DBMS > LeanXcale vs. Spark SQL vs. VoltDB vs. XTDB

System Properties Comparison LeanXcale vs. Spark SQL vs. VoltDB vs. XTDB

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Editorial information provided by DB-Engines

Name

Description

A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

Spark SQL is a component on top of 'Spark Core' for structured data processing

Distributed In-Memory NewSQL RDBMS

Used for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory

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

Primary database model

Key-value store
Relational DBMS

Relational DBMS

Document store

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

Score	0.36
Rank	#280	Overall
	#40	Key-value stores
	#129	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	1.47
Rank	#157	Overall
	#73	Relational DBMS

Score	0.18
Rank	#332	Overall
	#46	Document stores

Website

www.leanxcale.com

spark.apache.org/sql

www.voltdb.com

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

Technical documentation

spark.apache.org/docs/latest/sql-programming-guide.html

docs.voltdb.com

www.xtdb.com/docs

Developer

LeanXcale

Apache Software Foundation

VoltDB Inc.

Juxt Ltd.

Initial release

2015

2014

2010

2019

Current release

3.5.0 ( 2.13), September 2023

11.3, April 2022

1.19, September 2021

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

AGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions

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

Scala

Java, C++

Clojure

Server operating systems

Linux
OS X
Windows

Linux
OS X

for development

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

Data scheme

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

yes

through Apache Derby

SQL-like DML and DDL statements

yes

only a subset of SQL 99

limited SQL, making use of Apache Calcite

APIs and other access methods

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

JDBC
ODBC

Java API
JDBC
RESTful HTTP/JSON API

HTTP REST
JDBC

Supported programming languages

C
Java
Scala

Java
Python
R
Scala

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Clojure
Java

Server-side scripts

Stored procedures

Java

Triggers

Partitioning methods

Methods for storing different data on different nodes

yes, utilizing Spark Core

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication
Source-replica replication

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

FOREIGN KEY constraints are not supported

Transaction concepts

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

ACID

Transactions are executed single-threaded within stored procedures

ACID

Concurrency

Support for concurrent manipulation of data

yes

Data access is serialized by the server

yes

Durability

Support for making data persistent

yes

Snapshots and command logging

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

Users and roles with access to stored procedures

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