DBMS > Apache Drill vs. Apache Phoenix vs. PlanetScale vs. Tarantool

System Properties Comparison Apache Drill vs. Apache Phoenix vs. PlanetScale vs. Tarantool

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Editorial information provided by DB-Engines

Name

Description

Schema-free SQL Query Engine for Hadoop, NoSQL and Cloud Storage

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

Scalable, distributed, serverless MySQL database platform built on top of Vitess

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

Primary database model

Document store
Relational DBMS

Relational DBMS

Document store
Key-value store
Relational DBMS

Secondary database models

Document store
Spatial DBMS

Spatial DBMS

with Tarantool/GIS extension

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

Score	1.90
Rank	#126	Overall
	#23	Document stores
	#60	Relational DBMS

Score	1.90
Rank	#125	Overall
	#59	Relational DBMS

Score	1.09
Rank	#178	Overall
	#82	Relational DBMS

Score	1.63
Rank	#139	Overall
	#25	Document stores
	#24	Key-value stores
	#65	Relational DBMS

Website

Technical documentation

drill.apache.org/docs

phoenix.apache.org

planetscale.com/docs

www.tarantool.io/en/doc

Developer

Apache Software Foundation

PlanetScale

Initial release

2012

2014

2020

2008

Current release

1.20.3, January 2023

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

2.10.0, May 2022

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache Version 2.0

commercial

Open Source

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

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

C and C++

Server operating systems

Linux
OS X
Windows

Linux
Unix
Windows

Docker
Linux
macOS

BSD
Linux
macOS

Data scheme

schema-free

yes

late-bound, schema-on-read capabilities

yes

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

Typing

predefined data types such as float or date

yes

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

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

SQL SELECT statement is SQL:2003 compliant

yes

with proprietary extensions

Full-featured ANSI SQL support

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

JDBC

ADO.NET
JDBC
MySQL protocol
ODBC

Open binary protocol

Supported programming languages

C++

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

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

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

Server-side scripts

Stored procedures

user defined functions

yes

proprietary syntax

Lua, C and SQL stored procedures

Triggers

yes

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

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

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

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency or Eventual Consistency

Eventual Consistency across shards
Immediate Consistency within a shard

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

not for MyISAM storage engine

yes

Transaction concepts

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

ACID

ACID at shard level

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

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

yes, cooperative multitasking

Durability

Support for making data persistent

Depending on the underlying data source

yes

yes, write ahead logging

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

Depending on the underlying data source

yes

yes, full featured in-memory storage engine with persistence

User concepts

Access control

Depending on the underlying data source

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

Users with fine-grained authorization concept

no user groups or roles

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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Recent citations in the news

Apache Drill case study: A tutorial on processing CSV files
9 June 2016, TheServerSide.com

Analyse Kafka messages with SQL queries using Apache Drill
27 September 2019, Towards Data Science

MapR Advances Support for Flexible and High Performance Analytics on JSON and S3 Data with Apache Drill
30 January 2019, Business Wire

Apache Drill Eliminates ETL, Data Transformation for MapR Database
11 April 2016, The New Stack

Drill Mines Diverse Data Sets, Google Style
20 May 2015, The Next Platform

provided by Google News

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

Deep dive into Azure HDInsight 4.0
25 September 2018, azure.microsoft.com

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

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

Sundeck Announces the Public Availability of Its Query Engineering Platform for Snowflake
31 May 2023, Newswire

provided by Google News

PlanetScale ends free tier bid, sheds staff in profitability bid
11 March 2024, The Register

PlanetScale Insights Anomalies introduces smart query monitoring
29 November 2023, SDTimes.com

PlanetScale forks MySQL to add vector support
3 October 2023, TechCrunch

Top Database as a Service (DBaaS) Startups
28 August 2024, Tracxn

PlanetScale review: Horizontally scalable MySQL in the cloud
1 September 2021, InfoWorld

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Deploying Tarantool Cartridge applications with zero effort (Part 1)
16 December 2019, Хабр

VShard — horizontal scaling in Tarantool
7 March 2019, Хабр

Tarantool Data Grid: Architecture and Features
16 October 2019, Хабр

Accelerating PHP connectors for Tarantool using Async, Swoole, and Parallel
18 December 2019, Хабр

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