DBMS > CockroachDB vs. HarperDB vs. Sqrrl vs. VoltDB

System Properties Comparison CockroachDB vs. HarperDB vs. Sqrrl vs. VoltDB

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Editorial information provided by DB-Engines

Name

Sqrrl has been acquired by Amazon and became a part of Amazon Web Services. It has been removed from the DB-Engines ranking.

Description

CockroachDB is a distributed database architected for modern cloud applications. It is wire compatible with PostgreSQL and backed by a Key-Value Store, which is either RocksDB or a purpose-built derivative, called Pebble.

Ultra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.

Adaptable, secure NoSQL built on Apache Accumulo

Distributed In-Memory NewSQL RDBMS

Used for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory

Primary database model

Relational DBMS

Document store

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

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

Score	5.73
Rank	#58	Overall
	#34	Relational DBMS

Score	0.60
Rank	#244	Overall
	#38	Document stores

Score	1.47
Rank	#157	Overall
	#73	Relational DBMS

Website

www.cockroachlabs.com

www.harperdb.io

sqrrl.com

www.voltdb.com

Technical documentation

www.cockroachlabs.com/docs

docs.harperdb.io/docs

docs.voltdb.com

Developer

Cockroach Labs

HarperDB

Amazon

originally Sqrrl Data, Inc.

VoltDB Inc.

Initial release

2015

2017

2012

2010

Current release

24.1.0, May 2024

3.1, August 2021

11.3, April 2022

License

Commercial or Open Source

Open Source

Apache 2.0, commercial license available

commercial

free community edition available

commercial

Open Source

AGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions

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.

Implementation language

Node.js

Java

Java, C++

Server operating systems

Linux
macOS
Windows

Linux
OS X

Linux

Linux
OS X

for development

Data scheme

dynamic schema

schema-free

yes

Typing

predefined data types such as float or date

yes

JSON data types

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

SQL

Support of SQL

yes, wire compatible with PostgreSQL

SQL-like data manipulation statements

yes

only a subset of SQL 99

APIs and other access methods

JDBC

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

Accumulo Shell
Java API
JDBC
ODBC
RESTful HTTP API
Thrift

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

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

.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift

Actionscript
C

using GLib
C#
C++
Cocoa
Delphi
Erlang
Go
Haskell
Java
JavaScript
OCaml
Perl
PHP
Python
Ruby
Smalltalk

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

Custom Functions

since release 3.1

Java

Triggers

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning (by key range)

all tables are translated to an ordered KV store and then broken down into 64MB ranges, which are then used as replicas in RAFT

A table resides as a whole on one (or more) nodes in a cluster

Sharding

making use of Hadoop

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using RAFT

yes

the nodes on which a table resides can be defined

selectable replication factor

making use of Hadoop

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Document store kept consistent with combination of global timestamping, row-level transactions, and server-side consistency resolution.

Foreign keys

Referential integrity

yes

FOREIGN KEY constraints are not supported

Transaction concepts

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

ACID

Atomic execution of specific operations

Atomic updates per row, document, or graph entity

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes

yes, using LMDB

yes

Snapshots and command logging

In-memory capabilities

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

yes

User concepts

Access control

Role-based access control

Access rights for users and roles

Cell-level Security, Data-Centric Security, Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC)

Users and roles with access to stored procedures

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More resources

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