DBMS > Apache Impala vs. Prometheus vs. Realm vs. Tigris

System Properties Comparison Apache Impala vs. Prometheus vs. Realm vs. Tigris

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

Open-source Time Series DBMS and monitoring system

A DBMS built for use on mobile devices that’s a fast, easy to use alternative to SQLite and Core Data

A horizontally scalable, ACID transactional, document database available both as a fully managed cloud service and for deployment on self-managed infrastructure

Primary database model

Relational DBMS

Time Series DBMS

Document store

Document store
Key-value store
Search engine
Time Series DBMS

Secondary database models

Document store

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

Score	10.63
Rank	#40	Overall
	#24	Relational DBMS

Score	7.56
Rank	#49	Overall
	#3	Time Series DBMS

Score	7.18
Rank	#52	Overall
	#8	Document stores

Score	0.05
Rank	#359	Overall
	#50	Document stores
	#52	Key-value stores
	#23	Search engines
	#37	Time Series DBMS

Website

Technical documentation

impala.apache.org/impala-docs.html

prometheus.io/docs

realm.io/docs

www.tigrisdata.com/docs

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Realm, acquired by MongoDB in May 2019

Tigris Data, Inc.

Initial release

2013

2015

2014

2022

Current release

4.1.0, June 2022

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache 2.0

Open Source

Apache Version 2.0

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++

Server operating systems

Linux

Linux
Windows

Android
Backend: server-less
iOS
Windows

Linux
macOS
Windows

Data scheme

yes

Typing

predefined data types such as float or date

yes

Numeric data only

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.

Import of XML data possible

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC

RESTful HTTP/JSON API

CLI Client
gRPC
RESTful HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

.Net
C++
Go
Haskell
Java
JavaScript (Node.js)
Python
Ruby

.Net
Java

with Android only
Objective-C
React Native
Swift

Go
Java
JavaScript (Node.js)

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

runs within the applications so server-side scripts are unnecessary

Triggers

yes

Change Listeners

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

by Federation

none

yes

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

none

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, using FoundationDB

In-memory capabilities

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

yes

In-Memory realm

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

yes

Access rights for users and roles

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More resources

DB-Engines blog posts

MySQL, PostgreSQL and Redis are the winners of the March ranking
2 March 2016, Paul Andlinger

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Recent citations in the news

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