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DBMS > Apache Doris vs. Drizzle vs. EsgynDB vs. Google Cloud Bigtable vs. Google Cloud Datastore

System Properties Comparison Apache Doris vs. Drizzle vs. EsgynDB vs. Google Cloud Bigtable vs. Google Cloud Datastore

Editorial information provided by DB-Engines
NameApache Doris  Xexclude from comparisonDrizzle  Xexclude from comparisonEsgynDB  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonGoogle Cloud Datastore  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.
DescriptionAn MPP-based analytics DBMS embracing the MySQL protocolMySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.Enterprise-class SQL-on-Hadoop solution, powered by Apache TrafodionGoogle's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform
Primary database modelRelational DBMSRelational DBMSRelational DBMSKey-value store
Wide column store
Document store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.57
Rank#244  Overall
#113  Relational DBMS
Score0.16
Rank#329  Overall
#146  Relational DBMS
Score3.26
Rank#92  Overall
#13  Key-value stores
#8  Wide column stores
Score4.47
Rank#76  Overall
#12  Document stores
Websitedoris.apache.org
github.com/­apache/­doris
www.esgyn.cncloud.google.com/­bigtablecloud.google.com/­datastore
Technical documentationgithub.com/­apache/­doris/­wikicloud.google.com/­bigtable/­docscloud.google.com/­datastore/­docs
DeveloperApache Software Foundation, originally contributed from BaiduDrizzle project, originally started by Brian AkerEsgynGoogleGoogle
Initial release20172008201520152008
Current release1.2.2, February 20237.2.4, September 2012
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoGNU GPLcommercialcommercialcommercial
Cloud-based only infoOnly available as a cloud servicenononoyesyes
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaC++C++, Java
Server operating systemsLinuxFreeBSD
Linux
OS X
Linuxhostedhosted
Data schemeyesyesyesschema-freeschema-free
Typing infopredefined data types such as float or dateyesyesyesnoyes, 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 indexesyesyesyesnoyes
SQL infoSupport of SQLyesyes infowith proprietary extensionsyesnoSQL-like query language (GQL)
APIs and other access methodsJDBC
MySQL client
JDBCADO.NET
JDBC
ODBC
gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
gRPC (using protocol buffers) API
RESTful HTTP/JSON API
Supported programming languagesJavaC
C++
Java
PHP
All languages supporting JDBC/ODBC/ADO.NetC#
C++
Go
Java
JavaScript (Node.js)
Python
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Server-side scripts infoStored proceduresuser defined functionsnoJava Stored Proceduresnousing Google App Engine
Triggersnono infohooks for callbacks inside the server can be used.nonoCallbacks using the Google Apps Engine
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioningShardingShardingShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesnoneMulti-source replication
Source-replica replication
Multi-source replication between multi datacentersInternal replication in Colossus, and regional replication between two clusters in different zonesMulti-source replication using Paxos
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoyesyesyes infousing Google Cloud Dataflow
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)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 integritynoyesyesnoyes infovia ReferenceProperties or Ancestor paths
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDAtomic single-row operationsACID infoSerializable Isolation within Transactions, Read Committed outside of Transactions
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nononono
User concepts infoAccess controlfine grained access rights according to SQL-standardPluggable authentication mechanisms infoe.g. LDAP, HTTPfine grained access rights according to SQL-standardAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

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More resources
Apache DorisDrizzleEsgynDBGoogle Cloud BigtableGoogle Cloud Datastore
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