DBMS > Apache Impala vs. GBase vs. H2 vs. Riak KV vs. TimescaleDB

System Properties Comparison Apache Impala vs. GBase vs. H2 vs. Riak KV vs. TimescaleDB

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

Widely used RDBMS in China, including analytical, transactional, distributed transactional, and cloud-native data warehousing.

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

Distributed, fault tolerant key-value store

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

Primary database model

Relational DBMS

Key-value store

with links between data sets and object tags for the creation of secondary indexes

Time Series DBMS

Secondary database models

Document store

Spatial DBMS

Relational DBMS

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

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	1.05
Rank	#186	Overall
	#86	Relational DBMS

Score	8.33
Rank	#46	Overall
	#30	Relational DBMS

Score	4.01
Rank	#79	Overall
	#9	Key-value stores

Score	4.46
Rank	#71	Overall
	#5	Time Series DBMS

Website

Technical documentation

impala.apache.org/impala-docs.html

www.h2database.com/html/main.html

www.tiot.jp/riak-docs/riak/kv/latest

docs.timescale.com

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

General Data Technology Co., Ltd.

Thomas Mueller

OpenSource, formerly Basho Technologies

Timescale

Initial release

2013

2004

2005

2009

2017

Current release

4.1.0, June 2022

GBase 8a, GBase 8s, GBase 8c

2.2.220, July 2023

3.2.0, December 2022

2.15.0, May 2024

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

dual-licence (Mozilla public license, Eclipse public license)

Open Source

Apache version 2, commercial enterprise edition

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

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Implementation language

C++

C, Java, Python

Java

Erlang

Server operating systems

Linux

All OS with a Java VM

Linux
OS X

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types

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

restricted

yes

SQL

Support of SQL

SQL-like DML and DDL statements

Standard with numerous extensions

yes

full PostgreSQL SQL syntax

APIs and other access methods

JDBC
ODBC

ADO.NET
C API
JDBC
ODBC

JDBC
ODBC

HTTP API
Native Erlang Interface

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Supported programming languages

All languages supporting JDBC/ODBC

Java

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

user defined functions

Java Stored Procedures and User-Defined Functions

Erlang

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

Triggers

yes

pre-commit hooks and post-commit hooks

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning (by range, list and hash) and vertical partitioning

none

Sharding

no "single point of failure"

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

With clustering: 2 database servers on different computers operate on identical copies of a database

selectable replication factor

Source-replica replication with hot standby and reads on replicas

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

links between data sets can be stored

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

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

yes

fine grained access rights according to SQL-standard

yes, using Riak Security

fine grained access rights according to SQL-standard

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More resources

Recent citations in the news

Apache Impala 4 Supports Operator Multi-Threading
29 July 2021, iProgrammer

Apache Impala becomes Top-Level Project
28 November 2017, SDTimes.com

Cloudera Bringing Impala to AWS Cloud
28 November 2017, Datanami

Hudi: Uber Engineering’s Incremental Processing Framework on Apache Hadoop
12 March 2017, Uber

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23 December 2011, InfoQ.com

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13 January 2015, Data Center Knowledge

Riak NoSQL snapped up by Bet365
12 September 2017, ComputerWeekly.com

Basho to Bolster Riak with DB Plug-Ins
5 May 2014, Datanami

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TimescaleDB Is a Vector Database Now, Too
25 September 2023, Datanami

Timescale Acquires PopSQL to Bring a Modern, Collaborative SQL GUI to PostgreSQL Developers
4 April 2024, PR Newswire

Power IoT and time-series workloads with TimescaleDB for Azure Database for PostgreSQL
18 March 2019, Microsoft

Timescale Valuation Rockets to Over $1B with $110M Round, Marking the Explosive Rise of Time-Series Data
22 February 2022, businesswire.com

TimescaleDB goes distributed; implements ‘Chunking’ over ‘Sharding’ for scaling-out
22 August 2019, Packt Hub

provided by Google News

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