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DBMS > EsgynDB vs. Google Cloud Bigtable vs. GridGain vs. Hive vs. HugeGraph

System Properties Comparison EsgynDB vs. Google Cloud Bigtable vs. GridGain vs. Hive vs. HugeGraph

Editorial information provided by DB-Engines
NameEsgynDB  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonGridGain  Xexclude from comparisonHive  Xexclude from comparisonHugeGraph  Xexclude from comparison
DescriptionEnterprise-class SQL-on-Hadoop solution, powered by Apache TrafodionGoogle's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.GridGain is an in-memory computing platform, built on Apache Ignitedata warehouse software for querying and managing large distributed datasets, built on HadoopA fast-speed and highly-scalable Graph DBMS
Primary database modelRelational DBMSKey-value store
Wide column store
Key-value store
Relational DBMS
Relational DBMSGraph DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.23
Rank#319  Overall
#141  Relational DBMS
Score3.58
Rank#92  Overall
#14  Key-value stores
#8  Wide column stores
Score1.53
Rank#155  Overall
#26  Key-value stores
#73  Relational DBMS
Score62.59
Rank#18  Overall
#12  Relational DBMS
Score0.15
Rank#339  Overall
#33  Graph DBMS
Websitewww.esgyn.cncloud.google.com/­bigtablewww.gridgain.comhive.apache.orggithub.com/­hugegraph
hugegraph.apache.org
Technical documentationcloud.google.com/­bigtable/­docswww.gridgain.com/­docs/­index.htmlcwiki.apache.org/­confluence/­display/­Hive/­Homehugegraph.apache.org/­docs
DeveloperEsgynGoogleGridGain Systems, Inc.Apache Software Foundation infoinitially developed by FacebookBaidu
Initial release20152015200720122018
Current releaseGridGain 8.5.13.1.3, April 20220.9
License infoCommercial or Open SourcecommercialcommercialcommercialOpen Source infoApache Version 2Open Source infoApache Version 2.0
Cloud-based only infoOnly available as a cloud servicenoyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++, JavaJava, C++, .NetJavaJava
Server operating systemsLinuxhostedLinux
OS X
Solaris
Windows
All OS with a Java VMLinux
macOS
Unix
Data schemeyesschema-freeyesyesyes
Typing infopredefined data types such as float or dateyesnoyesyesyes
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.nonoyesno
Secondary indexesyesnoyesyesyes infoalso supports composite index and range index
SQL infoSupport of SQLyesnoANSI-99 for query and DML statements, subset of DDLSQL-like DML and DDL statementsno
APIs and other access methodsADO.NET
JDBC
ODBC
gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data
JDBC
ODBC
Thrift
Java API
RESTful HTTP API
TinkerPop Gremlin
Supported programming languagesAll languages supporting JDBC/ODBC/ADO.NetC#
C++
Go
Java
JavaScript (Node.js)
Python
C#
C++
Java
PHP
Python
Ruby
Scala
C++
Java
PHP
Python
Groovy
Java
Python
Server-side scripts infoStored proceduresJava Stored Proceduresnoyes (compute grid and cache interceptors can be used instead)yes infouser defined functions and integration of map-reduceasynchronous Gremlin script jobs
Triggersnonoyes (cache interceptors and events)nono
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardingShardingyes infodepending on used storage backend, e.g. Cassandra and HBase
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication between multi datacentersInternal replication in Colossus, and regional replication between two clusters in different zonesyes (replicated cache)selectable replication factoryes infodepending on used storage backend, e.g. Cassandra and HBase
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesyesyes (compute grid and hadoop accelerator)yes infoquery execution via MapReducevia hugegraph-spark
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)Immediate ConsistencyEventual ConsistencyEventual Consistency
Foreign keys infoReferential integrityyesnononoyes infoedges in graph
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDAtomic single-row operationsACIDnoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyesyes
User concepts infoAccess controlfine grained access rights according to SQL-standardAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Security Hooks for custom implementationsAccess rights for users, groups and rolesUsers, roles and permissions

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More resources
EsgynDBGoogle Cloud BigtableGridGainHiveHugeGraph
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