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DBMS > Drizzle vs. Google Cloud Bigtable vs. IBM Db2 Event Store vs. Spark SQL

System Properties Comparison Drizzle vs. Google Cloud Bigtable vs. IBM Db2 Event Store vs. Spark SQL

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Editorial information provided by DB-Engines
NameDrizzle  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonIBM Db2 Event Store  Xexclude from comparisonSpark SQL  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.
DescriptionMySQL 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.Distributed Event Store optimized for Internet of Things use casesSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelRelational DBMSKey-value store
Wide column store
Event Store
Time Series DBMS
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score3.15
Rank#95  Overall
#14  Key-value stores
#8  Wide column stores
Score0.27
Rank#309  Overall
#2  Event Stores
#28  Time Series DBMS
Score18.04
Rank#33  Overall
#20  Relational DBMS
Websitecloud.google.com/­bigtablewww.ibm.com/­products/­db2-event-storespark.apache.org/­sql
Technical documentationcloud.google.com/­bigtable/­docswww.ibm.com/­docs/­en/­db2-event-storespark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperDrizzle project, originally started by Brian AkerGoogleIBMApache Software Foundation
Initial release2008201520172014
Current release7.2.4, September 20122.03.5.0 ( 2.13), September 2023
License infoCommercial or Open SourceOpen Source infoGNU GPLcommercialcommercial infofree developer edition availableOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++C and C++Scala
Server operating systemsFreeBSD
Linux
OS X
hostedLinux infoLinux, macOS, Windows for the developer additionLinux
OS X
Windows
Data schemeyesschema-freeyesyes
Typing infopredefined data types such as float or dateyesnoyesyes
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 indexesyesnonono
SQL infoSupport of SQLyes infowith proprietary extensionsnoyes infothrough the embedded Spark runtimeSQL-like DML and DDL statements
APIs and other access methodsJDBCgRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API
JDBC
ODBC
Supported programming languagesC
C++
Java
PHP
C#
C++
Go
Java
JavaScript (Node.js)
Python
C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic
Java
Python
R
Scala
Server-side scripts infoStored proceduresnonoyesno
Triggersno infohooks for callbacks inside the server can be used.nonono
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardingyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
Internal replication in Colossus, and regional replication between two clusters in different zonesActive-active shard replicationnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)Eventual Consistency
Foreign keys infoReferential integrityyesnonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDAtomic single-row operationsnono
Concurrency infoSupport for concurrent manipulation of datayesyesNo - written data is immutableyes
Durability infoSupport for making data persistentyesyesYes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storageyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesno
User concepts infoAccess controlPluggable authentication mechanisms infoe.g. LDAP, HTTPAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)fine grained access rights according to SQL-standardno

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More resources
DrizzleGoogle Cloud BigtableIBM Db2 Event StoreSpark SQL
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