DBMS > chDB vs. etcd vs. GigaSpaces vs. Google BigQuery vs. Sphinx

System Properties Comparison chDB vs. etcd vs. GigaSpaces vs. Google BigQuery vs. Sphinx

Editorial information provided by DB-Engines

Name

Description

An embedded SQL OLAP Engine powered by ClickHouse

High performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented Transactions

Large scale data warehouse service with append-only tables

Open source search engine for searching in data from different sources, e.g. relational databases

Primary database model

Relational DBMS

Key-value store

Document store
Object oriented DBMS

Values are user defined objects

Relational DBMS

Search engine

Secondary database models

Time Series DBMS

Graph DBMS
Search engine

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

Score	0.07
Rank	#376	Overall
	#158	Relational DBMS

Score	7.03
Rank	#54	Overall
	#5	Key-value stores

Score	1.03
Rank	#188	Overall
	#32	Document stores
	#6	Object oriented DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	5.95
Rank	#55	Overall
	#5	Search engines

Website

github.com/chdb-io/chdb

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

www.gigaspaces.com

cloud.google.com/bigquery

sphinxsearch.com

Technical documentation

doc.chdb.io

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

docs.gigaspaces.com/latest/landing.html

cloud.google.com/bigquery/docs

sphinxsearch.com/docs

Developer

Gigaspaces Technologies

Google

Sphinx Technologies Inc.

Initial release

2023

2000

2010

2001

Current release

3.4, August 2019

15.5, September 2020

3.5.1, February 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2.0

Open Source

Apache Version 2; Commercial licenses available

commercial

Open Source

GPL version 2, commercial licence available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java, C++, .Net

C++

Server operating systems

server-less

FreeBSD
Linux
Windows

experimental

Linux
macOS
Solaris
Windows

hosted

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

schema-free

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.

XML can be used for describing objects metadata

Secondary indexes

yes

full-text index on all search fields

SQL

Support of SQL

Close to ANSI SQL (SQL/JSON + extensions)

SQL-99 for query and DML statements

yes

SQL-like query language (SphinxQL)

APIs and other access methods

gRPC
JSON over HTTP

GigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data

RESTful HTTP/JSON API

Proprietary protocol

Supported programming languages

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

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

.Net
C++
Java
Python
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

yes

user defined functions

in JavaScript

Triggers

yes, watching key changes

yes, event driven architecture

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Partitioning is done manually, search queries against distributed index is supported

Replication methods

Methods for redundantly storing data on multiple nodes

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

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Map-Reduce pattern can be built with XAP task executors

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Sphinx index.

In-memory capabilities

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

yes

User concepts

Access control

Role-based access control

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

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