DBMS > Hawkular Metrics vs. Hive vs. RocksDB vs. SAP Advantage Database Server vs. Solr

System Properties Comparison Hawkular Metrics vs. Hive vs. RocksDB vs. SAP Advantage Database Server vs. Solr

Editorial information provided by DB-Engines

Name

SAP Advantage Database Server seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

Hawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.

data warehouse software for querying and managing large distributed datasets, built on Hadoop

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

Low-cost RDBMS with access to ISAM and FoxPro data structures

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

Primary database model

Secondary database models

Spatial DBMS

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

Score	0.08
Rank	#366	Overall
	#39	Time Series DBMS

Score	59.76
Rank	#18	Overall
	#12	Relational DBMS

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

Score	41.02
Rank	#24	Overall
	#3	Search engines

Website

www.hawkular.org

hive.apache.org

rocksdb.org

www.sap.com/products/advantage-database-server.html

solr.apache.org

Technical documentation

www.hawkular.org/hawkular-metrics/docs/user-guide

cwiki.apache.org/confluence/display/Hive/Home

github.com/facebook/rocksdb/wiki

solr.apache.org/resources.html

Developer

Community supported by Red Hat

Apache Software Foundation

initially developed by Facebook

Facebook, Inc.

Sybase, SAP

Apache Software Foundation

Initial release

2014

2012

2013

1993

2006

Current release

3.1.3, April 2022

9.2.1, May 2024

9.6.1, May 2024

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache Version 2

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

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C++

Java

Server operating systems

Linux
OS X
Windows

All OS with a Java VM

Linux

Linux
Windows

All OS with a Java VM

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

Data scheme

schema-free

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

HTTP REST

JDBC
ODBC
Thrift

C++ API
Java API

.NET Client API
ADO.NET
JDBC
ODBC
OLE DB

Java API
RESTful HTTP/JSON API

Supported programming languages

Go
Java
Python
Ruby

C++
Java
PHP
Python

C
C++
Go
Java
Perl
Python
Ruby

Delphi
Perl
PHP

.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

Java plugins

Triggers

yes

via Hawkular Alerting

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

Sharding

horizontal partitioning

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

selectable replication factor

yes

Multi-source replication

CONFLICT Trigger to resolve replication conflicts
Source-replica replication

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

based on Cassandra
Immediate Consistency

based on Cassandra

Eventual Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

ACID

optimistic locking

Concurrency

Support for concurrent manipulation of data

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

caching of (temporary) tables can be configured

yes

User concepts

Access control

Access rights for users, groups and roles

fine grained access rights according to SQL-standard

yes

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