DBMS > Apache Impala vs. chDB vs. Drizzle vs. EDB Postgres vs. Lovefield

System Properties Comparison Apache Impala vs. chDB vs. Drizzle vs. EDB Postgres vs. Lovefield

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

An embedded SQL OLAP Engine powered by ClickHouse

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

The EDB Postgres Platform is an enterprise-class data management platform based on the open source database PostgreSQL with flexible deployment options and Oracle compatibility features, complemented by tool kits for management, integration, and migration.

Embeddable relational database for web apps written in pure JavaScript

Primary database model

Relational DBMS

Secondary database models

Document store

Time Series DBMS

Document store
Spatial DBMS

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	0.00
Rank	#383	Overall
	#158	Relational DBMS

Score	1.90
Rank	#135	Overall
	#63	Relational DBMS

Score	0.29
Rank	#293	Overall
	#133	Relational DBMS

Website

impala.apache.org

github.com/chdb-io/chdb

www.enterprisedb.com

google.github.io/lovefield

Technical documentation

impala.apache.org/impala-docs.html

doc.chdb.io

www.enterprisedb.com/docs

github.com/google/lovefield/blob/master/docs/spec_index.md

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Drizzle project, originally started by Brian Aker

EnterpriseDB

Google

Initial release

2013

2023

2008

2005

2014

Current release

4.1.0, June 2022

7.2.4, September 2012

14, December 2021

2.1.12, February 2017

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache Version 2.0

Open Source

GNU GPL

commercial

BSD for PostgreSQL-components

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

C++

JavaScript

Server operating systems

Linux

server-less

FreeBSD
Linux
OS X

Linux
Windows

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

Data scheme

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

specific XML-type available, but no XML query functionality.

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

Close to ANSI SQL (SQL/JSON + extensions)

yes

with proprietary extensions

yes

standard with numerous extensions

SQL-like query language

via JavaScript builder pattern

APIs and other access methods

JDBC
ODBC

JDBC

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

Supported programming languages

All languages supporting JDBC/ODBC

Bun
C
C++
Go
JavaScript (Node.js)
Python
Rust

C
C++
Java
PHP

.Net
C
C++
Delphi
Java
Perl
PHP
Python
Tcl

JavaScript

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

Triggers

hooks for callbacks inside the server can be used.

yes

Using read-only observers

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

by hash, list or range

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Multi-source replication
Source-replica replication

Multi-source replication

none

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

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

yes, by using IndexedDB or the cloud service Firebase Realtime Database

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

using MemoryDB

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

Pluggable authentication mechanisms

e.g. LDAP, HTTP

fine grained access rights according to SQL-standard

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More resources

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