DBMS > LeanXcale vs. TimescaleDB vs. TimesTen vs. ToroDB vs. XTDB

System Properties Comparison LeanXcale vs. TimescaleDB vs. TimesTen vs. ToroDB vs. XTDB

Editorial information provided by DB-Engines

Name

ToroDB seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

In-Memory RDBMS compatible to Oracle

A MongoDB-compatible JSON document store, built on top of PostgreSQL

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Key-value store
Relational DBMS

Time Series DBMS

Relational DBMS

Document store

Secondary database models

Relational DBMS

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

Score	0.36
Rank	#280	Overall
	#40	Key-value stores
	#129	Relational DBMS

Score	4.46
Rank	#71	Overall
	#5	Time Series DBMS

Score	1.36
Rank	#161	Overall
	#75	Relational DBMS

Score	0.18
Rank	#332	Overall
	#46	Document stores

Website

www.leanxcale.com

www.timescale.com

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

github.com/torodb/server

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

docs.timescale.com

docs.oracle.com/database/timesten-18.1

www.xtdb.com/docs

Developer

LeanXcale

Timescale

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

8Kdata

Juxt Ltd.

Initial release

2015

2017

1998

2016

2019

Current release

2.15.0, May 2024

11 Release 2 (11.2.2.8.0)

1.19, September 2021

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

Open Source

AGPL-V3

Open Source

MIT License

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

Java

Clojure

Server operating systems

Linux
OS X
Windows

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

All OS with a Java 7 VM

All OS with a Java 8 (and higher) VM
Linux

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types

yes

string, integer, double, boolean, date, object_id

yes, extensible-data-notation format

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

yes

through Apache Derby

yes

full PostgreSQL SQL syntax

yes

limited SQL, making use of Apache Calcite

APIs and other access methods

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

HTTP REST
JDBC

Supported programming languages

C
Java
Scala

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

C
C++
Java
PL/SQL

Clojure
Java

Server-side scripts

Stored procedures

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

PL/SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

yes, across time and space (hash partitioning) attributes

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication with hot standby and reads on replicas

Multi-source replication
Source-replica replication

Source-replica replication

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

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

by means of logfiles and checkpoints

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

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

Access rights for users and roles

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More resources

Recent citations in the news

TimescaleDB Is a Vector Database Now, Too
25 September 2023, Datanami

Timescale Acquires PopSQL to Bring a Modern, Collaborative SQL GUI to PostgreSQL Developers
4 April 2024, PR Newswire

Power IoT and time-series workloads with TimescaleDB for Azure Database for PostgreSQL
18 March 2019, Microsoft

Timescale Valuation Rockets to Over $1B with $110M Round, Marking the Explosive Rise of Time-Series Data
22 February 2022, Business Wire

Timescale announces $15M investment and new enterprise version of TimescaleDB
29 January 2019, TechCrunch

provided by Google News

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