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DBMS > Cloudflare Workers KV vs. CrateDB vs. DuckDB vs. Ehcache

System Properties Comparison Cloudflare Workers KV vs. CrateDB vs. DuckDB vs. Ehcache

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Editorial information provided by DB-Engines
NameCloudflare Workers KV  Xexclude from comparisonCrateDB  Xexclude from comparisonDuckDB  Xexclude from comparisonEhcache  Xexclude from comparison
DescriptionA global, low-latency, key-value store for applications on Cloudflare with exceptionally high read volumes and low-latency.Distributed Database based on LuceneAn embeddable, in-process, column-oriented SQL OLAP RDBMSA widely adopted Java cache with tiered storage options
Primary database modelKey-value storeDocument store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS
Relational DBMSKey-value store
Secondary database modelsRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.39
Rank#274  Overall
#39  Key-value stores
Score0.71
Rank#227  Overall
#37  Document stores
#5  Spatial DBMS
#16  Search engines
#19  Time Series DBMS
#8  Vector DBMS
Score4.63
Rank#69  Overall
#37  Relational DBMS
Score4.64
Rank#68  Overall
#8  Key-value stores
Websitewww.cloudflare.com/­developer-platform/­workers-kvcratedb.comduckdb.orgwww.ehcache.org
Technical documentationdevelopers.cloudflare.com/­kv/­apicratedb.com/­docsduckdb.org/­docswww.ehcache.org/­documentation
DeveloperCloudflareCrateTerracotta Inc, owned by Software AG
Initial release2018201320182009
Current release1.0.0, June 20243.10.0, March 2022
License infoCommercial or Open SourcecommercialOpen SourceOpen Source infoMIT LicenseOpen Source infoApache Version 2; commercial licenses available
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
CrateDB Cloud: a distributed SQL database that spreads data and processing across an elastic cluster of shared nothing nodes. CrateDB Cloud enables data insights at scale on Microsoft Azure, AWS and Google Cloud Platform.
Implementation languageJavaC++Java
Server operating systemshostedAll Operating Systems, including Kubernetes with CrateDB Kubernetes Operator supportserver-lessAll OS with a Java VM
Data schemeschema-freeFlexible Schema (defined schema, partial schema, schema free)yesschema-free
Typing infopredefined data types such as float or datenoyesyesyes
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.nononono
Secondary indexesnoyesyesno
SQL infoSupport of SQLnoyes, but no triggers and constraints, and PostgreSQL compatibilityyesno
APIs and other access methodsHTTP REST
Proprietary protocol
ADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API
Arrow Database Connectivity (ADBC)
CLI Client
JDBC
ODBC
JCache
Supported programming languagesC
C++
Dart
JavaScript
Kotlin
Python
Rust
Scala
.NET
Erlang
Go infocommunity maintained client
Java
JavaScript (Node.js) infocommunity maintained client
Perl infocommunity maintained client
PHP
Python
R
Ruby infocommunity maintained client
Scala infocommunity maintained client
C
C# info3rd party driver
C++
Crystal info3rd party driver
Go info3rd party driver
Java
Lisp info3rd party driver
Python
R
Ruby info3rd party driver
Rust
Swift
Zig info3rd party driver
Java
Server-side scripts infoStored proceduresnouser defined functions (Javascript)nono
Triggersnononoyes infoCache Event Listeners
Partitioning methods infoMethods for storing different data on different nodesShardingnoneSharding infoby using Terracotta Server
Replication methods infoMethods for redundantly storing data on multiple nodesyesConfigurable replication on table/partition-levelnoneyes infoby using Terracotta Server
MapReduce infoOffers an API for user-defined Map/Reduce methodsnononono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyEventual Consistency
Read-after-write consistency on record level
Immediate ConsistencyTunable Consistency (Strong, Eventual, Weak)
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanono infounique row identifiers can be used for implementing an optimistic concurrency control strategyACIDyes infosupports JTA and can work as an XA resource
Concurrency infoSupport for concurrent manipulation of datayesyesyes, multi-version concurrency control (MVCC)yes
Durability infoSupport for making data persistentyesyesyesyes infousing a tiered cache-storage approach
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyesyes
User concepts infoAccess controlrights management via user accountsnono
More information provided by the system vendor
Cloudflare Workers KVCrateDBDuckDBEhcache
Specific characteristicsThe enterprise database for time series, documents, and vectors. Distributed - Native...
» more
Competitive advantagesResponse time in milliseconds: e ven for complex ad-hoc queries. Massive scaling...
» more
Typical application scenarios​ IoT: accelerate your IIoT projects with CrateDB, delivering real-time analytics...
» more
Key customersAcross all continents, CrateDB is used by companies of all sizes to meet the most...
» more
Market metricsThe CrateDB open source project was started in 2013 Honorable Mention in 2021 Gartner®...
» more
Licensing and pricing modelsSee CrateDB pricing >
» more

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Cloudflare Workers KVCrateDBDuckDBEhcache
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