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

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

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

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

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

A fully ACID transactional, developer-friendly, multi-model DBMS

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

Primary database model

Relational DBMS

Document store
Graph DBMS

Document store

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

Score	1.97
Rank	#126	Overall
	#59	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	0.86
Rank	#203	Overall
	#34	Document stores
	#18	Graph DBMS

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

phoenix.apache.org

spark.apache.org/sql

surrealdb.com

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

Technical documentation

phoenix.apache.org

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

surrealdb.com/docs

www.xtdb.com/docs

Developer

Apache Software Foundation

SurrealDB Ltd

Juxt Ltd.

Initial release

2014

2022

2019

Current release

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

3.5.0 ( 2.13), September 2023

v1.5.0, May 2024

1.19, September 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

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

Scala

Rust

Clojure

Server operating systems

Linux
Unix
Windows

Linux
OS X
Windows

Linux
macOS
Windows

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

Data scheme

yes

late-bound, schema-on-read capabilities

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

SQL-like query language

limited SQL, making use of Apache Calcite

APIs and other access methods

JDBC

JDBC
ODBC

GraphQL
RESTful HTTP API
WebSocket

HTTP REST
JDBC

Supported programming languages

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

Java
Python
R
Scala

Deno
Go
JavaScript (Node.js)
Rust

Clojure
Java

Server-side scripts

Stored procedures

user defined functions

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

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

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

yes, based on authentication and database rules

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

DB-Engines blog posts

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

show all

Recent citations in the news

Supercharge SQL on Your Data in Apache HBase with Apache Phoenix | Amazon Web Services
2 June 2016, AWS Blog

Bridge the SQL-NoSQL gap with Apache Phoenix
4 February 2016, InfoWorld

Hortonworks Starts Hadoop Summit with Data Platform Update -- ADTmag
28 June 2016, ADT Magazine

Amazon EMR 4.7.0 – Apache Tez & Phoenix, Updates to Existing Apps | Amazon Web Services
2 June 2016, AWS Blog

Quadrant takes over Apache Australian business
9 June 2015, Offshore Engineer

provided by Google News

Use Amazon Athena with Spark SQL for your open-source transactional table formats | Amazon Web Services
24 January 2024, AWS Blog

What is Apache Spark? The big data platform that crushed Hadoop
3 April 2024, InfoWorld

Cracking the Apache Spark Interview: 80+ Top Questions and Answers for 2024
1 April 2024, Simplilearn

Feature Engineering for Time-Series Using PySpark on Databricks
8 May 2024, Towards Data Science

18 Top Big Data Tools and Technologies to Know About in 2024
24 January 2024, TechTarget

provided by Google News

SD Times Open-Source Project of the Week: SurrealDB
10 May 2024, SDTimes.com

Meet Tobie Morgan Hitchcock, CEO & Co-Founder Of SurrealDB
25 April 2024, TechRound

Cloud, privacy and AI: Trends defining the future of data and databases
27 September 2023, Sifted

SurrealDB raises $6M for its database-as-a-service offering
4 January 2023, TechCrunch

Introducing SurrealDB: A Quantum Leap in Database Technology
11 September 2023, TechRound

provided by Google News

Share this page