DBMS > Apache Impala vs. Immudb vs. MySQL vs. Splice Machine vs. STSdb

System Properties Comparison Apache Impala vs. Immudb vs. MySQL vs. Splice Machine vs. STSdb

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

An open source immutable (append-only) database with cryptographic verification which makes it tamper-resistant and fully auditable.

Widely used open source RDBMS

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Relational DBMS

Key-value store

Relational DBMS

Key/Value like access via memcached API

Relational DBMS

Key-value store

Secondary database models

Document store

Relational DBMS

Document store
Spatial DBMS

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	0.24
Rank	#305	Overall
	#43	Key-value stores

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Score	0.04
Rank	#360	Overall
	#52	Key-value stores

Website

impala.apache.org

github.com/codenotary/immudb
immudb.io

www.mysql.com

splicemachine.com

github.com/STSSoft/STSdb4

Technical documentation

impala.apache.org/impala-docs.html

docs.immudb.io

dev.mysql.com/doc

splicemachine.com/how-it-works

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Codenotary

Oracle

since 2010, originally MySQL AB, then Sun

Splice Machine

STS Soft SC

Initial release

2013

2020

1995

2014

2011

Current release

4.1.0, June 2022

1.2.3, April 2022

8.4.0, April 2024

3.1, March 2021

4.0.8, September 2015

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache Version 2.0

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

AGPL 3.0, commercial license available

Open Source

GPLv2, commercial license available

Cloud-based only

Only available as a cloud service

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Implementation language

C++

C and C++

Java

Server operating systems

Linux

BSD
Linux
macOS
Solaris
Windows
z/OS

FreeBSD
Linux
OS X
Solaris
Windows

Linux
OS X
Solaris
Windows

Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

primitive types and user defined types (classes)

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

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like syntax

yes

with proprietary extensions

yes

APIs and other access methods

JDBC
ODBC

gRPC protocol
PostgreSQL wire protocol

ADO.NET
JDBC
ODBC
Proprietary native API

JDBC
Native Spark Datasource
ODBC

.NET Client API

Supported programming languages

All languages supporting JDBC/ODBC

.Net
Go
Java
JavaScript (Node.js)
Python

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

C#
Java

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

proprietary syntax

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Shared Nothhing Auto-Sharding, Columnar Partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

yes

Transaction concepts

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

ACID

not for MyISAM storage engine

ACID

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

Users with fine-grained authorization concept

no user groups or roles

Access rights for users, groups and roles according to SQL-standard

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