DBMS > AlaSQL vs. Apache Drill vs. BigObject vs. ClickHouse vs. etcd

System Properties Comparison AlaSQL vs. Apache Drill vs. BigObject vs. ClickHouse vs. etcd

Editorial information provided by DB-Engines

Name

Description

JavaScript DBMS library

Schema-free SQL Query Engine for Hadoop, NoSQL and Cloud Storage

Analytic DBMS for real-time computations and queries

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.

A distributed reliable key-value store

Primary database model

Document store
Relational DBMS

Relational DBMS

a hierachical model (tree) can be imposed

Relational DBMS

Key-value store

Secondary database models

Time Series DBMS

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

Score	0.46
Rank	#260	Overall
	#40	Document stores
	#121	Relational DBMS

Score	1.95
Rank	#127	Overall
	#23	Document stores
	#60	Relational DBMS

Score	0.13
Rank	#333	Overall
	#147	Relational DBMS

Score	16.34
Rank	#38	Overall
	#23	Relational DBMS

Score	7.25
Rank	#54	Overall
	#5	Key-value stores

Website

etcd.io
github.com/etcd-io/etcd

Technical documentation

github.com/AlaSQL/alasql

drill.apache.org/docs

docs.bigobject.io

clickhouse.com/docs

etcd.io/docs
github.com/etcd-io/etcd/tree/master/Documentation

Developer

Andrey Gershun & Mathias R. Wulff

Apache Software Foundation

BigObject, Inc.

Clickhouse Inc.

Initial release

2014

2012

2015

2016

Current release

1.20.3, January 2023

v24.4.1.2088-stable, May 2024

3.4, August 2019

License

Commercial or Open Source

Open Source

MIT-License

Open Source

Apache Version 2

commercial

free community edition available

Open Source

Apache 2.0

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

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Implementation language

JavaScript

C++

Server operating systems

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

Linux
OS X
Windows

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

FreeBSD
Linux
macOS

FreeBSD
Linux
Windows

experimental

Data scheme

schema-free

yes

schema-free

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.

Secondary indexes

yes

SQL

Support of SQL

Close to SQL99, but no user access control, stored procedures and host language bindings.

SQL SELECT statement is SQL:2003 compliant

SQL-like DML and DDL statements

Close to ANSI SQL (SQL/JSON + extensions)

APIs and other access methods

JavaScript API

JDBC
ODBC
RESTful HTTP API

fluentd
ODBC
RESTful HTTP API

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

gRPC
JSON over HTTP

Supported programming languages

JavaScript

C++

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

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Rust
Scala
Tcl

Server-side scripts

Stored procedures

user defined functions

Lua

yes

Triggers

yes

yes, watching key changes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

key based and custom

Replication methods

Methods for redundantly storing data on multiple nodes

none

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

Using Raft consensus algorithm to ensure data replication with strong consistency among multiple replicas.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

Transaction concepts

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

yes

only for local storage and DOM-storage

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

yes

Durability

Support for making data persistent

yes

by using IndexedDB, SQL.JS or proprietary FileStorage

Depending on the underlying data source

yes

In-memory capabilities

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

yes

Depending on the underlying data source

yes

User concepts

Access control

Depending on the underlying data source

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.

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