DBMS > Apache IoTDB vs. CrateDB vs. jBASE vs. Splice Machine

System Properties Comparison Apache IoTDB vs. CrateDB vs. jBASE vs. Splice Machine

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Editorial information provided by DB-Engines

Name

Description

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

Distributed Database based on Lucene

A robust multi-value DBMS comprising development tools and middleware

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Primary database model

Time Series DBMS

Document store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS

Multivalue DBMS

Relational DBMS

Secondary database models

Relational DBMS

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

Score	1.31
Rank	#159	Overall
	#14	Time Series DBMS

Score	0.69
Rank	#226	Overall
	#37	Document stores
	#5	Spatial DBMS
	#16	Search engines
	#19	Time Series DBMS
	#11	Vector DBMS

Score	1.36
Rank	#157	Overall
	#3	Multivalue DBMS

Score	0.54
Rank	#244	Overall
	#114	Relational DBMS

Website

iotdb.apache.org

cratedb.com

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-jbase

splicemachine.com

Technical documentation

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

cratedb.com/docs

docs.rocketsoftware.com/bundle?labelkey=jbase_5.9

splicemachine.com/how-it-works

Developer

Apache Software Foundation

Crate

Rocket Software (formerly Zumasys)

Splice Machine

Initial release

2018

2013

1991

2014

Current release

1.1.0, April 2023

5.8.1, August 2024

5.7

3.1, March 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

commercial

Open Source

AGPL 3.0, commercial license available

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

Server operating systems

All OS with a Java VM (>= 1.8)

All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator support

AIX
Linux
Windows

Linux
OS X
Solaris
Windows

Data scheme

yes

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

schema-free

yes

Typing

predefined data types such as float or date

yes

optional

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.

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes, but no triggers and constraints, and PostgreSQL compatibility

Embedded SQL for jBASE in BASIC

yes

APIs and other access methods

JDBC
Native API

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

JDBC
ODBC
Proprietary protocol
RESTful HTTP API
SOAP-based API

JDBC
Native Spark Datasource
ODBC

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

.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

.Net
Basic
Jabbascript
Java

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

yes

user defined functions (Javascript)

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

Sharding

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

Configurable replication on table/partition-level

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Eventual Consistency
Read-after-write consistency on record level

Immediate Consistency

Foreign keys

Referential integrity

yes

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

yes, multi-version concurrency control (MVCC)

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

yes

rights management via user accounts

Access rights can be defined down to the item level

Access rights for users, groups and roles according to SQL-standard

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