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DBMS > Apache Druid vs. Badger vs. Faircom DB vs. Google Cloud Datastore

System Properties Comparison Apache Druid vs. Badger vs. Faircom DB vs. Google Cloud Datastore

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Editorial information provided by DB-Engines
NameApache Druid  Xexclude from comparisonBadger  Xexclude from comparisonFaircom DB infoformerly c-treeACE  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparison
DescriptionOpen-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality dataAn embeddable, persistent, simple and fast Key-Value Store, written purely in Go.Native high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform
Primary database modelRelational DBMS
Time Series DBMS
Key-value storeKey-value store
Relational DBMS
Document store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score3.25
Rank#90  Overall
#47  Relational DBMS
#7  Time Series DBMS
Score0.22
Rank#320  Overall
#47  Key-value stores
Score0.29
Rank#304  Overall
#43  Key-value stores
#136  Relational DBMS
Score4.36
Rank#72  Overall
#12  Document stores
Websitedruid.apache.orggithub.com/­dgraph-io/­badgerwww.faircom.com/­products/­faircom-dbcloud.google.com/­datastore
Technical documentationdruid.apache.org/­docs/­latest/­designgodoc.org/­github.com/­dgraph-io/­badgerdocs.faircom.com/­docs/­en/­UUID-7446ae34-a1a7-c843-c894-d5322e395184.htmlcloud.google.com/­datastore/­docs
DeveloperApache Software Foundation and contributorsDGraph LabsFairCom CorporationGoogle
Initial release2012201719792008
Current release29.0.1, April 2024V12, November 2020
License infoCommercial or Open SourceOpen Source infoApache license v2Open Source infoApache 2.0commercial infoRestricted, free version availablecommercial
Cloud-based only infoOnly available as a cloud servicenononoyes
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaGoANSI C, C++
Server operating systemsLinux
OS X
Unix
BSD
Linux
OS X
Solaris
Windows
AIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows infoeasily portable to other OSs
hosted
Data schemeyes infoschema-less columns are supportedschema-freeschema free, schema optional, schema required, partial schema,schema-free
Typing infopredefined data types such as float or dateyesnoyes, ANSI SQL Types, JSON, typed binary structuresyes, details here
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 indexesyesnoyesyes
SQL infoSupport of SQLSQL for queryingnoyes, ANSI SQL with proprietary extensionsSQL-like query language (GQL)
APIs and other access methodsJDBC
RESTful HTTP/JSON API
ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC
gRPC (using protocol buffers) API
RESTful HTTP/JSON API
Supported programming languagesClojure
JavaScript
PHP
Python
R
Ruby
Scala
Go.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Server-side scripts infoStored proceduresnonoyes info.Net, JavaScript, C/C++using Google App Engine
TriggersnonoyesCallbacks using the Google Apps Engine
Partitioning methods infoMethods for storing different data on different nodesSharding infomanual/auto, time-basednoneFile partitioning, horizontal partitioning, sharding infoCustomizable business rules for table partitioningSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyes, via HDFS, S3 or other storage enginesnoneyes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).Multi-source replication using Paxos
MapReduce infoOffers an API for user-defined Map/Reduce methodsnononoyes infousing Google Cloud Dataflow
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencynoneEventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction
Immediate 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.
Foreign keys infoReferential integritynonoyesyes infovia ReferenceProperties or Ancestor paths
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonotunable from ACID to Eventually ConsistentACID infoSerializable Isolation within Transactions, Read Committed outside of Transactions
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesYes, tunable from durable to delayed durability to in-memoryyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyesno
User concepts infoAccess controlRBAC using LDAP or Druid internals for users and groups for read/write by datasource and systemnoFine grained access rights according to SQL-standard with additional protections for filesAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

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More resources
Apache DruidBadgerFaircom DB infoformerly c-treeACEGoogle Cloud Datastore
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