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DBMS > Databend vs. IRONdb vs. LMDB vs. PouchDB vs. Splice Machine

System Properties Comparison Databend vs. IRONdb vs. LMDB vs. PouchDB vs. Splice Machine

Editorial information provided by DB-Engines
NameDatabend  Xexclude from comparisonIRONdb  Xexclude from comparisonLMDB  Xexclude from comparisonPouchDB  Xexclude from comparisonSplice Machine  Xexclude from comparison
IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.
DescriptionAn open-source, elastic, and workload-aware cloud data warehouse designed to meet businesses' massive-scale analytics needs at low cost and with low complexityA distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicityA high performant, light-weight, embedded key-value database libraryJavaScript DBMS with an API inspired by CouchDBOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark
Primary database modelRelational DBMSTime Series DBMSKey-value storeDocument storeRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.34
Rank#283  Overall
#130  Relational DBMS
Score2.09
Rank#121  Overall
#20  Key-value stores
Score2.34
Rank#112  Overall
#21  Document stores
Score0.54
Rank#252  Overall
#115  Relational DBMS
Websitegithub.com/­datafuselabs/­databend
www.databend.com
www.circonus.com/solutions/time-series-database/www.symas.com/­symas-embedded-database-lmdbpouchdb.comsplicemachine.com
Technical documentationdocs.databend.comdocs.circonus.com/irondb/category/getting-startedwww.lmdb.tech/­docpouchdb.com/­guidessplicemachine.com/­how-it-works
DeveloperDatabend LabsCirconus LLC.SymasApache Software FoundationSplice Machine
Initial release20212017201120122014
Current release1.0.59, April 2023V0.10.20, January 20180.9.32, January 20247.1.1, June 20193.1, March 2021
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialOpen SourceOpen SourceOpen Source infoAGPL 3.0, commercial license available
Cloud-based only infoOnly available as a cloud servicenonononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageRustC and C++CJavaScriptJava
Server operating systemshosted
Linux
macOS
LinuxLinux
Unix
Windows
server-less, requires a JavaScript environment (browser, Node.js)Linux
OS X
Solaris
Windows
Data schemeyesschema-freeschema-freeschema-freeyes
Typing infopredefined data types such as float or dateyesyes infotext, numeric, histogramsnoyes
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 indexesnononoyes infovia viewsyes
SQL infoSupport of SQLyesSQL-like query language (Circonus Analytics Query Language: CAQL)nonoyes
APIs and other access methodsCLI Client
JDBC
RESTful HTTP API
HTTP APIHTTP REST infoonly for PouchDB Server
JavaScript API
JDBC
Native Spark Datasource
ODBC
Supported programming languagesGo
Java
JavaScript (Node.js)
Python
Rust
.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala
.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
JavaScriptC#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Server-side scripts infoStored proceduresnoyes, in LuanoView functions in JavaScriptyes infoJava
Triggersnononoyesyes
Partitioning methods infoMethods for storing different data on different nodesnoneAutomatic, metric affinity per nodenoneSharding infowith a proxy-based framework, named couchdb-loungeShared Nothhing Auto-Sharding, Columnar Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesnoneconfigurable replication factor, datacenter awarenoneMulti-source replication infoalso with CouchDB databases
Source-replica replication infoalso with CouchDB databases
Multi-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnononoyesYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate consistency per node, eventual consistency across nodesImmediate ConsistencyEventual ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonononoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datayesnoACIDnoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes, multi-version concurrency control (MVCC)
Durability infoSupport for making data persistentyesyesyesyes infoby using IndexedDB, WebSQL or LevelDB as backendyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesyesyes
User concepts infoAccess controlUsers with fine-grained authorization concept, user rolesnononoAccess rights for users, groups and roles according to SQL-standard

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More resources
DatabendIRONdbLMDBPouchDBSplice Machine
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