DBMS > Apache Impala vs. Datomic vs. JaguarDB vs. OpenMLDB

System Properties Comparison Apache Impala vs. Datomic vs. JaguarDB vs. OpenMLDB

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

Performant, highly scalable DBMS for AI and IoT applications

An open-source machine learning database that provides a feature platform for training and inference

Primary database model

Relational DBMS

Key-value store
Vector DBMS

Time Series DBMS

Secondary database models

Document store

Relational DBMS

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

Score	10.63
Rank	#40	Overall
	#24	Relational DBMS

Score	1.55
Rank	#144	Overall
	#67	Relational DBMS

Score	0.00
Rank	#385	Overall
	#61	Key-value stores
	#16	Vector DBMS

Score	0.11
Rank	#339	Overall
	#30	Time Series DBMS

Website

Technical documentation

impala.apache.org/impala-docs.html

docs.datomic.com

www.jaguardb.com/support.html

openmldb.ai/docs/zh/main

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Cognitect

DataJaguar, Inc.

4 Paradigm Inc.

Initial release

2013

2012

2015

2020

Current release

4.1.0, June 2022

1.0.7180, July 2024

3.3 July 2023

2024-2 February 2024

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

limited edition free

Open Source

GPL V3.0

Open Source

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Java, Clojure

C++

the server part. Clients available in other languages

C++, Java, Scala

Server operating systems

Linux

All OS with a Java VM

Linux

Data scheme

yes

Fixed schema

Typing

predefined data types such as float or date

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 DML and DDL statements

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

yes

APIs and other access methods

JDBC
ODBC

RESTful HTTP API

JDBC
ODBC

JDBC
SQLAlchemy

Supported programming languages

All languages supporting JDBC/ODBC

Clojure
Java

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Scala

C++
Go
Java
Python
Scala

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

Transaction Functions

Triggers

By using transaction functions

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

But extensive use of caching in the application peers

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

But extensive use of caching in the application peers

Multi-source replication

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Eventual Consistency

Immediate Consistency

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

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

recommended only for testing and development

yes

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

rights management via user accounts

fine grained access rights according to SQL-standard

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More resources

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