DBMS > Couchbase vs. Google Cloud Datastore vs. SAP Adaptive Server vs. SAP SQL Anywhere vs. SingleStore
System Properties Comparison Couchbase vs. Google Cloud Datastore vs. SAP Adaptive Server vs. SAP SQL Anywhere vs. SingleStore
Editorial information provided by DB-Engines | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Name | Couchbase Originally called Membase Xexclude from comparison | Google Cloud Datastore Xexclude from comparison | SAP Adaptive Server former name: Sybase ASE Xexclude from comparison | SAP SQL Anywhere formerly called Adaptive Server Anywhere Xexclude from comparison | SingleStore former name was MemSQL Xexclude from comparison | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Description | A distributed document store with integrated cache, a powerful search engine, in-built operational and analytical capabilities, and an embedded mobile database | Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform | The SAP (Sybase) Adaptive Server Enterprise (ASE) is an enterprise-class RDBMS | RDBMS database and synchronization technologies for server, desktop, remote office, and mobile environments | MySQL wire-compliant distributed RDBMS that combines an in-memory row-oriented and a disc-based column-oriented storage with patented universal storage to handle transactional and analytical workloads in one single table type | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Primary database model | Document store | Document store | Relational DBMS | Relational DBMS | Relational DBMS | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Secondary database models | Key-value store originating from the former Membase product and supporting the Memcached protocol Spatial DBMS using the Geocouch extension Search engine Time Series DBMS Vector DBMS | Spatial DBMS with Boeing's Spatial Query Server | Document store Spatial DBMS Time Series DBMS Vector DBMS | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Website | www.couchbase.com | cloud.google.com/datastore | www.sap.com/products/technology-platform/sybase-ase.html | www.sap.com/products/technology-platform/sql-anywhere.html | www.singlestore.com | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Technical documentation | docs.couchbase.com | cloud.google.com/datastore/docs | help.sap.com/docs/SAP_ASE | help.sap.com/docs/SAP_SQL_Anywhere | docs.singlestore.com | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Developer | Couchbase, Inc. | SAP, Sybase | SAP formerly Sybase | SingleStore Inc. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Initial release | 2011 | 2008 | 1987 | 1992 | 2013 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Current release | Server: 7.2, June 2023; Mobile: 3.1, March 2022; Couchbase Capella (DBaaS), June 2023 | 16.0 | 17, July 2015 | 8.5, January 2024 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
License Commercial or Open Source | Open Source Business Source License (BSL 1.1); Commercial licenses also available | commercial | commercial | commercial | commercial free developer edition available | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Cloud-based only Only available as a cloud service | no | yes | no | no | no | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
DBaaS offerings (sponsored links) Database as a Service Providers of DBaaS offerings, please contact us to be listed. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Implementation language | C, C++, Go and Erlang | C and C++ | C++, Go | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Server operating systems | Linux OS X Windows | hosted | AIX HP-UX Linux Solaris Unix Windows | AIX HP-UX Linux OS X Solaris Windows | Linux 64 bit version required | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Data scheme | schema-free | schema-free | yes | yes | yes | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Typing predefined data types such as float or date | yes | yes, details here | yes | yes | yes | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
XML support Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT. | no | no | yes | no | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Secondary indexes | yes | yes | yes | yes | yes | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
SQL Support of SQL | SQL++, extends ANSI SQL to JSON for operational, transactional, and analytic use cases | SQL-like query language (GQL) | yes | yes | yes but no triggers and foreign keys | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
APIs and other access methods | CLI Client HTTP REST Kafka Connector Native language bindings for CRUD, Query, Search and Analytics APIs Spark Connector Spring Data | gRPC (using protocol buffers) API RESTful HTTP/JSON API | ADO.NET JDBC ODBC OLE DB Tabular Data Stream (TDS) | ADO.NET HTTP API JDBC ODBC | Cluster Management API as HTTP Rest and CLI HTTP API JDBC MongoDB API ODBC | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Supported programming languages | .Net C Go Java JavaScript Node.js Kotlin PHP Python Ruby Scala | .Net Go Java JavaScript (Node.js) PHP Python Ruby | C C++ Cobol Java Java Perl PHP Python | C C# C++ Delphi Java JavaScript (Node.js) Perl PHP Python Ruby | Bash C C# Java JavaScript (Node.js) Python | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Server-side scripts Stored procedures | Functions and timers in JavaScript and UDFs in Java, Python, SQL++ | using Google App Engine | Java and Transact-SQL | yes, in C/C++, Java, .Net or Perl | yes | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Triggers | yes via the TAP protocol | Callbacks using the Google Apps Engine | yes | yes | no | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Partitioning methods Methods for storing different data on different nodes | Automatic Sharding | Sharding | horizontal partitioning | none | Sharding hash partitioning | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Replication methods Methods for redundantly storing data on multiple nodes | Multi-source replication including cross data center replication Source-replica replication | Multi-source replication using Paxos | Multi-source replication Source-replica replication | Source-replica replication Database mirroring | Source-replica replication stores two copies of each physical data partition on two separate nodes | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
MapReduce Offers an API for user-defined Map/Reduce methods | yes | yes using Google Cloud Dataflow | no | no | no can define user-defined aggregate functions for map-reduce-style calculations | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Consistency concepts Methods to ensure consistency in a distributed system | Eventual Consistency Immediate Consistency selectable on a per-operation basis | Immediate Consistency or Eventual Consistency depending on type of query and configuration Strong 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 Consistency | Immediate Consistency | Immediate Consistency | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Foreign keys Referential integrity | no | yes via ReferenceProperties or Ancestor paths | yes | yes | no | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Transaction concepts Support to ensure data integrity after non-atomic manipulations of data | ACID | ACID Serializable Isolation within Transactions, Read Committed outside of Transactions | ACID | ACID | ACID | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Concurrency Support for concurrent manipulation of data | yes | yes | yes | yes | yes, multi-version concurrency control (MVCC) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Durability Support for making data persistent | yes | yes | yes | yes | yes All updates are persistent, including those to disk-based columnstores and memory-based row stores. Transaction commits are supported via write-ahead log. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
In-memory capabilities Is there an option to define some or all structures to be held in-memory only. | yes Ephemeral buckets | no | yes | yes | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
User concepts Access control | User and Administrator separation with password-based and LDAP integrated Authentication. Role-base access control. | Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM) | fine grained access rights according to SQL-standard | fine grained access rights according to SQL-standard | Fine grained access control via users, groups and roles | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Couchbase Originally called Membase | Google Cloud Datastore | SAP Adaptive Server former name: Sybase ASE | SAP SQL Anywhere formerly called Adaptive Server Anywhere | SingleStore former name was MemSQL | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
DB-Engines blog posts | Couchbase climbs up the DB-Engines Ranking, increasing its popularity by 10% every month | Turbocharge Your Application Development Using WebAssembly With SingleStoreDB Cloud-Based Analytics With SingleStoreDB SingleStore: The Increasing Momentum of Multi-Model Database Systems Couchbase’s stock crumbles on anticipation of significant growth slowdown Interview with SWE, SJSU ranks no. 4 with WSJ, Couchbase moves to San Jose, and more! Couchbase, Inc. (BASE) Reports Q2 Loss, Tops Revenue Estimates Couchbase reports 20% Q2 revenue growth, though analysts expected more Why Couchbase Stock Plummeted Today provided by Google News Google Cloud vs AWS: Which Cloud Computing Platform is Better? Google Gets Rid of Fees To Transfer Data Out of Cloud Platform Best cloud storage of 2024 What Is Google Cloud? Platform, Benefits & More Explained Google App Engine provided by Google News Achieve zero RPO for your SAP ASE (Sybase) databases with Amazon FSx for NetApp ONTAP New Services in SAP HANA Cloud Lower TCO for Customers in Data-Intensive, Highly Regulated Industries Patch SAP Adaptive Server Enterprise now to avoid takeover risk Newly Patched SAP ASE Flaws Could Let Attackers Hack Database Servers Critical SAP ASE Flaws Allow Complete Control of Databases provided by Google News SAP Products & Services Data Portfolio SAP vulnerabilities Let Attacker Inject OS Commands—Patch Now! Securing SAP with AWS Network Firewall: Part 2 – Managed Rules MindsDB is now the leading and fastest growing applied ML platform in the world Gartner’s Magic Quadrant for Cloud Database Management Systems provided by Google News SingleStore Partners With Snowflake to Help Users Build Faster, More Efficient Real Time AI Applications Third time was the charm for SingleStore in the cloud, CEO says Achieve near real-time analytics on Amazon DynamoDB with SingleStore SingleStore CEO sees little future for purpose-built vector databases Building a Modern Database: Nikita Shamgunov on Postgres and Beyond provided by Google News |
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