DBMS > Amazon Redshift vs. BigObject vs. LMDB vs. Transwarp Hippo

System Properties Comparison Amazon Redshift vs. BigObject vs. LMDB vs. Transwarp Hippo

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Analytic DBMS for real-time computations and queries

A high performant, light-weight, embedded key-value database library

Cloud-native distributed Vector DBMS that supports storage, retrieval, and management of massive vector-based datasets

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Key-value store

Vector DBMS

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	0.19
Rank	#329	Overall
	#146	Relational DBMS

Score	2.09
Rank	#121	Overall
	#20	Key-value stores

Score	0.05
Rank	#386	Overall
	#16	Vector DBMS

Website

aws.amazon.com/redshift

bigobject.io

www.symas.com/symas-embedded-database-lmdb

www.transwarp.cn/en/subproduct/hippo

Technical documentation

docs.aws.amazon.com/redshift

docs.bigobject.io

www.lmdb.tech/doc

Developer

Amazon (based on PostgreSQL)

BigObject, Inc.

Symas

Initial release

2012

2015

2011

2023

Current release

0.9.32, January 2024

1.0, May 2023

License

Commercial or Open Source

commercial

free community edition available

Open Source

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C++

Server operating systems

hosted

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

Linux
Unix
Windows

Linux
macOS

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

Vector, Numeric and String

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

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC

fluentd
ODBC
RESTful HTTP API

RESTful HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

.Net
C
C++
Clojure
Go
Haskell
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Nim
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Rust
Swift
Tcl

C++
Java
Python

Server-side scripts

Stored procedures

user defined functions

in Python

Lua

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

automatically between fact table and dimension tables

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

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

fine grained access rights according to SQL-standard

Role based access control and fine grained access rights

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