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

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

Editorial information provided by DB-Engines

Name

Description

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

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

Time Series DBMS and big data platform

Primary database model

Document store
Key-value store

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	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

Score	2.60
Rank	#107	Overall
	#8	Time Series DBMS

Website

aws.amazon.com/dynamodb

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

machbase.com

sphinxsearch.com

github.com/taosdata/TDengine
tdengine.com

Technical documentation

docs.aws.amazon.com/dynamodb

www.lmdb.tech/doc

machbase.com/dbms

sphinxsearch.com/docs

docs.tdengine.com

Developer

Amazon

Symas

Machbase

Sphinx Technologies Inc.

TDEngine, previously Taos Data

Initial release

2012

2011

2013

2001

2019

Current release

0.9.32, January 2024

V8.0, August 2023

3.5.1, February 2023

3.0, August 2022

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

Open Source

commercial

free test version available

Open Source

GPL version 2, commercial licence available

Open Source

AGPL V3, also commercial editions 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
Unix
Windows

Linux
macOS
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Linux
Windows

Data scheme

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.

Secondary indexes

yes

full-text index on all search fields

SQL

Support of SQL

SQL-like query language

SQL-like query language (SphinxQL)

Standard SQL with extensions for time-series applications

APIs and other access methods

RESTful HTTP API

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

Proprietary protocol

JDBC
RESTful HTTP API

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

.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

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

Server-side scripts

Stored procedures

Triggers

yes

by integration with AWS Lambda

yes, via alarm monitoring

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

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

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

selectable replication factor

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

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

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.

yes

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)

simple password-based access control

yes

More information provided by the system vendor

Specific characteristics

TDengine™ is a next generation data historian purpose-built for Industry 4.0 and...
» more

Competitive advantages

High Performance at any Scale: TDengine is purpose-built for handling massive industrial...
» more

Typical application scenarios

TDengine is designed for Industrial IoT scenarios, including: Manufacturing Connected...
» more

Market metrics

TDengine has garnered over 22,500 stars on GitHub and is used in over 50 countries...
» more

Licensing and pricing models

TDengine OSS is an open source, cloud native time series database. It includes built-in...
» more

News

Can Typical Time-Series Databases Replace Data Historians?
8 May 2024

TDengine 3.3.0.0 Release Notes
7 May 2024

How to Unlock Value from Industrial Data with AI and ML Technology
6 May 2024

Compare InfluxDB vs. TDengine
19 April 2024

Why We Need the Next Generation Data Historian
15 April 2024

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.