DBMS > Apache Druid vs. IBM Db2 Event Store vs. Microsoft SQL Server vs. Rockset vs. RRDtool

System Properties Comparison Apache Druid vs. IBM Db2 Event Store vs. Microsoft SQL Server vs. Rockset vs. RRDtool

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

Distributed Event Store optimized for Internet of Things use cases

Microsofts flagship relational DBMS

A scalable, reliable search and analytics service in the cloud, built on RocksDB

Industry standard data logging and graphing tool for time series data. RRD is an acronym for round-robin database.

The data is stored in a circular buffer, thus the system storage footprint remains constant over time.

Primary database model

Relational DBMS
Time Series DBMS

Event Store
Time Series DBMS

Relational DBMS

Document store

Time Series DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Relational DBMS
Search engine

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

Score	3.34
Rank	#88	Overall
	#48	Relational DBMS
	#7	Time Series DBMS

Score	0.19
Rank	#323	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	0.79
Rank	#211	Overall
	#35	Document stores

Score	1.87
Rank	#136	Overall
	#11	Time Series DBMS

Website

druid.apache.org

www.ibm.com/products/db2-event-store

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

rockset.com

oss.oetiker.ch/rrdtool

Technical documentation

druid.apache.org/docs/latest/design

www.ibm.com/docs/en/db2-event-store

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

docs.rockset.com

oss.oetiker.ch/rrdtool/doc

Developer

Apache Software Foundation and contributors

IBM

Microsoft

Rockset

Tobias Oetiker

Initial release

2012

2017

1989

2019

1999

Current release

29.0.1, April 2024

2.0

SQL Server 2022, November 2022

1.8.0, 2022

License

Commercial or Open Source

Open Source

Apache license v2

commercial

free developer edition available

commercial

restricted free version is available

commercial

Open Source

GPL V2 and FLOSS

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C and C++

C++

Implementations in Java (e.g. RRD4J) and C# available

Server operating systems

Linux
OS X
Unix

Linux

Linux, macOS, Windows for the developer addition

Linux
Windows

hosted

HP-UX
Linux

Data scheme

yes

schema-less columns are supported

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

dynamic typing

Numeric data only

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

ingestion from XML files supported

Exporting into and restoring from XML files possible

Secondary indexes

yes

all fields are automatically indexed

SQL

Support of SQL

SQL for querying

yes

through the embedded Spark runtime

yes

Read-only SQL queries, including JOINs

APIs and other access methods

JDBC
RESTful HTTP/JSON API

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

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

HTTP REST

in-process shared library
Pipes

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

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

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

Go
Java
JavaScript (Node.js)
Python

with librrd library
C#

with a different implementation of RRDTool
Java

with a different implementation of RRDTool
JavaScript (Node.js)

with a different implementation of RRDTool
Lua
Perl
PHP

with a wrapper library
Python
Ruby

Server-side scripts

Stored procedures

yes

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

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Sharding

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

Automatic sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

Active-active shard replication

yes, but depending on the SQL-Server Edition

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Eventual Consistency

none

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

No - written data is immutable

yes

by using the rrdcached daemon

Durability

Support for making data persistent

yes

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

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

fine grained access rights according to SQL-standard

Access rights for users and organizations can be defined via Rockset console

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