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DBMS > Apache Druid vs. EsgynDB vs. Google Cloud Bigtable vs. Hive

System Properties Comparison Apache Druid vs. EsgynDB vs. Google Cloud Bigtable vs. Hive

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Editorial information provided by DB-Engines
NameApache Druid  Xexclude from comparisonEsgynDB  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonHive  Xexclude from comparison
DescriptionOpen-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality dataEnterprise-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.data warehouse software for querying and managing large distributed datasets, built on Hadoop
Primary database modelRelational DBMS
Time Series DBMS
Relational DBMSKey-value store
Wide column store
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score3.29
Rank#95  Overall
#49  Relational DBMS
#7  Time Series DBMS
Score0.23
Rank#319  Overall
#141  Relational DBMS
Score3.58
Rank#92  Overall
#14  Key-value stores
#8  Wide column stores
Score62.59
Rank#18  Overall
#12  Relational DBMS
Websitedruid.apache.orgwww.esgyn.cncloud.google.com/­bigtablehive.apache.org
Technical documentationdruid.apache.org/­docs/­latest/­designcloud.google.com/­bigtable/­docscwiki.apache.org/­confluence/­display/­Hive/­Home
DeveloperApache Software Foundation and contributorsEsgynGoogleApache Software Foundation infoinitially developed by Facebook
Initial release2012201520152012
Current release29.0.1, April 20243.1.3, April 2022
License infoCommercial or Open SourceOpen Source infoApache license v2commercialcommercialOpen Source infoApache Version 2
Cloud-based only infoOnly available as a cloud servicenonoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC++, JavaJava
Server operating systemsLinux
OS X
Unix
LinuxhostedAll OS with a Java VM
Data schemeyes infoschema-less columns are supportedyesschema-freeyes
Typing infopredefined data types such as float or dateyesyesnoyes
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.nonono
Secondary indexesyesyesnoyes
SQL infoSupport of SQLSQL for queryingyesnoSQL-like DML and DDL statements
APIs and other access methodsJDBC
RESTful HTTP/JSON API
ADO.NET
JDBC
ODBC
gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
JDBC
ODBC
Thrift
Supported programming languagesClojure
JavaScript
PHP
Python
R
Ruby
Scala
All languages supporting JDBC/ODBC/ADO.NetC#
C++
Go
Java
JavaScript (Node.js)
Python
C++
Java
PHP
Python
Server-side scripts infoStored proceduresnoJava Stored Proceduresnoyes infouser defined functions and integration of map-reduce
Triggersnononono
Partitioning methods infoMethods for storing different data on different nodesSharding infomanual/auto, time-basedShardingShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyes, via HDFS, S3 or other storage enginesMulti-source replication between multi datacentersInternal replication in Colossus, and regional replication between two clusters in different zonesselectable replication factor
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyesyesyes infoquery execution via MapReduce
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)Eventual Consistency
Foreign keys infoReferential integritynoyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDAtomic single-row operationsno
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.nonono
User concepts infoAccess controlRBAC using LDAP or Druid internals for users and groups for read/write by datasource and systemfine 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

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Apache DruidEsgynDBGoogle Cloud BigtableHive
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