DBMS > Apache Druid vs. FileMaker vs. Google Cloud Spanner vs. RocksDB vs. Teradata

System Properties Comparison Apache Druid vs. FileMaker vs. Google Cloud Spanner vs. RocksDB vs. Teradata

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.

A horizontally scalable, globally consistent, relational database service. It is the externalization of the core Google database that runs the biggest aspects of Google, like Ads and Google Play.

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

A hybrid cloud data analytics software platform (Teradata Vantage)

Primary database model

Relational DBMS
Time Series DBMS

Relational DBMS

Key-value store

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial 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	2.84
Rank	#100	Overall
	#51	Relational DBMS

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

Score	44.87
Rank	#22	Overall
	#15	Relational DBMS

Website

druid.apache.org

www.claris.com/filemaker

cloud.google.com/spanner

rocksdb.org

www.teradata.com

Technical documentation

druid.apache.org/docs/latest/design

www.claris.com/resources/documentation

cloud.google.com/spanner/docs

github.com/facebook/rocksdb/wiki

docs.teradata.com

Developer

Apache Software Foundation and contributors

Claris

a subsidiary of Apple

Google

Facebook, Inc.

Teradata

Initial release

2012

1983

2017

2013

1984

Current release

29.0.1, April 2024

19.4.1, November 2021

9.2.1, May 2024

Teradata Vantage 1.0 MU2, January 2019

License

Commercial or Open Source

Open Source

Apache license v2

commercial

Open Source

BSD

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++

Server operating systems

Linux
OS X
Unix

iOS

client part only
Linux
OS X
Windows

hosted

Linux

hosted
Linux

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

yes

Secondary indexes

yes

Join-index to prejoin tables, aggregate index, sparse index, hash index

SQL

Support of SQL

SQL for querying

yes

via plugins

yes

Query statements complying to ANSI 2011

yes

SQL 2016 + extensions

APIs and other access methods

JDBC
RESTful HTTP/JSON API

Filemaker WebDirect
JDBC
ODBC

gRPC (using protocol buffers) API
JDBC

At present, JDBC supports read-only queries. No support for DDL or DML statements.
RESTful HTTP API

C++ API
Java API

.NET Client API
HTTP REST
JDBC
JMS Adapter
ODBC
OLE DB

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

PHP

Go
Java
JavaScript (Node.js)
Python

C
C++
Go
Java
Perl
Python
Ruby

C
C++
Cobol
Java (JDBC-ODBC)
Perl
PL/1
Python
R
Ruby

Server-side scripts

Stored procedures

yes

UDFs, stored procedures, table functions in parallel

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

none

Sharding

horizontal partitioning

Sharding

Hashing

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

Multi-source replication with 3 replicas for regional instances.

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

by using interleaved tables, this features focuses more on performance improvements than on referential integrity

yes

Transaction concepts

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

ACID

Strict serializable isolation

yes

ACID

Concurrency

Support for concurrent manipulation of data

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

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 based on Google Cloud Identity and Access Management (IAM)

fine grained access rights according to SQL-standard

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