---
# System prepended metadata

title: opp-statistics center tech stack

---


## Backend
### GO + Python

This combination of technologies is commonly used for building web applications. GO is a powerful and performant language that excels in web service development, while Python's versatility makes it suitable for analytics and data science tasks.

#### GO
##### Benefits:
- **Performance**: GO's compiled nature ensures high performance.
- **Concurrency**: Built-in support for concurrency makes it ideal for handling a high volume of requests.
- **Simplicity**: Clean syntax simplifies learning and usage.

##### Drawbacks:
- **Learning curve**: Slightly different development philosophy may require some adjustment for new developers.

#### Python
##### Benefits:
- **Versatility**: Applicable to various tasks, including web development, data science, and machine learning.
- **Libraries**: Extensive library ecosystem for almost any task imaginable.

##### Drawbacks:
- **Performance**: Interpreted language, potentially slower than compiled languages like GO.
- **Scalability**: Challenges in scaling Python applications for high traffic volumes.


## Frontend
### Web: React.js
#### Benefits:

- **Flexibility**: React is a library, not a framework, so it provides more flexibility and allows you to choose the libraries and tools you want to use for file storage. This can be beneficial if you have specific requirements or want to integrate with existing systems.
- **Component-based architecture**: React's component-based architecture makes it easy to create reusable components for different parts of your file storage application, such as file upload, file management, and file display.
- **Virtual DOM**: React's virtual DOM can improve the performance of your application by minimizing the number of DOM manipulations needed when files are added, edited, or deleted.
- **Large community and ecosystem**: React has a large and active community, which means that there are many resources available to help you learn and build your application. Additionally, there are many libraries and tools available that can be used with React for file storage, such as React Dropzone and React FilePond.

##### Drawbacks:

- More complex setup: React requires setting up a build process and other tooling, which can be more complex than other frameworks.

### Mobile: React Native or Flutter

These popular cross-platform frameworks enable mobile app development. React Native utilizes JavaScript, while Flutter is based on Dart.

#### React Native
##### Benefits:
- **Performance**: Native code compilation ensures native app-like performance.
- **Developer experience**: Leverages JavaScript, a popular language with a large community.

##### Drawbacks:
- **Learning curve**: Slightly different syntax compared to native development, requiring some adaptation.

#### Flutter
##### Benefits:
- **Performance**: Native code compilation ensures native app-like performance.
- **Native widgets**: Native widgets provide a native look and feel for apps.

##### Drawbacks:
- **Learning curve**: Relatively new language, requiring some time to learn.
- **Community**: Relatively young community with limited resources compared to established ones.

## Database

### Survey questionnaires: MongoDB

Since survey questionnaires will not have strictly defined schema and structure we consider that using NoSQL database such as MongoDB will be more beneficial than using Relational DB.

#### MongoDB:
##### Benefits:

- **Schema Flexibility**: MongoDB's document-oriented structure allows for flexible data modeling, adapting to evolving data requirements without schema changes.
- **High Performance**: In-memory storage and efficient querying capabilities enable fast data access and manipulation.
- **Scalability**: Horizontal scalability ensures seamless growth to accommodate increasing data volumes.
- **Developer Productivity**: Familiar and intuitive data model resembles JSON, reducing the learning curve and accelerating development.
- **Rich Query Language**: Expressive and intuitive query language, MQL, supports complex queries and data analysis.
- **Versatility**: Caters to a wide range of applications, including web, mobile, data analytics, and content management systems.

##### Drawbacks:

- **Data Integrity**: Eventual consistency can lead to temporary data inconsistencies.
- **Join Operations**: Complex joins can be inefficient, requiring denormalization or specialized techniques.
- **Limited ACID Compliance**: Transactions provide weaker ACID (Atomicity, Consistency, Isolation, Durability) guarantees compared to relational databases.


### Survey results: Cassandra + OpenSearch or Elasticsearch

These NoSQL databases are suitable for storing and analyzing survey data. Cassandra's distributed nature provides scalability and high performance, while OpenSearch and Elasticsearch excel in indexing and searching survey data.

#### Cassandra
##### Benefits:
- **Scalability**: Distributed architecture enables handling large amounts of data.
- **Performance**: Highly performant database, crucial for processing large datasets.

##### Drawbacks:
- **Complexity**: More complex than other NoSQL databases, making learning and usage more challenging.
- **Consistency**: Eventual consistency may cause data to not be immediately available after writing.

#### OpenSearch or Elasticsearch
##### Benefits:
- **Search**: Powerful search engines for indexing and searching survey data.
- **Scalability**: Scalable search engines for handling large amounts of data.
- OpenSearch has open licensing, while Elastic has more closed license.


##### Drawbacks:
- **Ecosystem**: OpenSearch's relatively new ecosystem may not be as mature as Elasticsearch's. 
- **Performance**: in some cases OpenSearch is less performant than Elastic

## Dashboard
### Kibana or Grafana

These data visualization tools create dashboards for survey data analysis and insights.

#### Kibana
##### Benefits:
- **Integration**: Seamless integration with Elastic Stack for comprehensive data analysis.
- **Visualization**: Powerful data visualization capabilities for effective data presentation.
##### Drawbacks:
- **Learning curve**: Slightly steeper learning curve compared to Grafana.
- **Customization**: Limited customization options compared to Grafana.

#### Grafana
##### Benefits:
- **Ease of use**: User-friendly interface for easy data exploration and visualization.
- **Customization**: Highly customizable dashboard creation for tailored visualizations.

##### Drawbacks:
- **Integration**: Requires integration with external data sources for analysis.
- **Visualization**: Less advanced data visualization capabilities compared to Kibana.

## Notifications and Events

### Kafka and ActiveMQ

#### Kafka

- **Scalability**: Kafka excels at handling large volumes of messages, making it ideal for broadcasting notifications to many users simultaneously. Its distributed architecture can easily scale to accommodate growing notification demands.
- **Decoupling**: Kafka acts as a decoupling mechanism, allowing notification producers (e.g., application servers) to send messages without knowing who will receive them. This decoupling improves resilience and flexibility.
- **Persistent Delivery**: Kafka guarantees message delivery at least once, ensuring notifications reach their intended recipients even in case of failures.
- **Durability**: Kafka stores messages on disk, making them recoverable even if the system restarts. This durability is crucial for critical notifications.


#### ActiveMQ

- **Flexibility**: ActiveMQ supports various message routing patterns, including point-to-point, publish-subscribe, and topic-based routing. This flexibility allows you to tailor your notification system to specific needs.
- **Integrations**: ActiveMQ seamlessly integrates with diverse message formats, protocols (e.g., STOMP, AMQP), and client libraries, enabling easy integration with your existing infrastructure.
- **Reliability**: ActiveMQ offers advanced features like message redelivery, retry attempts, and dead-letter queues to ensure reliable message delivery even in case of transient errors.
- **Real-time notifications**: ActiveMQ supports low-latency messaging, enabling real-time notification delivery for urgent situations.

### Combining Kafka and ActiveMQ

- **High-performance notification delivery**: Leveraging Kafka's scalability and ActiveMQ's routing capabilities, you can achieve high-performance delivery of notifications to a large user base.
- **Resilient and fault-tolerant**: The combined system can handle failures gracefully and ensure notification delivery even if individual components experience hiccups.
- **Flexibility for diverse needs**: This stack caters to various notification types, ranging from real-time alerts to batch updates, with customizable routing and delivery guarantees.
- **Microservices-friendly**: Both technologies are well-suited for microservices architectures, facilitating modularity and independent deployment of notification producers and consumers.

### Potential drawbacks

- **Complexity**: Managing and monitoring two separate systems might increase operational overhead compared to using a single platform.
- **Learning curve**: Familiarity with both Kafka and ActiveMQ is required for optimal utilization.

Overall, the Kafka + ActiveMQ combination provides a powerful and flexible foundation for building a robust and scalable notification system. Its strengths lie in decoupling, scalability, diverse routing options, and reliable message delivery, making it suitable for handling a wide range of notification needs in modern applications.

For permanent event log/storage we should consider using `Cassandra`, which was proposed above.

## File Storage

Storing large survey related files such as Audio/Video requires file storage.

### Ceph

Ceph provides a flexible foundation for all data storage, uniting object, block and file types in a single unified RADOS cluster. For enterprises with multiple storage type requirements, Ceph provides a simplified, flexible solution.

Take multiple forms of data from multiple sources, and turn it into a powerful tool for driving artificial intelligence (AI), machine learning (ML), and drawing valuable insights for business decisions. 

#### Ceph
##### Benefits:

- **Open-source**: Free to use and customize, offering greater control and flexibility.
- **On-premises deployment**: Allows private storage within your infrastructure, ideal for security-conscious environments or applications requiring low latency.
- **High performance**: Capable of handling massive data volumes with low latency and high throughput, suitable for demanding workloads.
- **Scalability**: Horizontally scalable to accommodate growing storage needs without limitations.
- **Block, object, and file storage**: Supports various data access patterns through unified storage, simplifying management.
- **Compatibility**: Ceph supports a RESTful API that is compatible with the basic data access model of the *Amazon S3 API*.

##### Drawbacks:

- **Complexity**: Requires technical expertise for setup, configuration, and maintenance.
- **Monitoring and management**: Managing a large Ceph cluster can be resource-intensive.
- **Limited SLA options**: May require custom configurations for desired availability and durability guarantees.


## Infrastructure

###  Docker, Kubernetes and Terraform

Overall, using Docker, Kubernetes, and Terraform together provides a powerful and flexible solution for building, deploying, and managing containerized applications in a modern and efficient manner.

- **Simplified application development and deployment**: Docker, Kubernetes, and Terraform together provide a streamlined and efficient workflow for developing, deploying, and managing containerized applications.

- **Resource optimization and scalability**: The combination of Docker, Kubernetes, and Terraform optimizes resource utilization and enables seamless scaling of containerized applications to meet changing demands.

- **Resilient and highly available applications**: Kubernetes' self-healing capabilities ensure that applications remain resilient in the face of failures, while Terraform's automation and consistency ensure that infrastructure is always available to support applications.

- **Reduced operational overhead**: The automation and efficiency provided by Docker, Kubernetes, and Terraform reduce operational overhead and free up IT teams to focus on more strategic initiatives.

- **Cost-effectiveness**: The combination of Docker, Kubernetes, and Terraform can reduce infrastructure costs by optimizing resource utilization and eliminating manual configuration errors.

#### Docker
- **Lightweight and isolated containers**: Docker containers are lightweight and isolated environments that share the underlying host operating system, reducing resource overhead compared to traditional virtual machines (VMs). This makes Docker containers ideal for running microservices and other applications that require high performance and isolation.

- **Streamlined development and deployment**: Docker provides a consistent and isolated environment for developing, testing, and deploying applications. This makes it easy to develop and test applications locally and then deploy them to production without any changes.

- **Portability and flexibility**: Docker images are portable across different infrastructure environments, including on-premises, cloud, and hybrid deployments. This makes Docker containers ideal for running applications on a variety of platforms.

#### Kubernetes
- **Automated deployment and scaling**: Kubernetes automates the deployment and scaling of containerized applications across a cluster of machines. This eliminates manual intervention and reduces operational overhead. Kubernetes can dynamically adjust the number of container replicas based on demand, ensuring optimal resource utilization and performance.

- **Resource optimization**: Kubernetes efficiently manages resource allocation across a cluster of machines, ensuring that applications receive the necessary CPU, memory, and storage resources to function optimally. It prevents resource contention and maximizes resource utilization.

- **Self-healing capabilities**: Kubernetes' self-healing capabilities ensure that applications remain resilient in the face of failures. It automatically restarts failed containers, replaces unhealthy nodes, and rebalances workloads, maintaining application uptime and availability.

#### Terraform
- **Infrastructure provisioning and management**: Terraform automates the provisioning and management of infrastructure resources, such as cloud instances, networking, and storage, required for running Docker containers and Kubernetes clusters. It simplifies infrastructure configuration and deployment, ensuring consistency and repeatability across environments.

- **Infrastructure as Code (IaC)**: Terraform promotes Infrastructure as Code (IaC) principles, enabling infrastructure to be defined and managed as code, using declarative configuration files. This approach facilitates version control, collaboration, and integration with CI/CD pipelines for automated infrastructure provisioning and updates.

- **Environment consistency**: Terraform ensures consistent and repeatable infrastructure provisioning across different environments, including development, testing, and production. It eliminates manual configuration errors and ensures that deployments adhere to defined infrastructure standards.

- **Modular infrastructure management**: Terraform's modular approach allows for breaking down infrastructure into manageable modules, simplifying complex infrastructure configurations. This modularity promotes code reuse, maintainability, and scalability for large-scale infrastructure deployments.

##### Integration with Docker and Kubernetes
- **Automated infrastructure provisioning for Docker and Kubernetes**: Terraform can automatically provision and configure Docker containers and Kubernetes clusters, including nodes, networking, and storage. This simplifies the process of deploying containerized applications to production.

- **Integration with CI/CD pipelines**: Terraform can be integrated with CI/CD pipelines to automate infrastructure provisioning and updates as part of the application deployment process. This ensures that infrastructure changes are coordinated with application deployments.


## User management

Regarding user management there are several options that should be assesed in deeper research and highly dependent on SSO providers, preferred policies/etc.

