DBMS > CrateDB vs. Google Cloud Datastore vs. KairosDB vs. STSdb

System Properties Comparison CrateDB vs. Google Cloud Datastore vs. KairosDB vs. STSdb

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Editorial information provided by DB-Engines

Name

Description

Distributed Database based on Lucene

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

Distributed Time Series DBMS based on Cassandra or H2

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Document store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS

Document store

Time Series DBMS

Key-value store

Secondary database models

Relational DBMS

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

Score	0.69
Rank	#226	Overall
	#37	Document stores
	#5	Spatial DBMS
	#16	Search engines
	#19	Time Series DBMS
	#11	Vector DBMS

Score	4.13
Rank	#71	Overall
	#12	Document stores

Score	0.60
Rank	#238	Overall
	#20	Time Series DBMS

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

Website

cratedb.com

cloud.google.com/datastore

github.com/kairosdb/kairosdb

github.com/STSSoft/STSdb4

Technical documentation

cratedb.com/docs

cloud.google.com/datastore/docs

kairosdb.github.io

Developer

Crate

Google

STS Soft SC

Initial release

2013

2008

2013

2011

Current release

5.8.1, August 2024

1.2.2, November 2018

4.0.8, September 2015

License

Commercial or Open Source

Open Source

commercial

Open Source

Apache 2.0

Open Source

GPLv2, commercial license available

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

Server operating systems

All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator support

hosted

Linux
OS X
Windows

Windows

Data scheme

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

schema-free

yes

Typing

predefined data types such as float or date

yes

yes, details here

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.

Secondary indexes

yes

SQL

Support of SQL

yes, but no triggers and constraints, and PostgreSQL compatibility

SQL-like query language (GQL)

APIs and other access methods

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

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

Graphite protocol
HTTP REST
Telnet API

.NET Client 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

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Java
JavaScript

Node.js
PHP
Python

C#
Java

Server-side scripts

Stored procedures

user defined functions (Javascript)

using Google App Engine

Triggers

Callbacks using the Google Apps Engine

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

none

Replication methods

Methods for redundantly storing data on multiple nodes

Configurable replication on table/partition-level

Multi-source replication using Paxos

selectable replication factor

based on Cassandra

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Read-after-write consistency on record level

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

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

Serializable Isolation within Transactions, Read Committed outside of Transactions

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

rights management via user accounts

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

simple password-based access control

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More resources

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