CrateIO vs Scylla

Overview

CrateIO

Stacks19

Followers39

Votes7

GitHub Stars4.3K

Forks581

ScyllaDB

Stacks143

Followers197

Votes8

CrateIO vs Scylla: What are the differences?

Introduction: In the world of data management systems, CrateIO and Scylla are two popular options with distinctive features. Below are the key differences between CrateIO and Scylla.

Architecture: CrateIO follows a distributed SQL engine architecture, allowing users to query data in real-time using SQL. On the other hand, Scylla uses a shared-nothing architecture based on Apache Cassandra, providing high availability and fault tolerance through sharding and replication.
Consistency Model: CrateIO supports eventual consistency, making it suitable for applications where eventual consistency is acceptable. In contrast, Scylla offers strong consistency by default, ensuring data integrity and reliability in critical applications.
Performance: CrateIO is optimized for OLAP (Online Analytical Processing) workloads, enabling high-speed analytics on large datasets. Scylla, on the other hand, excels in OLTP (Online Transaction Processing) scenarios, providing low-latency and high-throughput for transaction-heavy applications.
Data Model: CrateIO supports a wide range of data types and complex data structures like arrays and objects, making it versatile for handling diverse data sets. Meanwhile, Scylla focuses on a tabular data model similar to Cassandra, which is conducive to efficient storage and retrieval of structured data.
Query Language: CrateIO supports SQL queries, making it easy for users familiar with SQL to interact with the database. In contrast, Scylla uses CQL (Cassandra Query Language), a query language similar to SQL with additional features specific to Cassandra databases.
Ecosystem: CrateIO has a relatively small ecosystem compared to Scylla, limiting the availability of integrations and tools. Scylla, being built on Apache Cassandra, benefits from a thriving ecosystem of tools, libraries, and community support.

In Summary, CrateIO and Scylla differ significantly in their architecture, consistency model, performance focus, data model, query language, and ecosystem support.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs

CLI (Node.js)

Manual

Advice on CrateIO, ScyllaDB

Tom

CEO at Gentlent

Jun 9, 2020

Decided

The Gentlent Tech Team made lots of updates within the past year. The biggest one being our database:

We decided to migrate our #PostgreSQL -based database systems to a custom implementation of #Cassandra . This allows us to integrate our product data perfectly in a system that just makes sense. High availability and scalability are supported out of the box.

387k views387k

Comments

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

CrateIO	ScyllaDB
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.	ScyllaDB is the database for data-intensive apps that require high performance and low latency. It enables teams to harness the ever-increasing computing power of modern infrastructures – eliminating barriers to scale as data grows.
Familiar SQL syntax;Semi-structured data;High availability, resiliency, and scalability in a distributed design;Powerful Lucene based full-text search	High availability; horizontal scalability; vertical scalability; Cassandra compatible; DynamoDB compatible; wide column; NoSQL; lightweight transactions; change data capture; workload prioritization; shard-per-core; IO scheduler; self-tuning
Statistics
GitHub Stars 4.3K	GitHub Stars -
GitHub Forks 581	GitHub Forks -
Stacks 19	Stacks 143
Followers 39	Followers 197
Votes 7	Votes 8
Pros & Cons
Pros 3 Simplicity 2 Open source 2 Scale	Pros 2 Replication 1 Scale up 1 Distributed 1 Fewer nodes 1 High performance
Integrations
Docker	KairosDB Wireshark JanusGraph Grafana Hackolade Prometheus Kubernetes Datadog Kafka Apache Spark

What are some alternatives to CrateIO, ScyllaDB?

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.

Related Comparisons

Bootstrap vs Materialize

Django vs Laravel vs Node.js

Bootstrap vs Foundation vs Material UI

Node.js vs Spring-Boot

Flyway vs Liquibase

CrateIO vs Scylla: What are the differences?

Introduction: In the world of data management systems, CrateIO and Scylla are two popular options with distinctive features. Below are the key differences between CrateIO and Scylla.

Architecture: CrateIO follows a distributed SQL engine architecture, allowing users to query data in real-time using SQL. On the other hand, Scylla uses a shared-nothing architecture based on Apache Cassandra, providing high availability and fault tolerance through sharding and replication.
Consistency Model: CrateIO supports eventual consistency, making it suitable for applications where eventual consistency is acceptable. In contrast, Scylla offers strong consistency by default, ensuring data integrity and reliability in critical applications.
Performance: CrateIO is optimized for OLAP (Online Analytical Processing) workloads, enabling high-speed analytics on large datasets. Scylla, on the other hand, excels in OLTP (Online Transaction Processing) scenarios, providing low-latency and high-throughput for transaction-heavy applications.
Data Model: CrateIO supports a wide range of data types and complex data structures like arrays and objects, making it versatile for handling diverse data sets. Meanwhile, Scylla focuses on a tabular data model similar to Cassandra, which is conducive to efficient storage and retrieval of structured data.
Query Language: CrateIO supports SQL queries, making it easy for users familiar with SQL to interact with the database. In contrast, Scylla uses CQL (Cassandra Query Language), a query language similar to SQL with additional features specific to Cassandra databases.
Ecosystem: CrateIO has a relatively small ecosystem compared to Scylla, limiting the availability of integrations and tools. Scylla, being built on Apache Cassandra, benefits from a thriving ecosystem of tools, libraries, and community support.

In Summary, CrateIO and Scylla differ significantly in their architecture, consistency model, performance focus, data model, query language, and ecosystem support.