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DBMS > EJDB vs. Google Cloud Bigtable vs. GridGain vs. HarperDB

System Properties Comparison EJDB vs. Google Cloud Bigtable vs. GridGain vs. HarperDB

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Editorial information provided by DB-Engines
NameEJDB  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonGridGain  Xexclude from comparisonHarperDB  Xexclude from comparison
DescriptionEmbeddable document-store database library with JSON representation of queries (in MongoDB style)Google'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 IgniteHarperDB takes the "stack" out of "tech stack" by combining an ultra-fast document-style data store, in-memory cache, real-time message broker, and your application components into a single distributed technology.
Primary database modelDocument storeKey-value store
Wide column store
Columnar
Key-value store
Object oriented DBMS
Relational DBMS
Document store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.13
Rank#331  Overall
#46  Document stores
Score2.97
Rank#92  Overall
#15  Key-value stores
#8  Wide column stores
Score1.48
Rank#150  Overall
#1  Columnar
#26  Key-value stores
#2  Object oriented DBMS
#69  Relational DBMS
Score0.55
Rank#243  Overall
#38  Document stores
Websitegithub.com/­Softmotions/­ejdbcloud.google.com/­bigtablewww.gridgain.comwww.harperdb.io
Technical documentationgithub.com/­Softmotions/­ejdb/­blob/­master/­README.mdcloud.google.com/­bigtable/­docswww.gridgain.com/­docs/­index.htmldocs.harperdb.io/­docs
DeveloperSoftmotionsGoogleGridGain Systems, Inc.HarperDB
Initial release2012201520072017
Current releaseGridGain 8.5.13.1, August 2021
License infoCommercial or Open SourceOpen Source infoGPLv2commercialcommercial, open sourcecommercial infofree community edition available
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageCJava, C++, .Net, Python, REST, SQLNode.js
Server operating systemsserver-lesshostedLinux
OS X
Solaris
Windows
z/OS
Linux
OS X
Data schemeschema-freeschema-freeyesdynamic schema
Typing infopredefined data types such as float or dateyes infostring, integer, double, bool, date, object_idnoyesyes infoJSON data types
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.noyesno
Secondary indexesnonoyesyes
SQL infoSupport of SQLnonoANSI-99 for query and DML statements, subset of DDLSQL-like data manipulation statements
APIs and other access methodsin-process shared librarygRPC (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
React Hooks
RESTful HTTP/JSON API
WebSocket
Supported programming languagesActionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby
C#
C++
Go
Java
JavaScript (Node.js)
Python
C#
C++
Java
PHP
Python
Ruby
Scala
.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift
Server-side scripts infoStored proceduresnonoyes (compute grid and cache interceptors can be used instead)Custom Functions infosince release 3.1
Triggersnonoyes (cache interceptors and events)no
Partitioning methods infoMethods for storing different data on different nodesnoneShardingShardingA table resides as a whole on one (or more) nodes in a cluster
Replication methods infoMethods for redundantly storing data on multiple nodesnoneInternal replication in Colossus, and regional replication between two clusters in different zonesyes (replicated cache)yes infothe nodes on which a table resides can be defined
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyesyes (compute grid and hadoop accelerator)no
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)Immediate ConsistencyImmediate Consistency
Foreign keys infoReferential integrityno infotypically not needed, however similar functionality with collection joins possiblenonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoAtomic single-row operationsACIDAtomic execution of specific operations
Concurrency infoSupport for concurrent manipulation of datayes infoRead/Write Lockingyesyesyes
Durability infoSupport for making data persistentyesyesyesyes, using LMDB
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesyes
User concepts infoAccess controlnoAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Role-based access control
Security Hooks for custom implementations
Access rights for users and roles

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More resources
EJDBGoogle Cloud BigtableGridGainHarperDB
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