DBMS > Apache Drill vs. Geode vs. Microsoft SQL Server vs. Splice Machine vs. TerarkDB

System Properties Comparison Apache Drill vs. Geode vs. Microsoft SQL Server vs. Splice Machine vs. TerarkDB

Editorial information provided by DB-Engines

Name

Description

Schema-free SQL Query Engine for Hadoop, NoSQL and Cloud Storage

Geode is a distributed data container, pooling memory, CPU, network resources, and optionally local disk across multiple processes

Microsofts flagship relational DBMS

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

A key-value store forked from RocksDB with advanced compression algorithms. It can be used standalone or as a storage engine for MySQL and MongoDB

Primary database model

Document store
Relational DBMS

Key-value store

Relational DBMS

Key-value store

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	1.95
Rank	#127	Overall
	#23	Document stores
	#60	Relational DBMS

Score	1.92
Rank	#131	Overall
	#23	Key-value stores

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Score	0.00
Rank	#383	Overall
	#60	Key-value stores

Website

drill.apache.org

geode.apache.org

www.microsoft.com/en-us/sql-server

splicemachine.com

github.com/bytedance/terarkdb

Technical documentation

drill.apache.org/docs

geode.apache.org/docs

learn.microsoft.com/en-US/sql/sql-server

splicemachine.com/how-it-works

bytedance.larkoffice.com/docs/doccnZmYFqHBm06BbvYgjsHHcKc

Developer

Apache Software Foundation

Originally developed by Gemstone. They outsourced the project to Apache in 2015 but still deliver a commercial version as Gemfire.

Microsoft

Splice Machine

ByteDance, originally Terark

Initial release

2012

2002

1989

2014

2016

Current release

1.20.3, January 2023

1.1, February 2017

SQL Server 2022, November 2022

3.1, March 2021

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache Version 2; commercial licenses available as Gemfire

commercial

restricted free version is available

Open Source

AGPL 3.0, commercial license available

commercial

restricted open source version available

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

Java

C++

Java

C++

Server operating systems

Linux
OS X
Windows

All OS with a Java VM

the JDK (8 or later) is also required

Linux
Windows

Linux
OS X
Solaris
Windows

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

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 SELECT statement is SQL:2003 compliant

SQL-like query language (OQL)

yes

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

Java Client API
Memcached protocol
RESTful HTTP API

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

JDBC
Native Spark Datasource
ODBC

C++ API
Java API

Supported programming languages

C++

.Net
All JVM based languages
C++
Groovy
Java
Scala

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

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

C++
Java

Server-side scripts

Stored procedures

user defined functions

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

yes

Java

Triggers

yes

Cache Event Listeners

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

tables can be distributed across several files (horizontal partitioning); sharding through federation

Shared Nothhing Auto-Sharding, Columnar Partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes, but depending on the SQL-Server Edition

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Yes, via Full Spark Integration

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, on a single node

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

Depending on the underlying data source

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

Depending on the underlying data source

yes

User concepts

Access control

Depending on the underlying data source

Access rights per client and object definable

fine grained access rights according to SQL-standard

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

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