DBMS > Apache Druid vs. CockroachDB vs. Pinecone vs. Snowflake vs. STSdb

System Properties Comparison Apache Druid vs. CockroachDB vs. Pinecone vs. Snowflake vs. STSdb

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

CockroachDB is a distributed database architected for modern cloud applications. It is wire compatible with PostgreSQL and backed by a Key-Value Store, which is either RocksDB or a purpose-built derivative, called Pebble.

A managed, cloud-native vector database

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

Primary database model

Relational DBMS
Time Series DBMS

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

Score	2.85
Rank	#96	Overall
	#50	Relational DBMS
	#6	Time Series DBMS

Score	4.74
Rank	#67	Overall
	#36	Relational DBMS

Score	3.02
Rank	#87	Overall
	#3	Vector DBMS

Score	133.72
Rank	#7	Overall
	#5	Relational DBMS

Score	0.03
Rank	#365	Overall
	#54	Key-value stores

Website

druid.apache.org

www.cockroachlabs.com

www.pinecone.io

www.snowflake.com

github.com/STSSoft/STSdb4

Technical documentation

druid.apache.org/docs/latest/design

www.cockroachlabs.com/docs

docs.pinecone.io/docs/overview

docs.snowflake.net/manuals/index.html

Developer

Apache Software Foundation and contributors

Cockroach Labs

Pinecone Systems, Inc

Snowflake Computing Inc.

STS Soft SC

Initial release

2012

2015

2019

2014

2011

Current release

30.0.0, June 2024

24.1.0, May 2024

4.0.8, September 2015

License

Commercial or Open Source

Open Source

Apache license v2

Open Source

Apache 2.0, commercial license available

commercial

Open Source

GPLv2, commercial license available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Server operating systems

Linux
OS X
Unix

Linux
macOS
Windows

hosted

Windows

Data scheme

yes

schema-less columns are supported

dynamic schema

yes

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

yes

Typing

predefined data types such as float or date

yes

String, Number, Boolean

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, wire compatible with PostgreSQL

yes

APIs and other access methods

JDBC
RESTful HTTP/JSON API

JDBC

RESTful HTTP API

CLI Client
JDBC
ODBC

.NET Client API

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

C#
C++
Clojure
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Rust

Python

JavaScript (Node.js)
Python

C#
Java

Server-side scripts

Stored procedures

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

horizontal partitioning (by key range)

all tables are translated to an ordered KV store and then broken down into 64MB ranges, which are then used as replicas in RAFT

yes

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

Multi-source replication using RAFT

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

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.

User concepts

Access control

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

Role-based access control

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

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