DBMS > Heroic vs. LevelDB vs. RocksDB vs. Spark SQL vs. Teradata

System Properties Comparison Heroic vs. LevelDB vs. RocksDB vs. Spark SQL vs. Teradata

Editorial information provided by DB-Engines

Name

Description

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

Embeddable fast key-value storage library that provides an ordered mapping from string keys to string values

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

A hybrid cloud data analytics software platform (Teradata Vantage)

Primary database model

Time Series 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	0.46
Rank	#265	Overall
	#22	Time Series DBMS

Score	2.25
Rank	#115	Overall
	#19	Key-value stores

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

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	44.87
Rank	#22	Overall
	#15	Relational DBMS

Website

github.com/spotify/heroic

github.com/google/leveldb

rocksdb.org

spark.apache.org/sql

www.teradata.com

Technical documentation

spotify.github.io/heroic

github.com/google/leveldb/blob/main/doc/index.md

github.com/facebook/rocksdb/wiki

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

docs.teradata.com

Developer

Spotify

Google

Facebook, Inc.

Apache Software Foundation

Teradata

Initial release

2014

2011

2013

2014

1984

Current release

1.23, February 2021

9.2.1, May 2024

3.5.0 ( 2.13), September 2023

Teradata Vantage 1.0 MU2, January 2019

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

BSD

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

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Implementation language

Java

C++

Scala

Server operating systems

Illumos
Linux
OS X
Windows

Linux

Linux
OS X
Windows

hosted
Linux

Data scheme

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

Secondary indexes

yes

via Elasticsearch

yes

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

SQL

Support of SQL

SQL-like DML and DDL statements

yes

SQL 2016 + extensions

APIs and other access methods

HQL (Heroic Query Language, a JSON-based language)
HTTP API

C++ API
Java API

JDBC
ODBC

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

Supported programming languages

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

C
C++
Go
Java
Perl
Python
Ruby

Java
Python
R
Scala

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

none

horizontal partitioning

yes, utilizing Spark Core

Sharding

Hashing

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

yes

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

with automatic compression on writes

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

fine grained access rights according to SQL-standard

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