DBMS > Apache Impala vs. Datomic vs. RocksDB vs. SAP SQL Anywhere vs. Sphinx

System Properties Comparison Apache Impala vs. Datomic vs. RocksDB vs. SAP SQL Anywhere vs. Sphinx

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

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

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

RDBMS database and synchronization technologies for server, desktop, remote office, and mobile environments

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

Primary database model

Secondary database models

Document store

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Score	4.25
Rank	#79	Overall
	#43	Relational DBMS

Score	5.98
Rank	#56	Overall
	#5	Search engines

Website

impala.apache.org

www.datomic.com

rocksdb.org

www.sap.com/products/technology-platform/sql-anywhere.html

sphinxsearch.com

Technical documentation

impala.apache.org/impala-docs.html

docs.datomic.com

github.com/facebook/rocksdb/wiki

help.sap.com/docs/SAP_SQL_Anywhere

sphinxsearch.com/docs

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Cognitect

Facebook, Inc.

SAP

formerly Sybase

Sphinx Technologies Inc.

Initial release

2013

2012

2013

1992

2001

Current release

4.1.0, June 2022

1.0.6735, June 2023

8.11.4, April 2024

17, July 2015

3.5.1, February 2023

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

limited edition free

Open Source

BSD

commercial

Open Source

GPL version 2, commercial licence available

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.

Implementation language

C++

Java, Clojure

C++

Server operating systems

Linux

All OS with a Java VM

Linux

AIX
HP-UX
Linux
OS X
Solaris
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

yes

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.

yes

Secondary indexes

yes

full-text index on all search fields

SQL

Support of SQL

SQL-like DML and DDL statements

yes

SQL-like query language (SphinxQL)

APIs and other access methods

JDBC
ODBC

RESTful HTTP API

C++ API
Java API

ADO.NET
HTTP API
JDBC
ODBC

Proprietary protocol

Supported programming languages

All languages supporting JDBC/ODBC

Clojure
Java

C
C++
Go
Java
Perl
Python
Ruby

C
C#
C++
Delphi
Java
JavaScript (Node.js)
Perl
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 and integration of map-reduce

yes

Transaction Functions

yes, in C/C++, Java, .Net or Perl

Triggers

By using transaction functions

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

But extensive use of caching in the application peers

horizontal partitioning

none

Sharding

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

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

But extensive use of caching in the application peers

yes

Source-replica replication

Database mirroring

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

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

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

recommended only for testing and development

yes

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

fine grained access rights according to SQL-standard

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