DBMS > Apache Druid vs. Brytlyt vs. RocksDB vs. Splunk vs. Warp 10

System Properties Comparison Apache Druid vs. Brytlyt vs. RocksDB vs. Splunk vs. Warp 10

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

Scalable GPU-accelerated RDBMS for very fast analytic and streaming workloads, leveraging PostgreSQL

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

Analytics Platform for Big Data

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

Primary database model

Relational DBMS
Time Series DBMS

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	0.29
Rank	#288	Overall
	#131	Relational DBMS

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Score	86.45
Rank	#14	Overall
	#2	Search engines

Score	0.07
Rank	#349	Overall
	#32	Time Series DBMS

Website

Technical documentation

druid.apache.org/docs/latest/design

docs.brytlyt.io

github.com/facebook/rocksdb/wiki

docs.splunk.com/Documentation/Splunk

www.warp10.io/content/02_Getting_started

Developer

Apache Software Foundation and contributors

Brytlyt

Facebook, Inc.

Splunk Inc.

SenX

Initial release

2012

2016

2013

2003

2015

Current release

29.0.1, April 2024

5.0, August 2023

8.11.4, April 2024

License

Commercial or Open Source

Open Source

Apache license v2

commercial

Open Source

BSD

commercial

Limited free edition and free developer edition available

Open Source

Apache License 2.0

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, C++ and CUDA

C++

Java

Server operating systems

Linux
OS X
Unix

Linux
OS X
Windows

Linux

Linux
OS X
Solaris
Windows

Linux
OS X
Windows

Data scheme

yes

schema-less columns are supported

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.

yes

specific XML-type available, but no XML query functionality.

yes

Secondary indexes

yes

SQL

Support of SQL

SQL for querying

yes

Splunk Search Processing Language for search commands

APIs and other access methods

JDBC
RESTful HTTP/JSON API

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

C++ API
Java API

HTTP REST

HTTP API
Jupyter
WebSocket

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

.Net
C
C++
Delphi
Java
Perl
Python
Tcl

C
C++
Go
Java
Perl
Python
Ruby

C#
Java
JavaScript
PHP
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

in PL/pgSQL

yes

WarpScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

horizontal partitioning

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

Source-replica replication

yes

Multi-source replication

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

based on HBase

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

A 'Transaction' in Splunk has a different meaning: grouping related events into a single one for later searching

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

fine grained access rights according to SQL-standard

Access rights for users and roles

Mandatory use of cryptographic tokens, containing fine-grained authorizations

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