DBMS > Amazon CloudSearch vs. CrateDB vs. Splice Machine vs. SQLite

System Properties Comparison Amazon CloudSearch vs. CrateDB vs. Splice Machine vs. SQLite

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Editorial information provided by DB-Engines

Name

Description

A hosted search engine service by Amazon with the data stored in Amazons cloud

Distributed Database based on Lucene

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Widely used embeddable, in-process RDBMS

Primary database model

Search engine

Document store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS

Relational DBMS

Secondary database models

Relational DBMS

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

Score	1.86
Rank	#133	Overall
	#12	Search engines

Score	0.69
Rank	#226	Overall
	#37	Document stores
	#5	Spatial DBMS
	#16	Search engines
	#19	Time Series DBMS
	#11	Vector DBMS

Score	0.54
Rank	#244	Overall
	#114	Relational DBMS

Score	103.35
Rank	#10	Overall
	#7	Relational DBMS

Website

aws.amazon.com/cloudsearch

cratedb.com

splicemachine.com

www.sqlite.org

Technical documentation

docs.aws.amazon.com/cloudsearch

cratedb.com/docs

splicemachine.com/how-it-works

www.sqlite.org/docs.html

Developer

Amazon

Crate

Splice Machine

Dwayne Richard Hipp

Initial release

2012

2013

2014

2000

Current release

5.8.1, August 2024

3.1, March 2021

3.46.1 (13 August 2024), August 2024

License

Commercial or Open Source

commercial

Open Source

AGPL 3.0, commercial license available

Open Source

Public Domain

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

Server operating systems

hosted

All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator support

Linux
OS X
Solaris
Windows

server-less

Data scheme

yes

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

yes

dynamic column types

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

all search fields are automatically indexed

yes

SQL

Support of SQL

yes, but no triggers and constraints, and PostgreSQL compatibility

yes

SQL-92 is not fully supported

APIs and other access methods

HTTP API

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

JDBC
Native Spark Datasource
ODBC

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

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

C#
C++
Java
JavaScript (Node.js)
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

Server-side scripts

Stored procedures

user defined functions (Javascript)

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

yes

automatic partitioning across Amazon Search Instance as required

Sharding

Shared Nothhing Auto-Sharding, Columnar Partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

managed transparently by AWS

Configurable replication on table/partition-level

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Read-after-write consistency on record level

Immediate Consistency

Foreign keys

Referential integrity

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

via file-system locks

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

authentication via encrypted signatures

rights management via user accounts

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

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