DB-EnginesInfluxDB: Focus on building software with an easy-to-use serverless, scalable time series platformEnglish
Deutsch
Knowledge Base of Relational and NoSQL Database Management Systemsprovided by solid IT

DBMS > Drizzle vs. Milvus vs. Rockset vs. Spark SQL

System Properties Comparison Drizzle vs. Milvus vs. Rockset vs. Spark SQL

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines
NameDrizzle  Xexclude from comparisonMilvus  Xexclude from comparisonRockset  Xexclude from comparisonSpark SQL  Xexclude from comparison
Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.
DescriptionMySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.A DBMS designed for efficient storage of vector data and vector similarity searchesA scalable, reliable search and analytics service in the cloud, built on RocksDBSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelRelational DBMSVector DBMSDocument storeRelational DBMS
Secondary database modelsRelational DBMS
Search engine
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.78
Rank#103  Overall
#3  Vector DBMS
Score0.82
Rank#212  Overall
#36  Document stores
Score18.04
Rank#33  Overall
#20  Relational DBMS
Websitemilvus.iorockset.comspark.apache.org/­sql
Technical documentationmilvus.io/­docs/­overview.mddocs.rockset.comspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperDrizzle project, originally started by Brian AkerRocksetApache Software Foundation
Initial release2008201920192014
Current release7.2.4, September 20122.3.4, January 20243.5.0 ( 2.13), September 2023
License infoCommercial or Open SourceOpen Source infoGNU GPLOpen Source infoApache Version 2.0commercialOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenonoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Zilliz Cloud – Cloud-native service for Milvus
Implementation languageC++C++, GoC++Scala
Server operating systemsFreeBSD
Linux
OS X
Linux
macOS info10.14 or later
Windows infowith WSL 2 enabled
hostedLinux
OS X
Windows
Data schemeyesschema-freeyes
Typing infopredefined data types such as float or dateyesVector, Numeric and Stringdynamic typingyes
XML support infoSome form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.nono infoingestion from XML files supportedno
Secondary indexesyesnoall fields are automatically indexedno
SQL infoSupport of SQLyes infowith proprietary extensionsnoRead-only SQL queries, including JOINsSQL-like DML and DDL statements
APIs and other access methodsJDBCRESTful HTTP APIHTTP RESTJDBC
ODBC
Supported programming languagesC
C++
Java
PHP
C++
Go
Java
JavaScript (Node.js)
Python
Go
Java
JavaScript (Node.js)
Python
Java
Python
R
Scala
Server-side scripts infoStored proceduresnononono
Triggersno infohooks for callbacks inside the server can be used.nonono
Partitioning methods infoMethods for storing different data on different nodesShardingShardingAutomatic shardingyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
yesnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonono
Consistency concepts infoMethods to ensure consistency in a distributed systemBounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency
Eventual Consistency
Foreign keys infoReferential integrityyesnonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnonono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesno
User concepts infoAccess controlPluggable authentication mechanisms infoe.g. LDAP, HTTPRole based access control and fine grained access rightsAccess rights for users and organizations can be defined via Rockset consoleno
More information provided by the system vendor
DrizzleMilvusRocksetSpark SQL
Specific characteristicsMilvus is an open-source and cloud-native vector database built for production-ready...
» more
Competitive advantagesHighly available, versatile, and robust with millisecond latency. Supports batch...
» more
Typical application scenariosRAG: retrieval augmented generation Video media : video understanding, video deduplication....
» more
Key customersMilvus is trusted by thousands of enterprises, including PayPal, eBay, IKEA, LINE,...
» more
Market metricsAs of January 2024, 25k+ GitHub stars 10M+ downloads and installations​ ​ 3k+ enterprise...
» more
Licensing and pricing modelsMilvus was released under the open-source Apache License 2.0 in October 2019. Fully-managed...
» more

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
DrizzleMilvusRocksetSpark SQL
DB-Engines blog posts

MySQL won the April ranking; did its forks follow?
1 April 2015, Paul Andlinger

Has MySQL finally lost its mojo?
1 July 2013, Matthias Gelbmann

show all

Vector databases
2 June 2023, Matthias Gelbmann

show all

Recent citations in the news

How NVIDIA GPU Acceleration Supercharged Milvus Vector Database
26 March 2024, The New Stack

AI-Powered Search Engine With Milvus Vector Database on Vultr
31 January 2024, SitePoint

Milvus 2.4 Unveils Game-Changing Features for Enhanced Vector Search
20 March 2024, GlobeNewswire

Zilliz Unveils Game-Changing Features for Vector Search
22 March 2024, Datanami

IBM watsonx.data’s integrated vector database: unify, prepare, and deliver your data for AI
9 April 2024, ibm.com

provided by Google News

Rockset Announces Native Support for Hybrid Search to Power AI Apps
17 May 2024, Datanami

Rockset upgrades database to meet the needs of AI hybrid search – Blocks and Files
20 May 2024, Blocks & Files

Rockset launches native support for hybrid vector and text search to power AI apps
16 May 2024, SiliconANGLE News

Data Management News for the Week of May 17; Updates from Anomalo, DataStax, Rockset & More
16 May 2024, Solutions Review

Rockset targets cost control with latest database update
31 January 2024, TechTarget

provided by Google News

Performance Insights from Sigma Rule Detections in Spark Streaming
1 June 2024, Towards Data Science

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

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

provided by Google News



Share this page

Featured Products

Neo4j logo

See for yourself how a graph database can make your life easier.
Use Neo4j online for free.

Milvus logo

Vector database designed for GenAI, fully equipped for enterprise implementation.
Try Managed Milvus for Free

Datastax Astra logo

Bring all your data to Generative AI applications with vector search enabled by the most scalable
vector database available.
Try for Free

Present your product here