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DBMS > Datomic vs. Hypertable vs. Spark SQL vs. Tarantool

System Properties Comparison Datomic vs. Hypertable vs. Spark SQL vs. Tarantool

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Editorial information provided by DB-Engines
NameDatomic  Xexclude from comparisonHypertable  Xexclude from comparisonSpark SQL  Xexclude from comparisonTarantool  Xexclude from comparison
Hypertable has stopped its further development with March 2016 and is removed 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 HadoopSpark SQL is a component on top of 'Spark Core' for structured data processingIn-memory computing platform with a flexible data schema for efficiently building high-performance applications
Primary database modelRelational DBMSWide column storeRelational DBMSDocument store
Key-value store
Relational DBMS
Secondary database modelsSpatial DBMS infowith Tarantool/GIS extension
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.76
Rank#145  Overall
#66  Relational DBMS
Score19.15
Rank#33  Overall
#20  Relational DBMS
Score1.67
Rank#150  Overall
#25  Document stores
#25  Key-value stores
#69  Relational DBMS
Websitewww.datomic.comspark.apache.org/­sqlwww.tarantool.io
Technical documentationdocs.datomic.comspark.apache.org/­docs/­latest/­sql-programming-guide.htmlwww.tarantool.io/­en/­doc
DeveloperCognitectHypertable Inc.Apache Software FoundationVK
Initial release2012200920142008
Current release1.0.6735, June 20230.9.8.11, March 20163.5.0 ( 2.13), September 20232.10.0, May 2022
License infoCommercial or Open Sourcecommercial infolimited edition freeOpen Source infoGNU version 3. Commercial license availableOpen Source infoApache 2.0Open Source infoBSD-2, source-available extensions (modules), commercial licenses for Tarantool Enterprise
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJava, ClojureC++ScalaC and C++
Server operating systemsAll OS with a Java VMLinux
OS X
Windows infoan inofficial Windows port is available
Linux
OS X
Windows
BSD
Linux
macOS
Data schemeyesschema-freeyesFlexible data schema: relational definition for tables with ability to store json-like documents in columns
Typing infopredefined data types such as float or dateyesnoyesstring, 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.nonono
Secondary indexesyesrestricted infoonly exact value or prefix value scansnoyes
SQL infoSupport of SQLnonoSQL-like DML and DDL statementsFull-featured ANSI SQL support
APIs and other access methodsRESTful HTTP APIC++ API
Thrift
JDBC
ODBC
Open binary protocol
Supported programming languagesClojure
Java
C++
Java
Perl
PHP
Python
Ruby
Java
Python
R
Scala
C
C#
C++
Erlang
Go
Java
JavaScript
Lua
Perl
PHP
Python
Rust
Server-side scripts infoStored proceduresyes infoTransaction FunctionsnonoLua, C and SQL stored procedures
TriggersBy using transaction functionsnonoyes, 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 peersShardingyes, utilizing Spark CoreSharding, 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 levelnoneAsynchronous 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 methodsnoyes
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate 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 integritynononoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnonoACID, with serializable isolation and linearizable read (within partition); Configurable MVCC (within partition); No cross-shard distributed transactions
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes, cooperative multitasking
Durability infoSupport for making data persistentyes infousing external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)yesyesyes, 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 developmentnoyes, full featured in-memory storage engine with persistence
User concepts infoAccess controlnononoAccess 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
DatomicHypertableSpark SQLTarantool
DB-Engines blog posts

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

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