DBMS > Apache Impala vs. Graphite vs. Microsoft Azure Cosmos DB vs. QuestDB vs. Snowflake

System Properties Comparison Apache Impala vs. Graphite vs. Microsoft Azure Cosmos DB vs. QuestDB vs. Snowflake

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

Data logging and graphing tool for time series data

The storage layer (fixed size database) is called Whisper

Globally distributed, horizontally scalable, multi-model database service

A high performance open source SQL database for time series data

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

Primary database model

Relational DBMS

Time Series DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Time Series DBMS

Relational DBMS

Secondary database models

Document store

Spatial DBMS

Relational DBMS

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

Score	10.63
Rank	#40	Overall
	#24	Relational DBMS

Score	5.19
Rank	#62	Overall
	#4	Time Series DBMS

Score	24.97
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	2.81
Rank	#98	Overall
	#7	Time Series DBMS

Score	133.72
Rank	#7	Overall
	#5	Relational DBMS

Website

impala.apache.org

github.com/graphite-project/graphite-web

azure.microsoft.com/services/cosmos-db

questdb.io

www.snowflake.com

Technical documentation

impala.apache.org/impala-docs.html

graphite.readthedocs.io

learn.microsoft.com/azure/cosmos-db

questdb.io/docs

docs.snowflake.net/manuals/index.html

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Chris Davis

Microsoft

QuestDB Technology Inc

Snowflake Computing Inc.

Initial release

2013

2006

2014

Current release

4.1.0, June 2022

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache 2.0

commercial

Open Source

Apache 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++

Python

Java (Zero-GC), C++, Rust

Server operating systems

Linux

Linux
Unix

hosted

Linux
macOS
Windows

hosted

Data scheme

yes

schema-free

yes

schema-free via InfluxDB Line Protocol

yes

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

Typing

predefined data types such as float or date

yes

Numeric data only

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

SQL with time-series extensions

yes

APIs and other access methods

JDBC
ODBC

HTTP API
Sockets

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

HTTP REST
InfluxDB Line Protocol (TCP/UDP)
JDBC
PostgreSQL wire protocol

CLI Client
JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

JavaScript (Node.js)
Python

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

PostgreSQL driver
C++
Go
Java
JavaScript (Node.js)
Python
Rust

over HTTP

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

JavaScript

user defined functions

Triggers

JavaScript

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Implicit feature of the cloud service

horizontal partitioning (by timestamps)

yes

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

yes

Implicit feature of the cloud service

Source-replica replication with eventual consistency

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

none

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

Multi-item ACID transactions with snapshot isolation within a partition

ACID for single-table writes

ACID

Concurrency

Support for concurrent manipulation of data

yes

locking

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

through memory mapped files

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

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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