#dbhouseparty - Should I be building Microservices?

1 Copyright © 2020, Oracle and/or its affiliates | Confidential: Internal/Restricted/Highly Restricted
[Date]
Should I be building Microservices?
Paul Parkinson
Cloud Platform Dev
Lead
Microservices
Sanjay Goil
VP Product
Management
Database Server Tech
(microservices)

2 Copyright © 2020, Oracle and/or its affiliates | Confidential: Internal/Restricted/Highly Restricted
[Date]
Sanjay Goil
VP Product Management
At Oracle for 2+ years
Messaging & Microservices; Previously Event
Hub Cloud Service (Kafka); AI Service
Past 20 years:
AdTech Startup: Social Media & TV Ads Platform
HP/Autonomy : Big Data platform (IDOL,
Vertica, Logger, and Hadoop)
Sun & Intel : Built HPC Systems and Developer
Tools for Converged Parallelism
About your presenters:
Paul Parkinson
Cloud Platform Dev Lead
Dev lead for Transaction Processing in the mid-
tier for 19 years at Oracle
Helidon Microservices Platform Dev Lead

Two incredible voices of Experience!
Copyright © 2019 Oracle and/or its affiliates.

Sanjay
Paul
Rolando
Guido
WeWelcome to you all from around theWorld.

5
6: Should I Build Microservices?
Sanjay Goil Paul Parkinson
Vice President, Product Management Tx Processing Dev Lead
Database Messaging and Microservices Microservices Cloud Platform
With Insights From GUIDO And ROLANDO, Oracle Aces

Agenda
• Introductions – 5 mins (All)
• What are Microservices – 5 mins (Sanjay)
• Recommendations from Guido – 10 mins
• Recommendations from Rolando – 10 mins
• Converged Database Simplicity (Sanjay) – 5 mins
• Building a Microservice Example (Paul) – 20 mins
• Quick walkthrough of the LiveLabs for Microservices
• Summary + Q&A – 5 mins

Can we make Microservices SIMPLE ?
Help you build Microservices with open
interfaces
• Simplify data platform
• Simplify the code
• Simplify integration
Data-driven Applications at Scale

What are Microservices?
“loosely coupled service oriented
architecture with bounded contexts.”
• Adrian Cockroft, while at Netflix
If every service has to be updated at the same
time it’s not loosely coupled
If you know too much about the surrounding
services you don’t have a bounded context
Source: 12factor.net

Complexity of Lambda and Kappa
• Many technologies
• Multiple data stores
• Copy contagion
– Data copied many times
• Complex and difficult to
maintain
• Skill set is fragmented
9
Apache
Kafka
Web servers
Order service
API/Brokers
Mobile
IoT
Producers
Databases
Oracle
NoSQL
MySQL
GG Kafka
Connector
Mobile App1
Consumers
Search Portal
Ops Dashboards
Realtime
Analytics, Alerts
ML Model
Training
Analytic Reports
Adhoc
ExplorationHadoop data lake
Vertica/Hive
Event and data streams
Pub/Sub
AWS S3
Mobile Platform
ELK Platform
Apache Flink
Transactions
Hadoop Stream Everything
A move from DB + DW  Special purpose databases ; Event hub ; Streaming data

How do we think about Microservices?
• Application Platform
• Data Platform
• Simplicity of Development, Deployment, Upgrades, Maintenance, Scale,
Cost effectiveness,Agility, …
Oracle Restricted

Some (not so) simple
questions first !
• Is a separate database per
(micro)service recommended?
• How about REST APIs vs Event
sourcing for Microservices?
• Does expertise on a team
benefit from a Converged
approach?

Microservice Approach
Tightly Scoped behind interfaces & highly
decoupled
Polyglot Programming/Persistence => Skills
Own their data != absolutely need their own
database instance!
Communication between services is complex
3 Mechanisms Service can interact: Command &
Query but also Event
Smart Endpoints and Dumb Pipes
Finding the right granularity of a service is
challenging
Really something new?Or just SOA done right?

Service 1
Service 2
Service 3
Service 4
Smart Endpoints and Dumb Pipes!
– no complex Integration in the middle!
Integration
X
Service Bus
Service
Orchestration
Service 1
Service 2
Service 3
Service 4
X
X
X
X

Modernizing SOA
• SOA is a similar paradigm
as Microservices
• Reasons to move from SOA
to Microservices
• 7 suggestions to get started

First & foremost. SOA is a discipline and the Microservices
architecture shares some of its principles
• The first thing to understand is that if you are really doing
SOA & you are looking to implement microservices, then
the path is pretty much open for you
• Services Design Principles such as Abstraction, Loosely
Coupling, Autonomy, Composability, Standardized Service
Contract; applies perfectly for your Microservices
• As the title says, SOA is a discipline is not a technology.
• If you are really designing services, then you are in very
good position
Microservices
SOA

But…what about Oracle SOA Suite?
• Now we are talking about technology
• Oracle SOA Suite is a great platform, no doubt about it. Personally, I’ve been
using it since the very beginning (Oracle BPEL in 2003)
• Sometimes people uses it as a Services platform and sometimes as an
Integration Platform
• But, if you are going into a direction where some of your services running on top
of Oracle Suite, needs to be moved to a microservices architecture, then we can
give you some suggestions.That architecture can be deployed on Oracle
Kubernetes Engine, for example.

Reasons of doing this type of change
1. Business drivers and needs
2. Different consumers generation different dynamics for your services and you
need to respond accordingly with agility
3. If you are looking to isolate your services and reduce the dependencies (the
metadata DB for example)
4. Autoscaling for your services
5. Service Isolation
6. Service Performance
7. Service Containerization
8. Service isolation needs
https://innovationmanagement.se/imtool-articles/improvement-is-not-innovation/

Seven (7) suggestions for you
1. This is a progressive change. Do not try to make a big bang plan
2. Identify services that really need microservices dynamics. If they are
OSB based (Proxy, Pipelines, Business), even better. Migrate your
pipeline code
3. Reuse your contracts, if you really have applied Standardized Service
Contracts
4. Do not try to migrate dependencies, that is exactly what you are trying
to avoid. Example: database dehydration store
5. If you are using JMS for communication among your services, you can
use similar approaches such as Oracle AQ/TEQ
6. Business Rules engine is a good candidate, since most of the times the
rules are stand alone and they do not depend on others
7. Cross cutting functionality, such as OWSM policies, can be expressed
like service proxies or sidecars, and depending on the dynamics you can
implement the service mesh pattern

Multitenant for Efficient,Agile DatabaseClouds
In-Memory for DatabaseAcceleration
Sharding for Hyperscale and Geo Distribution
Native JSON for Document Data
In-Memory Ingest for Fastest IoT
Cloud SQL for integratingObject Store Data Lake
AutoML for simple integrated Machine Learning
Persistent Memory Store for Lowest Latency
BlockchainTables for Preventing Fraud
Spatial and Graph for Mapping and Social Networks
Events forTransactional Event-driven Microservices
And many more …
Oracle Autonomous Database
Converged Features In-Memory
Analytics
Hyperscale
Machine
Learning
Blockchain
Cloud
Integration
{ }
JSON In-Memory
IoT
Spatial Graph
Multitenant
Persistent
Memory
Events

Web servers
Order service
API/Brokers
Mobile
IoT
Producers Consumers
Mobile App1
Search Portal
Ops
Dashboards
Realtime
Analytics,
Alerts
ML Model
Training
Analytic
Reports
Adhoc
Exploration
Converged database
with
Event Queues
+
Managed
+
Cloud Scale
Transactions
20
• Microservices support
• Open interfaces
• Easy app building blocks & APIs
• Less to learn, manage, backup,
upgrade, secure
• Self-managing with Autonomous
Database
Database Convergence Simplicity
OCI Object Store
Converged DB
JSON

Converged Database Architecture
• Event Queues supports transactional
messaging - microservice state and
events can be persisted by the same
local transaction (not 2 phase commit)
• Simplify development of fault-tolerant
microservices
• Error recovery logic is typically 90% of the
code. And this code is often poorly tested
Confidential – Gartner SAS Day 7/18/2019
compatible
Multimodel PDBs withTransactional EventQueues
Order Inventory Delivery
JSON JSON
JSON

A Modern Mobile Food Delivery Application
order
inventory
delivery
supplier
Transactions
Data
AI/ML
Source: Doordash

LiveLabs to get hands-on experience
Lab3
Multitenant
TEQ
Multitenant
Lab1
Lab4 APP PLATFORM
Lab5 DATA PLATFORM
Lab6 Polyglot Coding in Java, Python, node.js
Lab2
OCI Registry

LiveLabs for
Microservices
• https://oracle.github.io/learning-library/developer-
library/microservices-with-converged-db/#Let'sGetStarted!

Quick Walkthrough of
Microservices on Converged DB with
Helidon

Food order App – Microservices and message flow
Mobile App
Order μService
Helidon MP
Data type: JSON
Delivery μService
WebLogic
Data type: Spatial
Inventory μService
Helidon MP
Data type: relational
ATP PDBS with propagated AQ queues
add inventory call
over gRPC
Supplier μService
Helidon SE
Data type: relational
routing and
visualization API
over REST
place order and
show order call
over REST
Streaming Order
μService
Helidon SE
Data type: JSON
insert order (SODA API) +
send event
over AQ
OCI
Streaming
Service
produce/stream
orders
over Kakfa
consume/stream
orders
over Kakfa
receive inventory
status
and other events
over AQ receive order
event
over AQ
send inventory
status event
over AQ
stream orders call
over gRPC
OKE
cluster
OCI
Monitoring
Service
update inventory
(SQL – commutative
operation
over reactive)
OCIR
OCI
Object
Storage
Event sourcing
and
choreography
saga

Sagas | For Transactions across Microservices
Order
Service
Inventory
Service
Delivery
Service
Kafka or JMS
client API
Kafka or JMS
in DB
VeggieDash
App
HTTP
Begin Saga
1. Place order
(local commit)
2. Check Inventory
(local commit)
3. Delivery Service
(rollback)
4. If all 3 succeed
Commit Saga [Id];
Record overall
booking
Else
Abort Saga [Id]
(Compensation)
End Saga
Transparent TEQ message propagation cross PDBs and CDB
Saga pattern requires compensation code (~80%) | Can we make it simpler ?

No Matter Where you Are? You can touch a Microservice?
build

Future Labs Ideas – Collaborate with us!
• Spatial Optimization for order delivery team. (Spatial data)
Step 1: Multiple routes planning for a deliverer picking up 5 orders
Step 2: Plot the routes on a map
Step 3: Optimize for all delivery persons for optimal delivery time
• Social Graph Recommendations (Graph data)
Step 1: Social graph for recommendations of restaurants
Step 2: Provide Top 3 recommendations for Thai food
• Ops Analytics (Analytics dashboards)
Step 1: Order analysis and prediction of delivery times
Step 2: Delivery Ops dashboard on spatial map in real time
Step 3: Compare predicted delivery times vs actual times with feedback learning
• Machine Learning (Machine Learning)
Step 1: Dynamic pricing algorithms based on demand and supply
Step 2: Profitability predictions with surge pricing
Step 3: Predicting models for bringing surge pricing to normal
• Performance, HA, and Scaling of Microservices Platform (Performance/HA)
Step 1: Adding capacity to the system
Step 2: 100 orders to 10,000 orders per minute horizontal scaling of platform
Step 3: App, Messaging, Data layer cost scaling
Step 4: HA best practices for always on service experience

For Microservices Build the App and the Data Platform together!
FINAL WORD
DATA PLATFORM
APP

Questions?
Guido.Schmutz@trivadis.com | Trivadis
rcarrasco@spsolutions.com.mx | S&P Solutions
Paul.Parkinson@oracle.com | Oracle
Sanjay.Goil@oracle.com | Oracle

#dbhouseparty - Should I be building Microservices?

#dbhouseparty - Should I be building Microservices?

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