DBMS > Cassandra vs. EsgynDB vs. Spark SQL vs. YugabyteDB

System Properties Comparison Cassandra vs. EsgynDB vs. Spark SQL vs. YugabyteDB

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Editorial information provided by DB-Engines

Name

Description

Wide-column store based on ideas of BigTable and DynamoDB

Optimized for write access

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

Spark SQL is a component on top of 'Spark Core' for structured data processing

High-performance distributed SQL database for global, internet-scale applications. Wire and feature compatible with PostgreSQL.

Primary database model

Wide column store

Relational DBMS

Secondary database models

Vector DBMS

starting with release V5

Document store
Wide column store

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	101.89
Rank	#12	Overall
	#1	Wide column stores

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	2.91
Rank	#102	Overall
	#51	Relational DBMS

Website

Technical documentation

cassandra.apache.org/doc/latest

spark.apache.org/docs/latest/sql-programming-guide.html

docs.yugabyte.com
github.com/yugabyte/yugabyte-db

Developer

Apache Software Foundation

Apache top level project, originally developped by Facebook

Esgyn

Apache Software Foundation

Yugabyte Inc.

Initial release

2008

2015

2014

2017

Current release

4.1.3, July 2023

3.5.0 ( 2.13), September 2023

2.19, September 2023

License

Commercial or Open Source

Open Source

Apache version 2

commercial

Open Source

Apache 2.0

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Astra DB: Multi-cloud DBaaS built on Apache Cassandra.

YugabyteDB Managed is the fully managed database-as-a-service offering of YugabyteDB. Get started quickly, and effortlessly ensure continuous availability and limitless scale of your cloud native applications.

Implementation language

Java

C++, Java

Scala

C and C++

Server operating systems

BSD
Linux
OS X
Windows

Linux

Linux
OS X
Windows

Linux
OS X

Data scheme

schema-free

yes

depending on used data model

Typing

predefined data types such as float or date

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

Secondary indexes

restricted

only equality queries, not always the best performing solution

yes

SQL

Support of SQL

SQL-like SELECT, DML and DDL statements (CQL)

yes

SQL-like DML and DDL statements

yes, PostgreSQL compatible

APIs and other access methods

Proprietary protocol

CQL (Cassandra Query Language, an SQL-like language)
Thrift

ADO.NET
JDBC
ODBC

JDBC
ODBC

JDBC
YCQL, an SQL-based flexible-schema API with its roots in Cassandra Query Language
YSQL - a fully relational SQL API that is wire compatible with the SQL language in PostgreSQL

Supported programming languages

C#
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript

Node.js
Perl
PHP
Python
Ruby
Scala

All languages supporting JDBC/ODBC/ADO.Net

Java
Python
R
Scala

C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Rust
Scala

Server-side scripts

Stored procedures

Java Stored Procedures

yes

sql, plpgsql, C

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

no "single point of failure"

Sharding

yes, utilizing Spark Core

Hash and Range Sharding, row-level geo-partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Representation of geographical distribution of servers is possible

Multi-source replication between multi datacenters

none

Based on Raft distributed consensus protocol, minimum 3 replicas for continuous availability

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be individually decided for each write operation

Immediate Consistency

Strong consistency on writes and tunable consistency on reads

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

Atomicity and isolation are supported for single operations

ACID

Distributed ACID with Serializable & Snapshot Isolation. Inspired by Google Spanner architecture.

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

based on RocksDB

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

User concepts

Access control

Access rights for users can be defined per object

fine grained access rights according to SQL-standard

yes

More information provided by the system vendor

Specific characteristics

Apache Cassandra is the leading NoSQL, distributed database management system, well...
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YugabyteDB is an open source distributed SQL database for cloud native transactional...
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Competitive advantages

No single point of failure ensures 100% availability . Operational simplicity for...
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PostgreSQL compatible: Get instantly productive with a PostgreSQL compatible RDBMS....
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Typical application scenarios

Internet of Things (IOT), fraud detection applications, recommendation engines, product...
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Systems of record and engagement for cloud native applications that require resilience,...
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Key customers

Apple, Netflix, Uber, ING,, Intuit,Fidelity, NY Times, Outbrain, BazaarVoice, Best...
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Market metrics

Cassandra is used by 40% of the Fortune 100.
» more

2 Million+ lifetime clusters deployed, 6.5K+ GitHub stars, 7K YugabyteDB Community...
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Licensing and pricing models

Apache licenseÂ Pricing for commercial distributions provided by DataStax and available...
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Apache 2.0 license for the database
» more

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