DBMS > Apache Impala vs. Drizzle vs. HarperDB vs. Microsoft SQL Server vs. Sphinx

System Properties Comparison Apache Impala vs. Drizzle vs. HarperDB vs. Microsoft SQL Server vs. Sphinx

Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

Description

Analytic DBMS for Hadoop

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

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.

Microsofts flagship relational DBMS

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

Primary database model

Secondary database models

Document store

Document store
Graph DBMS
Spatial DBMS

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	0.55
Rank	#248	Overall
	#38	Document stores

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	5.98
Rank	#56	Overall
	#5	Search engines

Website

impala.apache.org

www.harperdb.io

www.microsoft.com/en-us/sql-server

sphinxsearch.com

Technical documentation

impala.apache.org/impala-docs.html

docs.harperdb.io/docs

learn.microsoft.com/en-US/sql/sql-server

sphinxsearch.com/docs

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Drizzle project, originally started by Brian Aker

HarperDB

Microsoft

Sphinx Technologies Inc.

Initial release

2013

2008

2017

1989

2001

Current release

4.1.0, June 2022

7.2.4, September 2012

3.1, August 2021

SQL Server 2022, November 2022

3.5.1, February 2023

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

GNU GPL

commercial

free community edition available

commercial

restricted free version is available

Open Source

GPL version 2, commercial licence available

Cloud-based only

Only available as a cloud service

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Implementation language

C++

Node.js

C++

Server operating systems

Linux

FreeBSD
Linux
OS X

Linux
OS X

Linux
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

yes

dynamic schema

yes

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.

yes

Secondary indexes

yes

full-text index on all search fields

SQL

Support of SQL

SQL-like DML and DDL statements

yes

with proprietary extensions

SQL-like data manipulation statements

yes

SQL-like query language (SphinxQL)

APIs and other access methods

JDBC
ODBC

JDBC

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

Proprietary protocol

Supported programming languages

All languages supporting JDBC/ODBC

C
C++
Java
PHP

.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

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

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

Custom Functions

since release 3.1

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

Triggers

hooks for callbacks inside the server can be used.

yes

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

tables can be distributed across several files (horizontal partitioning); sharding through federation

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

Multi-source replication
Source-replica replication

yes

the nodes on which a table resides can be defined

yes, but depending on the SQL-Server Edition

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

Atomic execution of specific operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, using LMDB

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

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

Pluggable authentication mechanisms

e.g. LDAP, HTTP

Access rights for users and roles

fine grained access rights according to SQL-standard

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