Speaker: Andrew Morgan
Organizations are building their applications around microservice architectures because of the flexibility, speed of delivery, and maintainability they deliver. Want to try out MongoDB on your laptop? Execute a single command and you have a lightweight, self-contained sandbox; another command removes all trace when you're done. Replicate your complete application for your development, test, operations, and support teams. This session introduces you to technologies such as Docker, Kubernetes, and Kafka, which are driving the microservices revolution. Learn about containers and orchestration, and most importantly, how to exploit them for stateful services such as MongoDB.
MongoDB .local Paris 2020: Les bonnes pratiques pour sécuriser MongoDB
Powering Microservices with Docker, Kubernetes, Kafka, and MongoDB
1. # M D B l o c a l
Andrew Morgan
Powering Microservices with Docker,
Kubernetes, Kafka, & MongoDB
MongoDB
London
@andrewmorgan
2. # M D B l o c a l
Agenda
Microservic
es
What, Why,
When
MongoDB
Why
Containers
Docker
Messaging
Kafka
Orchestrati
on
Kubernetes
Deploying
MongoDB
Options, How
1 2 3 4 5 6
4. # M D B l o c a l
Why Use Microservices? (tl;dr WebScale)
Speed Change Maintain Scale Empower
Build MVP quickly Rapid iterations Simple components Product Team == Component
React to market Isolated impact Team Committees
5. # M D B l o c a l
Why Use Microservices? (tl;dr WebScale)
Speed Change Maintain Scale Empower
Build MVP quickly Rapid iterations Simple components Product Team == Component
React to market Isolated impact Team Committees
6. # M D B l o c a l
Why Use Microservices? (tl;dr WebScale)
Speed Change Maintain Scale Empower
Build MVP quickly Rapid iterations Simple components Product Team == Component
React to market Isolated impact Team Committees
7. # M D B l o c a l
Why Use Microservices? (tl;dr WebScale)
Speed Change Maintain Scale Empower
Build MVP quickly Rapid iterations Simple components Product Team == Component
React to market Isolated impact Team Committees
8. # M D B l o c a l
Why Use Microservices? (tl;dr WebScale)
Speed Change Maintain Scale Empower
Build MVP quickly Rapid iterations Simple components Product Team == Component
React to market Isolated impact Team Committees
9. # M D B l o c a l
Why Use Microservices? (tl;dr WebScale)
Speed Change Maintain Scale Empower
Build MVP quickly Rapid iterations Simple components Product Team == Component
React to market Isolated impact Team Committees
26. # M D B l o c a l
Software containers
• 1 image -> Many containers
• Laptop, DC, cloud
• Dev, QA, production, support
• Efficient
• Isolation
• Constraints
Containers – Powering Microservices
27. # M D B l o c a l
• Simple to use
• 100K+ images on Docker
Hub
• Build images from images
• Platforms
• Linux, OS X, Windows
• Laptop, VM, Cloud,…
• Cloud services
Docker
28. # M D B l o c a l
docker run -d mongo
Run MongoDB
29. # M D B l o c a l
FROM debian:jessie-slim
RUN groupadd -r mongodb && useradd -r -g mongodb mongodb
...
ARG MONGO_PACKAGE=mongodb-org
ENV MONGO_VERSION 3.4.9
...
RUN echo "deb http://$MONGO_REPO/apt/debian jessie/${MONGO_PACKAGE%-unstable}/$MONGO_MAJOR main" | tee "/etc/apt/sources.list.d/${MONGO_PACKAGE%-unstable}.list”
RUN set -x
&& apt-get update
&& apt-get install -y ${MONGO_PACKAGE}=$MONGO_VERSION
${MONGO_PACKAGE}-server=$MONGO_VERSION
${MONGO_PACKAGE}-shell=$MONGO_VERSION
${MONGO_PACKAGE}-mongos=$MONGO_VERSION
${MONGO_PACKAGE}-tools=$MONGO_VERSION
&& rm -rf /var/lib/apt/lists/*
&& rm -rf /var/lib/mongodb
&& mv /etc/mongod.conf /etc/mongod.conf.orig
RUN mkdir -p /data/db /data/configdb && chown -R mongodb:mongodb /data/db
/data/configdb
VOLUME /data/db /data/configdb
COPY docker-entrypoint.sh /usr/local/bin/
ENTRYPOINT ["docker-entrypoint.sh"]
EXPOSE 27017
CMD ["mongod"]
https://github.com/docker-library/mongo
30. # M D B l o c a l
FROM debian:jessie-slim
RUN groupadd -r mongodb && useradd -r -g mongodb mongodb
...
ARG MONGO_PACKAGE=mongodb-org
ENV MONGO_VERSION 3.4.9
...
RUN echo "deb http://$MONGO_REPO/apt/debian jessie/${MONGO_PACKAGE%-unstable}/$MONGO_MAJOR main" | tee "/etc/apt/sources.list.d/${MONGO_PACKAGE%-unstable}.list”
RUN set -x
&& apt-get update
&& apt-get install -y ${MONGO_PACKAGE}=$MONGO_VERSION
${MONGO_PACKAGE}-server=$MONGO_VERSION
${MONGO_PACKAGE}-shell=$MONGO_VERSION
${MONGO_PACKAGE}-mongos=$MONGO_VERSION
${MONGO_PACKAGE}-tools=$MONGO_VERSION
&& rm -rf /var/lib/apt/lists/*
&& rm -rf /var/lib/mongodb
&& mv /etc/mongod.conf /etc/mongod.conf.orig
RUN mkdir -p /data/db /data/configdb && chown -R mongodb:mongodb /data/db
/data/configdb
VOLUME /data/db /data/configdb
COPY docker-entrypoint.sh /usr/local/bin/
ENTRYPOINT ["docker-entrypoint.sh"]
EXPOSE 27017
CMD ["mongod"]
https://github.com/docker-library/mongo
31. # M D B l o c a l
FROM debian:jessie-slim
RUN groupadd -r mongodb && useradd -r -g mongodb
mongodb
...
ARG MONGO_PACKAGE=mongodb-org
ENV MONGO_VERSION 3.4.9
...
RUN echo "deb http://$MONGO_REPO/apt/debian jessie/${MONGO_PACKAGE%-unstable}/$MONGO_MAJOR main" | tee "/etc/apt/sources.list.d/${MONGO_PACKAGE%-unstable}.list”
RUN set -x
&& apt-get update
&& apt-get install -y ${MONGO_PACKAGE}=$MONGO_VERSION
${MONGO_PACKAGE}-server=$MONGO_VERSION
${MONGO_PACKAGE}-shell=$MONGO_VERSION
${MONGO_PACKAGE}-mongos=$MONGO_VERSION
${MONGO_PACKAGE}-tools=$MONGO_VERSION
&& rm -rf /var/lib/apt/lists/*
&& rm -rf /var/lib/mongodb
&& mv /etc/mongod.conf /etc/mongod.conf.orig
RUN mkdir -p /data/db /data/configdb && chown -R mongodb:mongodb /data/db
/data/configdb
VOLUME /data/db /data/configdb
COPY docker-entrypoint.sh /usr/local/bin/
ENTRYPOINT ["docker-entrypoint.sh"]
EXPOSE 27017
CMD ["mongod"]
https://github.com/docker-library/mongo
32. # M D B l o c a l
FROM debian:jessie-slim
RUN groupadd -r mongodb && useradd -r -g mongodb mongodb
...
ARG MONGO_PACKAGE=mongodb-org
ENV MONGO_VERSION 3.4.9
...
RUN echo "deb http://$MONGO_REPO/apt/debian jessie/${MONGO_PACKAGE%-unstable}/$MONGO_MAJOR main" | tee "/etc/apt/sources.list.d/${MONGO_PACKAGE%-unstable}.list”
RUN set -x
&& apt-get update
&& apt-get install -y ${MONGO_PACKAGE}=$MONGO_VERSION
${MONGO_PACKAGE}-server=$MONGO_VERSION
${MONGO_PACKAGE}-shell=$MONGO_VERSION
${MONGO_PACKAGE}-mongos=$MONGO_VERSION
${MONGO_PACKAGE}-tools=$MONGO_VERSION
&& rm -rf /var/lib/apt/lists/*
&& rm -rf /var/lib/mongodb
&& mv /etc/mongod.conf /etc/mongod.conf.orig
RUN mkdir -p /data/db /data/configdb && chown -R mongodb:mongodb /data/db /data/configdb
VOLUME /data/db /data/configdb
COPY docker-entrypoint.sh /usr/local/bin/
ENTRYPOINT ["docker-entrypoint.sh"]
EXPOSE 27017
CMD ["mongod"]
https://github.com/docker-library/mongo
33. # M D B l o c a l
FROM debian:jessie-slim
RUN groupadd -r mongodb && useradd -r -g mongodb mongodb
...
ARG MONGO_PACKAGE=mongodb-org
ENV MONGO_VERSION 3.4.9
...
RUN echo "deb http://$MONGO_REPO/apt/debian jessie/${MONGO_PACKAGE%-unstable}/$MONGO_MAJOR main" | tee "/etc/apt/sources.list.d/${MONGO_PACKAGE%-unstable}.list”
RUN set -x
&& apt-get update
&& apt-get install -y
${MONGO_PACKAGE}=$MONGO_VERSION
${MONGO_PACKAGE}-server=$MONGO_VERSION
${MONGO_PACKAGE}-shell=$MONGO_VERSION
${MONGO_PACKAGE}-mongos=$MONGO_VERSION
${MONGO_PACKAGE}-tools=$MONGO_VERSION
&& rm -rf /var/lib/apt/lists/*
&& rm -rf /var/lib/mongodb
&& mv /etc/mongod.conf /etc/mongod.conf.orig
RUN mkdir -p /data/db /data/configdb && chown -R mongodb:mongodb /data/db
/data/configdb
VOLUME /data/db /data/configdb
COPY docker-entrypoint.sh /usr/local/bin/
ENTRYPOINT ["docker-entrypoint.sh"]
EXPOSE 27017
CMD ["mongod"]
https://github.com/docker-library/mongo
34. # M D B l o c a l
Many small, focused containers ->
sophisticated services
• Well defined APIs
• Independent languages &
libraries
• Modular: easy maintenance +
reuse
• Fault tolerant
• Scalable
Microservice Architectures Built on
Containers
36. # M D B l o c a l
Connecting the Microservices – Kafka
Producer 987 123...
Topic A
Consumer
New Old
37. # M D B l o c a l
Connecting the Microservices – Kafka
Producer
987 123...
Topic A
Consumer
Producer Consumer
38. # M D B l o c a l
Connecting the Microservices – Kafka
Producer
987 123...
Partition 0
Topic A
Consumer
Producer Consumer
435 123...
Partition 1
39. # M D B l o c a l
Connecting the Microservices – Kafka
Producer
LEADER
Topic A / Partition 0
Broker 1
FOLLOWER
Topic A / Partition 1
FOLLOWER
Topic A / Partition 0
Broker 2
LEADER
Topic A / Partition 1
40. # M D B l o c a l
Connecting the Microservices – Kafka
Producer
Producer
Producer
9
8
7
123
...
Partition 0
4
3
5
123
...
Partition 1
7
3
2
123
...
Partition N
Topic A
Topic B
7
6
5
123
...
Partition 0
Consumer
Consumer
42. # M D B l o c a l
Created by Google, feature-rich and
widely adopted
• Deployment and ‘replication’
• On-line scale out/in
• Rolling upgrades
• High Availability
• Persistence
• Ports
• Load balancing
• Google Compute Engine
Kubernetes
44. # M D B l o c a l
How to use MongoDB with Containers
Twitter
Ingest
Snapchat
Ingest
Feed
merge
Facebook
Ingest
Whatsapp
Ingest
Snapchat
Ingest
Snapchat
Ingest
MongoDB Atlas
45. # M D B l o c a l
How to use MongoDB with Containers
Twitter
Ingest
Snapchat
Ingest
Feed
merge
Facebook
Ingest
Whatsapp
Ingest
Snapchat
Ingest
Snapchat
Ingest
Kubernetes
Ops Mgr
agent
Ops Mgr
agent
Ops Mgr
agent
46. # M D B l o c a l
How to use MongoDB with Containers
Twitter
Ingest
Snapchat
Ingest
Feed
merge
Facebook
Ingest
Whatsapp
Ingest
Snapchat
Ingest
Snapchat
Ingest
Kubernetes
mongod
mongod
mongod
47. # M D B l o c a l
Kubernetes Building Blocks
POD Host Host Service
50. # M D B l o c a l
Beta in Kubernetes 1.5+
• Stable, predictable, unique
network identifiers
• IP addresses may change
• Stable, persistent storage
• Ordered, graceful deployment
and scaling (0 N-1)
• Ordered, graceful deletion and
termination (N-1 0)
StatefulSets
51. # M D B l o c a l
MongoDB Replica
Set as StatefulSet
rs.initiate()
rs.add('mongo-1.mongo:27017')
rs.add('mongo-2.mongo:27017')
52. # M D B l o c a l
• Enabling Microservices: Containers & Orchestration Explained
https://www.mongodb.com/collateral/microservices-containers-and-orchestration-explained
• Microservices: The Evolution of Building Modern Applications
https://www.mongodb.com/collateral/microservices-the-evolution-of-building-modern-applications
• Data Streaming with Apache Kafka & MongoDB
https://www.mongodb.com/collateral/data-streaming-with-apache-kafka-and-mongodb
• Guidance and examples
http://k8smongodb.net/
References
53. # M D B l o c a l
MongoDB & Microservices in the Wild
2 mins
A microservice architecture is one where you break an application down into multiple processes; where each one:
Is self-contained
Has a single, well defined role
The microservices communicate with each other using network technologies (even if running on the same host)
3 min.
Web & Mobile Apps => Enterprise
Micro Web Services
Microservices were pioneered in the web and then mobile App worlds; at one time called micro-web-services. Now other enterprises are looking for the same benefits.
Microservice architectures implement applications as a series of small, self-contained, loosely coupled software components. Each has a specific and well understood role.
Benefits of microservices:
- Development Speed
- Rapid Iteration
Evolve quickly, continuous deployment
Isolate impact of changes to existing functions or just add a new one
Reactive development
Maintainable
Independent, empowered work teams
Web & Mobile Apps => Enterprise
Micro Web Services
Microservices were pioneered in the web and then mobile App worlds; at one time called micro-web-services. Now other enterprises are looking for the same benefits.
Microservice architectures implement applications as a series of small, self-contained, loosely coupled software components. Each has a specific and well understood role.
Benefits of microservices:
- Development Speed
- Rapid Iteration
Evolve quickly, continuous deployment
Isolate impact of changes to existing functions or just add a new one
Reactive development
Maintainable
Independent, empowered work teams
Web & Mobile Apps => Enterprise
Micro Web Services
Microservices were pioneered in the web and then mobile App worlds; at one time called micro-web-services. Now other enterprises are looking for the same benefits.
Microservice architectures implement applications as a series of small, self-contained, loosely coupled software components. Each has a specific and well understood role.
Benefits of microservices:
- Development Speed
- Rapid Iteration
Evolve quickly, continuous deployment
Isolate impact of changes to existing functions or just add a new one
Reactive development
Maintainable
Independent, empowered work teams
Web & Mobile Apps => Enterprise
Micro Web Services
Microservices were pioneered in the web and then mobile App worlds; at one time called micro-web-services. Now other enterprises are looking for the same benefits.
Microservice architectures implement applications as a series of small, self-contained, loosely coupled software components. Each has a specific and well understood role.
Benefits of microservices:
- Development Speed
- Rapid Iteration
Evolve quickly, continuous deployment
Isolate impact of changes to existing functions or just add a new one
Reactive development
Maintainable
Independent, empowered work teams
Web & Mobile Apps => Enterprise
Micro Web Services
Microservices were pioneered in the web and then mobile App worlds; at one time called micro-web-services. Now other enterprises are looking for the same benefits.
Microservice architectures implement applications as a series of small, self-contained, loosely coupled software components. Each has a specific and well understood role.
Benefits of microservices:
- Development Speed
- Rapid Iteration
Evolve quickly, continuous deployment
Isolate impact of changes to existing functions or just add a new one
Reactive development
Maintainable
Independent, empowered work teams
Web & Mobile Apps => Enterprise
Micro Web Services
Microservices were pioneered in the web and then mobile App worlds; at one time called micro-web-services. Now other enterprises are looking for the same benefits.
Microservice architectures implement applications as a series of small, self-contained, loosely coupled software components. Each has a specific and well understood role.
Benefits of microservices:
- Development Speed
- Rapid Iteration
Evolve quickly, continuous deployment
Isolate impact of changes to existing functions or just add a new one
Reactive development
Maintainable
Independent, empowered work teams
5 mins.
Add a new flavor independently.
Chef making chocolate knows that well but need not know the others
Blueberry goes out of fashion, remove them
Need more green cakes, add them
Improved pink frosting, throw out the old ones and the new ones.
Microservices are like Cupcakes
Can add new ones with different flavors, remove ones that you no longer need, add more pink ones if there’s greater demand
Developers can create and activate new microservices without prior coordination with others. Their adherence to MSA principles makes continuous delivery of new or modified services possible
Greater modularity, looser coupling.
Started in the web and mobile app world, moving to Enterprise. Big in media and startups
Plan for flexibility rather than reuse
7 mins.
Each of the ovals represents a microservice.
Each source of social media feeds has its own microservice which is specialised in interfacing with the relevant API.
Each of those microservices passes messages to the ‘feed merge’ microservice which can then make them available for further microservices to work with.
Communication between the microservices is over the network – they can be local to the same machine or distributed.
Best practice is for each microservice to be stateless and to have its own database or schema
Individual microservices can be updated in isolation or even removed if their role is no longer needed
When a new role (or even a change to an existing one) appears, best practice is to implement a new microservice rather than extending an existing one.
When a new role (or even a change to an existing one) appears, best practice is to implement a new microservice rather than extending an existing one.
Microservices allow scale-out.
Each type of microservice can be scaled independently – add extra instances just for the functions that are being overworked.
Multiple instances of each service can provide High Availability
9 mins
Fast > Elegant
Sagrada Familia –1882-2026 (144 years).
Frequent, localised changes
Localised scaling
Upgrades
Only if:
Scaling team
Designing for change
Fast is more important than elegant.
Change in the application’s functionality and usage is frequent.
Change occurs at different rates within the application, so functional isolation and simple integration are more important than module cohesiveness.
Functionality is easily separated into simple, isolatable components.
When you have the developer/DevOps skillsets.
Where development org boundaries match service boundaries.
Don’t forget that you’re building a distributed system -> complexity but there are precedents to read up on.
One argument is that you shouldn’t bother with microservices unless you need either:
- Scale your team
- Design for change
Sagrada Familia – designed by Gaudi; construction started on March 19, 1882. Expected to be finished in 2026.
11 mins
13 mins
Software containers
Build an image containing the full application stack only once
Spin up many containers from the same image in multiple environments
Laptop, data center, cloud
Development, QA, production, support
Simple to use & efficient
Contents of each container isolated from the others
Storage, memory, namespace
Constrain resources available to each container
Storage, memory, CPU, IO
16 mins
The most popular container technology
Simple to use and has a rich ecosystem
100,000+ images available from Docker Hub
Including mongo hub.docker.com/_/mongo/
Syncs with GitHub projects
Define new images built upon base images
Define interfaces between containers
LINUX, (and now) Windows, and OS X
Runs on bare metal, VMs, and cloud. Cloud providers supply the Docker infrastructure (e.g. Google Container Engine)
Not best practice
The ARG instruction defines a variable that users can pass at build-time to the builder with the docker build command using the --build-arg <varname>=<value> flag.
The ENV instruction sets the environment variable <key> to the value <value>.The environment variables set using ENV will persist when a container is run from the resulting image.
20 mins
Microservices built by combining multiple containers
Build sophisticated services from many small, focused processes (containers)
Well defined APIs between components
Each component can use different libraries, middleware & programming languages
Modular, decoupled architecture simplifies maintenance and enables reuse
Fault tolerant
Scalable
21 mins
21 mins
3,6 Change Streams
To do useful work, microservices need a way of communicating – Apache Kafka
Kafka provides a flexible, scalable, and reliable method to distribute streams of event data from one or more **producers** to one or more **consumers**.
Examples of **events** (or **messages**) include:
A periodic sensor reading such as the current temperature
A user adding an item to the shopping cart in an online store
A Tweet being sent with a specific hashtag
A log entry generated for each click in a web application
Streams of Kafka events are organized into **topics**. A producer chooses a topic to send a given event to and consumers select which topics they pull events from. For example, a financial application could pull NYSE stock trades from one topic, and company financial announcements from another in order to look for trading opportunities.
Kafka actually stores all of the messages that it passes around – this makes it ideal for production microservice deployments
A microservice can be upgraded and then catch up on everything it missed
Or even apply its updated business logic to the full history of events
A new microservice can be added and it can be brought up to speed with everything that’s gone before
If one service is generating more work than another can keep up with then Kafka operates as a buffer
To do useful work, microservices need a way of communicating – Apache Kafka
Kafka provides a flexible, scalable, and reliable method to distribute streams of event data from one or more **producers** to one or more **consumers**.
Examples of **events** (or **messages**) include:
A periodic sensor reading such as the current temperature
A user adding an item to the shopping cart in an online store
A Tweet being sent with a specific hashtag
A log entry generated for each click in a web application
Streams of Kafka events are organized into **topics**. A producer chooses a topic to send a given event to and consumers select which topics they pull events from. For example, a financial application could pull NYSE stock trades from one topic, and company financial announcements from another in order to look for trading opportunities.
Kafka actually stores all of the messages that it passes around – this makes it ideal for production microservice deployments
A microservice can be upgraded and then catch up on everything it missed
Or even apply its updated business logic to the full history of events
A new microservice can be added and it can be brought up to speed with everything that’s gone before
If one service is generating more work than another can keep up with then Kafka operates as a buffer
To do useful work, microservices need a way of communicating – Apache Kafka
Kafka provides a flexible, scalable, and reliable method to distribute streams of event data from one or more **producers** to one or more **consumers**.
Examples of **events** (or **messages**) include:
A periodic sensor reading such as the current temperature
A user adding an item to the shopping cart in an online store
A Tweet being sent with a specific hashtag
A log entry generated for each click in a web application
Streams of Kafka events are organized into **topics**. A producer chooses a topic to send a given event to and consumers select which topics they pull events from. For example, a financial application could pull NYSE stock trades from one topic, and company financial announcements from another in order to look for trading opportunities.
Kafka actually stores all of the messages that it passes around – this makes it ideal for production microservice deployments
A microservice can be upgraded and then catch up on everything it missed
Or even apply its updated business logic to the full history of events
A new microservice can be added and it can be brought up to speed with everything that’s gone before
If one service is generating more work than another can keep up with then Kafka operates as a buffer
To do useful work, microservices need a way of communicating – Apache Kafka
Kafka provides a flexible, scalable, and reliable method to distribute streams of event data from one or more **producers** to one or more **consumers**.
Examples of **events** (or **messages**) include:
A periodic sensor reading such as the current temperature
A user adding an item to the shopping cart in an online store
A Tweet being sent with a specific hashtag
A log entry generated for each click in a web application
Streams of Kafka events are organized into **topics**. A producer chooses a topic to send a given event to and consumers select which topics they pull events from. For example, a financial application could pull NYSE stock trades from one topic, and company financial announcements from another in order to look for trading opportunities.
Kafka actually stores all of the messages that it passes around – this makes it ideal for production microservice deployments
A microservice can be upgraded and then catch up on everything it missed
Or even apply its updated business logic to the full history of events
A new microservice can be added and it can be brought up to speed with everything that’s gone before
If one service is generating more work than another can keep up with then Kafka operates as a buffer
To do useful work, microservices need a way of communicating – Apache Kafka
Kafka provides a flexible, scalable, and reliable method to distribute streams of event data from one or more **producers** to one or more **consumers**.
Examples of **events** (or **messages**) include:
A periodic sensor reading such as the current temperature
A user adding an item to the shopping cart in an online store
A Tweet being sent with a specific hashtag
A log entry generated for each click in a web application
Streams of Kafka events are organized into **topics**. A producer chooses a topic to send a given event to and consumers select which topics they pull events from. For example, a financial application could pull NYSE stock trades from one topic, and company financial announcements from another in order to look for trading opportunities.
Kafka actually stores all of the messages that it passes around – this makes it ideal for production microservice deployments
A microservice can be upgraded and then catch up on everything it missed
Or even apply its updated business logic to the full history of events
A new microservice can be added and it can be brought up to speed with everything that’s gone before
If one service is generating more work than another can keep up with then Kafka operates as a buffer
23 mins
23 mins
Cloud Native Computing Foundation
Created by Google, feature-rich and widely adopted
Automated container deployment and ‘replication’
On-line scale out/in
Rolling upgrades
HA – automatic rescheduling of failed containers
Exposure of network ports to external apps
Load balancing over groups of containers providing a service
Provided as a service by Google Compute Engine
25.5 mins
27.5 mins
29.5 mins
Kubernetes.
Single Pod/comtainer/mongod in a ReplicationController.
Use external IP addresses (other IP addresses & hostnames are local to Kubernetes cluster and they change)
Refer to white paper for details
31 mins
31.5 mins
33.5 mins
35.5 mins
36.5 mins
Gap (flexibility): Monolith -> microservice (75 days). New types of PO took just days
FuboTV (scalability). Single cluster for dev, QA + production. Cope with 100x bursts. Run MongoDB on Kubernetes
Otto (arch == org). Fast, iterative delivery
Backcountry (> distributed dev team): Schema changes were taking 20% of dev time. Flexible schema
Compare The Market (Use Docker, Kafka, MongoDB & Ops Manager).
GAP moved their purchase order system from a monolith architecture to microservices. Due to MongoDB’s flexible schema, it took just 75 days to build the new system. When requirements changed and they had to add new types of purchase orders, it took days instead of months.
FuboTV is a North American soccer streaming service. Using Microservices with Kubernetes, Docker & MongoDB. Isolation means that they can use a single cluster of machines (in Google Cloud) for dev, QA & production. Very birsty application – scalability lets them handle 100x increases in traffic.
Otto – the key was to have an architecture that fits with their organization. Microservices empower loosely couple development teams (business, project management, IT). This is all enabling Fast test & deployment + Iterative, Continuous Delivery
Backcountry.com is an online specialty retailer that sells outdoor clothing and gear. The driver to Microservices for them was a growing, distributed development team. As more and more developers joined and made contributions to the code, the schemas became convoluted and harder to maintain; contributing to 20% of the Scrum backlog. Taking advantage of MongoDB’s flexible data model, Backcountry was able to iterate faster, reduce development time, and mitigate technical debt.
Compare The Market: In the cloud, each microservice, or logical grouping of related microservices, is provisioned with its own MongoDB replica set running in Encrypted storage engine to further reduce our security-related surface area. Use Docker, Kafka & MongoDB.