DBMS > Apache Phoenix vs. LevelDB vs. Spark SQL vs. XTDB

System Properties Comparison Apache Phoenix vs. LevelDB vs. Spark SQL vs. XTDB

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Editorial information provided by DB-Engines

Name

Description

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

Embeddable fast key-value storage library that provides an ordered mapping from string keys to string values

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

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

Score	1.97
Rank	#126	Overall
	#59	Relational DBMS

Score	2.35
Rank	#111	Overall
	#19	Key-value stores

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

phoenix.apache.org

github.com/google/leveldb

spark.apache.org/sql

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

Technical documentation

phoenix.apache.org

github.com/google/leveldb/blob/main/doc/index.md

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

www.xtdb.com/docs

Developer

Apache Software Foundation

Google

Apache Software Foundation

Juxt Ltd.

Initial release

2014

2011

2014

2019

Current release

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

1.23, February 2021

3.5.0 ( 2.13), September 2023

1.19, September 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

BSD

Open Source

Apache 2.0

Open Source

MIT License

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++

Scala

Clojure

Server operating systems

Linux
Unix
Windows

Illumos
Linux
OS X
Windows

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 DML and DDL statements

limited SQL, making use of Apache Calcite

APIs and other access methods

JDBC

JDBC
ODBC

HTTP REST
JDBC

Supported programming languages

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

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

Java
Python
R
Scala

Clojure
Java

Server-side scripts

Stored procedures

user defined functions

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

yes, utilizing Spark Core

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

none

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

with automatic compression on writes

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

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More resources

DB-Engines blog posts

Cloudera's HBase PaaS offering now supports Complex Transactions
11 August 2021, Krishna Maheshwari (sponsor)

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