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DBMS > CrateDB vs. Google Cloud Spanner vs. Microsoft Azure Data Explorer vs. Neo4j vs. SurrealDB

System Properties Comparison CrateDB vs. Google Cloud Spanner vs. Microsoft Azure Data Explorer vs. Neo4j vs. SurrealDB

Editorial information provided by DB-Engines
NameCrateDB  Xexclude from comparisonGoogle Cloud Spanner  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonNeo4j  Xexclude from comparisonSurrealDB  Xexclude from comparison
DescriptionDistributed Database based on LuceneA 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.Fully managed big data interactive analytics platformScalable, ACID-compliant graph database designed with a high-performance distributed cluster architecture, available in self-hosted and cloud offeringsA fully ACID transactional, developer-friendly, multi-model DBMS
Primary database modelDocument store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS
Relational DBMSRelational DBMS infocolumn orientedGraph DBMSDocument store
Graph DBMS
Secondary database modelsRelational DBMSDocument store infoIf a column is of type dynamic docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-types/­dynamic then it's possible to add arbitrary JSON documents in this cell
Event Store infothis is the general usage pattern at Microsoft. Billing, Logs, Telemetry events are stored in ADX and the state of an individual entity is defined by the arg_max(timestamps)
Spatial DBMS
Search engine infosupport for complex search expressions docs.microsoft.com/­en-us/­azure/­kusto/­query/­parseoperator FTS, Geospatial docs.microsoft.com/­en-us/­azure/­kusto/­query/­geo-point-to-geohash-function distributed search -> ADX acts as a distributed search engine
Time Series DBMS infosee docs.microsoft.com/­en-us/­azure/­data-explorer/­time-series-analysis
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.67
Rank#225  Overall
#37  Document stores
#5  Spatial DBMS
#17  Search engines
#19  Time Series DBMS
#16  Vector DBMS
Score2.56
Rank#97  Overall
#50  Relational DBMS
Score3.00
Rank#86  Overall
#47  Relational DBMS
Score47.91
Rank#20  Overall
#1  Graph DBMS
Score1.08
Rank#176  Overall
#30  Document stores
#16  Graph DBMS
Websitecratedb.comcloud.google.com/­spannerazure.microsoft.com/­services/­data-explorerneo4j.comsurrealdb.com
Technical documentationcratedb.com/­docscloud.google.com/­spanner/­docsdocs.microsoft.com/­en-us/­azure/­data-explorerneo4j.com/­docssurrealdb.com/­docs
DeveloperCrateGoogleMicrosoftNeo4j, Inc.SurrealDB Ltd
Initial release20132017201920072022
Current release5.8.1, August 2024cloud service with continuous releases5.23, August 2024v1.5.0, May 2024
License infoCommercial or Open SourceOpen SourcecommercialcommercialOpen Source infoGPL version3, commercial licenses availableOpen Source
Cloud-based only infoOnly available as a cloud servicenoyesyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaJava, ScalaRust
Server operating systemsAll Operating Systems, including Kubernetes with CrateDB Kubernetes Operator supporthostedhostedLinux infoCan also be used server-less as embedded Java database.
OS X
Solaris
Windows
Linux
macOS
Windows
Data schemeFlexible Schema (defined schema, partial schema, schema free)yesFixed schema with schema-less datatypes (dynamic)schema-free and schema-optionalschema-free
Typing infopredefined data types such as float or dateyesyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesyesyes
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.nonoyes
Secondary indexesyesyesall fields are automatically indexedyes infopluggable indexing subsystem, by default Apache Lucene
SQL infoSupport of SQLyes, but no triggers and constraints, and PostgreSQL compatibilityyes infoQuery statements complying to ANSI 2011Kusto Query Language (KQL), SQL subsetnoSQL-like query language
APIs and other access methodsADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API
gRPC (using protocol buffers) API
JDBC infoAt present, JDBC supports read-only queries. No support for DDL or DML statements.
RESTful HTTP API
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Bolt protocol
Cypher query language
Java API
Neo4j-OGM infoObject Graph Mapper
RESTful HTTP API
Spring Data Neo4j
TinkerPop 3
GraphQL
RESTful HTTP API
WebSocket
Supported programming languages.NET
Erlang
Go infocommunity maintained client
Java
JavaScript (Node.js) infocommunity maintained client
Perl infocommunity maintained client
PHP
Python
R
Ruby infocommunity maintained client
Scala infocommunity maintained client
Go
Java
JavaScript (Node.js)
Python
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
.Net
Clojure
Elixir
Go
Groovy
Haskell
Java
JavaScript
Perl
PHP
Python
Ruby
Scala
Deno
Go
JavaScript (Node.js)
Rust
Server-side scripts infoStored proceduresuser defined functions (Javascript)noYes, possible languages: KQL, Python, Ryes infoUser defined Procedures and Functions
Triggersnonoyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyyes infovia event handler
Partitioning methods infoMethods for storing different data on different nodesShardingShardingSharding infoImplicit feature of the cloud serviceyes using Neo4j Fabric
Replication methods infoMethods for redundantly storing data on multiple nodesConfigurable replication on table/partition-levelMulti-source replication with 3 replicas for regional instances.yes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.Causal Clustering using Raft protocol infoavailable in in Enterprise Version only
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infousing Google Cloud DataflowSpark connector (open source): github.com/­Azure/­azure-kusto-sparknono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Read-after-write consistency on record level
Immediate ConsistencyEventual Consistency
Immediate Consistency
Causal and Eventual Consistency configurable in Causal Cluster setup
Immediate Consistency in stand-alone mode
Foreign keys infoReferential integritynoyes infoby using interleaved tables, this features focuses more on performance improvements than on referential integritynoyes infoRelationships in graphs
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datano infounique row identifiers can be used for implementing an optimistic concurrency control strategyACID infoStrict serializable isolationnoACIDACID
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.nonono
User concepts infoAccess controlrights management via user accountsAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Azure Active Directory AuthenticationUsers, roles and permissions. Pluggable authentication with supported standards (LDAP, Active Directory, Kerberos)yes, based on authentication and database rules
More information provided by the system vendor
CrateDBGoogle Cloud SpannerMicrosoft Azure Data ExplorerNeo4jSurrealDB
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