DBMS > Apache Impala vs. BoltDB vs. Cassandra vs. DuckDB vs. SWC-DB

System Properties Comparison Apache Impala vs. BoltDB vs. Cassandra vs. DuckDB vs. SWC-DB

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

An embedded key-value store for Go.

Wide-column store based on ideas of BigTable and DynamoDB

Optimized for write access

An embeddable, in-process, column-oriented SQL OLAP RDBMS

A high performance, scalable Wide Column DBMS

Primary database model

Secondary database models

Document store

Vector DBMS

starting with release V5

Time Series DBMS

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

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	0.80
Rank	#215	Overall
	#31	Key-value stores

Score	98.83
Rank	#12	Overall
	#1	Wide column stores

Score	4.63
Rank	#69	Overall
	#37	Relational DBMS

Score	0.08
Rank	#364	Overall
	#13	Wide column stores

Website

impala.apache.org

github.com/boltdb/bolt

cassandra.apache.org

duckdb.org

github.com/kashirin-alex/swc-db
www.swcdb.org

Technical documentation

impala.apache.org/impala-docs.html

cassandra.apache.org/doc/latest

duckdb.org/docs

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Apache Software Foundation

Apache top level project, originally developped by Facebook

Alex Kashirin

Initial release

2013

2008

2018

2020

Current release

4.1.0, June 2022

4.1.3, July 2023

0.10, February 2024

0.5, April 2021

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

MIT License

Open Source

Apache version 2

Open Source

MIT License

Open Source

GPL V3

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.

Implementation language

C++

Java

C++

Server operating systems

Linux

BSD
Linux
OS X
Solaris
Windows

BSD
Linux
OS X
Windows

server-less

Linux

Data scheme

yes

schema-free

yes

schema-free

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

yes

restricted

only equality queries, not always the best performing solution

yes

SQL

Support of SQL

SQL-like DML and DDL statements

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

yes

SQL-like query language

APIs and other access methods

JDBC
ODBC

Proprietary protocol

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

Arrow Database Connectivity (ADBC)
CLI Client
JDBC
ODBC

Proprietary protocol
Thrift

Supported programming languages

All languages supporting JDBC/ODBC

C#
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript

Node.js
Perl
PHP
Python
Ruby
Scala

C
C#

3rd party driver
C++
Crystal

3rd party driver
Go

3rd party driver
Java
Lisp

3rd party driver
Python
R
Ruby

3rd party driver
Rust
Swift
Zig

3rd party driver

C++

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

no "single point of failure"

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

selectable replication factor

Representation of geographical distribution of servers is possible

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

none

Eventual Consistency
Immediate Consistency

can be individually decided for each write operation

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

yes

Atomicity and isolation are supported for single operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

In-memory capabilities

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

yes

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

Access rights for users can be defined per object

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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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.
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