DBMS > Apache Impala vs. Bangdb vs. Faircom DB vs. OpenTSDB

System Properties Comparison Apache Impala vs. Bangdb vs. Faircom DB vs. OpenTSDB

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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

Native high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.

Scalable Time Series DBMS based on HBase

Primary database model

Relational DBMS

Document store
Graph DBMS
Time Series DBMS

Key-value store
Relational DBMS

Time Series DBMS

Secondary database models

Document store

Spatial DBMS

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

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	0.16
Rank	#338	Overall
	#47	Document stores
	#32	Graph DBMS
	#31	Time Series DBMS

Score	0.29
Rank	#304	Overall
	#43	Key-value stores
	#136	Relational DBMS

Score	1.68
Rank	#142	Overall
	#12	Time Series DBMS

Website

impala.apache.org

bangdb.com

www.faircom.com/products/faircom-db

opentsdb.net

Technical documentation

impala.apache.org/impala-docs.html

docs.bangdb.com

docs.faircom.com/docs/en/UUID-7446ae34-a1a7-c843-c894-d5322e395184.html

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

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Sachin Sinha, BangDB

FairCom Corporation

currently maintained by Yahoo and other contributors

Initial release

2013

2012

1979

2011

Current release

4.1.0, June 2022

BangDB 2.0, October 2021

V12, November 2020

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

BSD 3

commercial

Restricted, free version available

Open Source

LGPL

Cloud-based only

Only available as a cloud service

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Database as a Service

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Implementation language

C++

C, C++

ANSI C, C++

Java

Server operating systems

Linux

AIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows

easily portable to other OSs

Linux
Windows

Data scheme

yes

schema-free

schema free, schema optional, schema required, partial schema,

schema-free

Typing

predefined data types such as float or date

yes

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

yes, ANSI SQL Types, JSON, typed binary structures

numeric data for metrics, strings for tags

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, ANSI SQL with proprietary extensions

APIs and other access methods

JDBC
ODBC

Proprietary protocol
RESTful HTTP API

ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC

HTTP API
Telnet API

Supported programming languages

All languages supporting JDBC/ODBC

C
C#
C++
Java
Python

.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic

Erlang
Go
Java
Python
R
Ruby

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

.Net, JavaScript, C/C++

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

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

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

yes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).

selectable replication factor

based on HBase

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

Eventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction

Immediate Consistency

based on HBase

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

tunable from ACID to Eventually Consistent

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

Yes, tunable from durable to delayed durability to in-memory

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)

Fine grained access rights according to SQL-standard with additional protections for files

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More resources

DB-Engines blog posts

Time Series DBMS are the database category with the fastest increase in popularity
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