DBMS > Apache Impala vs. Riak TS vs. RocksDB vs. Sphinx vs. Splice Machine

System Properties Comparison Apache Impala vs. Riak TS vs. RocksDB vs. Sphinx vs. Splice Machine

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

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

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

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

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	0.20
Rank	#319	Overall
	#27	Time Series DBMS

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

Score	5.98
Rank	#56	Overall
	#5	Search engines

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Website

Technical documentation

impala.apache.org/impala-docs.html

www.tiot.jp/riak-docs/riak/ts/latest

github.com/facebook/rocksdb/wiki

sphinxsearch.com/docs

splicemachine.com/how-it-works

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Open Source, formerly Basho Technologies

Facebook, Inc.

Sphinx Technologies Inc.

Splice Machine

Initial release

2013

2015

2013

2001

2014

Current release

4.1.0, June 2022

3.0.0, September 2022

8.11.4, April 2024

3.5.1, February 2023

3.1, March 2021

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

BSD

Open Source

GPL version 2, commercial licence available

Open Source

AGPL 3.0, commercial license available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Erlang

C++

Java

Server operating systems

Linux

Linux
OS X

Linux

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Linux
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.

Secondary indexes

yes

restricted

yes

full-text index on all search fields

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes, limited

SQL-like query language (SphinxQL)

yes

APIs and other access methods

JDBC
ODBC

HTTP API
Native Erlang Interface

C++ API
Java API

Proprietary protocol

JDBC
Native Spark Datasource
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

C
C++
Go
Java
Perl
Python
Ruby

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Erlang

yes

Java

Triggers

yes

pre-commit hooks and post-commit hooks

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Sharding

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

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

links between datasets can be stored

yes

Transaction concepts

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

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

Durability

Support for making data persistent

yes

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

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

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

Access rights for users, groups and roles according to SQL-standard

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