DBMS > Amazon DynamoDB vs. Kdb vs. LMDB vs. Machbase Neo vs. Sphinx

System Properties Comparison Amazon DynamoDB vs. Kdb vs. LMDB vs. Machbase Neo vs. Sphinx

Editorial information provided by DB-Engines

Name

Description

Hosted, scalable database service by Amazon with the data stored in Amazons cloud

High performance Time Series DBMS

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

TimeSeries DBMS for AIoT and BigData

Open source search engine for searching in data from different sources, e.g. relational databases

Primary database model

Document store
Key-value store

Time Series DBMS
Vector DBMS

Key-value store

Time Series DBMS

Search engine

Secondary database models

Relational DBMS

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

Score	74.07
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

Score	7.55
Rank	#53	Overall
	#3	Time Series DBMS
	#1	Vector DBMS

Score	1.99
Rank	#125	Overall
	#21	Key-value stores

Score	0.12
Rank	#339	Overall
	#30	Time Series DBMS

Score	5.98
Rank	#56	Overall
	#5	Search engines

Website

aws.amazon.com/dynamodb

kx.com

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

machbase.com

sphinxsearch.com

Technical documentation

docs.aws.amazon.com/dynamodb

code.kx.com

www.lmdb.tech/doc

machbase.com/dbms

sphinxsearch.com/docs

Developer

Amazon

Kx Systems, a division of First Derivatives plc

Symas

Machbase

Sphinx Technologies Inc.

Initial release

2012

2000

kdb was released 2000, kdb+ in 2003

2011

2013

2001

Current release

3.6, May 2018

0.9.32, January 2024

V8.0, August 2023

3.5.1, February 2023

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

commercial

free 32-bit version

Open Source

commercial

free test version available

Open Source

GPL version 2, commercial licence available

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
OS X
Solaris
Windows

Linux
Unix
Windows

Linux
macOS
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

schema-free

yes

schema-free

yes

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.

yes

Secondary indexes

yes

table attribute 'grouped'

yes

full-text index on all search fields

SQL

Support of SQL

SQL-like query language (q)

SQL-like query language

SQL-like query language (SphinxQL)

APIs and other access methods

RESTful HTTP API

HTTP API
JDBC
Jupyter
Kafka
ODBC
WebSocket

gRPC
HTTP REST
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC

Proprietary protocol

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

C
C#
C++
Go
J
Java
JavaScript
Lua
MatLab
Perl
PHP
Python
R
Scala

.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
C#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

user defined functions

Triggers

yes

by integration with AWS Lambda

yes

with views

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

none

Sharding

Partitioning is done manually, search queries against distributed index is supported

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

none

selectable replication factor

none

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

similar paradigm used for internal processing

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

ACID across one or more tables within a single AWS account and region

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Sphinx index.

In-memory capabilities

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

yes

volatile and lookup table

User concepts

Access control

Access rights for users and roles can be defined via the AWS Identity and Access Management (IAM)

rights management via user accounts

simple password-based access control

More information provided by the system vendor

Specific characteristics

Integrated columnar database & programming system for streaming, real time and historical...
» more

Competitive advantages

provides seamless scalability; runs on industry standard server platforms; is top-ranked...
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Typical application scenarios

tick database streaming sensor data massive intelligence applications oil and gas...
» more

Key customers

Goldman Sachs Morgan Stanley Merrill Lynch J.P. Morgan Deutsche Bank IEX Securities...
» more

Market metrics

kdb+ performance and reliability proven by our customers in critical infrastructure...
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Licensing and pricing models

upon request
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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.