DBMS > Apache Impala vs. Heroic vs. LevelDB vs. Splunk

System Properties Comparison Apache Impala vs. Heroic vs. LevelDB vs. Splunk

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

Embeddable fast key-value storage library that provides an ordered mapping from string keys to string values

Analytics Platform for Big Data

Primary database model

Secondary database models

Document store

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

Score	14.03
Rank	#40	Overall
	#24	Relational DBMS

Score	0.57
Rank	#250	Overall
	#21	Time Series DBMS

Score	2.75
Rank	#107	Overall
	#19	Key-value stores

Score	88.71
Rank	#14	Overall
	#2	Search engines

Website

impala.apache.org

github.com/spotify/heroic

github.com/google/leveldb

www.splunk.com

Technical documentation

impala.apache.org/impala-docs.html

spotify.github.io/heroic

github.com/google/leveldb/blob/main/doc/index.md

docs.splunk.com/Documentation/Splunk

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Spotify

Google

Splunk Inc.

Initial release

2013

2014

2011

2003

Current release

4.1.0, June 2022

1.23, February 2021

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache 2.0

Open Source

BSD

commercial

Limited free edition and free developer edition available

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++

Java

C++

Server operating systems

Linux

Illumos
Linux
OS X
Windows

Linux
OS X
Solaris
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

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.

yes

Secondary indexes

yes

via Elasticsearch

yes

SQL

Support of SQL

SQL-like DML and DDL statements

Splunk Search Processing Language for search commands

APIs and other access methods

JDBC
ODBC

HQL (Heroic Query Language, a JSON-based language)
HTTP API

HTTP REST

Supported programming languages

All languages supporting JDBC/ODBC

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

C#
Java
JavaScript
PHP
Python
Ruby

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

Triggers

yes

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

none

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

A 'Transaction' in Splunk has a different meaning: grouping related events into a single one for later searching

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

with automatic compression on writes

yes

In-memory capabilities

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

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

Access rights for users and roles

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More resources

DB-Engines blog posts

Enterprise Search Engines almost double their popularity in the last 12 months
2 July 2014, Paul Andlinger

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