DBMS > CrateDB vs. GeoSpock vs. GridGain vs. Snowflake vs. Yaacomo

System Properties Comparison CrateDB vs. GeoSpock vs. GridGain vs. Snowflake vs. Yaacomo

Editorial information provided by DB-Engines

Name

GeoSpock seems to be discontinued. Therefore it will be excluded from the DB-Engines ranking.

Yaacomo seems to be discontinued and is removed from the DB-Engines ranking

Description

Distributed Database based on Lucene

Spatial and temporal data processing engine for extreme data scale

GridGain is an in-memory computing platform, built on Apache Ignite

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

OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing

Primary database model

Document store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS

Relational DBMS

Key-value store
Relational DBMS

Relational DBMS

Secondary database models

Relational DBMS

Time Series DBMS

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

Score	0.73
Rank	#224	Overall
	#37	Document stores
	#5	Spatial DBMS
	#16	Search engines
	#19	Time Series DBMS
	#8	Vector DBMS

Score	1.47
Rank	#154	Overall
	#26	Key-value stores
	#72	Relational DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

cratedb.com

geospock.com

www.gridgain.com

www.snowflake.com

yaacomo.com

Technical documentation

cratedb.com/docs

www.gridgain.com/docs/index.html

docs.snowflake.net/manuals/index.html

Developer

Crate

GeoSpock

GridGain Systems, Inc.

Snowflake Computing Inc.

Q2WEB GmbH

Initial release

2013

2007

2014

2009

Current release

2.0, September 2019

GridGain 8.5.1

License

Commercial or Open Source

Open Source

commercial

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

Java, Javascript

Java, C++, .Net

Server operating systems

All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator support

hosted

Linux
OS X
Solaris
Windows

hosted

Android
Linux
Windows

Data scheme

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

yes

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

yes

Typing

predefined data types such as float or date

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

temporal, categorical

yes

SQL

Support of SQL

yes, but no triggers and constraints, and PostgreSQL compatibility

ANSI SQL for query only (using Presto)

ANSI-99 for query and DML statements, subset of DDL

yes

APIs and other access methods

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

JDBC

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

CLI Client
JDBC
ODBC

JDBC
ODBC

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

C#
C++
Java
PHP
Python
Ruby
Scala

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions (Javascript)

yes (compute grid and cache interceptors can be used instead)

user defined functions

Triggers

yes (cache interceptors and events)

similar concept for controling cloud resources

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic sharding

Sharding

yes

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Configurable replication on table/partition-level

yes (replicated cache)

yes

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

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

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

Access rights for users can be defined per table

Security Hooks for custom implementations

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

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

The enterprise database for time series, documents, and vectors. Distributed - Native...
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Competitive advantages

Response time in milliseconds: e ven for complex ad-hoc queries. Massive scaling...
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Typical application scenarios

IoT: accelerate your IIoT projects with CrateDB, delivering real-time analytics...
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Key customers

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

The CrateDB open source project was started in 2013 Honorable Mention in 2021 Gartner®...
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Licensing and pricing models

See CrateDB pricing >
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