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DBMS > Blueflood vs. Dgraph vs. Google Cloud Datastore vs. Tarantool

System Properties Comparison Blueflood vs. Dgraph vs. Google Cloud Datastore vs. Tarantool

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Editorial information provided by DB-Engines
NameBlueflood  Xexclude from comparisonDgraph  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonTarantool  Xexclude from comparison
DescriptionScalable TimeSeries DBMS based on CassandraDistributed and scalable native Graph DBMSAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformIn-memory computing platform with a flexible data schema for efficiently building high-performance applications
Primary database modelTime Series DBMSGraph DBMSDocument storeDocument 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
Score0.13
Rank#346  Overall
#33  Time Series DBMS
Score1.53
Rank#152  Overall
#15  Graph DBMS
Score4.36
Rank#72  Overall
#12  Document stores
Score1.67
Rank#143  Overall
#25  Document stores
#25  Key-value stores
#65  Relational DBMS
Websiteblueflood.iodgraph.iocloud.google.com/­datastorewww.tarantool.io
Technical documentationgithub.com/­rax-maas/­blueflood/­wikidgraph.io/­docscloud.google.com/­datastore/­docswww.tarantool.io/­en/­doc
DeveloperRackspaceDgraph Labs, Inc.GoogleVK
Initial release2013201620082008
Current release2.10.0, May 2022
License infoCommercial or Open SourceOpen Source infoApache 2.0Open Source infoApache 2.0commercialOpen Source infoBSD-2, source-available extensions (modules), commercial licenses for Tarantool Enterprise
Cloud-based only infoOnly available as a cloud servicenonoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaGoC and C++
Server operating systemsLinux
OS X
Linux
OS X
Windows
hostedBSD
Linux
macOS
Data schemepredefined schemeschema-freeschema-freeFlexible data schema: relational definition for tables with ability to store json-like documents in columns
Typing infopredefined data types such as float or dateyesyesyes, details herestring, 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 indexesnoyesyesyes
SQL infoSupport of SQLnonoSQL-like query language (GQL)Full-featured ANSI SQL support
APIs and other access methodsHTTP RESTGraphQL query language
gRPC (using protocol buffers) API
HTTP API
gRPC (using protocol buffers) API
RESTful HTTP/JSON API
Open binary protocol
Supported programming languagesC#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
C
C#
C++
Erlang
Go
Java
JavaScript
Lua
Perl
PHP
Python
Rust
Server-side scripts infoStored proceduresnonousing Google App EngineLua, C and SQL stored procedures
TriggersnonoCallbacks using the Google Apps Engineyes, before/after data modification events, on replication events, client session events
Partitioning methods infoMethods for storing different data on different nodesSharding infobased on CassandrayesShardingSharding, 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 factor infobased on CassandraSynchronous replication via RaftMulti-source replication using PaxosAsynchronous 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 methodsnonoyes infousing Google Cloud Dataflow
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency infobased on Cassandra
Immediate Consistency infobased on Cassandra
Immediate ConsistencyImmediate Consistency or Eventual Consistency depending on type of query and configuration infoStrong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.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 integritynonoyes infovia ReferenceProperties or Ancestor pathsyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsACID, 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.nonoyes, full featured in-memory storage engine with persistence
User concepts infoAccess controlnono infoPlanned for future releasesAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Access 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
BluefloodDgraphGoogle Cloud DatastoreTarantool
DB-Engines blog posts

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

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