DBMS > Apache Impala vs. BoltDB vs. Postgres-XL vs. RocksDB vs. Tarantool

System Properties Comparison Apache Impala vs. BoltDB vs. Postgres-XL vs. RocksDB vs. Tarantool

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

An embedded key-value store for Go.

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

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

Primary database model

Document store
Key-value store
Relational DBMS

Secondary database models

Document store

Document store
Spatial DBMS

Spatial DBMS

with Tarantool/GIS extension

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	0.74
Rank	#220	Overall
	#31	Key-value stores

Score	0.49
Rank	#256	Overall
	#117	Relational DBMS

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

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

Website

impala.apache.org

github.com/boltdb/bolt

www.postgres-xl.org

rocksdb.org

www.tarantool.io

Technical documentation

impala.apache.org/impala-docs.html

www.postgres-xl.org/documentation

github.com/facebook/rocksdb/wiki

www.tarantool.io/en/doc

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Facebook, Inc.

Initial release

2013

2014

since 2012, originally named StormDB

2013

2008

Current release

4.1.0, June 2022

10 R1, October 2018

8.11.4, April 2024

2.10.0, May 2022

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

MIT License

Open Source

Mozilla public license

Open Source

BSD

Open Source

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

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

C and C++

Server operating systems

Linux

BSD
Linux
OS X
Solaris
Windows

Linux
macOS

Linux

BSD
Linux
macOS

Data scheme

yes

schema-free

yes

schema-free

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

Typing

predefined data types such as float or date

yes

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

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

XML type, but no XML query functionality

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

distributed, parallel query execution

Full-featured ANSI SQL support

APIs and other access methods

JDBC
ODBC

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

C++ API
Java API

Open binary protocol

Supported programming languages

All languages supporting JDBC/ODBC

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

C
C++
Go
Java
Perl
Python
Ruby

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

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

user defined functions

Lua, C and SQL stored procedures

Triggers

yes

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

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal partitioning

Sharding, partitioned with virtual buckets by user defined affinity key. Live resharding for scale up and scale down without maintenance downtime.

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

yes

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

none

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

yes

ACID

MVCC

yes

ACID, with serializable isolation and linearizable read (within partition); Configurable MVCC (within partition); No cross-shard distributed transactions

Concurrency

Support for concurrent manipulation of data

yes

yes, cooperative multitasking

Durability

Support for making data persistent

yes

yes, write ahead logging

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

fine grained access rights according to SQL-standard

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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