Heroku Redis vs RocksDB

Overview

RocksDB

Stacks141

Followers290

Votes11

GitHub Stars30.9K

Forks6.6K

Heroku Redis

Stacks105

Followers163

Votes5

Heroku Redis vs RocksDB: What are the differences?

Introduction

This markdown code provides a comparison of the key differences between Heroku Redis and RocksDB. The following paragraphs highlight six specific differences between the two technologies.

Data Storage Mechanism: Heroku Redis is an in-memory data store that primarily uses RAM for fast data retrieval and is able to persist data on disk. On the other hand, RocksDB is an embeddable persistent key-value store that primarily uses disk storage for data persistence. While Heroku Redis focuses on fast data access, RocksDB prioritizes durability and reliable disk-based storage.
Deployment Model: Heroku Redis is offered as a managed service, ensuring ease of use and minimal maintenance requirements for developers. Conversely, RocksDB is an open-source library that can be incorporated into applications as a component, which requires developers to handle the operational aspects themselves.
Scalability: Heroku Redis provides built-in scalability options like vertical scaling (increasing memory size) and horizontal scaling (replication and sharding). RocksDB, on the other hand, lacks built-in scalability features and requires custom implementations for horizontal scaling.
Performance Focus: Heroku Redis emphasizes speed and low latency, making it suitable for use cases that require fast data retrieval. RocksDB, on the other hand, concentrates on data consistency and durability, making it more suitable for use cases that prioritize fault tolerance and long-term data storage.
Use Case Compatibility: Heroku Redis is commonly used for caching, session management, and real-time analytics due to its fast in-memory data access. RocksDB is typically utilized for scenarios that require persistent storage with low-latency point lookups, such as search indexes and message queues.
Cost Structure: Heroku Redis has a usage-based pricing model, where costs are calculated based on factors like memory size and data transfer. RocksDB, being an open-source library, does not have any direct cost associated with its usage apart from operational and maintenance expenses.

In summary, Heroku Redis and RocksDB differ in their data storage mechanisms, deployment models, scalability options, performance focus, use case compatibility, and cost structures.

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CLI (Node.js)

Manual

Detailed Comparison

RocksDB	Heroku Redis
RocksDB is an embeddable persistent key-value store for fast storage. RocksDB can also be the foundation for a client-server database but our current focus is on embedded workloads. RocksDB builds on LevelDB to be scalable to run on servers with many CPU cores, to efficiently use fast storage, to support IO-bound, in-memory and write-once workloads, and to be flexible to allow for innovation.	Heroku Redis is an in-memory key-value data store, run by Heroku, that is provisioned and managed as an add-on. Heroku Redis is accessible from any language with a Redis driver, including all languages and frameworks supported by Heroku.
Designed for application servers wanting to store up to a few terabytes of data on locally attached Flash drives or in RAM;Optimized for storing small to medium size key-values on fast storage -- flash devices or in-memory;Scales linearly with number of CPUs so that it works well on ARM processors	Easily Optimize;Vertically Scalable
Statistics
GitHub Stars 30.9K	GitHub Stars -
GitHub Forks 6.6K	GitHub Forks -
Stacks 141	Stacks 105
Followers 290	Followers 163
Votes 11	Votes 5
Pros & Cons
Pros 5 Very fast 3 Made by Facebook 2 Consistent performance 1 Ability to add logic to the database layer where needed	Pros 5 More reliable than the other Redis add-ons Cons 1 More expensive than the other options
Integrations
No integrations available	Heroku Redis

What are some alternatives to RocksDB, Heroku Redis?

MongoDB

MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Cassandra

Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

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Heroku Redis vs RocksDB: What are the differences?

Introduction

This markdown code provides a comparison of the key differences between Heroku Redis and RocksDB. The following paragraphs highlight six specific differences between the two technologies.

Data Storage Mechanism: Heroku Redis is an in-memory data store that primarily uses RAM for fast data retrieval and is able to persist data on disk. On the other hand, RocksDB is an embeddable persistent key-value store that primarily uses disk storage for data persistence. While Heroku Redis focuses on fast data access, RocksDB prioritizes durability and reliable disk-based storage.
Deployment Model: Heroku Redis is offered as a managed service, ensuring ease of use and minimal maintenance requirements for developers. Conversely, RocksDB is an open-source library that can be incorporated into applications as a component, which requires developers to handle the operational aspects themselves.
Scalability: Heroku Redis provides built-in scalability options like vertical scaling (increasing memory size) and horizontal scaling (replication and sharding). RocksDB, on the other hand, lacks built-in scalability features and requires custom implementations for horizontal scaling.
Performance Focus: Heroku Redis emphasizes speed and low latency, making it suitable for use cases that require fast data retrieval. RocksDB, on the other hand, concentrates on data consistency and durability, making it more suitable for use cases that prioritize fault tolerance and long-term data storage.
Use Case Compatibility: Heroku Redis is commonly used for caching, session management, and real-time analytics due to its fast in-memory data access. RocksDB is typically utilized for scenarios that require persistent storage with low-latency point lookups, such as search indexes and message queues.
Cost Structure: Heroku Redis has a usage-based pricing model, where costs are calculated based on factors like memory size and data transfer. RocksDB, being an open-source library, does not have any direct cost associated with its usage apart from operational and maintenance expenses.

In summary, Heroku Redis and RocksDB differ in their data storage mechanisms, deployment models, scalability options, performance focus, use case compatibility, and cost structures.