DBMS > Apache Impala vs. DolphinDB vs. Microsoft Azure Cosmos DB vs. Snowflake vs. Tkrzw

System Properties Comparison Apache Impala vs. DolphinDB vs. Microsoft Azure Cosmos DB vs. Snowflake vs. Tkrzw

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

DolphinDB is a high performance Time Series DBMS. It is integrated with an easy-to-use fully featured programming language and a high-volume high-velocity streaming analytics system. It offers operational simplicity, scalability, fault tolerance, and concurrency.

Globally distributed, horizontally scalable, multi-model database service

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

A concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet

Primary database model

Relational DBMS

Time Series DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Key-value store

Secondary database models

Document store

Relational 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	4.03
Rank	#78	Overall
	#6	Time Series 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

Score	0.07
Rank	#372	Overall
	#57	Key-value stores

Website

impala.apache.org

www.dolphindb.com

azure.microsoft.com/services/cosmos-db

www.snowflake.com

dbmx.net/tkrzw

Technical documentation

impala.apache.org/impala-docs.html

docs.dolphindb.cn/en/help200/index.html

learn.microsoft.com/azure/cosmos-db

docs.snowflake.net/manuals/index.html

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

DolphinDB, Inc

Microsoft

Snowflake Computing Inc.

Mikio Hirabayashi

Initial release

2013

2018

2014

2020

Current release

4.1.0, June 2022

v2.00.4, January 2022

0.9.3, August 2020

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

free community version available

commercial

Open Source

Apache Version 2.0

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.

Implementation language

C++

Server operating systems

Linux

Linux
Windows

hosted

Linux
macOS

Data scheme

yes

schema-free

yes

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

schema-free

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

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

JDBC
JSON over HTTP
Kafka
MQTT (Message Queue Telemetry Transport)
ODBC
OPC DA
OPC UA
RabbitMQ
WebSocket

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

CLI Client
JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

C#
C++
Go
Java
JavaScript
MatLab
Python
R
Rust

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

JavaScript (Node.js)
Python

C++
Java
Python
Ruby

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

JavaScript

user defined functions

Triggers

JavaScript

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Sharding

Implicit feature of the cloud service

yes

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

Implicit feature of the cloud service

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

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

using specific database classes

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

Administrators, Users, Groups

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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