DBMS > Amazon DynamoDB vs. BigObject vs. Solr vs. Sqrrl vs. Trafodion

System Properties Comparison Amazon DynamoDB vs. BigObject vs. Solr vs. Sqrrl vs. Trafodion

Editorial information provided by DB-Engines

Name

Sqrrl has been acquired by Amazon and became a part of Amazon Web Services. It has been removed from the DB-Engines ranking.

Apache Trafodion has been retired in 2021. Therefore it is excluded from the DB-Engines Ranking.

Description

Hosted, scalable database service by Amazon with the data stored in Amazons cloud

Analytic DBMS for real-time computations and queries

A widely used distributed, scalable search engine based on Apache Lucene

Adaptable, secure NoSQL built on Apache Accumulo

Transactional SQL-on-Hadoop DBMS

Primary database model

Document store
Key-value store

Relational DBMS

a hierachical model (tree) can be imposed

Search engine

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	74.45
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

Score	0.19
Rank	#329	Overall
	#146	Relational DBMS

Score	41.02
Rank	#24	Overall
	#3	Search engines

Website

aws.amazon.com/dynamodb

bigobject.io

solr.apache.org

sqrrl.com

trafodion.apache.org

Technical documentation

docs.aws.amazon.com/dynamodb

docs.bigobject.io

solr.apache.org/resources.html

trafodion.apache.org/documentation.html

Developer

Amazon

BigObject, Inc.

Apache Software Foundation

Amazon

originally Sqrrl Data, Inc.

Apache Software Foundation, originally developed by HP

Initial release

2012

2015

2006

2012

2014

Current release

9.6.1, May 2024

2.3.0, February 2019

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

commercial

free community edition available

Open Source

Apache Version 2

commercial

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

Java

C++, Java

Server operating systems

hosted

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Linux

Data scheme

schema-free

yes

Dynamic Fields enables on-the-fly addition of new fields

schema-free

yes

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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.

yes

Secondary indexes

yes

All search fields are automatically indexed

yes

SQL

Support of SQL

SQL-like DML and DDL statements

Solr Parallel SQL Interface

yes

APIs and other access methods

RESTful HTTP API

fluentd
ODBC
RESTful HTTP API

Java API
RESTful HTTP/JSON API

Accumulo Shell
Java API
JDBC
ODBC
RESTful HTTP API
Thrift

ADO.NET
JDBC
ODBC

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Actionscript
C

using GLib
C#
C++
Cocoa
Delphi
Erlang
Go
Haskell
Java
JavaScript
OCaml
Perl
PHP
Python
Ruby
Smalltalk

All languages supporting JDBC/ODBC/ADO.Net

Server-side scripts

Stored procedures

Lua

Java plugins

Java Stored Procedures

Triggers

yes

by integration with AWS Lambda

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

making use of Hadoop

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

yes

selectable replication factor

making use of Hadoop

yes, via HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

yes

via user defined functions and HBase

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

none

Eventual Consistency

Immediate Consistency

Document store kept consistent with combination of global timestamping, row-level transactions, and server-side consistency resolution.

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

ACID

ACID across one or more tables within a single AWS account and region

optimistic locking

Atomic updates per row, document, or graph entity

ACID

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

User concepts

Access control

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

yes

Cell-level Security, Data-Centric Security, Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC)

fine grained access rights according to SQL-standard

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