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DBMS > Hive vs. Microsoft Azure Table Storage vs. Tarantool vs. TigerGraph

System Properties Comparison Hive vs. Microsoft Azure Table Storage vs. Tarantool vs. TigerGraph

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Editorial information provided by DB-Engines
NameHive  Xexclude from comparisonMicrosoft Azure Table Storage  Xexclude from comparisonTarantool  Xexclude from comparisonTigerGraph  Xexclude from comparison
Descriptiondata warehouse software for querying and managing large distributed datasets, built on HadoopA Wide Column Store for rapid development using massive semi-structured datasetsIn-memory computing platform with a flexible data schema for efficiently building high-performance applicationsA complete, distributed, parallel graph computing platform supporting web-scale data analytics in real-time
Primary database modelRelational DBMSWide column storeDocument store
Key-value store
Relational DBMS
Graph DBMS
Secondary database modelsSpatial DBMS infowith Tarantool/GIS extension
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score59.76
Rank#18  Overall
#12  Relational DBMS
Score4.04
Rank#77  Overall
#6  Wide column stores
Score1.67
Rank#143  Overall
#25  Document stores
#25  Key-value stores
#65  Relational DBMS
Score1.80
Rank#138  Overall
#13  Graph DBMS
Websitehive.apache.orgazure.microsoft.com/­en-us/­services/­storage/­tableswww.tarantool.iowww.tigergraph.com
Technical documentationcwiki.apache.org/­confluence/­display/­Hive/­Homewww.tarantool.io/­en/­docdocs.tigergraph.com
DeveloperApache Software Foundation infoinitially developed by FacebookMicrosoftVK
Initial release2012201220082017
Current release3.1.3, April 20222.10.0, May 2022
License infoCommercial or Open SourceOpen Source infoApache Version 2commercialOpen Source infoBSD-2, source-available extensions (modules), commercial licenses for Tarantool Enterprisecommercial
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC and C++C++
Server operating systemsAll OS with a Java VMhostedBSD
Linux
macOS
Linux
Data schemeyesschema-freeFlexible data schema: relational definition for tables with ability to store json-like documents in columnsyes
Typing infopredefined data types such as float or dateyesyesstring, double, decimal, uuid, integer, blob, boolean, datetimeyes
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
SQL infoSupport of SQLSQL-like DML and DDL statementsnoFull-featured ANSI SQL supportSQL-like query language (GSQL)
APIs and other access methodsJDBC
ODBC
Thrift
RESTful HTTP APIOpen binary protocolGSQL (TigerGraph Query Language)
Kafka
RESTful HTTP/JSON API
Supported programming languagesC++
Java
PHP
Python
.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby
C
C#
C++
Erlang
Go
Java
JavaScript
Lua
Perl
PHP
Python
Rust
C++
Java
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reducenoLua, C and SQL stored proceduresyes
Triggersnonoyes, before/after data modification events, on replication events, client session eventsno
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infoImplicit feature of the cloud serviceSharding, partitioned with virtual buckets by user defined affinity key. Live resharding for scale up and scale down without maintenance downtime.
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factoryes infoimplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.Asynchronous replication with multi-master option
Configurable replication topology (full-mesh, chain, star)
Synchronous quorum replication (with Raft)
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReducenoyes
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate ConsistencyCasual consistency across sharding partitions
Eventual consistency within replicaset partition infowhen using asyncronous replication
Immediate Consistency within single instance
Sequential consistency including linearizable read within replicaset partition infowhen using Raft
Foreign keys infoReferential integritynonoyesyes infoRelationships in graphs
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanooptimistic lockingACID, with serializable isolation and linearizable read (within partition); Configurable MVCC (within partition); No cross-shard distributed transactionsACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes, cooperative multitaskingyes
Durability infoSupport for making data persistentyesyesyes, write ahead loggingyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes, full featured in-memory storage engine with persistenceno
User concepts infoAccess controlAccess rights for users, groups and rolesAccess rights based on private key authentication or shared access signaturesAccess Control Lists
Mutual TLS authentication for Tarantol Enterprise
Password based authentication
Role-based access control (RBAC) and LDAP for Tarantol Enterprise
Users and Roles
Role-based access control

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More resources
HiveMicrosoft Azure Table StorageTarantoolTigerGraph
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