DBMS > RocksDB vs. Spark SQL vs. Transwarp Hippo

System Properties Comparison RocksDB vs. Spark SQL vs. Transwarp Hippo

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

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

Cloud-native distributed Vector DBMS that supports storage, retrieval, and management of massive vector-based datasets

Primary database model

Key-value store

Relational DBMS

Vector DBMS

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

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

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	0.00
Rank	#383	Overall
	#13	Vector DBMS

Website

rocksdb.org

spark.apache.org/sql

www.transwarp.cn/en/subproduct/hippo

Technical documentation

github.com/facebook/rocksdb/wiki

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

Developer

Facebook, Inc.

Apache Software Foundation

Initial release

2013

2014

2023

Current release

8.11.4, April 2024

3.5.0 ( 2.13), September 2023

1.0, May 2023

License

Commercial or Open Source

Open Source

BSD

Open Source

Apache 2.0

commercial

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++

Server operating systems

Linux

Linux
OS X
Windows

Linux
macOS

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

Vector, Numeric and String

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

SQL

Support of SQL

SQL-like DML and DDL statements

APIs and other access methods

C++ API
Java API

JDBC
ODBC

RESTful HTTP API

Supported programming languages

C
C++
Go
Java
Perl
Python
Ruby

Java
Python
R
Scala

C++
Java
Python

Server-side scripts

Stored procedures

Triggers

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

yes, utilizing Spark Core

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

yes

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

Role based access control and fine grained access rights

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.