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DBMS > atoti vs. Faircom DB vs. Google Cloud Bigtable vs. HugeGraph

System Properties Comparison atoti vs. Faircom DB vs. Google Cloud Bigtable vs. HugeGraph

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Editorial information provided by DB-Engines
Nameatoti  Xexclude from comparisonFaircom DB infoformerly c-treeACE  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonHugeGraph  Xexclude from comparison
DescriptionAn in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.Native high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.A fast-speed and highly-scalable Graph DBMS
Primary database modelObject oriented DBMSKey-value store
Relational DBMS
Key-value store
Wide column store
Graph DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.56
Rank#245  Overall
#10  Object oriented DBMS
Score0.20
Rank#318  Overall
#48  Key-value stores
#141  Relational DBMS
Score3.26
Rank#92  Overall
#13  Key-value stores
#8  Wide column stores
Score0.13
Rank#336  Overall
#32  Graph DBMS
Websiteatoti.iowww.faircom.com/­products/­faircom-dbcloud.google.com/­bigtablegithub.com/­hugegraph
hugegraph.apache.org
Technical documentationdocs.atoti.iodocs.faircom.com/­docs/­en/­UUID-7446ae34-a1a7-c843-c894-d5322e395184.htmlcloud.google.com/­bigtable/­docshugegraph.apache.org/­docs
DeveloperActiveViamFairCom CorporationGoogleBaidu
Initial release197920152018
Current releaseV12, November 20200.9
License infoCommercial or Open Sourcecommercial infofree versions availablecommercial infoRestricted, free version availablecommercialOpen Source infoApache Version 2.0
Cloud-based only infoOnly available as a cloud servicenonoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaANSI C, C++Java
Server operating systemsAIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows infoeasily portable to other OSs
hostedLinux
macOS
Unix
Data schemeschema free, schema optional, schema required, partial schema,schema-freeyes
Typing infopredefined data types such as float or dateyes, ANSI SQL Types, JSON, typed binary structuresnoyes
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.nonono
Secondary indexesyesnoyes infoalso supports composite index and range index
SQL infoSupport of SQLMultidimensional Expressions (MDX)yes, ANSI SQL with proprietary extensionsnono
APIs and other access methodsADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC
gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
Java API
RESTful HTTP API
TinkerPop Gremlin
Supported programming languages.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic
C#
C++
Go
Java
JavaScript (Node.js)
Python
Groovy
Java
Python
Server-side scripts infoStored proceduresPythonyes info.Net, JavaScript, C/C++noasynchronous Gremlin script jobs
Triggersyesnono
Partitioning methods infoMethods for storing different data on different nodesSharding, horizontal partitioningFile 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 nodesyes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).Internal replication in Colossus, and regional replication between two clusters in different zonesyes 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 systemEventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction
Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)Eventual Consistency
Foreign keys infoReferential integrityyesnoyes infoedges in graph
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datatunable from ACID to Eventually ConsistentAtomic single-row operationsACID
Concurrency infoSupport for concurrent manipulation of datayes, multi-version concurrency control (MVCC)yesyesyes
Durability infoSupport for making data persistentYes, 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.yesyesnoyes
User concepts infoAccess controlFine grained access rights according to SQL-standard with additional protections for filesAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Users, roles and permissions

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More resources
atotiFaircom DB infoformerly c-treeACEGoogle Cloud BigtableHugeGraph
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