RethinkDB

Website

rethinkdb.com

$ Details

Categories

#Databases #NoSQL Databases #Relational Databases #Graph Databases

Apache Ignite

Website

ignite.apache.org

$ Details

Categories

#Databases #NoSQL Databases #Key-Value Database #Relational Databases

RethinkDB features and specs

• Real-time Capabilities
  RethinkDB offers built-in real-time capabilities that allow applications to subscribe to data changes. This makes it ideal for building real-time applications like live dashboards, multiplayer games, or collaborative tools.
• Flexible Query Language
  RethinkDB's query language, ReQL, is highly flexible and allows for complex queries. It supports operations such as joins, aggregations, and transformations directly within the database.
• Distributed Database
  RethinkDB is designed to be distributed from the ground up. It can automatically replicate data and distribute queries across multiple machines to improve scalability and fault tolerance.
• Web-based Administration
  RethinkDB provides a user-friendly web-based administration interface that simplifies database management and monitoring. This interface makes it easier for developers and system administrators to manage their databases.
• JSON Data Model
  RethinkDB uses JSON as its data model, which makes it easy to store and work with structured data. This can be especially beneficial for web developers who are used to working with JSON data.

Possible disadvantages of RethinkDB

• Limited Ecosystem
  Compared to more established databases like MySQL or MongoDB, RethinkDB has a smaller ecosystem. This means fewer third-party tools, libraries, and community support are available.
• Stalled Development
  After the company behind RethinkDB shut down in 2016, the development slowed significantly. Although the project was later open-sourced and the community keeps it alive, the updates and new features are not as frequent.
• Learning Curve
  While ReQL is powerful, it can have a steep learning curve for developers who are new to RethinkDB or who are more familiar with SQL or other NoSQL query languages.
• Resource Intensive
  RethinkDB can be more resource-intensive compared to some other databases. It may require more RAM and CPU resources to achieve optimal performance, which can be a consideration for resource-constrained environments.
• Fewer Enterprise Features
  RethinkDB lacks some enterprise-level features such as advanced security options, comprehensive backup solutions, and robust user management compared to other database systems like MongoDB or PostgreSQL.

Apache Ignite features and specs

• In-Memory Data Grid
  Apache Ignite provides a robust in-memory data grid that can drastically improve data access speeds by storing data in memory across distributed nodes.
• Scalability
  The system is designed to scale horizontally, allowing users to add more nodes to handle increased loads, thereby ensuring high availability and performance.
• Distributed Compute Capabilities
  Ignite supports parallel execution of tasks across cluster nodes, which is beneficial for complex computations and real-time processing.
• Persistence
  Although primarily in-memory, Ignite offers a durable and transactional Persistence layer that ensures data can be persisted on disk, providing a hybrid in-memory and persistent storage solution.
• SQL Queries
  Ignite offers support for ANSI-99 SQL, which allows users to execute complex SQL queries across distributed datasets easily.
• Integration
  It integrates well with existing Hadoop and Spark setups, allowing users to enhance their existing data pipelines with Ignite’s capabilities.
• Fault Tolerance
  Apache Ignite includes built-in mechanisms for recovery and ensures that data copies are maintained across nodes for resilience against node failures.

Possible disadvantages of Apache Ignite

• Complexity
  Apache Ignite can be complex to set up and manage, especially when configuring a large, distributed system with multiple nodes.
• Resource Intensive
  Running an in-memory data grid like Ignite requires significant memory resources, which can increase operational costs.
• Learning Curve
  Due to its comprehensive features and distributed nature, there is a steep learning curve associated with effectively utilizing Ignite.
• Configuration Overhead
  There is substantial configuration overhead involved to optimize performance and ensure proper cluster management.
• Community Support
  Although it has active development, the community support might not be as robust compared to other more mature solutions, possibly leading to challenges in finding solutions to niche issues.
• YARN Dependence
  For those looking to integrate with Hadoop, Ignite's optimal performance is sometimes reliant on Hadoop YARN, which can introduce additional complexity.

Mike Glukhovsky - RethinkDB Year in Review 2015

More videos:

Best Practices for a Microservices Architecture on Apache Ignite

More videos: