DBMS > AlaSQL vs. Apache Impala vs. H2 vs. Machbase Neo vs. XTDB

System Properties Comparison AlaSQL vs. Apache Impala vs. H2 vs. Machbase Neo vs. XTDB

Editorial information provided by DB-Engines

Name

Description

JavaScript DBMS library

Analytic DBMS for Hadoop

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

TimeSeries DBMS for AIoT and BigData

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

Primary database model

Document store
Relational DBMS

Relational DBMS

Time Series DBMS

Document store

Secondary database models

Document store

Spatial DBMS

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

Score	0.51
Rank	#256	Overall
	#40	Document stores
	#118	Relational DBMS

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	8.33
Rank	#46	Overall
	#30	Relational DBMS

Score	0.17
Rank	#337	Overall
	#30	Time Series DBMS

Score	0.18
Rank	#332	Overall
	#46	Document stores

Website

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

Technical documentation

github.com/AlaSQL/alasql

impala.apache.org/impala-docs.html

www.h2database.com/html/main.html

machbase.com/dbms

www.xtdb.com/docs

Developer

Andrey Gershun & Mathias R. Wulff

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Thomas Mueller

Machbase

Juxt Ltd.

Initial release

2014

2013

2005

2013

2019

Current release

4.1.0, June 2022

2.2.220, July 2023

V8.0, August 2023

1.19, September 2021

License

Commercial or Open Source

Open Source

MIT-License

Open Source

Apache Version 2

Open Source

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

commercial

free test version available

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

JavaScript

C++

Java

Clojure

Server operating systems

server-less, requires a JavaScript environment (browser, Node.js)

Linux

All OS with a Java VM

Linux
macOS
Windows

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

Data scheme

schema-free

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.

Secondary indexes

yes

SQL

Support of SQL

Close to SQL99, but no user access control, stored procedures and host language bindings.

SQL-like DML and DDL statements

yes

SQL-like query language

limited SQL, making use of Apache Calcite

APIs and other access methods

JavaScript API

JDBC
ODBC

gRPC
HTTP REST
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC

HTTP REST
JDBC

Supported programming languages

JavaScript

All languages supporting JDBC/ODBC

Java

C
C#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

Clojure
Java

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Java Stored Procedures and User-Defined Functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

selectable replication factor

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

selectable replication factor

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

none

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

only for local storage and DOM-storage

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

by using IndexedDB, SQL.JS or proprietary FileStorage

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

volatile and lookup table

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

fine grained access rights according to SQL-standard

simple password-based access control

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

Recent citations in the news

HarperDB - How and Why We Built It From The Ground Up on NodeJS
28 February 2021, hackernoon.com

Create a Marvel Database with SQL and Javascript, the easy way
2 July 2019, Towards Data Science

Multi faceted data exploration in the browser using Leaflet and amCharts
3 May 2020, Towards Data Science

provided by Google 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

Share this page