DBMS > AnzoGraph DB vs. Apache Impala vs. Dolt vs. Microsoft SQL Server vs. TerarkDB

System Properties Comparison AnzoGraph DB vs. Apache Impala vs. Dolt vs. Microsoft SQL Server vs. TerarkDB

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

Analytic DBMS for Hadoop

A MySQL compatible DBMS with Git-like versioning of data and schema

Microsofts flagship relational DBMS

A key-value store forked from RocksDB with advanced compression algorithms. It can be used standalone or as a storage engine for MySQL and MongoDB

Primary database model

Graph DBMS
RDF store

Relational DBMS

Key-value store

Secondary database models

Document store

Document store
Graph DBMS
Spatial DBMS

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

Score	0.29
Rank	#303	Overall
	#25	Graph DBMS
	#14	RDF stores

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	1.02
Rank	#191	Overall
	#89	Relational DBMS

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	0.08
Rank	#367	Overall
	#56	Key-value stores

Website

cambridgesemantics.com/anzograph

impala.apache.org

github.com/dolthub/dolt
www.dolthub.com

www.microsoft.com/en-us/sql-server

github.com/bytedance/terarkdb

Technical documentation

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

impala.apache.org/impala-docs.html

docs.dolthub.com

learn.microsoft.com/en-US/sql/sql-server

bytedance.larkoffice.com/docs/doccnZmYFqHBm06BbvYgjsHHcKc

Developer

Cambridge Semantics

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

DoltHub Inc

Microsoft

ByteDance, originally Terark

Initial release

2018

2013

2018

1989

2016

Current release

2.3, January 2021

4.1.0, June 2022

SQL Server 2022, November 2022

License

Commercial or Open Source

commercial

free trial version available

Open Source

Apache Version 2

Open Source

Apache Version 2.0

commercial

restricted free version is available

commercial

restricted open source version available

Cloud-based only

Only available as a cloud service

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Implementation language

C++

Server operating systems

Linux

Linux
macOS
Windows

Linux
Windows

Data scheme

Schema-free and OWL/RDFS-schema support

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.

yes

Secondary indexes

yes

SQL

Support of SQL

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

SQL-like DML and DDL statements

yes

APIs and other access methods

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

JDBC
ODBC

CLI Client
HTTP REST

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

C++ API
Java API

Supported programming languages

C++
Java
Python

All languages supporting JDBC/ODBC

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

C++
Java

Server-side scripts

Stored procedures

user defined functions and aggregates

yes

user defined functions and integration of map-reduce

yes

currently in alpha release

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

Sharding

none

tables can be distributed across several files (horizontal partitioning); sharding through federation

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

selectable replication factor

A database can be cloned to multiple locations and be used there in isolation. Data/schema changes can be pushed/pulled explicitly between locations.

yes, but depending on the SQL-Server Edition

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

yes

query execution via MapReduce

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

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

Only one user is configurable, and must be specified in the config file at startup

fine grained access rights according to SQL-standard

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