DBMS > Axibase vs. BigObject vs. CrateDB vs. DuckDB vs. SpaceTime

System Properties Comparison Axibase vs. BigObject vs. CrateDB vs. DuckDB vs. SpaceTime

Editorial information provided by DB-Engines

Name

Description

Scalable TimeSeries DBMS based on HBase with integrated rule engine and visualization

Analytic DBMS for real-time computations and queries

Distributed Database based on Lucene

An embeddable, in-process, column-oriented SQL OLAP RDBMS

SpaceTime is a spatio-temporal DBMS with a focus on performance.

Primary database model

Time Series DBMS

Relational DBMS

a hierachical model (tree) can be imposed

Document store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS

Relational DBMS

Spatial DBMS

Secondary database models

Relational DBMS

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

Score	0.29
Rank	#292	Overall
	#25	Time Series DBMS

Score	0.13
Rank	#333	Overall
	#147	Relational DBMS

Score	0.73
Rank	#224	Overall
	#37	Document stores
	#5	Spatial DBMS
	#16	Search engines
	#19	Time Series DBMS
	#8	Vector DBMS

Score	4.57
Rank	#74	Overall
	#40	Relational DBMS

Score	0.00
Rank	#383	Overall
	#7	Spatial DBMS

Website

axibase.com/docs/atsd/finance

bigobject.io

cratedb.com

duckdb.org

www.mireo.com/spacetime

Technical documentation

docs.bigobject.io

cratedb.com/docs

duckdb.org/docs

Developer

Axibase Corporation

BigObject, Inc.

Crate

Mireo

Initial release

2013

2015

2013

2018

2020

Current release

15585

0.10, February 2024

License

Commercial or Open Source

commercial

Community Edition (single node) is free, Enterprise Edition (distributed) is paid

commercial

free community edition available

Open Source

MIT License

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.

CrateDB Cloud: a distributed SQL database that spreads data and processing across an elastic cluster of shared nothing nodes. CrateDB Cloud enables data insights at scale on Microsoft Azure, AWS and Google Cloud Platform.

Implementation language

Java

C++

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator support

server-less

Linux

Data scheme

yes

Flexible Schema (defined schema, partial schema, schema free)

yes

Typing

predefined data types such as float or date

yes

short, integer, long, float, double, decimal, string

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

SQL

Support of SQL

SQL-like query language

SQL-like DML and DDL statements

yes, but no triggers and constraints, and PostgreSQL compatibility

yes

A subset of ANSI SQL is implemented

APIs and other access methods

JDBC
Proprietary protocol (Network API)
RESTful HTTP API

fluentd
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API

Arrow Database Connectivity (ADBC)
CLI Client
JDBC
ODBC

RESTful HTTP API

Supported programming languages

Go
Java
PHP
Python
R
Ruby

.NET
Erlang
Go

community maintained client
Java
JavaScript (Node.js)

community maintained client
Perl

community maintained client
PHP
Python
R
Ruby

community maintained client
Scala

community maintained client

C
C#

3rd party driver
C++
Crystal

3rd party driver
Go

3rd party driver
Java
Lisp

3rd party driver
Python
R
Ruby

3rd party driver
Rust
Swift
Zig

3rd party driver

C#
C++
Python

Server-side scripts

Stored procedures

yes

Lua

user defined functions (Javascript)

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

none

Fixed-grid hypercubes

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

Configurable replication on table/partition-level

none

Real-time block device replication (DRBD)

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Eventual Consistency
Read-after-write consistency on record level

Immediate Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

Transaction concepts

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

unique row identifiers can be used for implementing an optimistic concurrency control strategy

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

yes

yes, multi-version concurrency control (MVCC)

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

User concepts

Access control

rights management via user accounts

yes

More information provided by the system vendor

Specific characteristics

The enterprise database for time series, documents, and vectors. Distributed - Native...
» more

Competitive advantages

Response time in milliseconds: e ven for complex ad-hoc queries. Massive scaling...
» more

Typical application scenarios

IoT: accelerate your IIoT projects with CrateDB, delivering real-time analytics...
» more

Key customers

Across all continents, CrateDB is used by companies of all sizes to meet the most...
» more

Market metrics

The CrateDB open source project was started in 2013 Honorable Mention in 2021 Gartner®...
» more

Licensing and pricing models

See CrateDB pricing >
» more

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

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