DBMS > Amazon DocumentDB vs. RocksDB vs. Spark SQL vs. YugabyteDB

System Properties Comparison Amazon DocumentDB vs. RocksDB vs. Spark SQL vs. YugabyteDB

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Editorial information provided by DB-Engines

Name

Description

Fast, scalable, highly available, and fully managed MongoDB-compatible database service

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

Spark SQL is a component on top of 'Spark Core' for structured data processing

High-performance distributed SQL database for global, internet-scale applications. Wire and feature compatible with PostgreSQL.

Primary database model

Document store

Key-value store

Relational DBMS

Secondary database models

Document store
Wide column store

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

Score	1.91
Rank	#132	Overall
	#24	Document stores

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

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	2.91
Rank	#102	Overall
	#51	Relational DBMS

Website

aws.amazon.com/documentdb

rocksdb.org

spark.apache.org/sql

www.yugabyte.com

Technical documentation

aws.amazon.com/documentdb/resources

github.com/facebook/rocksdb/wiki

spark.apache.org/docs/latest/sql-programming-guide.html

docs.yugabyte.com
github.com/yugabyte/yugabyte-db

Developer

Facebook, Inc.

Apache Software Foundation

Yugabyte Inc.

Initial release

2019

2013

2014

2017

Current release

8.11.4, April 2024

3.5.0 ( 2.13), September 2023

2.19, September 2023

License

Commercial or Open Source

commercial

Open Source

BSD

Open Source

Apache 2.0

Open Source

Apache 2.0

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.

YugabyteDB Managed is the fully managed database-as-a-service offering of YugabyteDB. Get started quickly, and effortlessly ensure continuous availability and limitless scale of your cloud native applications.

Implementation language

C++

Scala

C and C++

Server operating systems

hosted

Linux

Linux
OS X
Windows

Linux
OS X

Data scheme

schema-free

yes

depending on used data model

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes, PostgreSQL compatible

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

C++ API
Java API

JDBC
ODBC

JDBC
YCQL, an SQL-based flexible-schema API with its roots in Cassandra Query Language
YSQL - a fully relational SQL API that is wire compatible with the SQL language in PostgreSQL

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

C
C++
Go
Java
Perl
Python
Ruby

Java
Python
R
Scala

C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Rust
Scala

Server-side scripts

Stored procedures

yes

sql, plpgsql, C

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

yes, utilizing Spark Core

Hash and Range Sharding, row-level geo-partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones for high availability, asynchronous replication for up to 15 read replicas

yes

none

Based on Raft distributed consensus protocol, minimum 3 replicas for continuous availability

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Strong consistency on writes and tunable consistency on reads

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

yes

Transaction concepts

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

Atomic single-document operations

yes

Distributed ACID with Serializable & Snapshot Isolation. Inspired by Google Spanner architecture.

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

based on RocksDB

In-memory capabilities

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

yes

User concepts

Access control

Access rights for users and roles

yes

More information provided by the system vendor

Specific characteristics

YugabyteDB is an open source distributed SQL database for cloud native transactional...
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Competitive advantages

PostgreSQL compatible: Get instantly productive with a PostgreSQL compatible RDBMS....
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Typical application scenarios

Systems of record and engagement for cloud native applications that require resilience,...
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Market metrics

2 Million+ lifetime clusters deployed, 6.5K+ GitHub stars, 7K YugabyteDB Community...
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Licensing and pricing models

Apache 2.0 license for the database
» more

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