DBMS > Apache Impala vs. Tarantool vs. VelocityDB

System Properties Comparison Apache Impala vs. Tarantool vs. VelocityDB

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

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

A .NET Object Database that can be embedded/distributed and extended to a graph data model (VelocityGraph)

Primary database model

Relational DBMS

Document store
Key-value store
Relational DBMS

Graph DBMS
Object oriented DBMS

Secondary database models

Document store

Spatial DBMS

with Tarantool/GIS extension

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

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	1.67
Rank	#143	Overall
	#25	Document stores
	#25	Key-value stores
	#65	Relational DBMS

Score	0.11
Rank	#354	Overall
	#37	Graph DBMS
	#15	Object oriented DBMS

Website

impala.apache.org

www.tarantool.io

velocitydb.com

Technical documentation

impala.apache.org/impala-docs.html

www.tarantool.io/en/doc

velocitydb.com/UserGuide

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

VelocityDB Inc

Initial release

2013

2008

2011

Current release

4.1.0, June 2022

2.10.0, May 2022

7.x

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

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

commercial

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

C++

C and C++

Server operating systems

Linux

BSD
Linux
macOS

Any that supports .NET

Data scheme

yes

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

yes

Typing

predefined data types such as float or date

yes

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

yes

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-like DML and DDL statements

Full-featured ANSI SQL support

APIs and other access methods

JDBC
ODBC

Open binary protocol

.Net

Supported programming languages

All languages supporting JDBC/ODBC

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

.Net

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Lua, C and SQL stored procedures

Triggers

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

Callbacks are triggered when data changes

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.

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

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

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

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

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

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

In-memory capabilities

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

yes, full featured in-memory storage engine with persistence

yes

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

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

Based on Windows Authentication

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More resources

DB-Engines blog posts

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 Impala becomes Top-Level Project
28 November 2017, SDTimes.com

Cloudera Bringing Impala to AWS Cloud
28 November 2017, Datanami

Apache Doris just 'graduated': Why care about this SQL data warehouse
24 June 2022, InfoWorld

Hudi: Uber Engineering’s Incremental Processing Framework on Apache Hadoop
12 March 2017, Uber

Updates & Upserts in Hadoop Ecosystem with Apache Kudu
27 October 2017, KDnuggets

provided by Google News

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

VShard — horizontal scaling in Tarantool
7 March 2019, Хабр

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

provided by Google News

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