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DBMS > Google Cloud Spanner vs. KeyDB vs. Microsoft Azure Synapse Analytics vs. Splice Machine

System Properties Comparison Google Cloud Spanner vs. KeyDB vs. Microsoft Azure Synapse Analytics vs. Splice Machine

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Editorial information provided by DB-Engines
NameGoogle Cloud Spanner  Xexclude from comparisonKeyDB  Xexclude from comparisonMicrosoft Azure Synapse Analytics infopreviously named Azure SQL Data Warehouse  Xexclude from comparisonSplice Machine  Xexclude from comparison
DescriptionA horizontally scalable, globally consistent, relational database service. It is the externalization of the core Google database that runs the biggest aspects of Google, like Ads and Google Play.An ultra-fast, open source Key-value store fully compatible with Redis API, modules, and protocolsElastic, large scale data warehouse service leveraging the broad eco-system of SQL ServerOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark
Primary database modelRelational DBMSKey-value storeRelational DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.89
Rank#103  Overall
#52  Relational DBMS
Score0.71
Rank#226  Overall
#33  Key-value stores
Score20.56
Rank#31  Overall
#19  Relational DBMS
Score0.54
Rank#250  Overall
#114  Relational DBMS
Websitecloud.google.com/­spannergithub.com/­Snapchat/­KeyDB
keydb.dev
azure.microsoft.com/­services/­synapse-analyticssplicemachine.com
Technical documentationcloud.google.com/­spanner/­docsdocs.keydb.devdocs.microsoft.com/­azure/­synapse-analyticssplicemachine.com/­how-it-works
DeveloperGoogleEQ Alpha Technology Ltd.MicrosoftSplice Machine
Initial release2017201920162014
Current release3.1, March 2021
License infoCommercial or Open SourcecommercialOpen Source infoBSD-3commercialOpen Source infoAGPL 3.0, commercial license available
Cloud-based only infoOnly available as a cloud serviceyesnoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++C++Java
Server operating systemshostedLinuxhostedLinux
OS X
Solaris
Windows
Data schemeyesschema-freeyesyes
Typing infopredefined data types such as float or dateyespartial infoSupported data types are strings, hashes, lists, sets and sorted sets, bit arrays, hyperloglogs and geospatial indexesyesyes
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 indexesyesyes infoby using the Redis Search moduleyesyes
SQL infoSupport of SQLyes infoQuery statements complying to ANSI 2011noyesyes
APIs and other access methodsgRPC (using protocol buffers) API
JDBC infoAt present, JDBC supports read-only queries. No support for DDL or DML statements.
RESTful HTTP API
Proprietary protocol infoRESP - REdis Serialization ProtocoADO.NET
JDBC
ODBC
JDBC
Native Spark Datasource
ODBC
Supported programming languagesGo
Java
JavaScript (Node.js)
Python
C
C#
C++
Clojure
Crystal
D
Dart
Elixir
Erlang
Fancy
Go
Haskell
Haxe
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Objective-C
OCaml
Pascal
Perl
PHP
Prolog
Pure Data
Python
R
Rebol
Ruby
Rust
Scala
Scheme
Smalltalk
Swift
Tcl
Visual Basic
C#
Java
PHP
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Server-side scripts infoStored proceduresnoLuaTransact SQLyes infoJava
Triggersnononoyes
Partitioning methods infoMethods for storing different data on different nodesShardingShardingSharding, horizontal partitioningShared Nothhing Auto-Sharding, Columnar Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication with 3 replicas for regional instances.Multi-source replication
Source-replica replication
yesMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infousing Google Cloud DataflownonoYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency
Strong eventual consistency with CRDTs
Immediate ConsistencyImmediate Consistency
Foreign keys infoReferential integrityyes infoby using interleaved tables, this features focuses more on performance improvements than on referential integritynono infodocs.microsoft.com/­en-us/­azure/­synapse-analytics/­sql-data-warehouse/­sql-data-warehouse-table-constraintsyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACID infoStrict serializable isolationOptimistic locking, atomic execution of commands blocks and scriptsACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes, multi-version concurrency control (MVCC)
Durability infoSupport for making data persistentyesyes infoConfigurable mechanisms for persistency via snapshots and/or operations logsyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesyes
User concepts infoAccess controlAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)simple password-based access control and ACLyesAccess rights for users, groups and roles according to SQL-standard

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More resources
Google Cloud SpannerKeyDBMicrosoft Azure Synapse Analytics infopreviously named Azure SQL Data WarehouseSplice Machine
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