DBMS > BigObject vs. CrateDB vs. Elasticsearch vs. Sadas Engine

System Properties Comparison BigObject vs. CrateDB vs. Elasticsearch vs. Sadas Engine

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for real-time computations and queries

Distributed Database based on Lucene

A distributed, RESTful modern search and analytics engine based on Apache Lucene

Elasticsearch lets you perform and combine many types of searches such as structured, unstructured, geo, and metric

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

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Document store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS

Search engine

Relational DBMS

Secondary database models

Relational DBMS

Document store
Spatial DBMS
Vector DBMS

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

Score	0.13
Rank	#333	Overall
	#147	Relational DBMS

Score	0.73
Rank	#224	Overall
	#37	Document stores
	#5	Spatial DBMS
	#16	Search engines
	#19	Time Series DBMS
	#8	Vector DBMS

Score	135.35
Rank	#7	Overall
	#1	Search engines

Score	0.00
Rank	#383	Overall
	#158	Relational DBMS

Website

bigobject.io

cratedb.com

www.elastic.co/elasticsearch

www.sadasengine.com

Technical documentation

docs.bigobject.io

cratedb.com/docs

www.elastic.co/guide/en/elasticsearch/reference/current/index.html

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

Developer

BigObject, Inc.

Crate

Elastic

SADAS s.r.l.

Initial release

2015

2013

2010

2006

Current release

8.6, January 2023

8.0

License

Commercial or Open Source

commercial

free community edition available

Open Source

Elastic License

commercial

free trial version available

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.

CrateDB Cloud: a distributed SQL database that spreads data and processing across an elastic cluster of shared nothing nodes. CrateDB Cloud enables data insights at scale on Microsoft Azure, AWS and Google Cloud Platform.

Implementation language

Java

C++

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator support

All OS with a Java VM

AIX
Linux
Windows

Data scheme

yes

Flexible Schema (defined schema, partial schema, schema free)

schema-free

Flexible type definitions. Once a type is defined, it is persistent

yes

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

All search fields are automatically indexed

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes, but no triggers and constraints, and PostgreSQL compatibility

SQL-like query language

yes

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API

Java API
RESTful HTTP/JSON API

JDBC
ODBC
Proprietary protocol

Supported programming languages

.NET
Erlang
Go

community maintained client
Java
JavaScript (Node.js)

community maintained client
Perl

community maintained client
PHP
Python
R
Ruby

community maintained client
Scala

community maintained client

.Net
Groovy
Community Contributed Clients
Java
JavaScript
Perl
PHP
Python
Ruby

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

Server-side scripts

Stored procedures

Lua

user defined functions (Javascript)

yes

Triggers

yes

by using the 'percolation' feature

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

Configurable replication on table/partition-level

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

ES-Hadoop Connector

Consistency concepts

Methods to ensure consistency in a distributed system

none

Eventual Consistency
Read-after-write consistency on record level

Eventual Consistency

Synchronous doc based replication. Get by ID may show delays up to 1 sec. Configurable write consistency: one, quorum, all

Immediate Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

yes

Transaction concepts

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

unique row identifiers can be used for implementing an optimistic concurrency control strategy

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

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

Memcached and Redis integration

yes

managed by 'Learn by Usage'

User concepts

Access control

rights management via user accounts

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

More information provided by the system vendor

Specific characteristics

The enterprise database for time series, documents, and vectors. Distributed - Native...
» more

Competitive advantages

Response time in milliseconds: e ven for complex ad-hoc queries. Massive scaling...
» more

Typical application scenarios

IoT: accelerate your IIoT projects with CrateDB, delivering real-time analytics...
» more

Key customers

Across all continents, CrateDB is used by companies of all sizes to meet the most...
» more

Market metrics

The CrateDB open source project was started in 2013 Honorable Mention in 2021 Gartner®...
» more

Licensing and pricing models

See CrateDB pricing >
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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More resources

DB-Engines blog posts

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2 March 2016, Paul Andlinger

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