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DBMS > BoltDB vs. CrateDB vs. Ehcache vs. Google Cloud Bigtable

System Properties Comparison BoltDB vs. CrateDB vs. Ehcache vs. Google Cloud Bigtable

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Editorial information provided by DB-Engines
NameBoltDB  Xexclude from comparisonCrateDB  Xexclude from comparisonEhcache  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparison
DescriptionAn embedded key-value store for Go.Distributed Database based on LuceneA widely adopted Java cache with tiered storage optionsGoogle's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.
Primary database modelKey-value storeDocument store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS
Key-value storeKey-value store
Wide column store
Secondary database modelsRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.74
Rank#220  Overall
#31  Key-value stores
Score0.73
Rank#224  Overall
#37  Document stores
#5  Spatial DBMS
#16  Search engines
#19  Time Series DBMS
#8  Vector DBMS
Score4.89
Rank#67  Overall
#8  Key-value stores
Score3.26
Rank#92  Overall
#13  Key-value stores
#8  Wide column stores
Websitegithub.com/­boltdb/­boltcratedb.comwww.ehcache.orgcloud.google.com/­bigtable
Technical documentationcratedb.com/­docswww.ehcache.org/­documentationcloud.google.com/­bigtable/­docs
DeveloperCrateTerracotta Inc, owned by Software AGGoogle
Initial release2013201320092015
Current release3.10.0, March 2022
License infoCommercial or Open SourceOpen Source infoMIT LicenseOpen SourceOpen Source infoApache Version 2; commercial licenses availablecommercial
Cloud-based only infoOnly available as a cloud servicenononoyes
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
CrateDB Cloud: a distributed SQL database that spreads data and processing across an elastic cluster of shared nothing nodes. CrateDB Cloud enables data insights at scale on Microsoft Azure, AWS and Google Cloud Platform.
Implementation languageGoJavaJava
Server operating systemsBSD
Linux
OS X
Solaris
Windows
All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator supportAll OS with a Java VMhosted
Data schemeschema-freeFlexible Schema (defined schema, partial schema, schema free)schema-freeschema-free
Typing infopredefined data types such as float or datenoyesyesno
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.nononono
Secondary indexesnoyesnono
SQL infoSupport of SQLnoyes, but no triggers and constraints, and PostgreSQL compatibilitynono
APIs and other access methodsADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API
JCachegRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
Supported programming languagesGo.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
JavaC#
C++
Go
Java
JavaScript (Node.js)
Python
Server-side scripts infoStored proceduresnouser defined functions (Javascript)nono
Triggersnonoyes infoCache Event Listenersno
Partitioning methods infoMethods for storing different data on different nodesnoneShardingSharding infoby using Terracotta ServerSharding
Replication methods infoMethods for redundantly storing data on multiple nodesnoneConfigurable replication on table/partition-levelyes infoby using Terracotta ServerInternal replication in Colossus, and regional replication between two clusters in different zones
MapReduce infoOffers an API for user-defined Map/Reduce methodsnononoyes
Consistency concepts infoMethods to ensure consistency in a distributed systemnoneEventual Consistency
Read-after-write consistency on record level
Tunable Consistency (Strong, Eventual, Weak)Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datayesno infounique row identifiers can be used for implementing an optimistic concurrency control strategyyes infosupports JTA and can work as an XA resourceAtomic single-row operations
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyes infousing a tiered cache-storage approachyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyesno
User concepts infoAccess controlnorights management via user accountsnoAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)
More information provided by the system vendor
BoltDBCrateDBEhcacheGoogle Cloud Bigtable
Specific characteristicsThe enterprise database for time series, documents, and vectors. Distributed - Native...
» more
Competitive advantagesResponse time in milliseconds: e ven for complex ad-hoc queries. Massive scaling...
» more
Typical application scenarios​ IoT: accelerate your IIoT projects with CrateDB, delivering real-time analytics...
» more
Key customersAcross all continents, CrateDB is used by companies of all sizes to meet the most...
» more
Market metricsThe CrateDB open source project was started in 2013 Honorable Mention in 2021 Gartner®...
» more
Licensing and pricing modelsSee CrateDB pricing >
» more

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More resources
BoltDBCrateDBEhcacheGoogle Cloud Bigtable
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