DBMS > Apache Impala vs. ClickHouse vs. EJDB vs. HarperDB

System Properties Comparison Apache Impala vs. ClickHouse vs. EJDB vs. HarperDB

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

A high-performance, column-oriented SQL DBMS for online analytical processing (OLAP) that uses all available system resources to their full potential to process each analytical query as fast as possible. It is available as both an open-source software and a cloud offering.

Embeddable document-store database library with JSON representation of queries (in MongoDB style)

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.

Primary database model

Relational DBMS

Document store

Secondary database models

Document store

Time Series DBMS

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

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	15.55
Rank	#38	Overall
	#23	Relational DBMS

Score	0.31
Rank	#296	Overall
	#44	Document stores

Score	0.60
Rank	#244	Overall
	#38	Document stores

Website

impala.apache.org

clickhouse.com

github.com/Softmotions/ejdb

www.harperdb.io

Technical documentation

impala.apache.org/impala-docs.html

clickhouse.com/docs

github.com/Softmotions/ejdb/blob/master/README.md

docs.harperdb.io/docs

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Clickhouse Inc.

Softmotions

HarperDB

Initial release

2013

2016

2012

2017

Current release

4.1.0, June 2022

v24.4.1.2088-stable, May 2024

3.1, August 2021

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache 2.0

Open Source

GPLv2

commercial

free community edition available

Cloud-based only

Only available as a cloud service

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Implementation language

C++

Node.js

Server operating systems

Linux

FreeBSD
Linux
macOS

server-less

Linux
OS X

Data scheme

yes

schema-free

dynamic schema

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

yes

JSON 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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

Close to ANSI SQL (SQL/JSON + extensions)

SQL-like data manipulation statements

APIs and other access methods

JDBC
ODBC

gRPC
HTTP REST
JDBC
MySQL wire protocol
ODBC
PostgreSQL wire protocol
Proprietary protocol

in-process shared library

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

Supported programming languages

All languages supporting JDBC/ODBC

3rd party library
C++
Elixir

3rd party library
Go

3rd party library
Java

3rd party library
JavaScript (Node.js)

3rd party library
Kotlin

3rd party library
Nim

3rd party library
Perl

3rd party library
PHP

3rd party library
Python

3rd party library
R

3rd party library
Ruby

3rd party library
Rust
Scala

3rd party library

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby

.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

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

Custom Functions

since release 3.1

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

key based and custom

none

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

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Asynchronous and synchronous physical replication; geographically distributed replicas; support for object storages.

none

yes

the nodes on which a table resides can be defined

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

typically not needed, however similar functionality with collection joins possible

Transaction concepts

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

Atomic execution of specific operations

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

yes

Durability

Support for making data persistent

yes

yes, using LMDB

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

Access rights for users and roles. Column and row based policies. Quotas and resource limits. Pluggable authentication with LDAP and Kerberos. Password based, X.509 certificate, and SSH key authentication.

Access rights for users and roles

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