A Guide to digital Cities

Digital Cities are changing the way we live, work and play. We’ve seen a recent focus on the idea of creating sustainable digital cities, which address how technology can be used to improve quality of life while reducing greenhouse gas emissions. But what is a digital city? In this article, you’ll learn all about the technology behind them and what they’re capable of.

What is a Digital City?

A digital city is a city that uses digital technologies to improve the quality of life of its citizens. The goal of a digital city is to use technology to make the city more efficient, sustainable, and livable. There are many different types of digital city initiatives, but they all share the same goal of using technology to make the city a better place to live.

Digital cities use a variety of different technologies to achieve their goals. Some of these technologies include:

-Sensors: Sensors are used to collect data about the city. This data can be used to monitor traffic, pollution, and other aspects of the city.

-Smart grids: Smart grids are used to manage the flow of electricity in the city. Smart grids can help reduce blackouts and brownouts, and can also help save energy.

-Smart buildings: Smart buildings use sensors and other technologies to automate heating, cooling, and lighting. This can save energy and improve the comfort of building occupants.

-Intelligent transportation systems: Intelligent transportation systems are used to manage traffic flow in the city. These systems can help reduce congestion and improve the efficiency of the city’s transportation network.

The Platform Approach to build a Digital City

Digital cities are built on a platform approach that enables different applications and services to be delivered through a shared infrastructure. The key components of a digital city platform include:

-An open data portal that provides access to city data and information

-A set of APIs that allow different applications to interoperate

-A cloud-based infrastructure that delivers scalability and flexibility

The advantages of this approach include lower costs, faster deployment of new services, and the ability to create an ecosystem of innovation around the city platform.

Pillars of Digital City

1. Connected Infrastructure:

A smart city is one with a digital infrastructure that allows for the easy flow of information and communication between city systems and its residents. This infrastructure must be secure and reliable in order to protect the data of both the city and its citizens.

2. Intelligent Transportation:

A key pillar of smart cities is intelligent transportation. This includes everything from real-time traffic monitoring to self-driving vehicles. By using data and technology to improve the efficiency of transportation, cities can reduce congestion, pollution, and accidents.

3. Sustainable Energy:

Smart cities use data and technology to make their energy usage more sustainable. This can include things like renewable energy sources, energy storage, and smart grids. By using sustainable energy, cities can reduce their carbon footprint and save money in the long run.

4. Resilient Buildings:

Smart buildings are those that are designed to be resilient to extreme weather events and other emergencies. They use things like sensors, big data, and AI to monitor conditions inside and outside the building. This information can then be used to make necessary adjustments to keep people safe and comfortable during an emergency.

5. Healthy Citizens:

A healthy citizenry is essential for any city to function

What is the Technological Components of a Digital City?

Digital cities are increasingly becoming a reality as more and more municipalities adopt the technology needed to create them. But what exactly is a digital city, and what are the technological components that make it up?

A digital city is an urban area that uses digital technologies to improve the livability, workability, and sustainability of the city. This can include everything from using sensors to monitor traffic and air quality, to using big data to make better decisions about city planning, to providing free public Wi-Fi.

The technological components of a digital city vary depending on the specific goals and needs of the municipality, but there are some common themes. These include:

Sensors: Sensors are used to collect data about various aspects of the city, such as traffic patterns, air quality, and weather conditions. This data can be used to improve the efficiency of city operations and services.

-Big Data: Big data is a term used to describe the large amounts of data that are generated by sensors and other sources. This data can be used to identify trends and patterns, which can be used to make better decisions about city planning and service delivery.

-Crowdsensing: Crowdsensing is another simple way for cities to obtain data from their citizens. In this form of crowdsourcing, users voluntarily participate in a collaborative effort that helps make better use of the data.

-Open Data: Open data is a concept where governments release public data for use by their citizens, who can then create innovative applications and services based on that data. One of the most exciting aspects of open data is that it creates opportunities for citizen-to-citizen engagement and collaboration.-Internet of Things: The

-Public Wi-Fi: Public Wi-Fi is one of the simplest ways for a city to provide quick access to information and services for its citizens. Public Wi-Fi not only has social benefits, but can also be used by cities to , empower citizens with information.

Digital Democracy: Digital democracy describes the implementation of digital technology by citizens to increase their participation in the democratic process. Digital democracy encompasses both online activism for political causes and the use of social media for political action.


Digital cities are becoming increasingly popular as more and more people move to urban areas. These cities use technology to improve the quality of life for residents and make the city more efficient. If you’re interested in learning more about digital cities and the technology behind them, this article has provided a good introduction.


Predicting Microservices ROI

Welcome to this series to cover various aspects of microservice migration. The first Article here is “How to Predict Microservices ROIs” .


Nowadays, there is a trend to migrate enterprise applications from monolithic to Microservices. The biggest business drivers for the same are 

  • IT Modernization
  • Digital Transformation
  • Growth & Expansion

Generally, we adopt a stranger pattern, where we migrate piece by piece to Microservices. Generally, we create a migration roadmap with side by side approach. The most difficult part of this migration proposal is to justify the cost of migration. ROI can be calculated by prediction cost and benefit analysis.

Apply Samling Technique to predict cost and benefits- 

Let’s take a sample of Future Microservice X, which will replace the monolithic Module Y .. let’s collect the following data 

Measuring Benefits

  • How many bugs have been reported for that module in the last 6 months or 1 year? You can assume that microservice will minimize that cost and all the effort and cost put in bugs will be future savings with Microservices.
  • What was the average time to deliver a story point/feature? WIth Microservice this should increase and you can calculate the delta.
  • What was the developer productivity?
  • Is there any feature missed due to stability? What would have been the business impact of that feature? With microservice, you will introduce agility and you can assume that chances of missing a feature will be negligible.
  • Has the current system missed some customers due to scalability issues ( You can predict, how many customers has been missed and what could have been a business impact)
  • How much average time and effort a typical deployment take? With Microservice all the deployments will be automated and you can predict the 

Estimating Cost 

  • The development cost for new microservice.
  • Routing workload to new microservice.
  • Infrastructure cost of microservice.

Does Kafka really preserve ordering in a partition?

It’s a general belief that Kafka guarantees the ordering within a partition. Same is claimed in official Kafka documentation. Below are the excerpts from Kafka documentation.

Topics are partitioned, meaning a topic is spread over a number of “buckets” located on different Kafka brokers. This distributed placement of your data is very important for scalability because it allows client applications to both read and write the data from/to many brokers at the same time. When a new event is published to a topic, it is actually appended to one of the topic’s partitions. Events with the same event key (e.g., a customer or vehicle ID) are written to the same partition, and Kafka guarantees that any consumer of a given topic-partition will always read that partition’s events in exactly the same order as they were written.

Figure: This example topic has four partitions P1–P4. Two different producer clients are publishing, independently from each other, new events to the topic by writing events over the network to the topic’s partitions. Events with the same key (denoted by their color in the figure) are written to the same partition. Note that both producers can write to the same partition if appropriate.


So, where is the problem?

If you run the Kafka with default configurations, there can be a scenario when a message, although produced earlier yet appended after the message produced later.

No, I don’t want to break your heart, but its the bitter truth. Let me unravel this mystery.

Default value for producer’s retries is Integer.MAX_VALUE, and the delivery timeout is 2 minutes. If a message is not acknowledged, producer will keep sending it again and again, unless it succeeds, retries are exhausted or timeout period expires.

There is another configuration “max.in.flight.requests.per.connection”. It is the maximum number of unacknowledged requests, the client will send on a single connection before blocking. Default value of this property is 5.

So if a message is unacknowledged, it will be retried. If messages sent after the failed message are acknowledged before this message succeeds in retry, then those messages will appear before in order i.e. reordering of the messages (et tu Kafka……).

Don’t get disheartened, although by default Kafka doesn’t preserve the order, yet a small tweak in the configurations would restore the guaranteed ordering and your faith in humanity.

You need to set either of following two configurations to ensure the ordering.

  1. max.in.flight.requests.per.connection =1


  1. enable.idempotence=true

Sigh!! After all Kafka is a loyal friend, Happy Messaging!!

Microservices Assessment Framework

Mohit is an experienced enterprise architect and blogger. He has consulted various organizations and trained multiple teams to enable them to successfully adopt and improve Microservices architecture.

Based on his experience,  Mohit is working on a Microservices assessment framework for following three objectives.

  1. Readiness – Access whether your organization is ready to adopt Microservices?
  2. Fitness – Access whether Microservices is good fit for your organization?
  3. Review– Evaluate your (Microservices) Architecture and identity area of improvements.



Proposed framework access Organization, processes and  base architecture.  You may find various questionnaires accessing following items.

  1. Business Drivers – Determine whether you have clear and valid business drivers for MSA.
  2. Development Velocity – Determine whether you can take benefits from MSA.
  3. Base Architecture – Determine whatever base architeure has all the required
  4. Infrastructure – Determine whether your organization has developer and MSA friendly infrastructure.
  5. Organization Structure – Determine whether you have correct organization structure required for MSA
  6. Processes – Determine whether you have correct organization processes required for MSA
  7. Individual Service Design – Determine capability of each service design.

Stay tuned for More Information. Please contact us for learning more.



Why Is Swagger JSON Better Than Swagger Java Client?

The Swagger Java-Based Client Using Java Annotations on the Controller layer

Pros and Cons

  • It’s the old way of creating web-based REST API documents through the Swagger Java library.
  • It’s easy for Java developers to code.
  • All API description of endpoints will be added in the Java annotations parameters.
  • Swagger API dependency has to be added to the Maven configuration file POM.xml.
  • It creates overhead on the performance because of extra processing time for creating Swagger GUI files (CSS, HTML, JS etc). Also, parsing the annotation logic on the controller classes creates overhead on the performance, as well. It makes the build a little heavy to deploy on microservices, where build size should be smaller.
  • The code looks dirty because the extra code has to be added to the Spring MVC Controller classes through the Spring annotations. Sometimes, if the description of the API contract is too long, then it makes code unreadable and maintainable.
  • Any change in an API contract requires Java to build changes and re-deployment, even if it’s only simple text changes, like API definition text.
  • The biggest challenge is to share with the clients/QA/BA teams before the actual development and to make frequent amendments. The service consumers may change their requirements frequently. Then, it’s very difficult to make these changes in code and create the Swagger GUI HTML pages by redeploying and sharing the updated Swagger dashboard on the actual deployed dev/QA env.

2. Swagger JSON File Can be Written Separately and Provide Browser-Based GUI

Pros and Cons

  • In this latest approach, all of the above challenges with Java-based client solution have been solved.
  • The developer initially creates a JSON file, shares, and agrees with the service consumer and stakeholders. They will get signed off after many amendments —no code change and re-deployment are required.
  • The code will be cleaner, readable, and maintainable.
  • There is no extra overhead for file creation and processing, performance is better, and the code is more lightweight for microservices, etc.
  • There is no code dependency for any API contract changes.
  • Swagger JSON file resides in the project binaries (inside src/main/resources/swagger_api_doc.json). We can deploy Swagger on one server and can switch to an environment like this.


You can copy and paste swagger_api_doc.json JSON file content on https://editor.swagger.io/. It will help you modify content and create an HTML page like the following.  Swagger GUI will provide the web-based interface like Postman.

10 Challenges of Microservices and Solutions – Tips & Tricks

I am cloud API developer and architect and currently working on Google’s GCP based microservices for a large retail client of USA.

Transitioning/implementing to microservices creates significant challenges for organizations. I have identified these challenges and solution based on my real exposure of microservices on PROD.

I am writing this white paper in June 2018. At this time, Microservices architecture is not matured enough to address completely all the existing challenges, however open source communities and IT product companies are trying to address all these open issues. All new researches on this topics are based on the finding solutions to the new challenges.

These are the major 10 challenges of microservices architecture and proposed solutions-

1. Data Synchronization – We have event sourcing architecture to address this issue using the async messaging platform. Saga design pattern can address this challenge.
2. Security – API Gateway can solve these challenges. Kong is very popular open source which is being used by many companies on the production system. The custom solution can also be developed for API security using JWT token, Spring Security, and Netflix Zuul/ Zuul2. There are enterprise solutions are also available like Apigee, Okta ( 2 step authentication). Openshift for public cloud security for its top features of RedHat Linux Kernal based security and namespace-based app to app security.
3. Versioning – It will be taken care by API registry and discovery APIs using dynamic Swagger API, which can be updated dynamically and shared with the consumers on the server.
4. Discovery – It will be addressed by API discovery tools like Kubernetes, OpenShift. It can also be done using Netflix Eureka at the code level. However, doing with the orchestration layer will be better and that can be managed by these tools rather doing and maintaining thru code and configuration.

5. Data Staleness – The database should be always updated to give recent data, API will fetch data from the recent and updated database. A timestamp entry can also be added with each record in the database to check and verify the recent data. Caching can be used and customized with an acceptable eviction policy based on the business requirement.
6. Debugging and Logging – There are multiple solutions- Externalised logging can be used by pushing log messages to an async messaging platform like Kafka, Google PubSub etc. A correlation ID can be provided by the client in the header to REST APIs to track the relevant logs across all the pods/docker containers. Also, local debugging can be done individually of each microservice using IDE or checking the logs.
7. Testing– This issue can be addressed using unit testing by mocking REST APIs, Mocking integrated/dependent APIs which are not available for testing using WireMock, BDD Cucumber integration testing, performance testing using Jmeter and any good profiling tool like Jprofiler, DynaTrace, YourToolKit, VisualVM etc.
8. Monitoring – Monitoring can be done using Open source tools Prometheus in the combination of Grafana by creating Gauge and matrices, Kubernetes/OpensShift, Influx DB, Apigee, the combination of Graphana and Graphite.
9. DevOps Support – Microservices deployment and support related challenges can be addressed using state on the art DevOps tools GCP Kubernetes, OpenShift with Jenkin.
10. Fault Tolerance – Netflix Hystrix can be used to break the circuit if there is no response from the API for the given SLA/ETA.