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DBMS > Datomic vs. HugeGraph vs. LevelDB vs. Tarantool

System Properties Comparison Datomic vs. HugeGraph vs. LevelDB vs. Tarantool

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Editorial information provided by DB-Engines
NameDatomic  Xexclude from comparisonHugeGraph  Xexclude from comparisonLevelDB  Xexclude from comparisonTarantool  Xexclude from comparison
DescriptionDatomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durabilityA fast-speed and highly-scalable Graph DBMSEmbeddable fast key-value storage library that provides an ordered mapping from string keys to string valuesIn-memory computing platform with a flexible data schema for efficiently building high-performance applications
Primary database modelRelational DBMSGraph DBMSKey-value storeDocument store
Key-value store
Relational DBMS
Secondary database modelsSpatial DBMS infowith Tarantool/GIS extension
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.66
Rank#144  Overall
#66  Relational DBMS
Score0.17
Rank#335  Overall
#31  Graph DBMS
Score2.25
Rank#115  Overall
#19  Key-value stores
Score1.67
Rank#143  Overall
#25  Document stores
#25  Key-value stores
#65  Relational DBMS
Websitewww.datomic.comgithub.com/­hugegraph
hugegraph.apache.org
github.com/­google/­leveldbwww.tarantool.io
Technical documentationdocs.datomic.comhugegraph.apache.org/­docsgithub.com/­google/­leveldb/­blob/­main/­doc/­index.mdwww.tarantool.io/­en/­doc
DeveloperCognitectBaiduGoogleVK
Initial release2012201820112008
Current release1.0.7075, December 20230.91.23, February 20212.10.0, May 2022
License infoCommercial or Open Sourcecommercial infolimited edition freeOpen Source infoApache Version 2.0Open Source infoBSDOpen Source infoBSD-2, source-available extensions (modules), commercial licenses for Tarantool Enterprise
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJava, ClojureJavaC++C and C++
Server operating systemsAll OS with a Java VMLinux
macOS
Unix
Illumos
Linux
OS X
Windows
BSD
Linux
macOS
Data schemeyesyesschema-freeFlexible data schema: relational definition for tables with ability to store json-like documents in columns
Typing infopredefined data types such as float or dateyesyesnostring, double, decimal, uuid, integer, blob, boolean, datetime
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 indexesyesyes infoalso supports composite index and range indexnoyes
SQL infoSupport of SQLnononoFull-featured ANSI SQL support
APIs and other access methodsRESTful HTTP APIJava API
RESTful HTTP API
TinkerPop Gremlin
Open binary protocol
Supported programming languagesClojure
Java
Groovy
Java
Python
C++
Go
Java info3rd party binding
JavaScript (Node.js) info3rd party binding
Python info3rd party binding
C
C#
C++
Erlang
Go
Java
JavaScript
Lua
Perl
PHP
Python
Rust
Server-side scripts infoStored proceduresyes infoTransaction Functionsasynchronous Gremlin script jobsnoLua, C and SQL stored procedures
TriggersBy using transaction functionsnonoyes, before/after data modification events, on replication events, client session events
Partitioning methods infoMethods for storing different data on different nodesnone infoBut extensive use of caching in the application peersyes infodepending on used storage backend, e.g. Cassandra and HBasenoneSharding, partitioned with virtual buckets by user defined affinity key. Live resharding for scale up and scale down without maintenance downtime.
Replication methods infoMethods for redundantly storing data on multiple nodesnone infoBut extensive use of caching in the application peersyes infodepending on used storage backend, e.g. Cassandra and HBasenoneAsynchronous replication with multi-master option
Configurable replication topology (full-mesh, chain, star)
Synchronous quorum replication (with Raft)
MapReduce infoOffers an API for user-defined Map/Reduce methodsnovia hugegraph-sparkno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual ConsistencyImmediate 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
Foreign keys infoReferential integritynoyes infoedges in graphnoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDnoACID, with serializable isolation and linearizable read (within partition); Configurable MVCC (within partition); No cross-shard distributed transactions
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes, cooperative multitasking
Durability infoSupport for making data persistentyes infousing external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)yesyes infowith automatic compression on writesyes, write ahead logging
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yes inforecommended only for testing and developmentyesyes, full featured in-memory storage engine with persistence
User concepts infoAccess controlnoUsers, roles and permissionsnoAccess Control Lists
Mutual TLS authentication for Tarantol Enterprise
Password based authentication
Role-based access control (RBAC) and LDAP for Tarantol Enterprise
Users and Roles

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

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