes POD
Sidecar Cache
Sidecar Cache
Similar to Embedded:
● the same physical machine
● the same resource pool
● scales up and down together
● no discovery needed (always localhost)
Similar to Client-Server:
● different programming language
● uses cache client to connect
● clear isolation between app and cache
@Configuration
public class HazelcastSidecarConfiguration {
@Bean
CacheManager cacheManager() {
ClientConfig clientConfig = new ClientConfig();
clientConfig.getNetworkConfig()
.addAddress("localhost:5701");
return new HazelcastCacheManager(HazelcastClient
.newHazelcastClient(clientConfig));
}
}
Sidecar Cache
Sidecar Cache
apiVersion: apps/v1
kind: Deployment
...
spec:
template:
spec:
containers:
- name: application
image: leszko/application
- name: hazelcast
image: hazelcast/hazelcast
Sidecar Cache
Pros Cons
● Simple configuration
● Programming-language
agnostic
● Low latency
● Some isolation of data and
applications
● Not flexible management
(scaling, backup)
● Data collocated with
application PODs
● Introduction ✔
● Caching Architectural Patterns
○ Embedded ✔
○ Embedded Distributed ✔
○ Client-Server ✔
○ Cloud ✔
○ Sidecar ✔
○ Reverse Proxy
○ Reverse Proxy Sidecar
● Summary
Agenda
4. Reverse Proxy
Application
Kubernetes
Service
Cache
Application
Request
Reverse Proxy Cache
Kubernetes Pod
Kubernetes Pod
Reverse Proxy Cache
http {
...
proxy_cache_path /data/nginx/cache
keys_zone=one:10m;
...
}
● Only for HTTP
● Not distributed
● No High Availability
● Data stored on the disk
NGINX Reverse Proxy Cache Issues
4*. Reverse Proxy Sidecar
Kubernetes Service
Request
Hazelcast
Cluster
Kubernetes POD
Application Container
Reverse Proxy Cache
Container
Application Container
Reverse Proxy Cache
Container
Kubernetes POD
Reverse Proxy Sidecar Cache
Good
Reverse Proxy Sidecar Cache
Service 1
Service 2
v1
Service 2
v2
Service 1
Service 4
v1
Service 4
v2
Service 4
v3
Ruby
Reverse Proxy Sidecar Cache
Service 1
Service 2
v1
Service 2
v2
Service 1
Service 4
v1
Service 4
v2
Service 4
v3
Ruby
Reverse Proxy Sidecar Cache
apiVersion: apps/v1
kind: Deployment
...
spec:
template:
spec:
initContainers:
- name: init-networking
image: leszko/init-networking
containers:
- name: caching-proxy
image: leszko/caching-proxy
- name: application
image: leszko/application
Reverse Proxy Sidecar Cache
Service 1
Service 2
v1
Service 2
v2
Service 1
Service 4
v1
Service 4
v2
Service 4
v3
Ruby
Reverse Proxy Sidecar Cache (Istio)
Reverse Proxy Sidecar Cache (Istio)
Bad
Reverse Proxy Cache
@CacheEvict(value = "someValue", allEntries = true)
public void evictAllCacheValues() {}
Application Cache:
Reverse Proxy Cache
@CacheEvict(value = "someValue", allEntries = true)
public void evictAllCacheValues() {}
Application Cache:
Proxy Cache:
http {
...
location / {
add_header Cache-Control public;
expires 86400;
etag on;
}
}
Reverse Proxy (Sidecar) Cache
Pros Cons
● Configuration-based (no
need to change
applications)
● Programming-language
agnostic
● Consistent with containers
and microservice world
● Difficult cache invalidation
● No mature solutions yet
● Protocol-based (e.g. works
only with HTTP)
● Introduction ✔
● Caching Architectural Patterns
○ Embedded ✔
○ Embedded Distributed ✔
○ Client-Server ✔
○ Cloud ✔
○ Sidecar ✔
○ Reverse Proxy ✔
○ Reverse Proxy Sidecar ✔
● Summary
Agenda
Summary
application-aware?
application-aware?
early adopter?
no
application-aware?
Reverse Proxy
no
no
early adopter?
application-aware?
Reverse Proxy
Reverse Proxy
Sidecar
no
yes no
early adopter?
application-aware?
Reverse Proxy
Reverse Proxy
Sidecar
lot of data?
security restrictions?
yes no
yes no
early adopter?
application-aware?
Reverse Proxy
Reverse Proxy
Sidecar
lot of data?
security restrictions?
language-agnostic?
yes no
yes no
no
early adopter?
application-aware?
Reverse Proxy
Reverse Proxy
Sidecar
lot of data?
security restrictions?
language-agnostic?
Embedded
(Distributed)
yes no
yes no
no
no
early adopter?
application-aware?
Reverse Proxy
Reverse Proxy
Sidecar
lot of data?
security restrictions?
language-agnostic?
Embedded
(Distributed)
Sidecar
yes no
yes
yes no
no
no
early adopter?
application-aware?
Reverse Proxy
Reverse Proxy
Sidecar
lot of data?
security restrictions?
language-agnostic?
Embedded
(Distributed)
Sidecar
cloud?
yes no
yes
yes
yes no
no
no
early adopter?
application-aware?
Reverse Proxy
Reverse Proxy
Sidecar
lot of data?
security restrictions?
language-agnostic?
Embedded
(Distributed)
Sidecar
cloud?
Client-Server
yes no
yes
yes
yes no
no
no
no
early adopter?
application-aware?
Reverse Proxy
Reverse Proxy
Sidecar
lot of data?
security restrictions?
language-agnostic?
Embedded
(Distributed)
Sidecar
cloud?
Client-Server
Cloud
yes no
yes
yes yes
yes no
no
no
no
early adopter?
Resources
● Code for this talk:
https://github.com/leszko/caching-patterns
● Hazelcast Sidecar Container Pattern:
https://hazelcast.com/blog/hazelcast-sidecar-container-pattern/
● Hazelcast Reverse Proxy Sidecar Caching Prototype:
https://github.com/leszko/caching-injector
● NGINX HTTP Reverse Proxy Caching:
https://www.nginx.com/resources/videos/best-practices-for-caching/
Thank You!
Rafał Leszko
@RafalLeszko

More Related Content

What's hot

Using MLOps to Bring ML to Production/The Promise of MLOps
Using MLOps to Bring ML to Production/The Promise of MLOpsUsing MLOps to Bring ML to Production/The Promise of MLOps
Using MLOps to Bring ML to Production/The Promise of MLOps
Weaveworks
 

What's hot (17)

Tag based recommender system
Tag based recommender systemTag based recommender system
Tag based recommender system
 
CG 論文講読会 2013/2/12 "A reconstruction filter for plausible motion blur"
CG 論文講読会 2013/2/12 "A reconstruction filter for plausible motion blur"CG 論文講読会 2013/2/12 "A reconstruction filter for plausible motion blur"
CG 論文講読会 2013/2/12 "A reconstruction filter for plausible motion blur"
 
Recent Trends in Personalization at Netflix
Recent Trends in Personalization at NetflixRecent Trends in Personalization at Netflix
Recent Trends in Personalization at Netflix
 
Past, Present and Future Challenges of Global Illumination in Games
Past, Present and Future Challenges of Global Illumination in GamesPast, Present and Future Challenges of Global Illumination in Games
Past, Present and Future Challenges of Global Illumination in Games
 
Scene Graphs & Component Based Game Engines
Scene Graphs & Component Based Game EnginesScene Graphs & Component Based Game Engines
Scene Graphs & Component Based Game Engines
 
Visibility Optimization for Games
Visibility Optimization for GamesVisibility Optimization for Games
Visibility Optimization for Games
 
smallpt: Global Illumination in 99 lines of C++
smallpt:  Global Illumination in 99 lines of C++smallpt:  Global Illumination in 99 lines of C++
smallpt: Global Illumination in 99 lines of C++
 
Siggraph2016 - The Devil is in the Details: idTech 666
Siggraph2016 - The Devil is in the Details: idTech 666Siggraph2016 - The Devil is in the Details: idTech 666
Siggraph2016 - The Devil is in the Details: idTech 666
 
Graph database Use Cases
Graph database Use CasesGraph database Use Cases
Graph database Use Cases
 
[Unity Forum 2019] Mobile Graphics Optimization Guides
[Unity Forum 2019] Mobile Graphics Optimization Guides[Unity Forum 2019] Mobile Graphics Optimization Guides
[Unity Forum 2019] Mobile Graphics Optimization Guides
 
Using MLOps to Bring ML to Production/The Promise of MLOps
Using MLOps to Bring ML to Production/The Promise of MLOpsUsing MLOps to Bring ML to Production/The Promise of MLOps
Using MLOps to Bring ML to Production/The Promise of MLOps
 
Design Principles
Design PrinciplesDesign Principles
Design Principles
 
Introduction to Neo4j and .Net
Introduction to Neo4j and .NetIntroduction to Neo4j and .Net
Introduction to Neo4j and .Net
 
Deep Learning for Personalized Search and Recommender Systems
Deep Learning for Personalized Search and Recommender SystemsDeep Learning for Personalized Search and Recommender Systems
Deep Learning for Personalized Search and Recommender Systems
 
Past present and future of Recommender Systems: an Industry Perspective
Past present and future of Recommender Systems: an Industry PerspectivePast present and future of Recommender Systems: an Industry Perspective
Past present and future of Recommender Systems: an Industry Perspective
 
Making connections matter: 2 use cases on graphs & analytics solutions
Making connections matter: 2 use cases on graphs & analytics solutionsMaking connections matter: 2 use cases on graphs & analytics solutions
Making connections matter: 2 use cases on graphs & analytics solutions
 
FOSS4G Firenze 2022 참가기
FOSS4G Firenze 2022 참가기FOSS4G Firenze 2022 참가기
FOSS4G Firenze 2022 참가기
 

Similar to Architectural caching patterns for kubernetes

Where is my cache? Architectural patterns for caching microservices by example
Where is my cache? Architectural patterns for caching microservices by exampleWhere is my cache? Architectural patterns for caching microservices by example
Where is my cache? Architectural patterns for caching microservices by example
Rafał Leszko
 
Deep dive into OpenStack storage, Sean Cohen, Red Hat
Deep dive into OpenStack storage, Sean Cohen, Red HatDeep dive into OpenStack storage, Sean Cohen, Red Hat
Deep dive into OpenStack storage, Sean Cohen, Red Hat
Sean Cohen
 

Similar to Architectural caching patterns for kubernetes (20)

Architectural caching patterns for kubernetes
Architectural caching patterns for kubernetesArchitectural caching patterns for kubernetes
Architectural caching patterns for kubernetes
 
Where is my cache? Architectural patterns for caching microservices by example
Where is my cache? Architectural patterns for caching microservices by exampleWhere is my cache? Architectural patterns for caching microservices by example
Where is my cache? Architectural patterns for caching microservices by example
 
Where is my cache? Architectural patterns for caching microservices by example
Where is my cache? Architectural patterns for caching microservices by exampleWhere is my cache? Architectural patterns for caching microservices by example
Where is my cache? Architectural patterns for caching microservices by example
 
Where is my cache? Architectural patterns for caching microservices by example
Where is my cache? Architectural patterns for caching microservices by exampleWhere is my cache? Architectural patterns for caching microservices by example
Where is my cache? Architectural patterns for caching microservices by example
 
Where is my cache architectural patterns for caching microservices by example
Where is my cache architectural patterns for caching microservices by exampleWhere is my cache architectural patterns for caching microservices by example
Where is my cache architectural patterns for caching microservices by example
 
Architectural patterns for caching microservices
Architectural patterns for caching microservicesArchitectural patterns for caching microservices
Architectural patterns for caching microservices
 
[jLove 2020] Where is my cache architectural patterns for caching microservi...
[jLove 2020] Where is my cache  architectural patterns for caching microservi...[jLove 2020] Where is my cache  architectural patterns for caching microservi...
[jLove 2020] Where is my cache architectural patterns for caching microservi...
 
Where is my cache architectural patterns for caching microservices by example
Where is my cache  architectural patterns for caching microservices by exampleWhere is my cache  architectural patterns for caching microservices by example
Where is my cache architectural patterns for caching microservices by example
 
[DevopsDays India 2019] Where is my cache? Architectural patterns for caching...
[DevopsDays India 2019] Where is my cache? Architectural patterns for caching...[DevopsDays India 2019] Where is my cache? Architectural patterns for caching...
[DevopsDays India 2019] Where is my cache? Architectural patterns for caching...
 
Architectural patterns for high performance microservices in kubernetes
Architectural patterns for high performance microservices in kubernetesArchitectural patterns for high performance microservices in kubernetes
Architectural patterns for high performance microservices in kubernetes
 
Where is my cache architectural patterns for caching microservices by example
Where is my cache architectural patterns for caching microservices by exampleWhere is my cache architectural patterns for caching microservices by example
Where is my cache architectural patterns for caching microservices by example
 
Second Skin: Real-Time Retheming a Legacy Web Application with Diazo in the C...
Second Skin: Real-Time Retheming a Legacy Web Application with Diazo in the C...Second Skin: Real-Time Retheming a Legacy Web Application with Diazo in the C...
Second Skin: Real-Time Retheming a Legacy Web Application with Diazo in the C...
 
Deep dive into OpenStack storage, Sean Cohen, Red Hat
Deep dive into OpenStack storage, Sean Cohen, Red HatDeep dive into OpenStack storage, Sean Cohen, Red Hat
Deep dive into OpenStack storage, Sean Cohen, Red Hat
 
Deep Dive into Openstack Storage, Sean Cohen, Red Hat
Deep Dive into Openstack Storage, Sean Cohen, Red HatDeep Dive into Openstack Storage, Sean Cohen, Red Hat
Deep Dive into Openstack Storage, Sean Cohen, Red Hat
 
Making Service Deployments to AWS a breeze with Nova
Making Service Deployments to AWS a breeze with NovaMaking Service Deployments to AWS a breeze with Nova
Making Service Deployments to AWS a breeze with Nova
 
What’s new in cas 4.2
What’s new in cas 4.2 What’s new in cas 4.2
What’s new in cas 4.2
 
Red Hat Container Development Kit
Red Hat Container Development KitRed Hat Container Development Kit
Red Hat Container Development Kit
 
GCP - Continuous Integration and Delivery into Kubernetes with GitHub, Travis...
GCP - Continuous Integration and Delivery into Kubernetes with GitHub, Travis...GCP - Continuous Integration and Delivery into Kubernetes with GitHub, Travis...
GCP - Continuous Integration and Delivery into Kubernetes with GitHub, Travis...
 
Como creamos QuestDB Cloud, un SaaS basado en Kubernetes alrededor de QuestDB...
Como creamos QuestDB Cloud, un SaaS basado en Kubernetes alrededor de QuestDB...Como creamos QuestDB Cloud, un SaaS basado en Kubernetes alrededor de QuestDB...
Como creamos QuestDB Cloud, un SaaS basado en Kubernetes alrededor de QuestDB...
 
Deploying windows containers with kubernetes
Deploying windows containers with kubernetesDeploying windows containers with kubernetes
Deploying windows containers with kubernetes
 

More from Rafał Leszko

More from Rafał Leszko (15)

Build Your Kubernetes Operator with the Right Tool!
Build Your Kubernetes Operator with the Right Tool!Build Your Kubernetes Operator with the Right Tool!
Build Your Kubernetes Operator with the Right Tool!
 
Mutation Testing with PIT
Mutation Testing with PITMutation Testing with PIT
Mutation Testing with PIT
 
Distributed Locking in Kubernetes
Distributed Locking in KubernetesDistributed Locking in Kubernetes
Distributed Locking in Kubernetes
 
Mutation testing with PIT
Mutation testing with PITMutation testing with PIT
Mutation testing with PIT
 
Build your operator with the right tool
Build your operator with the right toolBuild your operator with the right tool
Build your operator with the right tool
 
5 levels of high availability from multi instance to hybrid cloud
5 levels of high availability  from multi instance to hybrid cloud5 levels of high availability  from multi instance to hybrid cloud
5 levels of high availability from multi instance to hybrid cloud
 
5 Levels of High Availability: From Multi-instance to Hybrid Cloud
5 Levels of High Availability: From Multi-instance to Hybrid Cloud5 Levels of High Availability: From Multi-instance to Hybrid Cloud
5 Levels of High Availability: From Multi-instance to Hybrid Cloud
 
Stream Processing in the Cloud - Athens Kubernetes Meetup 16.07.2019
Stream Processing in the Cloud - Athens Kubernetes Meetup 16.07.2019Stream Processing in the Cloud - Athens Kubernetes Meetup 16.07.2019
Stream Processing in the Cloud - Athens Kubernetes Meetup 16.07.2019
 
Stream Processing with Hazelcast Jet - Voxxed Days Thessaloniki 19.11.2018
Stream Processing with Hazelcast Jet - Voxxed Days Thessaloniki 19.11.2018Stream Processing with Hazelcast Jet - Voxxed Days Thessaloniki 19.11.2018
Stream Processing with Hazelcast Jet - Voxxed Days Thessaloniki 19.11.2018
 
Mutation Testing - Voxxed Days Cluj-Napoca 2017
Mutation Testing - Voxxed Days Cluj-Napoca 2017Mutation Testing - Voxxed Days Cluj-Napoca 2017
Mutation Testing - Voxxed Days Cluj-Napoca 2017
 
Continuous Delivery - Voxxed Days Cluj-Napoca 2017
Continuous Delivery - Voxxed Days Cluj-Napoca 2017Continuous Delivery - Voxxed Days Cluj-Napoca 2017
Continuous Delivery - Voxxed Days Cluj-Napoca 2017
 
Continuous Delivery - Voxxed Days Bucharest 2017
Continuous Delivery - Voxxed Days Bucharest 2017Continuous Delivery - Voxxed Days Bucharest 2017
Continuous Delivery - Voxxed Days Bucharest 2017
 
Mutation Testing - Voxxed Days Bucharest 10.03.2017
Mutation Testing - Voxxed Days Bucharest 10.03.2017Mutation Testing - Voxxed Days Bucharest 10.03.2017
Mutation Testing - Voxxed Days Bucharest 10.03.2017
 
Continuous Delivery - Devoxx Morocco 2016
Continuous Delivery - Devoxx Morocco 2016Continuous Delivery - Devoxx Morocco 2016
Continuous Delivery - Devoxx Morocco 2016
 
Continuous Delivery - Voxxed Days Thessaloniki 21.10.2016
Continuous Delivery - Voxxed Days Thessaloniki 21.10.2016Continuous Delivery - Voxxed Days Thessaloniki 21.10.2016
Continuous Delivery - Voxxed Days Thessaloniki 21.10.2016
 

Recently uploaded

Abortion Pills For Sale WhatsApp[[+27737758557]] In Birch Acres, Abortion Pil...
Abortion Pills For Sale WhatsApp[[+27737758557]] In Birch Acres, Abortion Pil...Abortion Pills For Sale WhatsApp[[+27737758557]] In Birch Acres, Abortion Pil...
Abortion Pills For Sale WhatsApp[[+27737758557]] In Birch Acres, Abortion Pil...
drm1699
 

Recently uploaded (20)

Community is Just as Important as Code by Andrea Goulet
Community is Just as Important as Code by Andrea GouletCommunity is Just as Important as Code by Andrea Goulet
Community is Just as Important as Code by Andrea Goulet
 
Evolving Data Governance for the Real-time Streaming and AI Era
Evolving Data Governance for the Real-time Streaming and AI EraEvolving Data Governance for the Real-time Streaming and AI Era
Evolving Data Governance for the Real-time Streaming and AI Era
 
Workshop: Enabling GenAI Breakthroughs with Knowledge Graphs - GraphSummit Milan
Workshop: Enabling GenAI Breakthroughs with Knowledge Graphs - GraphSummit MilanWorkshop: Enabling GenAI Breakthroughs with Knowledge Graphs - GraphSummit Milan
Workshop: Enabling GenAI Breakthroughs with Knowledge Graphs - GraphSummit Milan
 
Microsoft365_Dev_Security_2024_05_16.pdf
Microsoft365_Dev_Security_2024_05_16.pdfMicrosoft365_Dev_Security_2024_05_16.pdf
Microsoft365_Dev_Security_2024_05_16.pdf
 
Your Ultimate Web Studio for Streaming Anywhere | Evmux
Your Ultimate Web Studio for Streaming Anywhere | EvmuxYour Ultimate Web Studio for Streaming Anywhere | Evmux
Your Ultimate Web Studio for Streaming Anywhere | Evmux
 
Abortion Pill Prices Mthatha (@](+27832195400*)[ 🏥 Women's Abortion Clinic In...
Abortion Pill Prices Mthatha (@](+27832195400*)[ 🏥 Women's Abortion Clinic In...Abortion Pill Prices Mthatha (@](+27832195400*)[ 🏥 Women's Abortion Clinic In...
Abortion Pill Prices Mthatha (@](+27832195400*)[ 🏥 Women's Abortion Clinic In...
 
Abortion Pill Prices Jane Furse ](+27832195400*)[ 🏥 Women's Abortion Clinic i...
Abortion Pill Prices Jane Furse ](+27832195400*)[ 🏥 Women's Abortion Clinic i...Abortion Pill Prices Jane Furse ](+27832195400*)[ 🏥 Women's Abortion Clinic i...
Abortion Pill Prices Jane Furse ](+27832195400*)[ 🏥 Women's Abortion Clinic i...
 
Spring into AI presented by Dan Vega 5/14
Spring into AI presented by Dan Vega 5/14Spring into AI presented by Dan Vega 5/14
Spring into AI presented by Dan Vega 5/14
 
Automate your OpenSIPS config tests - OpenSIPS Summit 2024
Automate your OpenSIPS config tests - OpenSIPS Summit 2024Automate your OpenSIPS config tests - OpenSIPS Summit 2024
Automate your OpenSIPS config tests - OpenSIPS Summit 2024
 
Navigation in flutter – how to add stack, tab, and drawer navigators to your ...
Navigation in flutter – how to add stack, tab, and drawer navigators to your ...Navigation in flutter – how to add stack, tab, and drawer navigators to your ...
Navigation in flutter – how to add stack, tab, and drawer navigators to your ...
 
GraphSummit Milan - Visione e roadmap del prodotto Neo4j
GraphSummit Milan - Visione e roadmap del prodotto Neo4jGraphSummit Milan - Visione e roadmap del prodotto Neo4j
GraphSummit Milan - Visione e roadmap del prodotto Neo4j
 
architecting-ai-in-the-enterprise-apis-and-applications.pdf
architecting-ai-in-the-enterprise-apis-and-applications.pdfarchitecting-ai-in-the-enterprise-apis-and-applications.pdf
architecting-ai-in-the-enterprise-apis-and-applications.pdf
 
Abortion Pills For Sale WhatsApp[[+27737758557]] In Birch Acres, Abortion Pil...
Abortion Pills For Sale WhatsApp[[+27737758557]] In Birch Acres, Abortion Pil...Abortion Pills For Sale WhatsApp[[+27737758557]] In Birch Acres, Abortion Pil...
Abortion Pills For Sale WhatsApp[[+27737758557]] In Birch Acres, Abortion Pil...
 
From Knowledge Graphs via Lego Bricks to scientific conversations.pptx
From Knowledge Graphs via Lego Bricks to scientific conversations.pptxFrom Knowledge Graphs via Lego Bricks to scientific conversations.pptx
From Knowledge Graphs via Lego Bricks to scientific conversations.pptx
 
OpenChain Webinar: AboutCode and Beyond - End-to-End SCA
OpenChain Webinar: AboutCode and Beyond - End-to-End SCAOpenChain Webinar: AboutCode and Beyond - End-to-End SCA
OpenChain Webinar: AboutCode and Beyond - End-to-End SCA
 
GraphSummit Milan - Neo4j: The Art of the Possible with Graph
GraphSummit Milan - Neo4j: The Art of the Possible with GraphGraphSummit Milan - Neo4j: The Art of the Possible with Graph
GraphSummit Milan - Neo4j: The Art of the Possible with Graph
 
Lessons Learned from Building a Serverless Notifications System.pdf
Lessons Learned from Building a Serverless Notifications System.pdfLessons Learned from Building a Serverless Notifications System.pdf
Lessons Learned from Building a Serverless Notifications System.pdf
 
Encryption Recap: A Refresher on Key Concepts
Encryption Recap: A Refresher on Key ConceptsEncryption Recap: A Refresher on Key Concepts
Encryption Recap: A Refresher on Key Concepts
 
BusinessGPT - Security and Governance for Generative AI
BusinessGPT  - Security and Governance for Generative AIBusinessGPT  - Security and Governance for Generative AI
BusinessGPT - Security and Governance for Generative AI
 
Incident handling is a clearly defined set of procedures to manage and respon...
Incident handling is a clearly defined set of procedures to manage and respon...Incident handling is a clearly defined set of procedures to manage and respon...
Incident handling is a clearly defined set of procedures to manage and respon...
 

Architectural caching patterns for kubernetes