DBMS > Apache Impala vs. KeyDB vs. Lovefield vs. Stardog

System Properties Comparison Apache Impala vs. KeyDB vs. Lovefield vs. Stardog

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

An ultra-fast, open source Key-value store fully compatible with Redis API, modules, and protocols

Embeddable relational database for web apps written in pure JavaScript

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

Primary database model

Secondary database models

Document store

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	0.71
Rank	#226	Overall
	#33	Key-value stores

Score	0.29
Rank	#293	Overall
	#133	Relational DBMS

Score	2.02
Rank	#123	Overall
	#11	Graph DBMS
	#6	RDF stores

Website

impala.apache.org

github.com/Snapchat/KeyDB
keydb.dev

google.github.io/lovefield

www.stardog.com

Technical documentation

impala.apache.org/impala-docs.html

docs.keydb.dev

github.com/google/lovefield/blob/master/docs/spec_index.md

docs.stardog.com

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

EQ Alpha Technology Ltd.

Google

Stardog-Union

Initial release

2013

2019

2014

2010

Current release

4.1.0, June 2022

2.1.12, February 2017

7.3.0, May 2020

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

BSD-3

Open Source

Apache 2.0

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

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++

JavaScript

Java

Server operating systems

Linux

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

Linux
macOS
Windows

Data scheme

yes

schema-free

yes

schema-free and OWL/RDFS-schema support

Typing

predefined data types such as float or date

yes

partial

Supported data types are strings, hashes, lists, sets and sorted sets, bit arrays, hyperloglogs and geospatial indexes

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.

Import/export of XML data possible

Secondary indexes

yes

by using the Redis Search module

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language

via JavaScript builder pattern

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

APIs and other access methods

JDBC
ODBC

Proprietary protocol

RESP - REdis Serialization Protoco

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

Supported programming languages

All languages supporting JDBC/ODBC

C
C#
C++
Clojure
Crystal
D
Dart
Elixir
Erlang
Fancy
Go
Haskell
Haxe
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Objective-C
OCaml
Pascal
Perl
PHP
Prolog
Pure Data
Python
R
Rebol
Ruby
Rust
Scala
Scheme
Smalltalk
Swift
Tcl
Visual Basic

JavaScript

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Lua

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

Using read-only observers

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Multi-source replication
Source-replica replication

none

Multi-source replication in HA-Cluster

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

Eventual Consistency
Strong eventual consistency with CRDTs

Immediate Consistency in HA-Cluster

Foreign keys

Referential integrity

yes

relationships in graphs

Transaction concepts

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

Optimistic locking, atomic execution of commands blocks and scripts

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Configurable mechanisms for persistency via snapshots and/or operations logs

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

In-memory capabilities

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

yes

using MemoryDB

yes

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

simple password-based access control and ACL

Access rights for users and roles

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More resources

Recent citations in the news

Apache Impala becomes Top-Level Project
28 November 2017, SDTimes.com

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28 November 2017, Datanami

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provided by Google News

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