DBMS > HarperDB vs. Hive vs. LevelDB vs. SQLite vs. TimescaleDB

System Properties Comparison HarperDB vs. Hive vs. LevelDB vs. SQLite vs. TimescaleDB

Editorial information provided by DB-Engines

Name

Description

Ultra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.

data warehouse software for querying and managing large distributed datasets, built on Hadoop

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

Widely used embeddable, in-process RDBMS

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

Primary database model

Secondary database models

Relational DBMS

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

Score	0.55
Rank	#248	Overall
	#38	Document stores

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	2.35
Rank	#111	Overall
	#19	Key-value stores

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Score	4.64
Rank	#71	Overall
	#4	Time Series DBMS

Website

www.harperdb.io

hive.apache.org

github.com/google/leveldb

www.sqlite.org

www.timescale.com

Technical documentation

docs.harperdb.io/docs

cwiki.apache.org/confluence/display/Hive/Home

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

www.sqlite.org/docs.html

docs.timescale.com

Developer

HarperDB

Apache Software Foundation

initially developed by Facebook

Google

Dwayne Richard Hipp

Timescale

Initial release

2017

2012

2011

2000

2017

Current release

3.1, August 2021

3.1.3, April 2022

1.23, February 2021

3.45.3 (15 April 2024), April 2024

2.13.0, November 2023

License

Commercial or Open Source

commercial

free community edition available

Open Source

Apache Version 2

Open Source

BSD

Open Source

Public Domain

Open Source

Apache 2.0

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

Node.js

Java

C++

Server operating systems

Linux
OS X

All OS with a Java VM

Illumos
Linux
OS X
Windows

server-less

Linux
OS X
Windows

Data scheme

dynamic schema

yes

schema-free

yes

dynamic column types

yes

Typing

predefined data types such as float or date

yes

JSON data types

yes

not rigid because of 'dynamic typing' concept.

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data 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 data manipulation statements

SQL-like DML and DDL statements

yes

SQL-92 is not fully supported

yes

full PostgreSQL SQL syntax

APIs and other access methods

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

JDBC
ODBC
Thrift

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Supported programming languages

.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

C++
Java
PHP
Python

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

Custom Functions

since release 3.1

yes

user defined functions and integration of map-reduce

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

none

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

yes

the nodes on which a table resides can be defined

selectable replication factor

none

Source-replica replication with hot standby and reads on replicas

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

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

via file-system locks

yes

Durability

Support for making data persistent

yes, using LMDB

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

Access rights for users and roles

Access rights for users, groups and roles

fine grained access rights according to SQL-standard

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