DBMS > Apache Drill vs. Apache IoTDB vs. Geode vs. Splice Machine vs. TerarkDB

System Properties Comparison Apache Drill vs. Apache IoTDB vs. Geode vs. Splice Machine vs. TerarkDB

Editorial information provided by DB-Engines

Name

Description

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

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

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

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

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	1.18
Rank	#173	Overall
	#15	Time Series DBMS

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

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

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

Website

github.com/bytedance/terarkdb

Technical documentation

drill.apache.org/docs

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

geode.apache.org/docs

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.

Splice Machine

ByteDance, originally Terark

Initial release

2012

2018

2002

2014

2016

Current release

1.20.3, January 2023

1.1.0, April 2023

1.1, February 2017

3.1, March 2021

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache Version 2.0

Open Source

Apache Version 2; commercial licenses available as Gemfire

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++

Server operating systems

Linux
OS X
Windows

All OS with a Java VM (>= 1.8)

All OS with a Java VM

the JDK (8 or later) is also required

Linux
OS X
Solaris
Windows

Data scheme

schema-free

yes

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.

Secondary indexes

yes

SQL

Support of SQL

SQL SELECT statement is SQL:2003 compliant

SQL-like query language

SQL-like query language (OQL)

yes

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

JDBC
Native API

Java Client API
Memcached protocol
RESTful HTTP API

JDBC
Native Spark Datasource
ODBC

C++ API
Java API

Supported programming languages

C++

C
C#
C++
Go
Java
Python
Scala

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

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

C++
Java

Server-side scripts

Stored procedures

user defined functions

yes

user defined functions

yes

Java

Triggers

yes

Cache Event Listeners

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

Sharding

Shared Nothhing Auto-Sharding, Columnar Partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

Multi-source replication

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Integration with Hadoop and Spark

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

none

Eventual Consistency
Strong Consistency with Raft

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

yes

Access rights per client and object definable

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

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