DBMS > Heroic vs. LMDB vs. MarkLogic vs. Snowflake

System Properties Comparison Heroic vs. LMDB vs. MarkLogic vs. Snowflake

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Editorial information provided by DB-Engines

Name

Description

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

A high performant, light-weight, embedded key-value database library

Operational and transactional Enterprise NoSQL database

Cloud-based data warehousing service for structured and semi-structured data

Primary database model

Time Series DBMS

Key-value store

Document store
Native XML DBMS
RDF store

as of version 7
Search engine

Relational DBMS

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

Score	0.13
Rank	#335	Overall
	#29	Time Series DBMS

Score	1.94
Rank	#120	Overall
	#20	Key-value stores

Score	4.15
Rank	#70	Overall
	#11	Document stores
	#1	Native XML DBMS
	#1	RDF stores
	#8	Search engines

Score	133.72
Rank	#7	Overall
	#5	Relational DBMS

Website

github.com/spotify/heroic

www.symas.com/symas-embedded-database-lmdb

www.progress.com/marklogic

www.snowflake.com

Technical documentation

spotify.github.io/heroic

www.lmdb.tech/doc

www.progress.com/marklogic/documentation

docs.snowflake.net/manuals/index.html

Developer

Spotify

Symas

MarkLogic Corp.

Snowflake Computing Inc.

Initial release

2014

2011

2001

2014

Current release

0.9.32, January 2024

11.0, December 2022

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

commercial

restricted free version is available

commercial

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++

Server operating systems

Linux
Unix
Windows

Linux
OS X
Windows

hosted

Data scheme

schema-free

Schema can be enforced

yes

support of semi-structured data formats (JSON, XML, Avro)

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

SQL

Support of SQL

yes

SQL92

yes

APIs and other access methods

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

Java API
Node.js Client API
ODBC
proprietary Optic API

Proprietary Query API, introduced with version 9
RESTful HTTP API
SPARQL
WebDAV
XDBC
XQuery
XSLT

CLI Client
JDBC
ODBC

Supported programming languages

.Net
C
C++
Clojure
Go
Haskell
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Nim
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Rust
Swift
Tcl

C
C#
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

via XQuery or JavaScript

user defined functions

Triggers

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Hadoop Connector, HDFS Direct Access and in-database MapReduce jobs

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

ACID

can act as a resource manager in an XA/JTA transaction

ACID

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

yes, with Range Indexes

User concepts

Access control

Role-based access control at the document and subdocument levels

Users with fine-grained authorization concept, user roles and pluggable authentication

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