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DBMS > Databend vs. Google Cloud Bigtable vs. Hive vs. Solr

System Properties Comparison Databend vs. Google Cloud Bigtable vs. Hive vs. Solr

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Editorial information provided by DB-Engines
NameDatabend  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonHive  Xexclude from comparisonSolr  Xexclude from comparison
DescriptionAn open-source, elastic, and workload-aware cloud data warehouse designed to meet businesses' massive-scale analytics needs at low cost and with low complexityGoogle's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.data warehouse software for querying and managing large distributed datasets, built on HadoopA widely used distributed, scalable search engine based on Apache Lucene
Primary database modelRelational DBMSKey-value store
Wide column store
Relational DBMSSearch engine
Secondary database modelsSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.34
Rank#283  Overall
#130  Relational DBMS
Score3.15
Rank#95  Overall
#14  Key-value stores
#8  Wide column stores
Score59.76
Rank#18  Overall
#12  Relational DBMS
Score41.02
Rank#24  Overall
#3  Search engines
Websitegithub.com/­datafuselabs/­databend
www.databend.com
cloud.google.com/­bigtablehive.apache.orgsolr.apache.org
Technical documentationdocs.databend.comcloud.google.com/­bigtable/­docscwiki.apache.org/­confluence/­display/­Hive/­Homesolr.apache.org/­resources.html
DeveloperDatabend LabsGoogleApache Software Foundation infoinitially developed by FacebookApache Software Foundation
Initial release2021201520122006
Current release1.0.59, April 20233.1.3, April 20229.6.1, May 2024
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialOpen Source infoApache Version 2Open Source infoApache Version 2
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageRustJavaJava
Server operating systemshosted
Linux
macOS
hostedAll OS with a Java VMAll OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)
Data schemeyesschema-freeyesyes infoDynamic Fields enables on-the-fly addition of new fields
Typing infopredefined data types such as float or dateyesnoyesyes 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 indexesnonoyesyes infoAll search fields are automatically indexed
SQL infoSupport of SQLyesnoSQL-like DML and DDL statementsSolr Parallel SQL Interface
APIs and other access methodsCLI Client
JDBC
RESTful HTTP API
gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
JDBC
ODBC
Thrift
Java API
RESTful HTTP/JSON API
Supported programming languagesGo
Java
JavaScript (Node.js)
Python
Rust
C#
C++
Go
Java
JavaScript (Node.js)
Python
C++
Java
PHP
Python
.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala
Server-side scripts infoStored proceduresnonoyes infouser defined functions and integration of map-reduceJava plugins
Triggersnononoyes infoUser configurable commands triggered on index changes
Partitioning methods infoMethods for storing different data on different nodesnoneShardingShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesnoneInternal replication in Colossus, and regional replication between two clusters in different zonesselectable replication factoryes
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyesyes infoquery execution via MapReducespark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reduce
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)Eventual ConsistencyEventual Consistency
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datayesAtomic single-row operationsnooptimistic locking
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes
User concepts infoAccess controlUsers with fine-grained authorization concept, user rolesAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Access rights for users, groups and rolesyes

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More resources
DatabendGoogle Cloud BigtableHiveSolr
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