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DBMS > Bangdb vs. Drizzle vs. Google Cloud Bigtable vs. Solr

System Properties Comparison Bangdb vs. Drizzle vs. Google Cloud Bigtable vs. Solr

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Editorial information provided by DB-Engines
NameBangdb  Xexclude from comparisonDrizzle  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonSolr  Xexclude from comparison
Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.
DescriptionConverged and high performance database for device data, events, time series, document and graphMySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.A widely used distributed, scalable search engine based on Apache Lucene
Primary database modelDocument store
Graph DBMS
Time Series DBMS
Relational DBMSKey-value store
Wide column store
Search engine
Secondary database modelsSpatial DBMSSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.08
Rank#347  Overall
#47  Document stores
#34  Graph DBMS
#31  Time Series DBMS
Score3.26
Rank#92  Overall
#13  Key-value stores
#8  Wide column stores
Score42.91
Rank#24  Overall
#3  Search engines
Websitebangdb.comcloud.google.com/­bigtablesolr.apache.org
Technical documentationdocs.bangdb.comcloud.google.com/­bigtable/­docssolr.apache.org/­resources.html
DeveloperSachin Sinha, BangDBDrizzle project, originally started by Brian AkerGoogleApache Software Foundation
Initial release2012200820152006
Current releaseBangDB 2.0, October 20217.2.4, September 20129.6.0, April 2024
License infoCommercial or Open SourceOpen Source infoBSD 3Open Source infoGNU GPLcommercialOpen Source infoApache Version 2
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 languageC, C++C++Java
Server operating systemsLinuxFreeBSD
Linux
OS X
hostedAll OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)
Data schemeschema-freeyesschema-freeyes infoDynamic Fields enables on-the-fly addition of new fields
Typing infopredefined data types such as float or dateyes: string, long, double, int, geospatial, stream, eventsyesnoyes infosupports customizable data types and automatic typing
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.nonoyes
Secondary indexesyes infosecondary, composite, nested, reverse, geospatialyesnoyes infoAll search fields are automatically indexed
SQL infoSupport of SQLSQL like support with command line toolyes infowith proprietary extensionsnoSolr Parallel SQL Interface
APIs and other access methodsProprietary protocol
RESTful HTTP API
JDBCgRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
Java API
RESTful HTTP/JSON API
Supported programming languagesC
C#
C++
Java
Python
C
C++
Java
PHP
C#
C++
Go
Java
JavaScript (Node.js)
Python
.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala
Server-side scripts infoStored proceduresnononoJava plugins
Triggersyes, Notifications (with Streaming only)no infohooks for callbacks inside the server can be used.noyes infoUser configurable commands triggered on index changes
Partitioning methods infoMethods for storing different data on different nodesSharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithmShardingShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factor, Knob for CAP (enterprise version only)Multi-source replication
Source-replica replication
Internal replication in Colossus, and regional replication between two clusters in different zonesyes
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoyesspark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reduce
Consistency concepts infoMethods to ensure consistency in a distributed systemTunable consistency, set CAP knob accordinglyImmediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)Eventual Consistency
Foreign keys infoReferential integritynoyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDAtomic single-row operationsoptimistic locking
Concurrency infoSupport for concurrent manipulation of datayes, optimistic concurrency controlyesyesyes
Durability infoSupport for making data persistentyes, implements WAL (Write ahead log) as wellyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yes, run db with in-memory only modenoyes
User concepts infoAccess controlyes (enterprise version only)Pluggable authentication mechanisms infoe.g. LDAP, HTTPAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)yes

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More resources
BangdbDrizzleGoogle Cloud BigtableSolr
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