DBMS > Apache Impala vs. ObjectBox vs. Sphinx vs. Warp 10

System Properties Comparison Apache Impala vs. ObjectBox vs. Sphinx vs. Warp 10

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

Extremely fast embedded database for small devices, IoT and Mobile

Open source search engine for searching in data from different sources, e.g. relational databases

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

Primary database model

Secondary database models

Document store

Time Series DBMS

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	1.20
Rank	#170	Overall
	#5	Object oriented DBMS

Score	5.98
Rank	#56	Overall
	#5	Search engines

Score	0.07
Rank	#349	Overall
	#32	Time Series DBMS

Website

Technical documentation

impala.apache.org/impala-docs.html

docs.objectbox.io

sphinxsearch.com/docs

www.warp10.io/content/02_Getting_started

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

ObjectBox Limited

Sphinx Technologies Inc.

SenX

Initial release

2013

2017

2001

2015

Current release

4.1.0, June 2022

3.5.1, February 2023

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache License 2.0

Open Source

GPL version 2, commercial licence available

Open Source

Apache License 2.0

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

C++

C and C++

C++

Java

Server operating systems

Linux

Android
iOS
Linux
macOS
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Linux
OS X
Windows

Data scheme

yes

schema-free

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

full-text index on all search fields

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language (SphinxQL)

APIs and other access methods

JDBC
ODBC

Proprietary native API

Proprietary protocol

HTTP API
Jupyter
WebSocket

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

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

WarpScript

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Partitioning is done manually, search queries against distributed index is supported

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

online/offline synchronization between client and server

none

selectable replication factor

based on HBase

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

based on HBase

Foreign keys

Referential integrity

yes

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

The original contents of fields are not stored in the Sphinx index.

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

Mandatory use of cryptographic tokens, containing fine-grained authorizations

More information provided by the system vendor

News

Evolution of search: traditional vs vector search
23 May 2024

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

The first On-Device Vector Database: ObjectBox 4.0
16 May 2024

Edge AI: The era of on-device AI
23 April 2024

In-Memory Database Use Cases
15 February 2024

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More resources

DB-Engines blog posts

The DB-Engines ranking includes now search engines
4 February 2013, Paul Andlinger

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Recent citations in the news

Apache Impala 4 Supports Operator Multi-Threading
29 July 2021, iProgrammer

Cloudera Bringing Impala to AWS Cloud
28 November 2017, Datanami

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28 November 2017, SDTimes.com

Apache Doris just 'graduated': Why care about this SQL data warehouse
24 June 2022, InfoWorld

Hudi: Uber Engineering’s Incremental Processing Framework on Apache Hadoop
12 March 2017, Uber

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