DBMS > GridGain vs. OpenTSDB vs. SAP HANA vs. SurrealDB

System Properties Comparison GridGain vs. OpenTSDB vs. SAP HANA vs. SurrealDB

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Editorial information provided by DB-Engines

Name

Description

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

Scalable Time Series DBMS based on HBase

In-memory, column based data store. Available as appliance or cloud service

A fully ACID transactional, developer-friendly, multi-model DBMS

Primary database model

Columnar
Key-value store
Object oriented DBMS
Relational DBMS

Time Series DBMS

Relational DBMS

Document store
Graph DBMS

Secondary database models

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	1.48
Rank	#150	Overall
	#1	Columnar
	#26	Key-value stores
	#2	Object oriented DBMS
	#69	Relational DBMS

Score	1.59
Rank	#140	Overall
	#12	Time Series DBMS

Score	41.32
Rank	#23	Overall
	#16	Relational DBMS

Score	1.11
Rank	#174	Overall
	#30	Document stores
	#17	Graph DBMS

Website

www.gridgain.com

opentsdb.net

www.sap.com/products/hana.html

surrealdb.com

Technical documentation

www.gridgain.com/docs/index.html

opentsdb.net/docs/build/html/index.html

help.sap.com/hana

surrealdb.com/docs

Developer

GridGain Systems, Inc.

currently maintained by Yahoo and other contributors

SAP

SurrealDB Ltd

Initial release

2007

2011

2010

2022

Current release

GridGain 8.5.1

2.0 SPS07 (April 4, 2023), April 2023

v1.5.0, May 2024

License

Commercial or Open Source

commercial, open source

Open Source

LGPL

commercial

Open Source

Cloud-based only

Only available as a cloud service

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java, C++, .Net, Python, REST, SQL

Java

Rust

Server operating systems

Linux
OS X
Solaris
Windows
z/OS

Linux
Windows

Appliance or cloud-service

Linux
macOS
Windows

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

numeric data for metrics, strings for tags

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

SQL

Support of SQL

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

yes

SQL-like query language

APIs and other access methods

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

HTTP API
Telnet API

JDBC
ODBC

GraphQL
RESTful HTTP API
WebSocket

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

Erlang
Go
Java
Python
R
Ruby

Deno
Go
JavaScript (Node.js)
Rust

Server-side scripts

Stored procedures

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

SQLScript, R

Triggers

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on HBase

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

selectable replication factor

based on HBase

yes

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

Immediate Consistency

based on HBase

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.

yes

User concepts

Access control

Role-based access control
Security Hooks for custom implementations

yes

yes, based on authentication and database rules

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