DBMS > Apache Impala vs. DuckDB vs. RocksDB vs. SAP SQL Anywhere vs. Solr

System Properties Comparison Apache Impala vs. DuckDB vs. RocksDB vs. SAP SQL Anywhere vs. Solr

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

An embeddable, in-process, column-oriented SQL OLAP RDBMS

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

A widely used distributed, scalable search engine based on Apache Lucene

Primary database model

Secondary database models

Document store

Spatial DBMS

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	4.57
Rank	#74	Overall
	#40	Relational DBMS

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

Score	4.25
Rank	#79	Overall
	#43	Relational DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

impala.apache.org

duckdb.org

rocksdb.org

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

solr.apache.org

Technical documentation

impala.apache.org/impala-docs.html

duckdb.org/docs

github.com/facebook/rocksdb/wiki

help.sap.com/docs/SAP_SQL_Anywhere

solr.apache.org/resources.html

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Facebook, Inc.

SAP

formerly Sybase

Apache Software Foundation

Initial release

2013

2018

2013

1992

2006

Current release

4.1.0, June 2022

0.10, February 2024

8.11.4, April 2024

17, July 2015

9.6.0, April 2024

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

MIT License

Open Source

BSD

commercial

Open Source

Apache Version 2

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Java

Server operating systems

Linux

server-less

Linux

AIX
HP-UX
Linux
OS X
Solaris
Windows

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Data scheme

yes

schema-free

yes

Dynamic Fields enables on-the-fly addition of new fields

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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

All search fields are automatically indexed

SQL

Support of SQL

SQL-like DML and DDL statements

yes

Solr Parallel SQL Interface

APIs and other access methods

JDBC
ODBC

Arrow Database Connectivity (ADBC)
CLI Client
JDBC
ODBC

C++ API
Java API

ADO.NET
HTTP API
JDBC
ODBC

Java API
RESTful HTTP/JSON API

Supported programming languages

All languages supporting JDBC/ODBC

C
C#

3rd party driver
C++
Crystal

3rd party driver
Go

3rd party driver
Java
Lisp

3rd party driver
Python
R
Ruby

3rd party driver
Rust
Swift
Zig

3rd party driver

C
C++
Go
Java
Perl
Python
Ruby

C
C#
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

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

Java plugins

Triggers

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal partitioning

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

yes

Source-replica replication

Database mirroring

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

ACID

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

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

fine grained access rights according to SQL-standard

yes

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