DBMS > Apache Impala vs. ObjectBox vs. RethinkDB vs. SAP HANA vs. Spark SQL

System Properties Comparison Apache Impala vs. ObjectBox vs. RethinkDB vs. SAP HANA vs. Spark SQL

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

Extremely fast embedded database for small devices, IoT and Mobile

DBMS for the Web with a mechanism to push updated query results to applications in realtime.

In-memory, column based data store. Available as appliance or cloud service

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Secondary database models

Document store

Time Series DBMS

Spatial DBMS

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	1.29
Rank	#166	Overall
	#5	Object oriented DBMS

Score	2.66
Rank	#107	Overall
	#20	Document stores

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

impala.apache.org

objectbox.io

rethinkdb.com

www.sap.com/products/hana.html

spark.apache.org/sql

Technical documentation

impala.apache.org/impala-docs.html

docs.objectbox.io

rethinkdb.com/docs

help.sap.com/hana

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

ObjectBox Limited

The Linux Foundation

since July 2017

SAP

Apache Software Foundation

Initial release

2013

2017

2009

2010

2014

Current release

4.1.0, June 2022

2.4.1, August 2020

2.0 SPS07 (April 4, 2023), April 2023

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache License 2.0

Open Source

Apache Version 2

commercial

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

C and C++

C++

Scala

Server operating systems

Linux

Android
iOS
Linux
macOS
Windows

Linux
OS X
Windows

Appliance or cloud-service

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

string, binary, float, bool, date, geometry

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

yes

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC

Proprietary native API

JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

C
C++
Dart
Go
Java
JavaScript

planned (as of Jan 2019)
Kotlin
Python

planned (as of Jan 2019)
Swift

community-supported driver
C#

community-supported driver
C++

community-supported driver
Clojure

community-supported driver
Dart

community-supported driver
Erlang

community-supported driver
Go

community-supported driver
Haskell

community-supported driver
Java

official driver
JavaScript (Node.js)

official driver
Lisp

community-supported driver
Lua

community-supported driver
Objective-C

community-supported driver
Perl

community-supported driver
PHP

community-supported driver
Python

official driver
Ruby

official driver
Scala

community-supported driver

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

SQLScript, R

Triggers

Client-side triggers through changefeeds

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

range based

yes

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

online/offline synchronization between client and server

Source-replica replication

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Atomic single-document operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

MVCC based

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

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

yes

users and table-level permissions

yes

More information provided by the system vendor

News

The on-device Vector Database for Android and Java
29 May 2024

Vector search: making sense of search queries
29 May 2024

Python on-device Vector and Object Database for Local AI
28 May 2024

Evolution of search: traditional vs vector search
23 May 2024

On-device Vector Database for Dart/Flutter
21 May 2024

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.