DBMS > Apache Phoenix vs. RavenDB vs. SAP HANA vs. Spark SQL vs. XTDB

System Properties Comparison Apache Phoenix vs. RavenDB vs. SAP HANA vs. Spark SQL vs. XTDB

Editorial information provided by DB-Engines

Name

Description

A scale-out RDBMS with evolutionary schema built on Apache HBase

Open Source Operational and Transactional Enterprise NoSQL Document Database

In-memory, column based data store. Available as appliance or cloud service

Spark SQL is a component on top of 'Spark Core' for structured data processing

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Secondary database models

Graph DBMS
Spatial DBMS
Time Series DBMS

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	1.97
Rank	#126	Overall
	#59	Relational DBMS

Score	2.92
Rank	#101	Overall
	#18	Document stores

Score	44.69
Rank	#22	Overall
	#16	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

phoenix.apache.org

ravendb.net

www.sap.com/products/hana.html

spark.apache.org/sql

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

phoenix.apache.org

ravendb.net/docs

help.sap.com/hana

spark.apache.org/docs/latest/sql-programming-guide.html

www.xtdb.com/docs

Developer

Apache Software Foundation

Hibernating Rhinos

SAP

Apache Software Foundation

Juxt Ltd.

Initial release

2014

2010

2014

2019

Current release

5.0-HBase2, July 2018 and 4.15-HBase1, December 2019

5.4, July 2022

2.0 SPS07 (April 4, 2023), April 2023

3.5.0 ( 2.13), September 2023

1.19, September 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

AGPL version 3, commercial license available

commercial

Open Source

Apache 2.0

Open Source

MIT License

Cloud-based only

Only available as a cloud service

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Scala

Clojure

Server operating systems

Linux
Unix
Windows

Linux
macOS
Raspberry Pi
Windows

Appliance or cloud-service

Linux
OS X
Windows

All OS with a Java 8 (and higher) VM
Linux

Data scheme

yes

late-bound, schema-on-read capabilities

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, extensible-data-notation format

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

SQL

Support of SQL

yes

SQL-like query language (RQL)

yes

SQL-like DML and DDL statements

limited SQL, making use of Apache Calcite

APIs and other access methods

JDBC

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API

JDBC
ODBC

HTTP REST
JDBC

Supported programming languages

C
C#
C++
Go
Groovy
Java
PHP
Python
Scala

.Net
C#
F#
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Java
Python
R
Scala

Clojure
Java

Server-side scripts

Stored procedures

user defined functions

yes

SQLScript, R

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

yes, utilizing Spark Core

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Multi-source replication

yes

none

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

ACID, Cluster-wide transaction available

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

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 Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancy

Authorization levels configured per client per database

yes

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