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DBMS > Faircom DB vs. GBase vs. Hazelcast vs. IBM Db2 Event Store vs. Microsoft Azure Data Explorer

System Properties Comparison Faircom DB vs. GBase vs. Hazelcast vs. IBM Db2 Event Store vs. Microsoft Azure Data Explorer

Editorial information provided by DB-Engines
NameFaircom DB infoformerly c-treeACE  Xexclude from comparisonGBase  Xexclude from comparisonHazelcast  Xexclude from comparisonIBM Db2 Event Store  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparison
DescriptionNative high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.Widely used RDBMS in China, including analytical, transactional, distributed transactional, and cloud-native data warehousing.A widely adopted in-memory data gridDistributed Event Store optimized for Internet of Things use casesFully managed big data interactive analytics platform
Primary database modelKey-value store
Relational DBMS
Relational DBMSKey-value storeEvent Store
Time Series DBMS
Relational DBMS infocolumn oriented
Secondary database modelsDocument store infoJSON support with IMDG 3.12Document store infoIf a column is of type dynamic docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-types/­dynamic then it's possible to add arbitrary JSON documents in this cell
Event Store infothis is the general usage pattern at Microsoft. Billing, Logs, Telemetry events are stored in ADX and the state of an individual entity is defined by the arg_max(timestamps)
Spatial DBMS
Search engine infosupport for complex search expressions docs.microsoft.com/­en-us/­azure/­kusto/­query/­parseoperator FTS, Geospatial docs.microsoft.com/­en-us/­azure/­kusto/­query/­geo-point-to-geohash-function distributed search -> ADX acts as a distributed search engine
Time Series DBMS infosee docs.microsoft.com/­en-us/­azure/­data-explorer/­time-series-analysis
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.20
Rank#318  Overall
#48  Key-value stores
#141  Relational DBMS
Score1.07
Rank#185  Overall
#86  Relational DBMS
Score5.97
Rank#57  Overall
#6  Key-value stores
Score0.19
Rank#323  Overall
#2  Event Stores
#28  Time Series DBMS
Score4.38
Rank#77  Overall
#41  Relational DBMS
Websitewww.faircom.com/­products/­faircom-dbwww.gbase.cnhazelcast.comwww.ibm.com/­products/­db2-event-storeazure.microsoft.com/­services/­data-explorer
Technical documentationdocs.faircom.com/­docs/­en/­UUID-7446ae34-a1a7-c843-c894-d5322e395184.htmlhazelcast.org/­imdg/­docswww.ibm.com/­docs/­en/­db2-event-storedocs.microsoft.com/­en-us/­azure/­data-explorer
DeveloperFairCom CorporationGeneral Data Technology Co., Ltd.HazelcastIBMMicrosoft
Initial release19792004200820172019
Current releaseV12, November 2020GBase 8a, GBase 8s, GBase 8c5.3.6, November 20232.0cloud service with continuous releases
License infoCommercial or Open Sourcecommercial infoRestricted, free version availablecommercialOpen Source infoApache Version 2; commercial licenses availablecommercial infofree developer edition availablecommercial
Cloud-based only infoOnly available as a cloud servicenonononoyes
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageANSI C, C++C, Java, PythonJavaC and C++
Server operating systemsAIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows infoeasily portable to other OSs
LinuxAll OS with a Java VMLinux infoLinux, macOS, Windows for the developer additionhosted
Data schemeschema free, schema optional, schema required, partial schema,yesschema-freeyesFixed schema with schema-less datatypes (dynamic)
Typing infopredefined data types such as float or dateyes, ANSI SQL Types, JSON, typed binary structuresyesyesyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-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.noyesyes infothe object must implement a serialization strategynoyes
Secondary indexesyesyesyesnoall fields are automatically indexed
SQL infoSupport of SQLyes, ANSI SQL with proprietary extensionsStandard with numerous extensionsSQL-like query languageyes infothrough the embedded Spark runtimeKusto Query Language (KQL), SQL subset
APIs and other access methodsADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC
ADO.NET
C API
JDBC
ODBC
JCache
JPA
Memcached protocol
RESTful HTTP API
ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Supported programming languages.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic
C#.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
Server-side scripts infoStored proceduresyes info.Net, JavaScript, C/C++user defined functionsyes infoEvent Listeners, Executor ServicesyesYes, possible languages: KQL, Python, R
Triggersyesyesyes infoEventsnoyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicy
Partitioning methods infoMethods for storing different data on different nodesFile partitioning, horizontal partitioning, sharding infoCustomizable business rules for table partitioninghorizontal partitioning (by range, list and hash) and vertical partitioningShardingShardingSharding infoImplicit feature of the cloud service
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).yesyes infoReplicated MapActive-active shard replicationyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyesnoSpark connector (open source): github.com/­Azure/­azure-kusto-spark
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction
Immediate ConsistencyImmediate Consistency or Eventual Consistency selectable by user infoRaft Consensus AlgorithmEventual ConsistencyEventual Consistency
Immediate Consistency
Foreign keys infoReferential integrityyesyesnonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datatunable from ACID to Eventually ConsistentACIDone or two-phase-commit; repeatable reads; read commitednono
Concurrency infoSupport for concurrent manipulation of datayesyesyesNo - written data is immutableyes
Durability infoSupport for making data persistentYes, tunable from durable to delayed durability to in-memoryyesyesYes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storageyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyesyesno
User concepts infoAccess controlFine grained access rights according to SQL-standard with additional protections for filesyesRole-based access controlfine grained access rights according to SQL-standardAzure Active Directory Authentication

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More resources
Faircom DB infoformerly c-treeACEGBaseHazelcastIBM Db2 Event StoreMicrosoft Azure Data Explorer
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