DBMS > Apache Impala vs. HugeGraph vs. Kinetica vs. Microsoft Azure Cosmos DB vs. Snowflake

System Properties Comparison Apache Impala vs. HugeGraph vs. Kinetica vs. Microsoft Azure Cosmos DB vs. Snowflake

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

A fast-speed and highly-scalable Graph DBMS

Fully vectorized database across both GPUs and CPUs

Globally distributed, horizontally scalable, multi-model database service

Cloud-based data warehousing service for structured and semi-structured data

Primary database model

Relational DBMS

Graph DBMS

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Secondary database models

Document store

Spatial DBMS
Time Series DBMS

Spatial DBMS

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

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	0.17
Rank	#335	Overall
	#31	Graph DBMS

Score	0.66
Rank	#234	Overall
	#107	Relational DBMS

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Website

impala.apache.org

github.com/hugegraph
hugegraph.apache.org

www.kinetica.com

azure.microsoft.com/services/cosmos-db

www.snowflake.com

Technical documentation

impala.apache.org/impala-docs.html

hugegraph.apache.org/docs

docs.kinetica.com

learn.microsoft.com/azure/cosmos-db

docs.snowflake.net/manuals/index.html

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Baidu

Kinetica

Microsoft

Snowflake Computing Inc.

Initial release

2013

2018

2012

2014

Current release

4.1.0, June 2022

0.9

7.1, August 2021

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache Version 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

C++

Java

C, C++

Server operating systems

Linux

Linux
macOS
Unix

Linux

hosted

Data scheme

yes

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

Typing

predefined data types such as float or date

yes

JSON types

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

also supports composite index and range index

yes

All properties auto-indexed by default

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language

yes

APIs and other access methods

JDBC
ODBC

Java API
RESTful HTTP API
TinkerPop Gremlin

JDBC
ODBC
RESTful HTTP API

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

CLI Client
JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

Groovy
Java
Python

C++
Java
JavaScript (Node.js)
Python

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

asynchronous Gremlin script jobs

user defined functions

JavaScript

user defined functions

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

JavaScript

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

depending on used storage backend, e.g. Cassandra and HBase

Sharding

Implicit feature of the cloud service

yes

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

depending on used storage backend, e.g. Cassandra and HBase

Source-replica replication

yes

Implicit feature of the cloud service

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

via hugegraph-spark

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

edges in graph

yes

Transaction concepts

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

ACID

Multi-item ACID transactions with snapshot isolation within a partition

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

GPU vRAM or System RAM

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

Users, roles and permissions

Access rights for users and roles on table level

Access rights can be defined down to the item level

Users with fine-grained authorization concept, user roles and pluggable authentication

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