DBMS > Apache Impala vs. GridGain vs. H2GIS vs. XTDB

System Properties Comparison Apache Impala vs. GridGain vs. H2GIS vs. XTDB

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

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

Spatial extension of H2

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

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Spatial DBMS

Document store

Secondary database models

Document store

Relational DBMS

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

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

Score	0.00
Rank	#383	Overall
	#7	Spatial DBMS

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

impala.apache.org

www.gridgain.com

www.h2gis.org

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

Technical documentation

impala.apache.org/impala-docs.html

www.gridgain.com/docs/index.html

www.h2gis.org/docs/home

www.xtdb.com/docs

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

GridGain Systems, Inc.

CNRS

Juxt Ltd.

Initial release

2013

2007

2013

2019

Current release

4.1.0, June 2022

GridGain 8.5.1

1.19, September 2021

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

LGPL 3.0

Open Source

MIT License

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C++

Java, C++, .Net

Java

Clojure

Server operating systems

Linux

Linux
OS X
Solaris
Windows

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

Data scheme

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

limited SQL, making use of Apache Calcite

APIs and other access methods

JDBC
ODBC

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

HTTP REST
JDBC

Supported programming languages

All languages supporting JDBC/ODBC

C#
C++
Java
PHP
Python
Ruby
Scala

Java

Clojure
Java

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

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

yes

based on H2

Triggers

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes (replicated cache)

yes

based on H2

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

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

yes, multi-version concurrency control (MVCC)

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

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

Security Hooks for custom implementations

yes

based on H2

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More resources

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