DBMS > CrateDB vs. Riak KV vs. Snowflake vs. Solr

System Properties Comparison CrateDB vs. Riak KV vs. Snowflake vs. Solr

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Editorial information provided by DB-Engines

Name

Description

Distributed Database based on Lucene

Distributed, fault tolerant key-value store

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

A widely used distributed, scalable search engine based on Apache Lucene

Primary database model

Document store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS

Key-value store

with links between data sets and object tags for the creation of secondary indexes

Relational DBMS

Search engine

Secondary database models

Relational DBMS

Spatial DBMS

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

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

Score	4.01
Rank	#79	Overall
	#9	Key-value stores

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Score	41.02
Rank	#24	Overall
	#3	Search engines

Website

cratedb.com

www.snowflake.com

solr.apache.org

Technical documentation

cratedb.com/docs

www.tiot.jp/riak-docs/riak/kv/latest

docs.snowflake.net/manuals/index.html

solr.apache.org/resources.html

Developer

Crate

OpenSource, formerly Basho Technologies

Snowflake Computing Inc.

Apache Software Foundation

Initial release

2013

2009

2014

2006

Current release

3.2.0, December 2022

9.6.1, May 2024

License

Commercial or Open Source

Open Source

Apache version 2, commercial enterprise edition

commercial

Open Source

Apache Version 2

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

Java

Erlang

Java

Server operating systems

All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator support

Linux
OS X

hosted

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Data scheme

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

schema-free

yes

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

yes

Dynamic Fields enables on-the-fly addition of new fields

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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

restricted

yes

All search fields are automatically indexed

SQL

Support of SQL

yes, but no triggers and constraints, and PostgreSQL compatibility

yes

Solr Parallel SQL Interface

APIs and other access methods

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

HTTP API
Native Erlang Interface

CLI Client
JDBC
ODBC

Java API
RESTful HTTP/JSON API

Supported programming languages

.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

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

JavaScript (Node.js)
Python

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

user defined functions (Javascript)

Erlang

user defined functions

Java plugins

Triggers

yes

pre-commit hooks and post-commit hooks

similar concept for controling cloud resources

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

no "single point of failure"

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Configurable replication on table/partition-level

selectable replication factor

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Read-after-write consistency on record level

Eventual Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

links between data sets can be stored

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

optimistic locking

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

rights management via user accounts

yes, using Riak Security

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

yes

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