DBMS > ClickHouse vs. GeoMesa vs. Google Cloud Bigtable vs. XTDB

System Properties Comparison ClickHouse vs. GeoMesa vs. Google Cloud Bigtable vs. XTDB

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Editorial information provided by DB-Engines

Name

Description

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.

GeoMesa is a distributed spatio-temporal DBMS based on various systems as storage layer.

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Relational DBMS

Spatial DBMS

Key-value store
Wide column store

Document store

Secondary database models

Time Series DBMS

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

Score	15.88
Rank	#37	Overall
	#22	Relational DBMS

Score	0.78
Rank	#214	Overall
	#4	Spatial DBMS

Score	2.97
Rank	#92	Overall
	#15	Key-value stores
	#8	Wide column stores

Score	0.13
Rank	#330	Overall
	#45	Document stores

Website

clickhouse.com

www.geomesa.org

cloud.google.com/bigtable

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

clickhouse.com/docs

www.geomesa.org/documentation/stable/user/index.html

cloud.google.com/bigtable/docs

www.xtdb.com/docs

Developer

Clickhouse Inc.

CCRi and others

Google

Juxt Ltd.

Initial release

2016

2014

2015

2019

Current release

v24.6.2.17-stable, July 2024

5.0.1, July 2024

1.19, September 2021

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache License 2.0

commercial

Open Source

MIT License

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.

ClickHouse Cloud: Get the performance you love from open source ClickHouse in a serverless offering that takes care of the details so you can spend more time getting insight out of the fastest database on earth.
DoubleCloud: Fully managed ClickHouse alongside best-in-class managed open-source services to build analytics at scale.

Implementation language

C++

Scala

Clojure

Server operating systems

FreeBSD
Linux
macOS

hosted

All OS with a Java 8 (and higher) VM
Linux

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, extensible-data-notation format

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 ANSI SQL (SQL/JSON + extensions)

limited SQL, making use of Apache Calcite

APIs and other access methods

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

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

HTTP REST
JDBC

Supported programming languages

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

C#
C++
Go
Java
JavaScript (Node.js)
Python

Clojure
Java

Server-side scripts

Stored procedures

yes

Triggers

Partitioning methods

Methods for storing different data on different nodes

key based and custom

depending on storage layer

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

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

depending on storage layer

Internal replication in Colossus, and regional replication between two clusters in different zones

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

depending on storage layer

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Foreign keys

Referential integrity

Transaction concepts

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

Atomic single-row operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

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

yes

depending on storage layer

User concepts

Access control

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.

yes

depending on the DBMS used for storage

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

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