DBMS > Apache Phoenix vs. Google BigQuery vs. Google Cloud Bigtable vs. JanusGraph vs. Sqrrl

System Properties Comparison Apache Phoenix vs. Google BigQuery vs. Google Cloud Bigtable vs. JanusGraph vs. Sqrrl

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.

Description

A scale-out RDBMS with evolutionary schema built on Apache HBase

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

Adaptable, secure NoSQL built on Apache Accumulo

Primary database model

Relational DBMS

Key-value store
Wide column store

Graph DBMS

Document store
Graph DBMS
Key-value store
Wide column store

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

Score	1.97
Rank	#126	Overall
	#59	Relational DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

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

Score	1.94
Rank	#129	Overall
	#12	Graph DBMS

Website

phoenix.apache.org

cloud.google.com/bigquery

cloud.google.com/bigtable

janusgraph.org

sqrrl.com

Technical documentation

phoenix.apache.org

cloud.google.com/bigquery/docs

cloud.google.com/bigtable/docs

docs.janusgraph.org

Developer

Apache Software Foundation

Google

Linux Foundation; originally developed as Titan by Aurelius

Amazon

originally Sqrrl Data, Inc.

Initial release

2014

2010

2015

2017

2012

Current release

5.0-HBase2, July 2018 and 4.15-HBase1, December 2019

0.6.3, February 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

Apache 2.0

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Server operating systems

Linux
Unix
Windows

hosted

Linux
OS X
Unix
Windows

Linux

Data scheme

yes

late-bound, schema-on-read capabilities

yes

schema-free

yes

schema-free

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

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

Accumulo Shell
Java API
JDBC
ODBC
RESTful HTTP API
Thrift

Supported programming languages

C
C#
C++
Go
Groovy
Java
PHP
Python
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

Clojure
Java
Python

Actionscript
C

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

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Triggers

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

making use of Hadoop

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

selectable replication factor

making use of Hadoop

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

yes

via Faunus, a graph analytics engine

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

Immediate Consistency

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

Eventual Consistency
Immediate Consistency

Immediate Consistency

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

Foreign keys

Referential integrity

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

Atomic updates per row, document, or graph entity

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

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

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 Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancy

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

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

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