DBMS > Apache Druid vs. CrateDB vs. Snowflake vs. STSdb vs. SwayDB

System Properties Comparison Apache Druid vs. CrateDB vs. Snowflake vs. STSdb vs. SwayDB

Editorial information provided by DB-Engines

Name

Description

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

Distributed Database based on Lucene

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

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

Primary database model

Relational DBMS
Time Series DBMS

Document store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS

Relational DBMS

Key-value store

Secondary database models

Relational DBMS

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

Score	3.25
Rank	#90	Overall
	#47	Relational DBMS
	#7	Time Series DBMS

Score	0.71
Rank	#227	Overall
	#37	Document stores
	#5	Spatial DBMS
	#16	Search engines
	#19	Time Series DBMS
	#8	Vector DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Score	0.10
Rank	#357	Overall
	#51	Key-value stores

Score	0.04
Rank	#387	Overall
	#61	Key-value stores

Website

druid.apache.org

cratedb.com

www.snowflake.com

github.com/STSSoft/STSdb4

swaydb.simer.au

Technical documentation

druid.apache.org/docs/latest/design

cratedb.com/docs

docs.snowflake.net/manuals/index.html

Developer

Apache Software Foundation and contributors

Crate

Snowflake Computing Inc.

STS Soft SC

Simer Plaha

Initial release

2012

2013

2014

2011

2018

Current release

29.0.1, April 2024

4.0.8, September 2015

License

Commercial or Open Source

Open Source

Apache license v2

Open Source

commercial

Open Source

GPLv2, commercial license available

Open Source

GNU Affero GPL V3.0

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.

CrateDB Cloud: a distributed SQL database that spreads data and processing across an elastic cluster of shared nothing nodes. CrateDB Cloud enables data insights at scale on Microsoft Azure, AWS and Google Cloud Platform.

Implementation language

Java

Scala

Server operating systems

Linux
OS X
Unix

All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator support

hosted

Windows

Data scheme

yes

schema-less columns are supported

Flexible Schema (defined schema, partial schema, schema free)

yes

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

yes

schema-free

Typing

predefined data types such as float or date

yes

primitive types and user defined types (classes)

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

SQL for querying

yes, but no triggers and constraints, and PostgreSQL compatibility

yes

APIs and other access methods

JDBC
RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API

CLI Client
JDBC
ODBC

.NET Client API

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

.NET
Erlang
Go

community maintained client
Java
JavaScript (Node.js)

community maintained client
Perl

community maintained client
PHP
Python
R
Ruby

community maintained client
Scala

community maintained client

JavaScript (Node.js)
Python

C#
Java

Java
Kotlin
Scala

Server-side scripts

Stored procedures

user defined functions (Javascript)

user defined functions

Triggers

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Sharding

yes

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

Configurable replication on table/partition-level

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Read-after-write consistency on record level

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

unique row identifiers can be used for implementing an optimistic concurrency control strategy

ACID

Atomic execution of operations

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

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

rights management via user accounts

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

More information provided by the system vendor

Specific characteristics

The enterprise database for time series, documents, and vectors. Distributed - Native...
» more

Competitive advantages

Response time in milliseconds: e ven for complex ad-hoc queries. Massive scaling...
» more

Typical application scenarios

IoT: accelerate your IIoT projects with CrateDB, delivering real-time analytics...
» more

Key customers

Across all continents, CrateDB is used by companies of all sizes to meet the most...
» more

Market metrics

The CrateDB open source project was started in 2013 Honorable Mention in 2021 Gartner®...
» more

Licensing and pricing models

See CrateDB pricing >
» more

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.