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

Cassandra vs CrateIO

OverviewDecisionsComparisonAlternatives

Overview

Cassandra
Cassandra
Stacks3.6K
Followers3.5K
Votes507
GitHub Stars9.5K
Forks3.8K
CrateIO
CrateIO
Stacks19
Followers39
Votes7
GitHub Stars4.3K
Forks581

Cassandra vs CrateIO: What are the differences?

Introduction Cassandra and CrateIO are both popular distributed database systems with different architectures and use cases. Understanding the key differences between the two can help in making an informed decision on which technology to choose.

  1. Data Model: Cassandra uses a column-family data model, which organizes data in rows and columns similar to a relational database, whereas CrateIO uses a JSON document store data model, storing data in JSON format with dynamic schemas allowing for flexibility in data storage and retrieval.
  2. Consistency Model: Cassandra employs tunable consistency levels, allowing users to choose between strong consistency or high availability, while CrateIO follows a strong consistency model by default to ensure data integrity, reducing the risk of conflicts but potentially impacting availability.
  3. Query Language: Cassandra uses CQL (Cassandra Query Language), a SQL-like language, for querying and manipulating data, whereas CrateIO supports SQL as its query language, making it more familiar to users already experienced with SQL databases.
  4. Data Distribution: Cassandra uses partitioning and replication to distribute data across multiple nodes in the cluster for high availability and fault tolerance, while CrateIO automatically shards data across the cluster based on primary keys to achieve horizontal scalability and distribute query loads.
  5. Indexing: Cassandra relies on secondary indexes for querying non-primary key columns, which can impact performance on large datasets, whereas CrateIO utilizes inverted indexes for efficient full-text search and filtering, offering better performance for complex queries.
  6. Community Support: Cassandra has a larger and more established community with extensive documentation, support, and active development, making it easier to find resources and solutions to common issues, while CrateIO, being a newer entrant, is rapidly growing its community but may have fewer resources available.

In Summary, understanding the key differences between Cassandra and CrateIO in terms of data model, consistency model, query language, data distribution, indexing, and community support can help in making an informed decision on the right database technology for specific use cases.

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Advice on Cassandra, CrateIO

Micha
Micha

CEO & Co-Founder at Dechea

May 27, 2022

Decided

Fauna is a serverless database where you store data as JSON. Also, you have build in a HTTP GraphQL interface with a full authentication & authorization layer. That means you can skip your Backend and call it directly from the Frontend. With the power, that you can write data transformation function within Fauna with her own language called FQL, we're getting a blazing fast application.

Also, Fauna takes care about scaling and backups (All data are sharded on three different locations on the globe). That means we can fully focus on writing business logic and don't have to worry anymore about infrastructure.

93k views93k
Comments
Krishna Chaitanya
Krishna Chaitanya

Head of Technology at Adonmo

Jun 27, 2021

Review

For such a more realtime-focused, data-centered application like an exchange, it's not the frontend or backend that matter much. In fact for that, they can do away with any of the popular frameworks like React/Vue/Angular for the frontend and Go/Python for the backend. For example uniswap's frontend (although much simpler than binance) is built in React. The main interesting part here would be how they are able to handle updating data so quickly. In my opinion, they might be heavily reliant on realtime processing systems like Kafka+Kafka Streams, Apache Flink or Apache Spark Stream or similar. For more processing heavy but not so real-time processing, they might be relying on OLAP and/or warehousing tools like Cassandra/Redshift. They could have also optimized few high frequent queries using NoSQL stores like mongodb (for persistance) and in-memory cache like Redis (for further perfomance boost to get millisecond latencies).

53.8k views53.8k
Comments
Umair
Umair

Technical Architect at ERP Studio

Feb 12, 2021

Needs adviceonPostgreSQLPostgreSQLTimescaleDBTimescaleDBDruidDruid

Developing a solution that collects Telemetry Data from different devices, nearly 1000 devices minimum and maximum 12000. Each device is sending 2 packets in 1 second. This is time-series data, and this data definition and different reports are saved on PostgreSQL. Like Building information, maintenance records, etc. I want to know about the best solution. This data is required for Math and ML to run different algorithms. Also, data is raw without definitions and information stored in PostgreSQL. Initially, I went with TimescaleDB due to PostgreSQL support, but to increase in sites, I started facing many issues with timescale DB in terms of flexibility of storing data.

My major requirement is also the replication of the database for reporting and different purposes. You may also suggest other options other than Druid and Cassandra. But an open source solution is appreciated.

462k views462k
Comments

Detailed Comparison

Cassandra
Cassandra
CrateIO
CrateIO

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.

Crate is a distributed data store. Simply install Crate directly on your application servers and make the big centralized database a thing of the past. Crate takes care of synchronization, sharding, scaling, and replication even for mammoth data sets.

-
Familiar SQL syntax;Semi-structured data;High availability, resiliency, and scalability in a distributed design;Powerful Lucene based full-text search
Statistics
GitHub Stars
9.5K
GitHub Stars
4.3K
GitHub Forks
3.8K
GitHub Forks
581
Stacks
3.6K
Stacks
19
Followers
3.5K
Followers
39
Votes
507
Votes
7
Pros & Cons
Pros
  • 119
    Distributed
  • 98
    High performance
  • 81
    High availability
  • 74
    Easy scalability
  • 53
    Replication
Cons
  • 3
    Reliability of replication
  • 1
    Updates
  • 1
    Size
Pros
  • 3
    Simplicity
  • 2
    Open source
  • 2
    Scale
Integrations
No integrations available
Docker
Docker

What are some alternatives to Cassandra, CrateIO?

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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