DBMS > Apache Impala vs. Drizzle vs. HarperDB vs. Sequoiadb vs. Transwarp Hippo

System Properties Comparison Apache Impala vs. Drizzle vs. HarperDB vs. Sequoiadb vs. Transwarp Hippo

Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

Description

Analytic DBMS for Hadoop

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

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.

NewSQL database with distributed OLTP and SQL

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

Primary database model

Relational DBMS

Document store

Document store
Relational DBMS

Vector DBMS

Secondary database models

Document store

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	0.55
Rank	#248	Overall
	#38	Document stores

Score	0.45
Rank	#261	Overall
	#41	Document stores
	#122	Relational DBMS

Score	0.00
Rank	#383	Overall
	#13	Vector DBMS

Website

impala.apache.org

www.harperdb.io

www.sequoiadb.com

www.transwarp.cn/en/subproduct/hippo

Technical documentation

impala.apache.org/impala-docs.html

docs.harperdb.io/docs

www.sequoiadb.com/en/index.php?m=Files&a=index

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Drizzle project, originally started by Brian Aker

HarperDB

Sequoiadb Ltd.

Initial release

2013

2008

2017

2013

2023

Current release

4.1.0, June 2022

7.2.4, September 2012

3.1, August 2021

1.0, May 2023

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

GNU GPL

commercial

free community edition available

Open Source

Server: AGPL; Client: Apache V2

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++

Node.js

C++

Server operating systems

Linux

FreeBSD
Linux
OS X

Linux
OS X

Linux

Linux
macOS

Data scheme

yes

dynamic schema

schema-free

Typing

predefined data types such as float or date

yes

JSON data types

yes

oid, date, timestamp, binary, regex

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

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

with proprietary extensions

SQL-like data manipulation statements

SQL-like query language

APIs and other access methods

JDBC
ODBC

JDBC

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

proprietary protocol using JSON

RESTful HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

C
C++
Java
PHP

.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++
Java
PHP
Python

C++
Java
Python

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Custom Functions

since release 3.1

JavaScript

Triggers

hooks for callbacks inside the server can be used.

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Multi-source replication
Source-replica replication

yes

the nodes on which a table resides can be defined

Source-replica replication

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

Eventual Consistency

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Atomic execution of specific operations

Document is locked during a transaction

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

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

Pluggable authentication mechanisms

e.g. LDAP, HTTP

Access rights for users and roles

simple password-based access control

Role based access control and fine grained access rights

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More resources

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