DBMS > AnzoGraph DB vs. Manticore Search vs. Riak KV vs. TimescaleDB

System Properties Comparison AnzoGraph DB vs. Manticore Search vs. Riak KV vs. TimescaleDB

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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

Multi-storage database for search, including full-text search.

Distributed, fault tolerant key-value store

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

Primary database model

Graph DBMS
RDF store

Search engine

Key-value store

with links between data sets and object tags for the creation of secondary indexes

Time Series DBMS

Secondary database models

Time Series DBMS

using the Manticore Columnar Library

Relational DBMS

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

Score	0.22
Rank	#302	Overall
	#24	Graph DBMS
	#13	RDF stores

Score	0.23
Rank	#301	Overall
	#21	Search engines

Score	3.84
Rank	#76	Overall
	#9	Key-value stores

Score	4.06
Rank	#73	Overall
	#5	Time Series DBMS

Website

cambridgesemantics.com/anzograph

manticoresearch.com

www.timescale.com

Technical documentation

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

manual.manticoresearch.com

www.tiot.jp/riak-docs/riak/kv/latest

docs.timescale.com

Developer

Cambridge Semantics

Manticore Software

OpenSource, formerly Basho Technologies

Timescale

Initial release

2018

2017

2009

2017

Current release

2.3, January 2021

6.0, February 2023

3.2.0, December 2022

2.15.0, May 2024

License

Commercial or Open Source

commercial

free trial version available

Open Source

GPL version 2

Open Source

Apache version 2, commercial enterprise edition

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Erlang

Server operating systems

Linux

FreeBSD
Linux
macOS
Windows

Linux
OS X

Linux
OS X
Windows

Data scheme

Schema-free and OWL/RDFS-schema support

Fixed schema

schema-free

yes

Typing

predefined data types such as float or date

Int, Bigint, Float, Timestamp, Bit, Int array, Bigint array, JSON, Boolean

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types

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.

Can index from XML

yes

Secondary indexes

yes

full-text index on all search fields

restricted

yes

SQL

Support of SQL

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

SQL-like query language

yes

full PostgreSQL SQL syntax

APIs and other access methods

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

Binary API
RESTful HTTP/JSON API
RESTful HTTP/SQL API
SQL over MySQL

HTTP API
Native Erlang Interface

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Supported programming languages

C++
Java
Python

Elixir
Go
Java
JavaScript (Node.js)
Perl
PHP
Python

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

user defined functions and aggregates

user defined functions

Erlang

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

Triggers

yes

pre-commit hooks and post-commit hooks

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

Sharding

Partitioning is done manually, search queries against distributed index is supported

Sharding

no "single point of failure"

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

Synchronous replication based on Galera library

selectable replication factor

Source-replica replication with hot standby and reads on replicas

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

not needed in graphs

links between data sets can be stored

yes

Transaction concepts

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

ACID

yes

isolated transactions for atomic changes and binary logging for safe writes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Manticore index.

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

yes, using Riak Security

fine grained access rights according to SQL-standard

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More resources

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