DBMS > AnzoGraph DB vs. Google BigQuery vs. PouchDB vs. SingleStore

System Properties Comparison AnzoGraph DB vs. Google BigQuery vs. PouchDB vs. SingleStore

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Editorial information provided by DB-Engines

Name

Description

Scalable graph database built for online analytics and data harmonization with MPP scaling, high-performance analytical algorithms and reasoning, and virtualization

Large scale data warehouse service with append-only tables

JavaScript DBMS with an API inspired by CouchDB

MySQL wire-compliant distributed RDBMS that combines an in-memory row-oriented and a disc-based column-oriented storage with patented universal storage to handle transactional and analytical workloads in one single table type

Primary database model

Secondary database models

Document store
Spatial DBMS
Time Series DBMS
Vector DBMS

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

Score	0.27
Rank	#302	Overall
	#24	Graph DBMS
	#13	RDF stores

Score	61.90
Rank	#19	Overall
	#13	Relational DBMS

Score	2.35
Rank	#116	Overall
	#22	Document stores

Score	5.91
Rank	#61	Overall
	#34	Relational DBMS

Website

cambridgesemantics.com/anzograph

cloud.google.com/bigquery

pouchdb.com

www.singlestore.com

Technical documentation

docs.cambridgesemantics.com/anzograph/userdoc/home.htm

cloud.google.com/bigquery/docs

pouchdb.com/guides

docs.singlestore.com

Developer

Cambridge Semantics

Google

Apache Software Foundation

SingleStore Inc.

Initial release

2018

2010

2012

2013

Current release

2.3, January 2021

7.1.1, June 2019

8.5, January 2024

License

Commercial or Open Source

commercial

free trial version available

commercial

Open Source

commercial

free developer 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.

SingleStoreDB Cloud: The world's fastest, modern cloud database for both operational (OLTP) and analytical (OLAP) workloads. Available instantly with multi-cloud and hybrid-cloud capabilities

Implementation language

JavaScript

C++, Go

Server operating systems

Linux

hosted

server-less, requires a JavaScript environment (browser, Node.js)

Linux

64 bit version required

Data scheme

Schema-free and OWL/RDFS-schema support

yes

schema-free

yes

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

via views

yes

SQL

Support of SQL

SPARQL and SPARQL* as primary query language. Cypher preview.

yes

but no triggers and foreign keys

APIs and other access methods

Apache Mule
gRPC
JDBC
Kafka
OData access for BI tools
OpenCypher
RESTful HTTP API
SPARQL

RESTful HTTP/JSON API

HTTP REST

only for PouchDB Server
JavaScript API

Cluster Management API

as HTTP Rest and CLI
HTTP API
JDBC
MongoDB API
ODBC

Supported programming languages

C++
Java
Python

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

JavaScript

Bash
C
C#
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions and aggregates

user defined functions

in JavaScript

View functions in JavaScript

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

none

Sharding

with a proxy-based framework, named couchdb-lounge

Sharding

hash partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

Source-replica replication

stores two copies of each physical data partition on two separate nodes

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

yes

can define user-defined aggregate functions for map-reduce-style calculations

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

not needed in graphs

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

Durability

Support for making data persistent

yes

by using IndexedDB, WebSQL or LevelDB as backend

yes

All updates are persistent, including those to disk-based columnstores and memory-based row stores. Transaction commits are supported via write-ahead log.

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

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

Google Cloud Identity & Access Management (IAM)

Fine grained access control via users, groups and roles

More information provided by the system vendor

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Key customers

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Market metrics

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Licensing and pricing models

F ree Tier and Enterprise Edition
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