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DBMS > Drizzle vs. Google Cloud Datastore vs. GridGain vs. TimescaleDB

System Properties Comparison Drizzle vs. Google Cloud Datastore vs. GridGain vs. TimescaleDB

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Editorial information provided by DB-Engines
NameDrizzle  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonGridGain  Xexclude from comparisonTimescaleDB  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.Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformGridGain is an in-memory computing platform, built on Apache IgniteA time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL
Primary database modelRelational DBMSDocument storeKey-value store
Relational DBMS
Time Series DBMS
Secondary database modelsRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score4.36
Rank#72  Overall
#12  Document stores
Score1.55
Rank#150  Overall
#26  Key-value stores
#70  Relational DBMS
Score4.46
Rank#71  Overall
#5  Time Series DBMS
Websitecloud.google.com/­datastorewww.gridgain.comwww.timescale.com
Technical documentationcloud.google.com/­datastore/­docswww.gridgain.com/­docs/­index.htmldocs.timescale.com
DeveloperDrizzle project, originally started by Brian AkerGoogleGridGain Systems, Inc.Timescale
Initial release2008200820072017
Current release7.2.4, September 2012GridGain 8.5.12.15.0, May 2024
License infoCommercial or Open SourceOpen Source infoGNU GPLcommercialcommercialOpen 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++Java, C++, .NetC
Server operating systemsFreeBSD
Linux
OS X
hostedLinux
OS X
Solaris
Windows
Linux
OS X
Windows
Data schemeyesschema-freeyesyes
Typing infopredefined data types such as float or dateyesyes, details hereyesnumerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types
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.noyesyes
Secondary indexesyesyesyesyes
SQL infoSupport of SQLyes infowith proprietary extensionsSQL-like query language (GQL)ANSI-99 for query and DML statements, subset of DDLyes infofull PostgreSQL SQL syntax
APIs and other access methodsJDBCgRPC (using protocol buffers) API
RESTful HTTP/JSON API
HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
Supported programming languagesC
C++
Java
PHP
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
C#
C++
Java
PHP
Python
Ruby
Scala
.Net
C
C++
Delphi
Java infoJDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl
Server-side scripts infoStored proceduresnousing Google App Engineyes (compute grid and cache interceptors can be used instead)user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell
Triggersno infohooks for callbacks inside the server can be used.Callbacks using the Google Apps Engineyes (cache interceptors and events)yes
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardingyes, across time and space (hash partitioning) attributes
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
Multi-source replication using Paxosyes (replicated cache)Source-replica replication with hot standby and reads on replicas info
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infousing Google Cloud Dataflowyes (compute grid and hadoop accelerator)no
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate 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.Immediate ConsistencyImmediate Consistency
Foreign keys infoReferential integrityyesyes infovia ReferenceProperties or Ancestor pathsnoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsACIDACID
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.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)Security Hooks for custom implementationsfine grained access rights according to SQL-standard

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More resources
DrizzleGoogle Cloud DatastoreGridGainTimescaleDB
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