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DBMS > Apache Impala vs. Atos Standard Common Repository vs. Faircom DB vs. HugeGraph vs. Microsoft Azure AI Search

System Properties Comparison Apache Impala vs. Atos Standard Common Repository vs. Faircom DB vs. HugeGraph vs. Microsoft Azure AI Search

Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonAtos Standard Common Repository  Xexclude from comparisonFaircom DB infoformerly c-treeACE  Xexclude from comparisonHugeGraph  Xexclude from comparisonMicrosoft Azure AI Search  Xexclude from comparison
This system has been discontinued and will be removed from the DB-Engines ranking.
DescriptionAnalytic DBMS for HadoopHighly scalable database system, designed for managing session and subscriber data in modern mobile communication networksNative high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.A fast-speed and highly-scalable Graph DBMSSearch-as-a-service for web and mobile app development
Primary database modelRelational DBMSDocument store
Key-value store
Key-value store
Relational DBMS
Graph DBMSSearch engine
Secondary database modelsDocument storeVector DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score13.77
Rank#40  Overall
#24  Relational DBMS
Score0.20
Rank#318  Overall
#48  Key-value stores
#141  Relational DBMS
Score0.13
Rank#336  Overall
#32  Graph DBMS
Score5.59
Rank#63  Overall
#7  Search engines
Websiteimpala.apache.orgatos.net/en/convergence-creators/portfolio/standard-common-repositorywww.faircom.com/­products/­faircom-dbgithub.com/­hugegraph
hugegraph.apache.org
azure.microsoft.com/­en-us/­services/­search
Technical documentationimpala.apache.org/­impala-docs.htmldocs.faircom.com/­docs/­en/­UUID-7446ae34-a1a7-c843-c894-d5322e395184.htmlhugegraph.apache.org/­docslearn.microsoft.com/­en-us/­azure/­search
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaAtos Convergence CreatorsFairCom CorporationBaiduMicrosoft
Initial release20132016197920182015
Current release4.1.0, June 20221703V12, November 20200.9V1
License infoCommercial or Open SourceOpen Source infoApache Version 2commercialcommercial infoRestricted, free version availableOpen Source infoApache Version 2.0commercial
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 languageC++JavaANSI C, C++Java
Server operating systemsLinuxLinuxAIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows infoeasily portable to other OSs
Linux
macOS
Unix
hosted
Data schemeyesSchema and schema-less with LDAP viewsschema free, schema optional, schema required, partial schema,yesyes
Typing infopredefined data types such as float or dateyesoptionalyes, 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.noyesnonono
Secondary indexesyesyesyesyes infoalso supports composite index and range indexyes
SQL infoSupport of SQLSQL-like DML and DDL statementsnoyes, ANSI SQL with proprietary extensionsnono
APIs and other access methodsJDBC
ODBC
LDAPADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC
Java API
RESTful HTTP API
TinkerPop Gremlin
RESTful HTTP API
Supported programming languagesAll languages supporting JDBC/ODBCAll languages with LDAP bindings.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic
Groovy
Java
Python
C#
Java
JavaScript
Python
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reducenoyes info.Net, JavaScript, C/C++asynchronous Gremlin script jobsno
Triggersnoyesyesnono
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infocell divisionFile partitioning, horizontal partitioning, sharding infoCustomizable business rules for table partitioningyes infodepending on used storage backend, e.g. Cassandra and HBaseSharding infoImplicit feature of the cloud service
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factoryesyes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).yes infodepending on used storage backend, e.g. Cassandra and HBaseyes infoImplicit feature of the cloud service
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReducenovia hugegraph-sparkno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate Consistency or Eventual Consistency depending on configurationEventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction
Eventual ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonoyesyes infoedges in graphno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoAtomic execution of specific operationstunable from ACID to Eventually ConsistentACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesYes, 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.noyesyesyesno
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and KerberosLDAP bind authenticationFine grained access rights according to SQL-standard with additional protections for filesUsers, roles and permissionsyes infousing Azure authentication

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More resources
Apache ImpalaAtos Standard Common RepositoryFaircom DB infoformerly c-treeACEHugeGraphMicrosoft Azure AI Search
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