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DBMS > Datomic vs. Hypertable vs. IRONdb vs. Microsoft Azure Data Explorer vs. Tarantool

System Properties Comparison Datomic vs. Hypertable vs. IRONdb vs. Microsoft Azure Data Explorer vs. Tarantool

Editorial information provided by DB-Engines
NameDatomic  Xexclude from comparisonHypertable  Xexclude from comparisonIRONdb  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonTarantool  Xexclude from comparison
Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.
DescriptionDatomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durabilityAn open source BigTable implementation based on distributed file systems such as HadoopA distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicityFully managed big data interactive analytics platformIn-memory computing platform with a flexible data schema for efficiently building high-performance applications
Primary database modelRelational DBMSWide column storeTime Series DBMSRelational DBMS infocolumn orientedDocument store
Key-value store
Relational DBMS
Secondary database modelsDocument 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
Spatial DBMS infowith Tarantool/GIS extension
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.08
Rank#132  Overall
#64  Relational DBMS
Score5.82
Rank#65  Overall
#37  Relational DBMS
Score1.88
Rank#146  Overall
#24  Document stores
#25  Key-value stores
#68  Relational DBMS
Websitewww.datomic.comwww.circonus.com/solutions/time-series-database/azure.microsoft.com/­services/­data-explorerwww.tarantool.io
Technical documentationdocs.datomic.comdocs.circonus.com/irondb/category/getting-starteddocs.microsoft.com/­en-us/­azure/­data-explorerwww.tarantool.io/­en/­doc
DeveloperCognitectHypertable Inc.Circonus LLC.MicrosoftVK
Initial release20122009201720192008
Current release1.0.6735, June 20230.9.8.11, March 2016V0.10.20, January 2018cloud service with continuous releases2.10.0, May 2022
License infoCommercial or Open Sourcecommercial infolimited edition freeOpen Source infoGNU version 3. Commercial license availablecommercialcommercialOpen Source infoBSD-2, source-available extensions (modules), commercial licenses for Tarantool Enterprise
Cloud-based only infoOnly available as a cloud servicenononoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJava, ClojureC++C and C++C and C++
Server operating systemsAll OS with a Java VMLinux
OS X
Windows infoan inofficial Windows port is available
LinuxhostedBSD
Linux
macOS
Data schemeyesschema-freeschema-freeFixed schema with schema-less datatypes (dynamic)Flexible data schema: relational definition for tables with ability to store json-like documents in columns
Typing infopredefined data types such as float or dateyesnoyes infotext, numeric, histogramsyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesstring, double, decimal, uuid, integer, blob, boolean, datetime
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 indexesyesrestricted infoonly exact value or prefix value scansnoall fields are automatically indexedyes
SQL infoSupport of SQLnonoSQL-like query language (Circonus Analytics Query Language: CAQL)Kusto Query Language (KQL), SQL subsetFull-featured ANSI SQL support
APIs and other access methodsRESTful HTTP APIC++ API
Thrift
HTTP APIMicrosoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Open binary protocol
Supported programming languagesClojure
Java
C++
Java
Perl
PHP
Python
Ruby
.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
C
C#
C++
Erlang
Go
Java
JavaScript
Lua
Perl
PHP
Python
Rust
Server-side scripts infoStored proceduresyes infoTransaction Functionsnoyes, in LuaYes, possible languages: KQL, Python, RLua, C and SQL stored procedures
TriggersBy using transaction functionsnonoyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyyes, before/after data modification events, on replication events, client session events
Partitioning methods infoMethods for storing different data on different nodesnone infoBut extensive use of caching in the application peersShardingAutomatic, metric affinity per nodeSharding 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 nodesnone infoBut extensive use of caching in the application peersselectable replication factor on file system levelconfigurable replication factor, datacenter awareyes 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 methodsnoyesnoSpark connector (open source): github.com/­Azure/­azure-kusto-spark
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate consistency per node, eventual consistency across nodesEventual Consistency
Immediate Consistency
Casual 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 integritynonononoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnononoACID, with serializable isolation and linearizable read (within partition); Configurable MVCC (within partition); No cross-shard distributed transactions
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes, cooperative multitasking
Durability infoSupport for making data persistentyes infousing external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)yesyesyesyes, write ahead logging
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yes inforecommended only for testing and developmentnonoyes, full featured in-memory storage engine with persistence
User concepts infoAccess controlnononoAzure Active Directory AuthenticationAccess Control Lists
Mutual TLS authentication for Tarantol Enterprise
Password based authentication
Role-based access control (RBAC) and LDAP for Tarantol Enterprise
Users and Roles

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More resources
DatomicHypertableIRONdbMicrosoft Azure Data ExplorerTarantool
DB-Engines blog posts

Data processing speed and reliability: in-memory synchronous replication
9 November 2021,  Vladimir Perepelytsya, Tarantool (sponsor) 

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