DBMS > InfluxDB vs. Oracle Berkeley DB vs. Transwarp StellarDB

System Properties Comparison InfluxDB vs. Oracle Berkeley DB vs. Transwarp StellarDB

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Editorial information provided by DB-Engines

Name

Description

DBMS for storing time series, events and metrics

Widely used in-process key-value store

A distributed graph DBMS built for enterprise-level graph applications

Primary database model

Time Series DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Graph DBMS

Secondary database models

Spatial DBMS

with GEO package

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

Score	21.54
Rank	#28	Overall
	#1	Time Series DBMS

Score	1.71
Rank	#129	Overall
	#21	Key-value stores
	#3	Native XML DBMS

Score	0.00
Rank	#382	Overall
	#40	Graph DBMS

Website

www.influxdata.com/products/influxdb-overview

www.oracle.com/database/technologies/related/berkeleydb.html

www.transwarp.cn/en/product/stellardb

Technical documentation

docs.influxdata.com/influxdb

docs.oracle.com/cd/E17076_05/html/index.html

Developer

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Transwarp

Initial release

2013

1994

Current release

2.7.6, April 2024

18.1.40, May 2020

License

Commercial or Open Source

Open Source

MIT-License; commercial enterprise version available

Open Source

commercial license available

commercial

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

C, Java, C++ (depending on the Berkeley DB edition)

Server operating systems

Linux
OS X

through Homebrew

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Data scheme

schema-free

Typing

predefined data types such as float or date

Numeric data and Strings

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

only with the Berkeley DB XML edition

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

SQL interfaced based on SQLite is available

SQL-like query language

APIs and other access methods

HTTP API
JSON over UDP

OpenCypher

Supported programming languages

.Net
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Perl
PHP
Python
R
Ruby
Rust
Scala

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

Server-side scripts

Stored procedures

Triggers

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

Sharding

in enterprise version only

none

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

in enterprise version only

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Foreign keys

Referential integrity

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

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

Depending on used storage engine

yes

User concepts

Access control

simple rights management via user accounts

yes

More information provided by the system vendor

News

Building Smarter Manufacturing Systems with Bosch Rexroth and InfluxDB
23 April 2025

Build a Time Series Forecasting Pipeline in InfluxDB 3 Without Writing Code
17 April 2025

InfluxDB 3 Core & Enterprise GA: The Next Generation Time Series Platform for Developers is Here
15 April 2025

Deadman Alerts with the Python Processing Engine
9 April 2025

Optimizing SQL (and DataFrames) in DataFusion: Part 2
3 April 2025

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More resources

DB-Engines blog posts

Why Build a Time Series Data Platform?
20 July 2017, Paul Dix (guest author)

Time Series DBMS are the database category with the fastest increase in popularity
4 July 2016, Matthias Gelbmann

Time Series DBMS as a new trend?
1 June 2015, Paul Andlinger

show all

Recent citations in the news

InfluxData clocks in on time-series software development
24 April 2025, techzine.eu

InfluxData rolls out InfluxDB 3 to power real-time apps at scale
15 April 2025, Blocks and Files

InfluxData Releases InfluxDB 3 Core and InfluxDB 3 Enterprise
16 April 2025, Database Trends and Applications

InfluxData Announces General Availability of InfluxDB 3 Core and InfluxDB 3 Enterprise, Simplifying How Developers Build with Time Series Data
15 April 2025, Business Wire

Time series data: AWS/InfluxDB team up for open-source innovation
7 April 2025, SiliconANGLE

provided by Google News

Recognition For NoSQL Pioneers
5 July 2021, i-programmer.info

ACM recognizes far-reaching technical achievements with special awards
26 May 2021, EurekAlert!

Which Are the Top Local Databases for React Native
15 October 2019, appinventiv.com

Database Trends Report: SQL Beats NoSQL, MySQL Most Popular
5 March 2019, ADTmag

Fig. 2. XML Methods for Various XML DBMS Types
29 October 2024, ResearchGate

provided by Google News

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