DBMS > CockroachDB vs. Spark SQL vs. Tarantool vs. TempoIQ

System Properties Comparison CockroachDB vs. Spark SQL vs. Tarantool vs. TempoIQ

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

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

Description

CockroachDB is a distributed database architected for modern cloud applications. It is wire compatible with PostgreSQL and backed by a Key-Value Store, which is either RocksDB or a purpose-built derivative, called Pebble.

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

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)

Primary database model

Relational DBMS

Document store
Key-value store
Relational DBMS

Time Series DBMS

Secondary database models

Spatial DBMS

with Tarantool/GIS extension

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

Score	6.15
Rank	#55	Overall
	#33	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

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

Website

www.cockroachlabs.com

spark.apache.org/sql

www.tarantool.io

tempoiq.com (offline)

Technical documentation

www.cockroachlabs.com/docs

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

www.tarantool.io/en/doc

Developer

Cockroach Labs

Apache Software Foundation

TempoIQ

Initial release

2015

2014

2008

2012

Current release

23.1.1, May 2023

3.5.0 ( 2.13), September 2023

2.10.0, May 2022

License

Commercial or Open Source

Open Source

Apache 2.0, commercial license available

Open Source

Apache 2.0

Open Source

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

commercial

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

Scala

C and C++

Server operating systems

Linux
macOS
Windows

Linux
OS X
Windows

BSD
Linux
macOS

Data scheme

dynamic schema

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

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

yes, wire compatible with PostgreSQL

SQL-like DML and DDL statements

Full-featured ANSI SQL support

APIs and other access methods

JDBC

JDBC
ODBC

Open binary protocol

HTTP API

Supported programming languages

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

Java
Python
R
Scala

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

C#
Java
JavaScript

Node.js
Python
Ruby

Server-side scripts

Stored procedures

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

horizontal partitioning (by key range)

all tables are translated to an ordered KV store and then broken down into 64MB ranges, which are then used as replicas in RAFT

yes, utilizing Spark Core

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

Multi-source replication using RAFT

none

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

Consistency concepts

Methods to ensure consistency in a distributed system

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

ACID

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

yes

Durability

Support for making data persistent

yes

yes, write ahead logging

yes

In-memory capabilities

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

yes, full featured in-memory storage engine with persistence

User concepts

Access control

Role-based access control

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

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

DB-Engines blog posts

Data processing speed and reliability: in-memory synchronous replication
9 November 2021, Vladimir Perepelytsya, Tarantool (sponsor)

show all

Recent citations in the news

CockroachDB 23.2 Enhances Enterprise Architectures with Improved Postgres Compatibility and Built-in Resilience
18 January 2024, PR Newswire

CockroachDB tempts legacy databases to crawl into the cloud age
29 January 2024, The Register

How to Unlock Real-Time Data Streams with CockroachDB and Amazon MSK | Amazon Web Services
6 November 2023, AWS Blog

How DoorDash Migrated from Aurora Postgres to CockroachDB
5 December 2023, The New Stack

CockroachDB's Latest Enhancements Focus on Resilience
18 January 2024, Database Trends and Applications

provided by Google News

Use Amazon Athena with Spark SQL for your open-source transactional table formats | Amazon Web Services
24 January 2024, AWS Blog

What is Apache Spark? The big data platform that crushed Hadoop
3 April 2024, InfoWorld

Cracking the Apache Spark Interview: 80+ Top Questions and Answers for 2024
1 April 2024, Simplilearn

1.5 Years of Spark Knowledge in 8 Tips | by Michael Berk
23 December 2023, Towards Data Science

Run Apache Hive workloads using Spark SQL with Amazon EMR on EKS | Amazon Web Services
18 October 2023, AWS Blog

provided by Google News

TaranHouse: New Big Data Warehouse Announced by Tarantool
4 April 2018, Newswire

In-Memory Showdown: Redis vs. Tarantool
1 September 2021, Хабр

Tarantool Announces New Enterprise Version With Enhanced Scaling and Monitoring Capabilities
18 May 2018, Newswire

Deploying Tarantool Cartridge applications with zero effort (Part 1)
16 December 2019, Хабр

Deploying Tarantool Cartridge applications with zero effort (Part 2)
13 April 2020, Хабр

provided by Google News

8 things to consider when applying for an accelerator from 2 C-level execs
29 May 2015, Built In Chicago

provided by Google News

Share this page