DBMS > Datomic vs. Google BigQuery vs. Sadas Engine vs. SiriDB vs. Sphinx

System Properties Comparison Datomic vs. Google BigQuery vs. Sadas Engine vs. SiriDB vs. Sphinx

Editorial information provided by DB-Engines

Name

Description

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

Large scale data warehouse service with append-only tables

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

Open Source Time Series DBMS

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

Primary database model

Relational DBMS

Time Series DBMS

Search engine

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

Score	1.55
Rank	#144	Overall
	#67	Relational DBMS

Score	52.67
Rank	#19	Overall
	#13	Relational DBMS

Score	0.00
Rank	#385	Overall
	#159	Relational DBMS

Score	0.00
Rank	#385	Overall
	#40	Time Series DBMS

Score	5.97
Rank	#56	Overall
	#5	Search engines

Website

www.datomic.com

cloud.google.com/bigquery

www.sadasengine.com

siridb.com

sphinxsearch.com

Technical documentation

docs.datomic.com

cloud.google.com/bigquery/docs

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

docs.siridb.com

sphinxsearch.com/docs

Developer

Cognitect

Google

SADAS s.r.l.

Cesbit

Sphinx Technologies Inc.

Initial release

2012

2010

2006

2017

2001

Current release

1.0.7180, July 2024

8.0

3.5.1, February 2023

License

Commercial or Open Source

commercial

limited edition free

commercial

free trial version available

Open Source

MIT License

Open Source

GPL version 2, commercial licence available

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

Java, Clojure

C++

Server operating systems

All OS with a Java VM

hosted

AIX
Linux
Windows

Linux

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

yes

Typing

predefined data types such as float or date

yes

Numeric data

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

yes

SQL-like query language (SphinxQL)

APIs and other access methods

RESTful HTTP API

RESTful HTTP/JSON API

JDBC
ODBC
Proprietary protocol

HTTP API

Proprietary protocol

Supported programming languages

Clojure
Java

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C
C#
C++
Groovy
Java
PHP
Python

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

yes

Transaction Functions

user defined functions

in JavaScript

Triggers

By using transaction functions

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

none

horizontal partitioning

Sharding

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

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

none

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

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

In-memory capabilities

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

yes

recommended only for testing and development

yes

managed by 'Learn by Usage'

yes

User concepts

Access control

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Access rights for users, groups and roles according to SQL-standard

simple rights management via user accounts

More information provided by the system vendor

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.