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DBMS > Databricks vs. LeanXcale vs. NSDb vs. Tkrzw

System Properties Comparison Databricks vs. LeanXcale vs. NSDb vs. Tkrzw

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Editorial information provided by DB-Engines
NameDatabricks  Xexclude from comparisonLeanXcale  Xexclude from comparisonNSDb  Xexclude from comparisonTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet  Xexclude from comparison
DescriptionThe Databricks Lakehouse Platform combines elements of data lakes and data warehouses to provide a unified view onto structured and unstructured data. It is based on Apache Spark.A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilitiesScalable, High-performance Time Series DBMS designed for Real-time Analytics on top of KubernetesA concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet
Primary database modelDocument store
Relational DBMS
Key-value store
Relational DBMS
Time Series DBMSKey-value store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score81.08
Rank#15  Overall
#2  Document stores
#10  Relational DBMS
Score0.36
Rank#280  Overall
#40  Key-value stores
#129  Relational DBMS
Score0.08
Rank#369  Overall
#40  Time Series DBMS
Score0.07
Rank#372  Overall
#57  Key-value stores
Websitewww.databricks.comwww.leanxcale.comnsdb.iodbmx.net/­tkrzw
Technical documentationdocs.databricks.comnsdb.io/­Architecture
DeveloperDatabricksLeanXcaleMikio Hirabayashi
Initial release2013201520172020
Current release0.9.3, August 2020
License infoCommercial or Open SourcecommercialcommercialOpen Source infoApache Version 2.0Open Source infoApache Version 2.0
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.
Implementation languageJava, ScalaC++
Server operating systemshostedLinux
macOS
Linux
macOS
Data schemeFlexible Schema (defined schema, partial schema, schema free)yesschema-free
Typing infopredefined data types such as float or dateyes: int, bigint, decimal, stringno
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.yesnono
Secondary indexesyesall fields are automatically indexed
SQL infoSupport of SQLwith Databricks SQLyes infothrough Apache DerbySQL-like query languageno
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector
gRPC
HTTP REST
WebSocket
Supported programming languagesPython
R
Scala
C
Java
Scala
Java
Scala
C++
Java
Python
Ruby
Server-side scripts infoStored proceduresuser defined functions and aggregatesnono
Triggersno
Partitioning methods infoMethods for storing different data on different nodesShardingnone
Replication methods infoMethods for redundantly storing data on multiple nodesyesnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyEventual ConsistencyImmediate Consistency
Foreign keys infoReferential integrityyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesUsing Apache Luceneyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesyes infousing specific database classes
User concepts infoAccess controlno
More information provided by the system vendor
DatabricksLeanXcaleNSDbTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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More resources
DatabricksLeanXcaleNSDbTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet
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