DBMS > RocksDB vs. Spark SQL vs. Sphinx vs. Sqrrl vs. STSdb

System Properties Comparison RocksDB vs. Spark SQL vs. Sphinx vs. Sqrrl vs. STSdb

Editorial information provided by DB-Engines

Name

Sqrrl has been acquired by Amazon and became a part of Amazon Web Services. It has been removed from the DB-Engines ranking.

Description

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

Spark SQL is a component on top of 'Spark Core' for structured data processing

Open source search engine for searching in data from different sources, e.g. relational databases

Adaptable, secure NoSQL built on Apache Accumulo

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Key-value store

Relational DBMS

Search engine

Document store
Graph DBMS
Key-value store
Wide column store

Key-value store

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

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

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	5.95
Rank	#55	Overall
	#5	Search engines

Score	0.10
Rank	#357	Overall
	#51	Key-value stores

Website

rocksdb.org

spark.apache.org/sql

sphinxsearch.com

sqrrl.com

github.com/STSSoft/STSdb4

Technical documentation

github.com/facebook/rocksdb/wiki

spark.apache.org/docs/latest/sql-programming-guide.html

sphinxsearch.com/docs

Developer

Facebook, Inc.

Apache Software Foundation

Sphinx Technologies Inc.

Amazon

originally Sqrrl Data, Inc.

STS Soft SC

Initial release

2013

2014

2001

2012

2011

Current release

9.2.1, May 2024

3.5.0 ( 2.13), September 2023

3.5.1, February 2023

4.0.8, September 2015

License

Commercial or Open Source

Open Source

BSD

Open Source

Apache 2.0

Open Source

GPL version 2, commercial licence available

commercial

Open Source

GPLv2, commercial license 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

C++

Scala

C++

Java

Server operating systems

Linux

Linux
OS X
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Linux

Windows

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

primitive types and user defined types (classes)

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

full-text index on all search fields

yes

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language (SphinxQL)

APIs and other access methods

C++ API
Java API

JDBC
ODBC

Proprietary protocol

Accumulo Shell
Java API
JDBC
ODBC
RESTful HTTP API
Thrift

.NET Client API

Supported programming languages

C
C++
Go
Java
Perl
Python
Ruby

Java
Python
R
Scala

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Actionscript
C

using GLib
C#
C++
Cocoa
Delphi
Erlang
Go
Haskell
Java
JavaScript
OCaml
Perl
PHP
Python
Ruby
Smalltalk

C#
Java

Server-side scripts

Stored procedures

Triggers

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

yes, utilizing Spark Core

Sharding

Partitioning is done manually, search queries against distributed index is supported

Sharding

making use of Hadoop

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

selectable replication factor

making use of Hadoop

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Document store kept consistent with combination of global timestamping, row-level transactions, and server-side consistency resolution.

Foreign keys

Referential integrity

Transaction concepts

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

yes

Atomic updates per row, document, or graph entity

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Sphinx index.

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

Cell-level Security, Data-Centric Security, Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC)

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