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DBMS > CrateDB vs. LMDB vs. ObjectBox vs. Splice Machine

System Properties Comparison CrateDB vs. LMDB vs. ObjectBox vs. Splice Machine

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Editorial information provided by DB-Engines
NameCrateDB  Xexclude from comparisonLMDB  Xexclude from comparisonObjectBox  Xexclude from comparisonSplice Machine  Xexclude from comparison
DescriptionDistributed Database based on LuceneA high performant, light-weight, embedded key-value database libraryExtremely fast embedded database for small devices, IoT and MobileOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark
Primary database modelDocument store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS
Key-value storeObject oriented DBMSRelational DBMS
Secondary database modelsRelational DBMSTime Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.73
Rank#224  Overall
#37  Document stores
#5  Spatial DBMS
#16  Search engines
#19  Time Series DBMS
#8  Vector DBMS
Score1.99
Rank#125  Overall
#21  Key-value stores
Score1.20
Rank#170  Overall
#5  Object oriented DBMS
Score0.54
Rank#250  Overall
#114  Relational DBMS
Websitecratedb.comwww.symas.com/­symas-embedded-database-lmdbobjectbox.iosplicemachine.com
Technical documentationcratedb.com/­docswww.lmdb.tech/­docdocs.objectbox.iosplicemachine.com/­how-it-works
DeveloperCrateSymasObjectBox LimitedSplice Machine
Initial release2013201120172014
Current release0.9.32, January 20243.1, March 2021
License infoCommercial or Open SourceOpen SourceOpen SourceOpen Source infoApache License 2.0Open Source infoAGPL 3.0, commercial license available
Cloud-based only infoOnly available as a cloud servicenononono
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 languageJavaCC and C++Java
Server operating systemsAll Operating Systems, including Kubernetes with CrateDB Kubernetes Operator supportLinux
Unix
Windows
Android
iOS
Linux
macOS
Windows
Linux
OS X
Solaris
Windows
Data schemeFlexible Schema (defined schema, partial schema, schema free)schema-freeyesyes
Typing infopredefined data types such as float or dateyesyesyes
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.nonono
Secondary indexesyesnoyesyes
SQL infoSupport of SQLyes, but no triggers and constraints, and PostgreSQL compatibilitynonoyes
APIs and other access methodsADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API
Proprietary native APIJDBC
Native Spark Datasource
ODBC
Supported programming languages.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
.Net
C
C++
Clojure
Go
Haskell
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Nim
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Rust
Swift
Tcl
C
C++
Dart
Go
Java
JavaScript infoplanned (as of Jan 2019)
Kotlin
Python infoplanned (as of Jan 2019)
Swift
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Server-side scripts infoStored proceduresuser defined functions (Javascript)nonoyes infoJava
Triggersnononoyes
Partitioning methods infoMethods for storing different data on different nodesShardingnonenoneShared Nothhing Auto-Sharding, Columnar Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesConfigurable replication on table/partition-levelnoneonline/offline synchronization between client and serverMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnononoYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Read-after-write consistency on record level
Immediate ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonoyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datano infounique row identifiers can be used for implementing an optimistic concurrency control strategyACIDACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes, multi-version concurrency control (MVCC)
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.noyesnoyes
User concepts infoAccess controlrights management via user accountsnoyesAccess rights for users, groups and roles according to SQL-standard
More information provided by the system vendor
CrateDBLMDBObjectBoxSplice Machine
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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14 November 2023

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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More resources
CrateDBLMDBObjectBoxSplice Machine
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