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  1. Stackups
  2. Application & Data
  3. Databases
  4. Databases
  5. Cassandra vs Heroku Postgres

Cassandra vs Heroku Postgres

OverviewDecisionsComparisonAlternatives

Overview

Cassandra
Cassandra
Stacks3.6K
Followers3.5K
Votes507
GitHub Stars9.5K
Forks3.8K
Heroku Postgres
Heroku Postgres
Stacks607
Followers314
Votes38

Cassandra vs Heroku Postgres: What are the differences?

Cassandra: A partitioned row store. Rows are organized into tables with a required primary key. 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; Heroku Postgres: Heroku's Database-as-a-Service. Based on the most powerful open-source database, PostgreSQL. Heroku Postgres provides a SQL database-as-a-service that lets you focus on building your application instead of messing around with database management.

Cassandra belongs to "Databases" category of the tech stack, while Heroku Postgres can be primarily classified under "PostgreSQL as a Service".

"Distributed" is the top reason why over 96 developers like Cassandra, while over 27 developers mention "Easy to setup" as the leading cause for choosing Heroku Postgres.

Cassandra is an open source tool with 5.27K GitHub stars and 2.35K GitHub forks. Here's a link to Cassandra's open source repository on GitHub.

According to the StackShare community, Cassandra has a broader approval, being mentioned in 342 company stacks & 240 developers stacks; compared to Heroku Postgres, which is listed in 74 company stacks and 39 developer stacks.

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Advice on Cassandra, Heroku Postgres

Jorge
Jorge

Jan 15, 2020

Needs advice

Considering moving part of our PostgreSQL database infrastructure to the cloud, however, not quite sure between AWS, Heroku, Azure and Google cloud. Things to consider: The main reason is for backing up and centralize all our data in the cloud. With that in mind the main elements are: -Pricing for storage. -Small team. -No need for high throughput. -Support for docker swarm and Kubernetes.

51.8k views51.8k
Comments
Vinay
Vinay

Head of Engineering

Sep 19, 2019

Needs advice

The problem I have is - we need to process & change(update/insert) 55M Data every 2 min and this updated data to be available for Rest API for Filtering / Selection. Response time for Rest API should be less than 1 sec.

The most important factors for me are processing and storing time of 2 min. There need to be 2 views of Data One is for Selection & 2. Changed data.

174k views174k
Comments

Detailed Comparison

Cassandra
Cassandra
Heroku Postgres
Heroku Postgres

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.

Heroku Postgres provides a SQL database-as-a-service that lets you focus on building your application instead of messing around with database management.

-
High Availability;Rollback;Dataclips;Automated Health Checks
Statistics
GitHub Stars
9.5K
GitHub Stars
-
GitHub Forks
3.8K
GitHub Forks
-
Stacks
3.6K
Stacks
607
Followers
3.5K
Followers
314
Votes
507
Votes
38
Pros & Cons
Pros
  • 119
    Distributed
  • 98
    High performance
  • 81
    High availability
  • 74
    Easy scalability
  • 53
    Replication
Cons
  • 3
    Reliability of replication
  • 1
    Size
  • 1
    Updates
Pros
  • 29
    Easy to setup
  • 3
    Extremely reliable
  • 3
    Dataclips for sharing queries
  • 3
    Follower databases
Cons
  • 2
    Super expensive
Integrations
No integrations available
PostgreSQL
PostgreSQL
Heroku
Heroku

What are some alternatives to Cassandra, Heroku Postgres?

MongoDB

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

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

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

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

SQLite

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.

Memcached

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

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

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

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.

InfluxDB

InfluxDB

InfluxDB is a scalable datastore for metrics, events, and real-time analytics. It has a built-in HTTP API so you don't have to write any server side code to get up and running. InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out.

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