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DBMS > Datomic vs. Faircom DB vs. Hazelcast vs. HugeGraph

System Properties Comparison Datomic vs. Faircom DB vs. Hazelcast vs. HugeGraph

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Editorial information provided by DB-Engines
NameDatomic  Xexclude from comparisonFaircom DB infoformerly c-treeACE  Xexclude from comparisonHazelcast  Xexclude from comparisonHugeGraph  Xexclude from comparison
DescriptionDatomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durabilityNative high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.A widely adopted in-memory data gridA fast-speed and highly-scalable Graph DBMS
Primary database modelRelational DBMSKey-value store
Relational DBMS
Key-value storeGraph DBMS
Secondary database modelsDocument store infoJSON support with IMDG 3.12
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.66
Rank#144  Overall
#66  Relational DBMS
Score0.29
Rank#304  Overall
#43  Key-value stores
#136  Relational DBMS
Score5.46
Rank#61  Overall
#7  Key-value stores
Score0.17
Rank#335  Overall
#31  Graph DBMS
Websitewww.datomic.comwww.faircom.com/­products/­faircom-dbhazelcast.comgithub.com/­hugegraph
hugegraph.apache.org
Technical documentationdocs.datomic.comdocs.faircom.com/­docs/­en/­UUID-7446ae34-a1a7-c843-c894-d5322e395184.htmlhazelcast.org/­imdg/­docshugegraph.apache.org/­docs
DeveloperCognitectFairCom CorporationHazelcastBaidu
Initial release2012197920082018
Current release1.0.7075, December 2023V12, November 20205.3.6, November 20230.9
License infoCommercial or Open Sourcecommercial infolimited edition freecommercial infoRestricted, free version availableOpen Source infoApache Version 2; commercial licenses availableOpen Source infoApache Version 2.0
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJava, ClojureANSI C, C++JavaJava
Server operating systemsAll OS with a Java VMAIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows infoeasily portable to other OSs
All OS with a Java VMLinux
macOS
Unix
Data schemeyesschema free, schema optional, schema required, partial schema,schema-freeyes
Typing infopredefined data types such as float or dateyesyes, ANSI SQL Types, JSON, typed binary structuresyesyes
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.nonoyes infothe object must implement a serialization strategyno
Secondary indexesyesyesyesyes infoalso supports composite index and range index
SQL infoSupport of SQLnoyes, ANSI SQL with proprietary extensionsSQL-like query languageno
APIs and other access methodsRESTful HTTP APIADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC
JCache
JPA
Memcached protocol
RESTful HTTP API
Java API
RESTful HTTP API
TinkerPop Gremlin
Supported programming languagesClojure
Java
.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic
.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
Groovy
Java
Python
Server-side scripts infoStored proceduresyes infoTransaction Functionsyes info.Net, JavaScript, C/C++yes infoEvent Listeners, Executor Servicesasynchronous Gremlin script jobs
TriggersBy using transaction functionsyesyes infoEventsno
Partitioning methods infoMethods for storing different data on different nodesnone infoBut extensive use of caching in the application peersFile partitioning, horizontal partitioning, sharding infoCustomizable business rules for table partitioningShardingyes infodepending on used storage backend, e.g. Cassandra and HBase
Replication methods infoMethods for redundantly storing data on multiple nodesnone infoBut extensive use of caching in the application peersyes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).yes infoReplicated Mapyes infodepending on used storage backend, e.g. Cassandra and HBase
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoyesvia hugegraph-spark
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction
Immediate Consistency or Eventual Consistency selectable by user infoRaft Consensus AlgorithmEventual Consistency
Foreign keys infoReferential integritynoyesnoyes infoedges in graph
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDtunable from ACID to Eventually Consistentone or two-phase-commit; repeatable reads; read commitedACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyes infousing external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)Yes, tunable from durable to delayed durability to in-memoryyesyes
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 developmentyesyesyes
User concepts infoAccess controlnoFine grained access rights according to SQL-standard with additional protections for filesRole-based access controlUsers, roles and permissions

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More resources
DatomicFaircom DB infoformerly c-treeACEHazelcastHugeGraph
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