DBMS > Apache Impala vs. HarperDB vs. Infobright vs. XTDB

System Properties Comparison Apache Impala vs. HarperDB vs. Infobright vs. XTDB

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

Ultra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.

High performant column-oriented DBMS for analytic workloads using MySQL or PostgreSQL as a frontend

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

Primary database model

Secondary database models

Document store

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

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	0.60
Rank	#244	Overall
	#38	Document stores

Score	1.02
Rank	#192	Overall
	#90	Relational DBMS

Score	0.18
Rank	#332	Overall
	#46	Document stores

Website

impala.apache.org

www.harperdb.io

ignitetech.com/softwarelibrary/infobrightdb

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

Technical documentation

impala.apache.org/impala-docs.html

docs.harperdb.io/docs

www.xtdb.com/docs

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

HarperDB

Ignite Technologies Inc.; formerly InfoBright Inc.

Juxt Ltd.

Initial release

2013

2017

2005

2019

Current release

4.1.0, June 2022

3.1, August 2021

1.19, September 2021

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

free community edition available

commercial

The open source (GPLv2) version did not support inserts/updates/deletes and was discontinued with July 2016

Open Source

MIT License

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Node.js

Clojure

Server operating systems

Linux

Linux
OS X

Linux
Windows

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

Data scheme

yes

dynamic schema

yes

schema-free

Typing

predefined data types such as float or date

yes

JSON data types

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.

Secondary indexes

yes

Knowledge Grid Technology used instead

yes

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like data manipulation statements

yes

limited SQL, making use of Apache Calcite

APIs and other access methods

JDBC
ODBC

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

ADO.NET
JDBC
ODBC

HTTP REST
JDBC

Supported programming languages

All languages supporting JDBC/ODBC

.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift

.Net
C
C#
C++
D
Eiffel
Erlang
Haskell
Java
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Clojure
Java

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Custom Functions

since release 3.1

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

A table resides as a whole on one (or more) nodes in a cluster

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

the nodes on which a table resides can be defined

Source-replica replication

yes, each node contains all data

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

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Atomic execution of specific operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, using LMDB

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

Access rights for users and roles

fine grained access rights according to SQL-standard

exploiting MySQL or PostgreSQL frontend capabilities

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More resources

Recent citations in the news

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

Cloudera Bringing Impala to AWS Cloud
28 November 2017, Datanami

Apache Doris just 'graduated': Why care about this SQL data warehouse
24 June 2022, InfoWorld

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

Updates & Upserts in Hadoop Ecosystem with Apache Kudu
27 October 2017, KDnuggets

provided by Google News

Serent Capital invests in software firm HarperDB
12 June 2024, PE Hub

HarperDB Attracts Significant Growth Investment from Serent Capital
12 June 2024, Business Wire

HarperDB Receives Growth Investment from Serent Capital
12 June 2024, FinSMEs

Startups of the Year 2023: Meet HarperDB - A Database and Application Development Platform
22 June 2023, hackernoon.com

Jaxon Repp on HarperDB Distributed Database Platform
23 March 2022, InfoQ.com

provided by Google News

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