DBMS > Amazon DocumentDB vs. Drizzle vs. Google Cloud Bigtable vs. SiriDB vs. XTDB

System Properties Comparison Amazon DocumentDB vs. Drizzle vs. Google Cloud Bigtable vs. SiriDB vs. XTDB

Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

Description

Fast, scalable, highly available, and fully managed MongoDB-compatible database service

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

Open Source Time Series DBMS

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Document store

Relational DBMS

Key-value store
Wide column store

Time Series DBMS

Document store

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

Score	1.91
Rank	#132	Overall
	#24	Document stores

Score	3.26
Rank	#92	Overall
	#13	Key-value stores
	#8	Wide column stores

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

aws.amazon.com/documentdb

cloud.google.com/bigtable

siridb.com

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

aws.amazon.com/documentdb/resources

cloud.google.com/bigtable/docs

docs.siridb.com

www.xtdb.com/docs

Developer

Drizzle project, originally started by Brian Aker

Google

Cesbit

Juxt Ltd.

Initial release

2019

2008

2015

2017

2019

Current release

7.2.4, September 2012

1.19, September 2021

License

Commercial or Open Source

commercial

Open Source

GNU GPL

commercial

Open Source

MIT License

Open Source

MIT License

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++

Clojure

Server operating systems

hosted

FreeBSD
Linux
OS X

hosted

Linux

All OS with a Java 8 (and higher) VM
Linux

Data scheme

schema-free

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

Numeric data

yes, extensible-data-notation format

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

SQL

Support of SQL

yes

with proprietary extensions

limited SQL, making use of Apache Calcite

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

JDBC

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

HTTP API

HTTP REST
JDBC

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

C
C++
Java
PHP

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

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

Clojure
Java

Server-side scripts

Stored procedures

Triggers

hooks for callbacks inside the server can be used.

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones for high availability, asynchronous replication for up to 15 read replicas

Multi-source replication
Source-replica replication

Internal replication in Colossus, and regional replication between two clusters in different zones

yes

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

yes

Transaction concepts

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

Atomic single-document operations

ACID

Atomic single-row operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

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

Pluggable authentication mechanisms

e.g. LDAP, HTTP

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

simple rights management via user accounts

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More resources

DB-Engines blog posts

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