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DBMS > Databricks vs. HugeGraph vs. Sphinx vs. VoltDB

System Properties Comparison Databricks vs. HugeGraph vs. Sphinx vs. VoltDB

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Editorial information provided by DB-Engines
NameDatabricks  Xexclude from comparisonHugeGraph  Xexclude from comparisonSphinx  Xexclude from comparisonVoltDB  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 fast-speed and highly-scalable Graph DBMSOpen source search engine for searching in data from different sources, e.g. relational databasesDistributed In-Memory NewSQL RDBMS infoUsed for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory
Primary database modelDocument store
Relational DBMS
Graph DBMSSearch engineRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score78.61
Rank#15  Overall
#2  Document stores
#10  Relational DBMS
Score0.13
Rank#336  Overall
#32  Graph DBMS
Score5.98
Rank#56  Overall
#5  Search engines
Score1.44
Rank#158  Overall
#73  Relational DBMS
Websitewww.databricks.comgithub.com/­hugegraph
hugegraph.apache.org
sphinxsearch.comwww.voltdb.com
Technical documentationdocs.databricks.comhugegraph.apache.org/­docssphinxsearch.com/­docsdocs.voltdb.com
DeveloperDatabricksBaiduSphinx Technologies Inc.VoltDB Inc.
Initial release2013201820012010
Current release0.93.5.1, February 202311.3, April 2022
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2.0Open Source infoGPL version 2, commercial licence availableOpen Source infoAGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC++Java, C++
Server operating systemshostedLinux
macOS
Unix
FreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Linux
OS X infofor development
Data schemeFlexible Schema (defined schema, partial schema, schema free)yesyesyes
Typing infopredefined data types such as float or dateyesnoyes
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.yesno
Secondary indexesyesyes infoalso supports composite index and range indexyes infofull-text index on all search fieldsyes
SQL infoSupport of SQLwith Databricks SQLnoSQL-like query language (SphinxQL)yes infoonly a subset of SQL 99
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
Java API
RESTful HTTP API
TinkerPop Gremlin
Proprietary protocolJava API
JDBC
RESTful HTTP/JSON API
Supported programming languagesPython
R
Scala
Groovy
Java
Python
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
C#
C++
Erlang infonot officially supported
Go
Java
JavaScript infoNode.js
PHP
Python
Server-side scripts infoStored proceduresuser defined functions and aggregatesasynchronous Gremlin script jobsnoJava
Triggersnonono
Partitioning methods infoMethods for storing different data on different nodesyes infodepending on used storage backend, e.g. Cassandra and HBaseSharding infoPartitioning is done manually, search queries against distributed index is supportedSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyesyes infodepending on used storage backend, e.g. Cassandra and HBasenoneMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsvia hugegraph-sparknono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency
Foreign keys infoReferential integrityyes infoedges in graphnono infoFOREIGN KEY constraints are not supported
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDnoACID infoTransactions are executed single-threaded within stored procedures
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes infoData access is serialized by the server
Durability infoSupport for making data persistentyesyesyes infoThe original contents of fields are not stored in the Sphinx index.yes infoSnapshots and command logging
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes
User concepts infoAccess controlUsers, roles and permissionsnoUsers and roles with access to stored procedures
More information provided by the system vendor
DatabricksHugeGraphSphinxVoltDB
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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