DBMS > Apache Impala vs. Bangdb vs. Graph Engine vs. Informix

System Properties Comparison Apache Impala vs. Bangdb vs. Graph Engine vs. Informix

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

Converged and high performance database for device data, events, time series, document and graph

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

A secure embeddable database from IBM, positioned besides IBM Db2 as a relatively low-cost product optimized for OLTP and Internet of Things data

Primary database model

Relational DBMS

Document store
Graph DBMS
Time Series DBMS

Graph DBMS
Key-value store

Relational DBMS

Since Version 12.10 support for JSON/BSON datatypes compatible with MongoDB

Secondary database models

Document store

Spatial DBMS

Document store
Spatial DBMS
Time Series DBMS

with Informix TimeSeries Extension

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	0.08
Rank	#347	Overall
	#47	Document stores
	#34	Graph DBMS
	#31	Time Series DBMS

Score	0.61
Rank	#240	Overall
	#21	Graph DBMS
	#35	Key-value stores

Score	17.87
Rank	#35	Overall
	#22	Relational DBMS

Website

impala.apache.org

bangdb.com

www.graphengine.io

www.ibm.com/products/informix

Technical documentation

impala.apache.org/impala-docs.html

docs.bangdb.com

www.graphengine.io/docs/manual

informix.hcldoc.com
www.ibm.com/support/knowledgecenter/SSGU8G/welcomeIfxServers.html

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Sachin Sinha, BangDB

Microsoft

IBM, HCL Technologies

Effective May 1st, 2017, HCL took on development, technical support, and product management teams, and works jointly with IBM on product strategy, marketing, and sales.

Initial release

2013

2012

2010

1984

Current release

4.1.0, June 2022

BangDB 2.0, October 2021

14.10.FC5, November 2020

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

BSD 3

Open Source

MIT License

commercial

free developer edition available

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++

C, C++

.NET and C

C, C++ and Java

Server operating systems

Linux

.NET

AIX
HP-UX
Linux
macOS
Solaris
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

yes: string, long, double, int, geospatial, stream, events

yes

Since Version 12.10 support for JSON/BSON datatypes

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

secondary, composite, nested, reverse, geospatial

yes

SQL

Support of SQL

SQL-like DML and DDL statements

SQL like support with command line tool

yes

APIs and other access methods

JDBC
ODBC

Proprietary protocol
RESTful HTTP API

RESTful HTTP API

JDBC
JSON API

MongoDB compatible
MQTT (Message Queue Telemetry Transport)
ODBC
RESTful HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

C
C#
C++
Java
Python

C#
C++
F#
Visual Basic

.Net
C
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

Triggers

yes, Notifications (with Streaming only)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

selectable replication factor, Knob for CAP (enterprise version only)

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Tunable consistency, set CAP knob accordingly

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, optimistic concurrency control

yes

Durability

Support for making data persistent

yes

yes, implements WAL (Write ahead log) as well

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

yes

In-memory capabilities

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

yes, run db with in-memory only mode

yes

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

yes (enterprise version only)

Users with fine-grained authentication, authorization, and auditing controls

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