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DBMS > Google Cloud Bigtable vs. Hazelcast vs. Kinetica vs. SingleStore

System Properties Comparison Google Cloud Bigtable vs. Hazelcast vs. Kinetica vs. SingleStore

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Editorial information provided by DB-Engines
NameGoogle Cloud Bigtable  Xexclude from comparisonHazelcast  Xexclude from comparisonKinetica  Xexclude from comparisonSingleStore infoformer name was MemSQL  Xexclude from comparison
DescriptionGoogle's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.A widely adopted in-memory data gridFully vectorized database across both GPUs and CPUsMySQL 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 modelKey-value store
Wide column store
Key-value storeRelational DBMSRelational DBMS
Secondary database modelsDocument store infoJSON support with IMDG 3.12Spatial DBMS
Time Series DBMS
Document store
Spatial DBMS
Time Series DBMS
Vector DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.97
Rank#92  Overall
#15  Key-value stores
#8  Wide column stores
Score5.72
Rank#59  Overall
#6  Key-value stores
Score0.42
Rank#261  Overall
#120  Relational DBMS
Score4.02
Rank#74  Overall
#39  Relational DBMS
Websitecloud.google.com/­bigtablehazelcast.comwww.kinetica.comwww.singlestore.com
Technical documentationcloud.google.com/­bigtable/­docshazelcast.org/­imdg/­docsdocs.kinetica.comdocs.singlestore.com
DeveloperGoogleHazelcastKineticaSingleStore Inc.
Initial release2015200820122013
Current release5.3.6, November 20237.1, August 20218.5, January 2024
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2; commercial licenses availablecommercialcommercial infofree developer edition available
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC, C++C++, Go
Server operating systemshostedAll OS with a Java VMLinuxLinux info64 bit version required
Data schemeschema-freeschema-freeyesyes
Typing infopredefined data types such as float or datenoyesyesyes
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.noyes infothe object must implement a serialization strategynono
Secondary indexesnoyesyesyes
SQL infoSupport of SQLnoSQL-like query languageSQL-like DML and DDL statementsyes infobut no triggers and foreign keys
APIs and other access methodsgRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
JCache
JPA
Memcached protocol
RESTful HTTP API
JDBC
ODBC
RESTful HTTP API
Cluster Management API infoas HTTP Rest and CLI
HTTP API
JDBC
MongoDB API
ODBC
Supported programming languagesC#
C++
Go
Java
JavaScript (Node.js)
Python
.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
C++
Java
JavaScript (Node.js)
Python
Bash
C
C#
Java
JavaScript (Node.js)
Python
Server-side scripts infoStored proceduresnoyes infoEvent Listeners, Executor Servicesuser defined functionsyes
Triggersnoyes infoEventsyes infotriggers when inserted values for one or more columns fall within a specified rangeno
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardingSharding infohash partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesInternal replication in Colossus, and regional replication between two clusters in different zonesyes infoReplicated MapSource-replica replicationSource-replica replication infostores two copies of each physical data partition on two separate nodes
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesyesnono infocan define user-defined aggregate functions for map-reduce-style calculations
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)Immediate Consistency or Eventual Consistency selectable by user infoRaft Consensus AlgorithmImmediate Consistency or Eventual Consistency depending on configurationImmediate Consistency
Foreign keys infoReferential integritynonoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic single-row operationsone or two-phase-commit; repeatable reads; read commitednoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes, multi-version concurrency control (MVCC)
Durability infoSupport for making data persistentyesyesyesyes infoAll 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 infoIs there an option to define some or all structures to be held in-memory only.noyesyes infoGPU vRAM or System RAMyes
User concepts infoAccess controlAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Role-based access controlAccess rights for users and roles on table levelFine grained access control via users, groups and roles

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More resources
Google Cloud BigtableHazelcastKineticaSingleStore infoformer name was MemSQL
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