DBMS > Drizzle vs. Google BigQuery vs. Google Cloud Bigtable vs. JanusGraph vs. Memgraph

System Properties Comparison Drizzle vs. Google BigQuery vs. Google Cloud Bigtable vs. JanusGraph vs. Memgraph

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

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

Large scale data warehouse service with append-only tables

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

A Graph DBMS optimized for distributed clusters

It was forked from the latest code base of Titan in January 2017

An open source graph database built for real-time streaming and compatible with Neo4j

Primary database model

Relational DBMS

Key-value store
Wide column store

Graph DBMS

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

Score	52.67
Rank	#19	Overall
	#13	Relational DBMS

Score	2.97
Rank	#92	Overall
	#15	Key-value stores
	#8	Wide column stores

Score	1.85
Rank	#134	Overall
	#12	Graph DBMS

Score	2.91
Rank	#94	Overall
	#7	Graph DBMS

Website

cloud.google.com/bigquery

cloud.google.com/bigtable

janusgraph.org

memgraph.com

Technical documentation

cloud.google.com/bigquery/docs

cloud.google.com/bigtable/docs

docs.janusgraph.org

memgraph.com/docs

Social network pages

Developer

Drizzle project, originally started by Brian Aker

Google

Linux Foundation; originally developed as Titan by Aurelius

Memgraph Ltd

Initial release

2008

2010

2015

2017

Current release

7.2.4, September 2012

1.0.0, October 2023

License

Commercial or Open Source

Open Source

GNU GPL

commercial

Open Source

Apache 2.0

Open Source

BSL 1.1; commercial license for enterprise edition 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

C++

Java

C and C++

Server operating systems

FreeBSD
Linux
OS X

hosted

Linux
OS X
Unix
Windows

Linux

Data scheme

yes

schema-free

yes

schema-free and schema-optional

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

SQL

Support of SQL

yes

with proprietary extensions

yes

APIs and other access methods

JDBC

RESTful HTTP/JSON API

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

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

Bolt protocol
Cypher query language

Supported programming languages

C
C++
Java
PHP

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

Clojure
Java
Python

.Net
C
C++
Elixir
Go
Haskell
Java
JavaScript
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Triggers

hooks for callbacks inside the server can be used.

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

yes

depending on the used storage backend (e.g. Cassandra, HBase, BerkeleyDB)

Sharding

dynamic graph partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

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

yes

Multi-source replication using RAFT

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Faunus, a graph analytics engine

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)

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

relationships in graphs

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

Atomic single-row operations

ACID

with snapshot isolation

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

Durability

Support for making data persistent

yes

Supports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast

yes

with periodic snapshot and write-ahead logging (WAL) of changes

In-memory capabilities

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

yes

User concepts

Access control

Pluggable authentication mechanisms

e.g. LDAP, HTTP

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

Google Cloud Identity & Access Management (IAM)

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

User authentification and security via Rexster Graph Server

Users, roles and permissions

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

Graph algorithms in bioinformatics Social network analysis Cryptocurrency network...
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Licensing and pricing models

You can check out our pricing model and licenses on the company website .
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