DBMS > Apache Drill vs. Coveo vs. Datomic vs. Faircom DB vs. RocksDB

System Properties Comparison Apache Drill vs. Coveo vs. Datomic vs. Faircom DB vs. RocksDB

Editorial information provided by DB-Engines

Name

Description

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

AI-powered hosted search, recommendation and personalization platform providing tools for both low-code and full-code development

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

Native high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Primary database model

Document store
Relational DBMS

Search engine

Relational DBMS

Key-value store
Relational DBMS

Key-value store

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

Score	2.02
Rank	#124	Overall
	#22	Document stores
	#59	Relational DBMS

Score	2.11
Rank	#118	Overall
	#11	Search engines

Score	1.66
Rank	#144	Overall
	#66	Relational DBMS

Score	0.29
Rank	#304	Overall
	#43	Key-value stores
	#136	Relational DBMS

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Website

drill.apache.org

www.coveo.com

www.datomic.com

www.faircom.com/products/faircom-db

rocksdb.org

Technical documentation

drill.apache.org/docs

docs.coveo.com

docs.datomic.com

docs.faircom.com/docs/en/UUID-7446ae34-a1a7-c843-c894-d5322e395184.html

github.com/facebook/rocksdb/wiki

Developer

Apache Software Foundation

Coveo

Cognitect

FairCom Corporation

Facebook, Inc.

Initial release

2012

1979

2013

Current release

1.20.3, January 2023

1.0.6735, June 2023

V12, November 2020

9.2.1, May 2024

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

limited edition free

commercial

Restricted, free version available

Open Source

BSD

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java, Clojure

ANSI C, C++

C++

Server operating systems

Linux
OS X
Windows

hosted

All OS with a Java VM

AIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows

easily portable to other OSs

Linux

Data scheme

schema-free

hybrid - fields need to be configured prior to indexing, but relationships can be exploited at query time without pre-configuration

yes

schema free, schema optional, schema required, partial schema,

schema-free

Typing

predefined data types such as float or date

yes

yes, ANSI SQL Types, JSON, typed binary structures

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 SELECT statement is SQL:2003 compliant

yes, ANSI SQL with proprietary extensions

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

RESTful HTTP API

ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC

C++ API
Java API

Supported programming languages

C++

C#
Java
JavaScript
Python

Clojure
Java

.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

yes

Transaction Functions

yes

.Net, JavaScript, C/C++

Triggers

yes

By using transaction functions

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

none

But extensive use of caching in the application peers

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

But extensive use of caching in the application peers

yes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Eventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

ACID

tunable from ACID to Eventually Consistent

yes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

Depending on the underlying data source

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

Yes, tunable from durable to delayed durability to in-memory

yes

In-memory capabilities

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

Depending on the underlying data source

yes

recommended only for testing and development

yes

User concepts

Access control

Depending on the underlying data source

granular access controls, API key management, content filters

Fine grained access rights according to SQL-standard with additional protections for files

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