DBMS > Hazelcast vs. TimescaleDB vs. XTDB

System Properties Comparison Hazelcast vs. TimescaleDB vs. XTDB

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

A widely adopted in-memory data grid

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

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

Primary database model

Key-value store

Time Series DBMS

Document store

Secondary database models

Document store

JSON support with IMDG 3.12

Relational DBMS

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

Score	5.32
Rank	#60	Overall
	#6	Key-value stores

Score	3.90
Rank	#74	Overall
	#5	Time Series DBMS

Score	0.12
Rank	#337	Overall
	#46	Document stores

Website

hazelcast.com

www.timescale.com

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

Technical documentation

hazelcast.org/imdg/docs

docs.timescale.com

www.xtdb.com/docs

Developer

Hazelcast

Timescale

Juxt Ltd.

Initial release

2008

2017

2019

Current release

5.3.6, November 2023

2.15.0, May 2024

1.19, September 2021

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available

Open Source

Apache 2.0

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

All OS with a Java VM

Linux
OS X
Windows

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

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

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

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

the object must implement a serialization strategy

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

full PostgreSQL SQL syntax

limited SQL, making use of Apache Calcite

APIs and other access methods

JCache
JPA
Memcached protocol
RESTful HTTP API

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

HTTP REST
JDBC

Supported programming languages

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Clojure
Java

Server-side scripts

Stored procedures

yes

Event Listeners, Executor Services

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

Triggers

yes

Events

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes, across time and space (hash partitioning) attributes

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Replicated Map

Source-replica replication with hot standby and reads on replicas

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

one or two-phase-commit; repeatable reads; read commited

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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

Role-based access control

fine grained access rights according to SQL-standard

More information provided by the system vendor

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.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

Recent citations in the news

Hazelcast Weaves Wider Logic Threads Through The Data Fabric
7 March 2024, Forbes

Hazelcast 5.4 real time data processing platform boosts AI and consistency
17 April 2024, VentureBeat

Hazelcast Showcases Real-Time Data Platform at 2024 Gartner Summit
15 May 2024, Datanami

Hazelcast Versus Redis: A Practical Comparison
4 January 2024, Database Trends and Applications

Hazelcast: The ‘true’ value of streaming real-time data
27 September 2023, ComputerWeekly.com

provided by Google 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

Understanding Hyperfunctions in TimescaleDB
11 August 2021, CDOTrends

provided by Google News

Share this page