DBMS > BigObject vs. GridGain vs. Hawkular Metrics vs. MySQL vs. XTDB

System Properties Comparison BigObject vs. GridGain vs. Hawkular Metrics vs. MySQL vs. XTDB

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for real-time computations and queries

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

Hawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.

Widely used open source RDBMS

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Key-value store
Relational DBMS

Time Series DBMS

Relational DBMS

Key/Value like access via memcached API

Document store

Secondary database models

Document store
Spatial DBMS

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

Score	0.19
Rank	#329	Overall
	#146	Relational DBMS

Score	1.55
Rank	#150	Overall
	#26	Key-value stores
	#70	Relational DBMS

Score	0.08
Rank	#366	Overall
	#39	Time Series DBMS

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Score	0.18
Rank	#332	Overall
	#46	Document stores

Website

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

docs.bigobject.io

www.gridgain.com/docs/index.html

www.hawkular.org/hawkular-metrics/docs/user-guide

dev.mysql.com/doc

www.xtdb.com/docs

Developer

BigObject, Inc.

GridGain Systems, Inc.

Community supported by Red Hat

Oracle

since 2010, originally MySQL AB, then Sun

Juxt Ltd.

Initial release

2015

2007

2014

1995

2019

Current release

GridGain 8.5.1

8.4.0, April 2024

1.19, September 2021

License

Commercial or Open Source

commercial

free community edition available

commercial

Open Source

Apache 2.0

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

MIT License

Cloud-based only

Only available as a cloud service

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Implementation language

Java, C++, .Net

Java

C and C++

Clojure

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

Linux
OS X
Solaris
Windows

Linux
OS X
Windows

FreeBSD
Linux
OS X
Solaris
Windows

All OS with a Java 8 (and higher) VM
Linux

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, extensible-data-notation format

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-like DML and DDL statements

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

yes

with proprietary extensions

limited SQL, making use of Apache Calcite

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

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

HTTP REST

ADO.NET
JDBC
ODBC
Proprietary native API

HTTP REST
JDBC

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

Go
Java
Python
Ruby

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Clojure
Java

Server-side scripts

Stored procedures

Lua

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

yes

proprietary syntax

Triggers

yes (cache interceptors and events)

yes

via Hawkular Alerting

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

based on Cassandra

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes (replicated cache)

selectable replication factor

based on Cassandra

Multi-source replication
Source-replica replication

yes, each node contains all data

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

none

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

yes

not for MyISAM storage engine

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

not for MyISAM storage engine

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

yes

table locks or row locks depending on storage engine

yes

Durability

Support for making data persistent

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

Security Hooks for custom implementations

Users with fine-grained authorization concept

no user groups or roles

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