DBMS > Amazon Neptune vs. IRONdb vs. Spark SQL vs. SQLite vs. TimescaleDB

System Properties Comparison Amazon Neptune vs. IRONdb vs. Spark SQL vs. SQLite vs. TimescaleDB

Editorial information provided by DB-Engines

Name

IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

Fast, reliable graph database built for the cloud

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

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

Widely used embeddable, in-process RDBMS

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

Primary database model

Secondary database models

Relational DBMS

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

Score	2.20
Rank	#119	Overall
	#9	Graph DBMS
	#5	RDF stores

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Score	4.64
Rank	#71	Overall
	#4	Time Series DBMS

Website

aws.amazon.com/neptune

www.circonus.com/solutions/time-series-database/

spark.apache.org/sql

www.sqlite.org

www.timescale.com

Technical documentation

aws.amazon.com/neptune/developer-resources

docs.circonus.com/irondb/category/getting-started

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

www.sqlite.org/docs.html

docs.timescale.com

Developer

Amazon

Circonus LLC.

Apache Software Foundation

Dwayne Richard Hipp

Timescale

Initial release

2017

2014

2000

2017

Current release

V0.10.20, January 2018

3.5.0 ( 2.13), September 2023

3.45.3 (15 April 2024), April 2024

2.15.0, May 2024

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Public Domain

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C and C++

Scala

Server operating systems

hosted

Linux

Linux
OS X
Windows

server-less

Linux
OS X
Windows

Data scheme

schema-free

yes

dynamic column types

yes

Typing

predefined data types such as float or date

yes

text, numeric, histograms

yes

not rigid because of 'dynamic typing' concept.

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types

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.

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language (Circonus Analytics Query Language: CAQL)

SQL-like DML and DDL statements

yes

SQL-92 is not fully supported

yes

full PostgreSQL SQL syntax

APIs and other access methods

OpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin

HTTP API

JDBC
ODBC

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Supported programming languages

C#
Go
Java
JavaScript
PHP
Python
Ruby
Scala

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala

Java
Python
R
Scala

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

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

yes, in Lua

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Automatic, metric affinity per node

yes, utilizing Spark Core

none

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones high availability, asynchronous replication for up to 15 read replicas within a single region. Global database clusters consists of a primary write DB cluster in one region, and up to five secondary read DB clusters in different regions. Each secondary region can have up to 16 reader instances.

configurable replication factor, datacenter aware

none

Source-replica replication with hot standby and reads on replicas

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency per node, eventual consistency across nodes

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

yes

Durability

Support for making data persistent

yes

with encyption-at-rest

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 and roles can be defined via the AWS Identity and Access Management (IAM)

fine grained access rights according to SQL-standard

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