DBMS > Apache IoTDB vs. HarperDB vs. Memcached vs. Snowflake vs. SQream DB

System Properties Comparison Apache IoTDB vs. HarperDB vs. Memcached vs. Snowflake vs. SQream DB

Editorial information provided by DB-Engines

Name

Description

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

HarperDB takes the "stack" out of "tech stack" by combining an ultra-fast document-style data store, in-memory cache, real-time message broker, and your application components into a single distributed technology.

In-memory key-value store, originally intended for caching

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

a GPU-based, columnar RDBMS for big data analytics workloads

Primary database model

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

Score	1.31
Rank	#159	Overall
	#14	Time Series DBMS

Score	0.55
Rank	#243	Overall
	#38	Document stores

Score	16.84
Rank	#32	Overall
	#4	Key-value stores

Score	133.72
Rank	#7	Overall
	#5	Relational DBMS

Score	0.62
Rank	#234	Overall
	#109	Relational DBMS

Website

Technical documentation

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

docs.harperdb.io/docs

github.com/memcached/memcached/wiki

docs.snowflake.net/manuals/index.html

docs.sqream.com

Developer

Apache Software Foundation

HarperDB

Danga Interactive

originally developed by Brad Fitzpatrick for LiveJournal

Snowflake Computing Inc.

SQream Technologies

Initial release

2018

2017

2003

2014

2017

Current release

1.1.0, April 2023

3.1, August 2021

1.6.29, June 2024

2022.1.6, December 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

free community edition available

Open Source

BSD license

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Node.js

C++, CUDA, Haskell, Java, Scala

Server operating systems

All OS with a Java VM (>= 1.8)

Linux
OS X

FreeBSD
Linux
OS X
Unix
Windows

hosted

Linux

Data scheme

yes

dynamic schema

schema-free

yes

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

yes

Typing

predefined data types such as float or date

yes

JSON data types

yes

yes, ANSI Standard SQL Types

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

SQL

Support of SQL

SQL-like query language

SQL-like data manipulation statements

yes

APIs and other access methods

JDBC
Native API

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

Proprietary protocol

CLI Client
JDBC
ODBC

.Net
JDBC
ODBC

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift

.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
Perl
PHP
Python
Ruby

JavaScript (Node.js)
Python

C++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

Custom Functions

since release 3.1

user defined functions

user defined functions in Python

Triggers

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

A table resides as a whole on one (or more) nodes in a cluster

none

yes

horizontal and vertical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

yes

the nodes on which a table resides can be defined

none

Repcached, a Memcached patch, provides this functionallity

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic execution of specific operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, using LMDB

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

yes

Access rights for users and roles

yes

using SASL (Simple Authentication and Security Layer) protocol

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

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