DBMS > Apache Impala vs. Graph Engine vs. Tarantool vs. TempoIQ vs. XTDB

System Properties Comparison Apache Impala vs. Graph Engine vs. Tarantool vs. TempoIQ vs. XTDB

Editorial information provided by DB-Engines

Name

TempoIQ seems to be decommissioned. It will be removed from the DB-Engines ranking.

Description

Analytic DBMS for Hadoop

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

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

Scalable analytics DBMS for sensor data, provided as a service (SaaS)

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

Primary database model

Relational DBMS

Graph DBMS
Key-value store

Document store
Key-value store
Relational DBMS

Time Series DBMS

Document store

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	0.67
Rank	#232	Overall
	#21	Graph DBMS
	#34	Key-value stores

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

Score	0.18
Rank	#332	Overall
	#46	Document stores

Website

impala.apache.org

www.graphengine.io

www.tarantool.io

tempoiq.com (offline)

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

Technical documentation

impala.apache.org/impala-docs.html

www.graphengine.io/docs/manual

www.tarantool.io/en/doc

www.xtdb.com/docs

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Microsoft

TempoIQ

Juxt Ltd.

Initial release

2013

2010

2008

2012

2019

Current release

4.1.0, June 2022

2.10.0, May 2022

1.19, September 2021

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

MIT License

Open Source

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

commercial

Open Source

MIT License

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

C++

.NET and C

C and C++

Clojure

Server operating systems

Linux

.NET

BSD
Linux
macOS

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

Data scheme

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

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

SQL-like DML and DDL statements

Full-featured ANSI SQL support

limited SQL, making use of Apache Calcite

APIs and other access methods

JDBC
ODBC

RESTful HTTP API

Open binary protocol

HTTP API

HTTP REST
JDBC

Supported programming languages

All languages supporting JDBC/ODBC

C#
C++
F#
Visual Basic

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

C#
Java
JavaScript

Node.js
Python
Ruby

Clojure
Java

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

Lua, C and SQL stored procedures

Triggers

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

yes

Realtime Alerts

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

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

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

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

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

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

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

yes, full featured in-memory storage engine with persistence

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

simple authentication-based access control

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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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