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DBMS > Firebase Realtime Database vs. Spark SQL vs. Tarantool vs. TigerGraph

System Properties Comparison Firebase Realtime Database vs. Spark SQL vs. Tarantool vs. TigerGraph

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Editorial information provided by DB-Engines
NameFirebase Realtime Database  Xexclude from comparisonSpark SQL  Xexclude from comparisonTarantool  Xexclude from comparisonTigerGraph  Xexclude from comparison
DescriptionCloud-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.Spark 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 applicationsA complete, distributed, parallel graph computing platform supporting web-scale data analytics in real-time
Primary database modelDocument storeRelational DBMSDocument 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
Score14.29
Rank#39  Overall
#6  Document stores
Score18.96
Rank#33  Overall
#20  Relational DBMS
Score1.72
Rank#144  Overall
#25  Document stores
#25  Key-value stores
#66  Relational DBMS
Score1.83
Rank#139  Overall
#13  Graph DBMS
Websitefirebase.google.com/­products/­realtime-databasespark.apache.org/­sqlwww.tarantool.iowww.tigergraph.com
Technical documentationfirebase.google.com/­docs/­databasespark.apache.org/­docs/­latest/­sql-programming-guide.htmlwww.tarantool.io/­en/­docdocs.tigergraph.com
DeveloperGoogle infoacquired by Google 2014Apache Software FoundationVK
Initial release2012201420082017
Current release3.5.0 ( 2.13), September 20232.10.0, May 2022
License infoCommercial or Open SourcecommercialOpen Source infoApache 2.0Open Source infoBSD-2, source-available extensions (modules), commercial licenses for Tarantool Enterprisecommercial
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageScalaC and C++C++
Server operating systemshostedLinux
OS X
Windows
BSD
Linux
macOS
Linux
Data schemeschema-freeyesFlexible 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.nononono
Secondary indexesyesnoyes
SQL infoSupport of SQLnoSQL-like DML and DDL statementsFull-featured ANSI SQL supportSQL-like query language (GSQL)
APIs and other access methodsAndroid
iOS
JavaScript API
RESTful HTTP API
JDBC
ODBC
Open binary protocolGSQL (TigerGraph Query Language)
Kafka
RESTful HTTP/JSON API
Supported programming languagesJava
JavaScript
Objective-C
Java
Python
R
Scala
C
C#
C++
Erlang
Go
Java
JavaScript
Lua
Perl
PHP
Python
Rust
C++
Java
Server-side scripts infoStored procedureslimited functionality with using 'rules'noLua, C and SQL stored proceduresyes
TriggersCallbacks are triggered when data changesnoyes, before/after data modification events, on replication events, client session eventsno
Partitioning methods infoMethods for storing different data on different nodesyes, 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 nodesnoneAsynchronous 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 systemEventual Consistency infoif the client is offline
Immediate Consistency infoif the client is online
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 integritynonoyesyes infoRelationships in graphs
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datayesnoACID, 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 controlyes, based on authentication and database rulesnoAccess 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
Firebase Realtime DatabaseSpark SQLTarantoolTigerGraph
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