DBMS > HarperDB vs. MySQL vs. Sqrrl vs. Typesense

System Properties Comparison HarperDB vs. MySQL vs. Sqrrl vs. Typesense

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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

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.

Widely used open source RDBMS

Adaptable, secure NoSQL built on Apache Accumulo

A typo-tolerant, in-memory search engine optimized for instant search-as-you-type experiences and developer productivity

Primary database model

Document store

Relational DBMS

Key/Value like access via memcached API

Document store
Graph DBMS
Key-value store
Wide column store

Search engine

Secondary database models

Document store
Spatial DBMS

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

Score	0.55
Rank	#248	Overall
	#38	Document stores

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	0.79
Rank	#210	Overall
	#14	Search engines

Website

www.harperdb.io

www.mysql.com

sqrrl.com

typesense.org

Technical documentation

docs.harperdb.io/docs

dev.mysql.com/doc

typesense.org/docs

Developer

HarperDB

Oracle

since 2010, originally MySQL AB, then Sun

Amazon

originally Sqrrl Data, Inc.

Initial release

2017

1995

2012

2015

Current release

3.1, August 2021

8.4.0, April 2024

License

Commercial or Open Source

commercial

free community edition available

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

commercial

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.

Aiven for MySQL: Fully managed MySQL, deployable in the cloud of your choice, with seamless integrations and lightning-fast setup.

Implementation language

Node.js

C and C++

Java

C++

Server operating systems

Linux
OS X

FreeBSD
Linux
OS X
Solaris
Windows

Linux

Data scheme

dynamic schema

yes

schema-free

pre-defined schema optional

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.

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like data manipulation statements

yes

with proprietary extensions

APIs and other access methods

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

ADO.NET
JDBC
ODBC
Proprietary native API

Accumulo Shell
Java API
JDBC
ODBC
RESTful HTTP API
Thrift

RESTful HTTP API

Supported programming languages

.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

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Actionscript
C

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

.Net

community maintained
Clojure

community maintained
Dart

community maintained
Go

community maintained
Java

community maintained
JavaScript
Perl

community maintained
PHP
Python
Ruby
Rust

community maintained
Swift

community maintained

Server-side scripts

Stored procedures

Custom Functions

since release 3.1

yes

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

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

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

making use of Hadoop

Replication methods

Methods for redundantly storing data on multiple nodes

yes

the nodes on which a table resides can be defined

Multi-source replication
Source-replica replication

selectable replication factor

making use of Hadoop

Multi-source replication using RAFT

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

not for MyISAM storage engine

Transaction concepts

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

Atomic execution of specific operations

ACID

not for MyISAM storage engine

Atomic updates per row, document, or graph entity

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

yes

Durability

Support for making data persistent

yes, using LMDB

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 and roles

Users with fine-grained authorization concept

no user groups or roles

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

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