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

System Properties Comparison Apache Phoenix vs. Spark SQL vs. Tarantool 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

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

In-memory computing platform with a flexible data schema for efficiently building high-performance applications

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

Primary database model

Relational DBMS

Document store
Key-value store
Relational DBMS

Document store

Secondary database models

Spatial DBMS

with Tarantool/GIS extension

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	1.72
Rank	#144	Overall
	#25	Document stores
	#25	Key-value stores
	#66	Relational DBMS

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

phoenix.apache.org

spark.apache.org/sql

www.tarantool.io

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

Technical documentation

phoenix.apache.org

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

www.tarantool.io/en/doc

www.xtdb.com/docs

Developer

Apache Software Foundation

Juxt Ltd.

Initial release

2014

2008

2019

Current release

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

3.5.0 ( 2.13), September 2023

2.10.0, May 2022

1.19, September 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache 2.0

Open Source

BSD-2, source-available extensions (modules), commercial licenses for Tarantool Enterprise

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

C and C++

Clojure

Server operating systems

Linux
Unix
Windows

Linux
OS X
Windows

BSD
Linux
macOS

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

Data scheme

yes

late-bound, schema-on-read capabilities

yes

Flexible data schema: relational definition for tables with ability to store json-like documents in columns

schema-free

Typing

predefined data types such as float or date

yes

string, double, decimal, uuid, integer, blob, boolean, datetime

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

Full-featured ANSI SQL support

limited SQL, making use of Apache Calcite

APIs and other access methods

JDBC

JDBC
ODBC

Open binary protocol

HTTP REST
JDBC

Supported programming languages

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

Java
Python
R
Scala

C
C#
C++
Erlang
Go
Java
JavaScript
Lua
Perl
PHP
Python
Rust

Clojure
Java

Server-side scripts

Stored procedures

user defined functions

Lua, C and SQL stored procedures

Triggers

yes, before/after data modification events, on replication events, client session events

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes, utilizing Spark Core

Sharding, partitioned with virtual buckets by user defined affinity key. Live resharding for scale up and scale down without maintenance downtime.

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

none

Asynchronous replication with multi-master option
Configurable replication topology (full-mesh, chain, star)
Synchronous quorum replication (with Raft)

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

Casual consistency across sharding partitions
Eventual consistency within replicaset partition

when using asyncronous replication
Immediate Consistency within single instance
Sequential consistency including linearizable read within replicaset partition

when using Raft

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

ACID, with serializable isolation and linearizable read (within partition); Configurable MVCC (within partition); No cross-shard distributed transactions

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, cooperative multitasking

yes

Durability

Support for making data persistent

yes

yes, write ahead logging

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

yes, full featured in-memory storage engine with persistence

User concepts

Access control

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

Access Control Lists
Mutual TLS authentication for Tarantol Enterprise
Password based authentication
Role-based access control (RBAC) and LDAP for Tarantol Enterprise
Users and Roles

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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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Data processing speed and reliability: in-memory synchronous replication
9 November 2021, Vladimir Perepelytsya, Tarantool (sponsor)

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