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DBMS > Badger vs. Google Cloud Firestore vs. HugeGraph vs. Tarantool

System Properties Comparison Badger vs. Google Cloud Firestore vs. HugeGraph vs. Tarantool

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Editorial information provided by DB-Engines
NameBadger  Xexclude from comparisonGoogle Cloud Firestore  Xexclude from comparisonHugeGraph  Xexclude from comparisonTarantool  Xexclude from comparison
DescriptionAn embeddable, persistent, simple and fast Key-Value Store, written purely in Go.Cloud Firestore is an auto-scaling document database for storing, syncing, and querying data for mobile and web apps. It offers seamless integration with other Firebase and Google Cloud Platform products.A fast-speed and highly-scalable Graph DBMSIn-memory computing platform with a flexible data schema for efficiently building high-performance applications
Primary database modelKey-value storeDocument storeGraph 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
Score0.20
Rank#325  Overall
#49  Key-value stores
Score8.96
Rank#48  Overall
#8  Document stores
Score0.15
Rank#339  Overall
#33  Graph DBMS
Score1.67
Rank#150  Overall
#25  Document stores
#25  Key-value stores
#69  Relational DBMS
Websitegithub.com/­dgraph-io/­badgerfirebase.google.com/­products/­firestoregithub.com/­hugegraph
hugegraph.apache.org
www.tarantool.io
Technical documentationgodoc.org/­github.com/­dgraph-io/­badgerfirebase.google.com/­docs/­firestorehugegraph.apache.org/­docswww.tarantool.io/­en/­doc
DeveloperDGraph LabsGoogleBaiduVK
Initial release2017201720182008
Current release0.92.10.0, May 2022
License infoCommercial or Open SourceOpen Source infoApache 2.0commercialOpen Source infoApache Version 2.0Open Source infoBSD-2, source-available extensions (modules), commercial licenses for Tarantool Enterprise
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageGoJavaC and C++
Server operating systemsBSD
Linux
OS X
Solaris
Windows
hostedLinux
macOS
Unix
BSD
Linux
macOS
Data schemeschema-freeschema-freeyesFlexible data schema: relational definition for tables with ability to store json-like documents in columns
Typing infopredefined data types such as float or datenoyesyesstring, 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.nononono
Secondary indexesnoyesyes infoalso supports composite index and range indexyes
SQL infoSupport of SQLnononoFull-featured ANSI SQL support
APIs and other access methodsAndroid
gRPC (using protocol buffers) API
iOS
JavaScript API
RESTful HTTP API
Java API
RESTful HTTP API
TinkerPop Gremlin
Open binary protocol
Supported programming languagesGoGo
Java
JavaScript
JavaScript (Node.js)
Objective-C
Python
Groovy
Java
Python
C
C#
C++
Erlang
Go
Java
JavaScript
Lua
Perl
PHP
Python
Rust
Server-side scripts infoStored proceduresnoyes, Firebase Rules & Cloud Functionsasynchronous Gremlin script jobsLua, C and SQL stored procedures
Triggersnoyes, with Cloud Functionsnoyes, before/after data modification events, on replication events, client session events
Partitioning methods infoMethods for storing different data on different nodesnoneShardingyes infodepending on used storage backend, e.g. Cassandra and HBaseSharding, 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 nodesnoneMulti-source replicationyes infodepending on used storage backend, e.g. Cassandra and HBaseAsynchronous 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 methodsnoUsing Cloud Dataflowvia hugegraph-spark
Consistency concepts infoMethods to ensure consistency in a distributed systemnoneImmediate ConsistencyEventual 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 integritynonoyes infoedges in graphyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoyesACIDACID, 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 persistentyesyesyesyes, write ahead logging
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesyes, full featured in-memory storage engine with persistence
User concepts infoAccess controlnoAccess rights for users, groups and roles based on Google Cloud Identity and Access Management. Security Rules for 3rd party authentication using Firebase Auth.Users, roles and permissionsAccess 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
BadgerGoogle Cloud FirestoreHugeGraphTarantool
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