DBMS > Hypertable vs. Tarantool vs. TerarkDB vs. XTDB

System Properties Comparison Hypertable vs. Tarantool vs. TerarkDB vs. XTDB

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Editorial information provided by DB-Engines

Name

Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.

Description

An open source BigTable implementation based on distributed file systems such as Hadoop

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

A key-value store forked from RocksDB with advanced compression algorithms. It can be used standalone or as a storage engine for MySQL and MongoDB

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

Primary database model

Wide column store

Document store
Key-value store
Relational DBMS

Key-value store

Document store

Secondary database models

Spatial DBMS

with Tarantool/GIS extension

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

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

Score	0.00
Rank	#383	Overall
	#60	Key-value stores

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

www.tarantool.io

github.com/bytedance/terarkdb

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

Technical documentation

www.tarantool.io/en/doc

bytedance.larkoffice.com/docs/doccnZmYFqHBm06BbvYgjsHHcKc

www.xtdb.com/docs

Developer

Hypertable Inc.

ByteDance, originally Terark

Juxt Ltd.

Initial release

2009

2008

2016

2019

Current release

0.9.8.11, March 2016

2.10.0, May 2022

1.19, September 2021

License

Commercial or Open Source

Open Source

GNU version 3. Commercial license available

Open Source

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

commercial

restricted open source version available

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

C++

C and C++

C++

Clojure

Server operating systems

Linux
OS X
Windows

an inofficial Windows port is available

BSD
Linux
macOS

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

Data scheme

schema-free

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

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

restricted

only exact value or prefix value scans

yes

SQL

Support of SQL

Full-featured ANSI SQL support

limited SQL, making use of Apache Calcite

APIs and other access methods

C++ API
Thrift

Open binary protocol

C++ API
Java API

HTTP REST
JDBC

Supported programming languages

C++
Java
Perl
PHP
Python
Ruby

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

C++
Java

Clojure
Java

Server-side scripts

Stored procedures

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

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

selectable replication factor on file system level

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

none

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate 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, 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

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, full featured in-memory storage engine with persistence

yes

User concepts

Access control

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

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

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