DBMS > Apache Druid vs. Apache Impala vs. GeoSpock vs. SQLite vs. SwayDB

System Properties Comparison Apache Druid vs. Apache Impala vs. GeoSpock vs. SQLite vs. SwayDB

Editorial information provided by DB-Engines

Name

GeoSpock seems to be discontinued. Therefore it will be excluded from the DB-Engines ranking.

Description

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

Analytic DBMS for Hadoop

Spatial and temporal data processing engine for extreme data scale

Widely used embeddable, in-process RDBMS

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

Primary database model

Relational DBMS
Time Series DBMS

Relational DBMS

Key-value store

Secondary database models

Document store

Time Series DBMS

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

Score	3.34
Rank	#88	Overall
	#48	Relational DBMS
	#7	Time Series DBMS

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Score	0.00
Rank	#382	Overall
	#59	Key-value stores

Website

geospock.com

Technical documentation

druid.apache.org/docs/latest/design

impala.apache.org/impala-docs.html

www.sqlite.org/docs.html

Developer

Apache Software Foundation and contributors

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

GeoSpock

Dwayne Richard Hipp

Simer Plaha

Initial release

2012

2013

2000

2018

Current release

29.0.1, April 2024

4.1.0, June 2022

2.0, September 2019

3.45.3 (15 April 2024), April 2024

License

Commercial or Open Source

Open Source

Apache license v2

Open Source

Apache Version 2

commercial

Open Source

Public Domain

Open Source

GNU Affero GPL V3.0

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java

C++

Java, Javascript

Scala

Server operating systems

Linux
OS X
Unix

Linux

hosted

server-less

Data scheme

yes

schema-less columns are supported

yes

dynamic column types

schema-free

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

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

temporal, categorical

yes

SQL

Support of SQL

SQL for querying

SQL-like DML and DDL statements

ANSI SQL for query only (using Presto)

yes

SQL-92 is not fully supported

APIs and other access methods

JDBC
RESTful HTTP/JSON API

JDBC
ODBC

JDBC

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

All languages supporting JDBC/ODBC

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Java
Kotlin
Scala

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Sharding

Automatic sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

selectable replication factor

none

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

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Atomic execution of operations

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

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

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

Access rights for users can be defined per table

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