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DBMS > HugeGraph vs. NSDb vs. Tarantool vs. TimesTen

System Properties Comparison HugeGraph vs. NSDb vs. Tarantool vs. TimesTen

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Editorial information provided by DB-Engines
NameHugeGraph  Xexclude from comparisonNSDb  Xexclude from comparisonTarantool  Xexclude from comparisonTimesTen  Xexclude from comparison
DescriptionA fast-speed and highly-scalable Graph DBMSScalable, High-performance Time Series DBMS designed for Real-time Analytics on top of KubernetesIn-memory computing platform with a flexible data schema for efficiently building high-performance applicationsIn-Memory RDBMS compatible to Oracle
Primary database modelGraph DBMSTime Series DBMSDocument store
Key-value store
Relational DBMS
Relational DBMS
Secondary database modelsSpatial DBMS infowith Tarantool/GIS extension
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.17
Rank#335  Overall
#31  Graph DBMS
Score0.08
Rank#369  Overall
#40  Time Series DBMS
Score1.67
Rank#143  Overall
#25  Document stores
#25  Key-value stores
#65  Relational DBMS
Score1.36
Rank#161  Overall
#75  Relational DBMS
Websitegithub.com/­hugegraph
hugegraph.apache.org
nsdb.iowww.tarantool.iowww.oracle.com/­database/­technologies/­related/­timesten.html
Technical documentationhugegraph.apache.org/­docsnsdb.io/­Architecturewww.tarantool.io/­en/­docdocs.oracle.com/­database/­timesten-18.1
DeveloperBaiduVKOracle, TimesTen Performance Software, HP infooriginally founded in HP Labs it was acquired by Oracle in 2005
Initial release2018201720081998
Current release0.92.10.0, May 202211 Release 2 (11.2.2.8.0)
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoApache Version 2.0Open Source infoBSD-2, source-available extensions (modules), commercial licenses for Tarantool Enterprisecommercial
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaJava, ScalaC and C++
Server operating systemsLinux
macOS
Unix
Linux
macOS
BSD
Linux
macOS
AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows
Data schemeyesFlexible data schema: relational definition for tables with ability to store json-like documents in columnsyes
Typing infopredefined data types such as float or dateyesyes: int, bigint, decimal, stringstring, double, decimal, uuid, integer, blob, boolean, datetimeyes
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 indexesyes infoalso supports composite index and range indexall fields are automatically indexedyesyes
SQL infoSupport of SQLnoSQL-like query languageFull-featured ANSI SQL supportyes
APIs and other access methodsJava API
RESTful HTTP API
TinkerPop Gremlin
gRPC
HTTP REST
WebSocket
Open binary protocolJDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)
Supported programming languagesGroovy
Java
Python
Java
Scala
C
C#
C++
Erlang
Go
Java
JavaScript
Lua
Perl
PHP
Python
Rust
C
C++
Java
PL/SQL
Server-side scripts infoStored proceduresasynchronous Gremlin script jobsnoLua, C and SQL stored proceduresPL/SQL
Triggersnoyes, before/after data modification events, on replication events, client session eventsno
Partitioning methods infoMethods for storing different data on different nodesyes infodepending on used storage backend, e.g. Cassandra and HBaseShardingSharding, partitioned with virtual buckets by user defined affinity key. Live resharding for scale up and scale down without maintenance downtime.none
Replication methods infoMethods for redundantly storing data on multiple nodesyes infodepending on used storage backend, e.g. Cassandra and HBaseAsynchronous replication with multi-master option
Configurable replication topology (full-mesh, chain, star)
Synchronous quorum replication (with Raft)
Multi-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 systemEventual ConsistencyEventual ConsistencyCasual consistency across sharding partitions
Eventual consistency within replicaset partition infowhen using asyncronous replication
Immediate Consistency within single instance
Sequential consistency including linearizable read within replicaset partition infowhen using Raft
Immediate Consistency or Eventual Consistency depending on configuration
Foreign keys infoReferential integrityyes infoedges in graphnoyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnoACID, with serializable isolation and linearizable read (within partition); Configurable MVCC (within partition); No cross-shard distributed transactionsACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes, cooperative multitaskingyes
Durability infoSupport for making data persistentyesUsing Apache Luceneyes, write ahead loggingyes infoby means of logfiles and checkpoints
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyes, full featured in-memory storage engine with persistenceyes
User concepts infoAccess controlUsers, roles and permissionsAccess Control Lists
Mutual TLS authentication for Tarantol Enterprise
Password based authentication
Role-based access control (RBAC) and LDAP for Tarantol Enterprise
Users and Roles
fine grained access rights according to SQL-standard

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More resources
HugeGraphNSDbTarantoolTimesTen
DB-Engines blog posts

Data processing speed and reliability: in-memory synchronous replication
9 November 2021,  Vladimir Perepelytsya, Tarantool (sponsor) 

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Recent citations in the news

POC exploit code published for 9.8-rated Apache HugeGraph RCE flaw
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