DBMS > CrateDB vs. etcd vs. HarperDB vs. Heroic

System Properties Comparison CrateDB vs. etcd vs. HarperDB vs. Heroic

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Editorial information provided by DB-Engines

Name

Description

Distributed Database based on Lucene

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.

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

Primary database model

Document store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS

Key-value store

Document store

Time Series DBMS

Secondary database models

Relational DBMS

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

Score	0.71
Rank	#227	Overall
	#37	Document stores
	#5	Spatial DBMS
	#16	Search engines
	#19	Time Series DBMS
	#8	Vector DBMS

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

Score	0.60
Rank	#244	Overall
	#38	Document stores

Score	0.46
Rank	#265	Overall
	#22	Time Series DBMS

Website

cratedb.com

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

www.harperdb.io

github.com/spotify/heroic

Technical documentation

cratedb.com/docs

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

docs.harperdb.io/docs

spotify.github.io/heroic

Developer

Crate

HarperDB

Spotify

Initial release

2013

2017

2014

Current release

3.4, August 2019

3.1, August 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

free community edition available

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.

CrateDB Cloud: a distributed SQL database that spreads data and processing across an elastic cluster of shared nothing nodes. CrateDB Cloud enables data insights at scale on Microsoft Azure, AWS and Google Cloud Platform.

Implementation language

Java

Node.js

Java

Server operating systems

All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator support

FreeBSD
Linux
Windows

experimental

Linux
OS X

Data scheme

Flexible Schema (defined schema, partial schema, schema free)

schema-free

dynamic schema

schema-free

Typing

predefined data types such as float or date

yes

JSON data types

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

via Elasticsearch

SQL

Support of SQL

yes, but no triggers and constraints, and PostgreSQL compatibility

SQL-like data manipulation statements

APIs and other access methods

ADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API

gRPC
JSON over HTTP

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

HQL (Heroic Query Language, a JSON-based language)
HTTP API

Supported programming languages

.NET
Erlang
Go

community maintained client
Java
JavaScript (Node.js)

community maintained client
Perl

community maintained client
PHP
Python
R
Ruby

community maintained client
Scala

community maintained client

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

.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

user defined functions (Javascript)

Custom Functions

since release 3.1

Triggers

yes, watching key changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Configurable replication on table/partition-level

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

yes

the nodes on which a table resides can be defined

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Read-after-write consistency on record level

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

unique row identifiers can be used for implementing an optimistic concurrency control strategy

Atomic execution of specific operations

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, using LMDB

yes

In-memory capabilities

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

yes

User concepts

Access control

rights management via user accounts

Access rights for users and roles

More information provided by the system vendor

Specific characteristics

The enterprise database for time series, documents, and vectors. Distributed - Native...
» more

Competitive advantages

Response time in milliseconds: e ven for complex ad-hoc queries. Massive scaling...
» more

Typical application scenarios

IoT: accelerate your IIoT projects with CrateDB, delivering real-time analytics...
» more

Key customers

Across all continents, CrateDB is used by companies of all sizes to meet the most...
» more

Market metrics

The CrateDB open source project was started in 2013 Honorable Mention in 2021 Gartner®...
» more

Licensing and pricing models

See CrateDB pricing >
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

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More resources

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