GridDB vs KeyDB

Overview

KeyDB

Stacks37

Followers62

Votes5

GridDB

Stacks3

Followers18

Votes0

GitHub Stars0

Forks0

GridDB vs KeyDB: What are the differences?

Introduction:

GridDB and KeyDB are two different database management systems that cater to specific needs based on their differences.

Data Model: GridDB utilizes a column-oriented data model, which is suitable for data warehousing and analytical processing, while KeyDB follows a key-value data model, ideal for caching and real-time applications requiring fast read and write operations.
Replication: GridDB offers both synchronous and asynchronous replication methods, ensuring data consistency and high availability, whereas KeyDB focuses on replication through Redis' built-in replication capabilities for data redundancy and fault tolerance.
Partitioning Strategy: GridDB utilizes sharding for efficient data distribution and load balancing across multiple nodes, enabling horizontal scaling and improved performance, whereas KeyDB relies on Redis Cluster for partitioning data, supporting distributed data storage and processing for high scalability.
Consistency Model: GridDB guarantees ACID properties for transactions, ensuring data integrity and reliability, while KeyDB offers eventual consistency by default, prioritizing high availability and partition tolerance over strong consistency.
Language Support: GridDB supports multiple programming languages such as Java, C, and Python, allowing developers to integrate the database with various applications easily, whereas KeyDB primarily emphasizes compatibility with Redis protocols and data structures, making it a preferred choice for existing Redis users seeking enhanced performance.
Use Cases: GridDB is ideal for enterprise applications requiring complex data analytics, time series data processing, and IoT data management, whereas KeyDB is well-suited for real-time applications, distributed caching, and high-performance solutions that demand low-latency data access.

In Summary, GridDB and KeyDB differ in their data models, replication methods, partitioning strategies, consistency models, language support, and preferred use cases, making them suitable for diverse application requirements.

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Detailed Comparison

KeyDB	GridDB
KeyDB is a fully open source database that aims to make use of all hardware resources. KeyDB makes it possible to breach boundaries often dictated by price and complexity.	It is a highly scalable, in-memory NoSQL time series database optimized for IoT and Big Data. It has a KVS (Key-Value Store)-type data model that is suitable for sensor data stored in a timeseries. It is a database that can be easily scaled-out according to the number of sensors.
Active Replication; FLASH storage support; direct backup to AWS S3; MultiMaster; Multithreaded	IoT Data Model; Distributed; Horizontal Scalability;In-memory;Hybrid Cluster Management;Fast Ingest;Composite Indexes;Petabyte-Scale DB size;Time series functions;Geometry data support
Statistics
GitHub Stars -	GitHub Stars 0
GitHub Forks -	GitHub Forks 0
Stacks 37	Stacks 3
Followers 62	Followers 18
Votes 5	Votes 0
Pros & Cons
Pros 3 Performance 2 Active Replication	No community feedback yet
Integrations
No integrations available	Python Ubuntu Node.js CentOS Fluentd openSUSE

What are some alternatives to KeyDB, GridDB?

Redis

Redis is an open source (BSD licensed), in-memory data structure store, used as a database, cache, and message broker. Redis provides data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes, and streams.

Hazelcast

With its various distributed data structures, distributed caching capabilities, elastic nature, memcache support, integration with Spring and Hibernate and more importantly with so many happy users, Hazelcast is feature-rich, enterprise-ready and developer-friendly in-memory data grid solution.

Aerospike

Aerospike is an open-source, modern database built from the ground up to push the limits of flash storage, processors and networks. It was designed to operate with predictable low latency at high throughput with uncompromising reliability – both high availability and ACID guarantees.

MemSQL

MemSQL converges transactions and analytics for sub-second data processing and reporting. Real-time businesses can build robust applications on a simple and scalable infrastructure that complements and extends existing data pipelines.

Apache Ignite

It is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads delivering in-memory speeds at petabyte scale

SAP HANA

It is an application that uses in-memory database technology that allows the processing of massive amounts of real-time data in a short time. The in-memory computing engine allows it to process data stored in RAM as opposed to reading it from a disk.

VoltDB

VoltDB is a fundamental redesign of the RDBMS that provides unparalleled performance and scalability on bare-metal, virtualized and cloud infrastructures. VoltDB is a modern in-memory architecture that supports both SQL + Java with data durability and fault tolerance.

Tarantool

It is designed to give you the flexibility, scalability, and performance that you want, as well as the reliability and manageability that you need in mission-critical applications

Azure Redis Cache

It perfectly complements Azure database services such as Cosmos DB. It provides a cost-effective solution to scale read and write throughput of your data tier. Store and share database query results, session states, static contents, and more using a common cache-aside pattern.

LokiJS

LokiJS is a document oriented database written in javascript, published under MIT License. Its purpose is to store javascript objects as documents in a nosql fashion and retrieve them with a similar mechanism. Runs in node (including cordova/phonegap and node-webkit), nativescript and the browser.

Related Comparisons

GridDB vs KeyDB: What are the differences?

Introduction:

GridDB and KeyDB are two different database management systems that cater to specific needs based on their differences.

Data Model: GridDB utilizes a column-oriented data model, which is suitable for data warehousing and analytical processing, while KeyDB follows a key-value data model, ideal for caching and real-time applications requiring fast read and write operations.
Replication: GridDB offers both synchronous and asynchronous replication methods, ensuring data consistency and high availability, whereas KeyDB focuses on replication through Redis' built-in replication capabilities for data redundancy and fault tolerance.
Partitioning Strategy: GridDB utilizes sharding for efficient data distribution and load balancing across multiple nodes, enabling horizontal scaling and improved performance, whereas KeyDB relies on Redis Cluster for partitioning data, supporting distributed data storage and processing for high scalability.
Consistency Model: GridDB guarantees ACID properties for transactions, ensuring data integrity and reliability, while KeyDB offers eventual consistency by default, prioritizing high availability and partition tolerance over strong consistency.
Language Support: GridDB supports multiple programming languages such as Java, C, and Python, allowing developers to integrate the database with various applications easily, whereas KeyDB primarily emphasizes compatibility with Redis protocols and data structures, making it a preferred choice for existing Redis users seeking enhanced performance.
Use Cases: GridDB is ideal for enterprise applications requiring complex data analytics, time series data processing, and IoT data management, whereas KeyDB is well-suited for real-time applications, distributed caching, and high-performance solutions that demand low-latency data access.

GridDB vs KeyDB

Overview