DBMS > Apache Druid vs. FileMaker vs. Riak TS vs. RocksDB vs. Splice Machine

System Properties Comparison Apache Druid vs. FileMaker vs. Riak TS vs. RocksDB vs. Splice Machine

Editorial information provided by DB-Engines

Name

Description

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

FileMaker is a cross-platform RDBMS that includes a GUI frontend.

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

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

Primary database model

Relational DBMS
Time Series DBMS

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

Score	3.25
Rank	#90	Overall
	#47	Relational DBMS
	#7	Time Series DBMS

Score	47.91
Rank	#20	Overall
	#14	Relational DBMS

Score	0.28
Rank	#307	Overall
	#27	Time Series DBMS

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Website

druid.apache.org

www.claris.com/filemaker

rocksdb.org

splicemachine.com

Technical documentation

druid.apache.org/docs/latest/design

www.claris.com/resources/documentation

www.tiot.jp/riak-docs/riak/ts/latest

github.com/facebook/rocksdb/wiki

splicemachine.com/how-it-works

Developer

Apache Software Foundation and contributors

Claris

a subsidiary of Apple

Open Source, formerly Basho Technologies

Facebook, Inc.

Splice Machine

Initial release

2012

1983

2015

2013

2014

Current release

29.0.1, April 2024

19.4.1, November 2021

3.0.0, September 2022

9.2.1, May 2024

3.1, March 2021

License

Commercial or Open Source

Open Source

Apache license v2

commercial

Open Source

BSD

Open Source

AGPL 3.0, commercial license available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Erlang

C++

Java

Server operating systems

Linux
OS X
Unix

iOS

client part only
Linux
OS X
Windows

Linux
OS X

Linux

Linux
OS X
Solaris
Windows

Data scheme

yes

schema-less columns are supported

yes

schema-free

yes

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

A http query request can return the data in XML format

Secondary indexes

yes

restricted

yes

SQL

Support of SQL

SQL for querying

yes

via plugins

yes, limited

yes

APIs and other access methods

JDBC
RESTful HTTP/JSON API

Filemaker WebDirect
JDBC
ODBC

HTTP API
Native Erlang Interface

C++ API
Java API

JDBC
Native Spark Datasource
ODBC

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

PHP

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

C
C++
Go
Java
Perl
Python
Ruby

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

Server-side scripts

Stored procedures

yes

Erlang

yes

Java

Triggers

yes

pre-commit hooks and post-commit hooks

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

none

Sharding

horizontal partitioning

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

Source-replica replication, warm standby

since Version 14

selectable replication factor

yes

Multi-source replication
Source-replica replication

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

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

links between datasets can be stored

yes

Transaction concepts

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

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

simple rights management via user accounts and connection to external directory services

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

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