Alternatives to MyBatis logo

Alternatives to MyBatis

Hibernate, Flyway, Spring Data, jOOQ, and MySQL are the most popular alternatives and competitors to MyBatis.
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What is MyBatis and what are its top alternatives?

It is a first class persistence framework with support for custom SQL, stored procedures and advanced mappings. It eliminates almost all of the JDBC code and manual setting of parameters and retrieval of results. It can use simple XML or Annotations for configuration and map primitives, Map interfaces and Java POJOs (Plain Old Java Objects) to database records.
MyBatis is a tool in the Object Relational Mapper (ORM) category of a tech stack.
MyBatis is an open source tool with 19.7K GitHub stars and 12.8K GitHub forks. Here’s a link to MyBatis's open source repository on GitHub

Top Alternatives to MyBatis

  • Hibernate
    Hibernate

    Hibernate is a suite of open source projects around domain models. The flagship project is Hibernate ORM, the Object Relational Mapper. ...

  • Flyway
    Flyway

    It lets you regain control of your database migrations with pleasure and plain sql. Solves only one problem and solves it well. It migrates your database, so you don't have to worry about it anymore. ...

  • Spring Data
    Spring Data

    It makes it easy to use data access technologies, relational and non-relational databases, map-reduce frameworks, and cloud-based data services. This is an umbrella project which contains many subprojects that are specific to a given database. ...

  • jOOQ
    jOOQ

    It implements the active record pattern. Its purpose is to be both relational and object oriented by providing a domain-specific language to construct queries from classes generated from a database schema. ...

  • MySQL
    MySQL

    The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software. ...

  • PostgreSQL
    PostgreSQL

    PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions. ...

  • MongoDB
    MongoDB

    MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding. ...

  • Redis
    Redis

    Redis is an open source (BSD licensed), in-memory data structure store, used as a database, cache, and message broker. Redis provides data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes, and streams. ...

MyBatis alternatives & related posts

Hibernate logo

Hibernate

1.5K
1.2K
34
Idiomatic persistence for Java and relational databases.
1.5K
1.2K
+ 1
34
PROS OF HIBERNATE
  • 22
    Easy ORM
  • 8
    Easy transaction definition
  • 3
    Is integrated with spring jpa
  • 1
    Open Source
CONS OF HIBERNATE
  • 3
    Can't control proxy associations when entity graph used

related Hibernate posts

Ganesa Vijayakumar
Full Stack Coder | Technical Architect · | 19 upvotes · 5.3M views

I'm planning to create a web application and also a mobile application to provide a very good shopping experience to the end customers. Shortly, my application will be aggregate the product details from difference sources and giving a clear picture to the user that when and where to buy that product with best in Quality and cost.

I have planned to develop this in many milestones for adding N number of features and I have picked my first part to complete the core part (aggregate the product details from different sources).

As per my work experience and knowledge, I have chosen the followings stacks to this mission.

UI: I would like to develop this application using React, React Router and React Native since I'm a little bit familiar on this and also most importantly these will help on developing both web and mobile apps. In addition, I'm gonna use the stacks JavaScript, jQuery, jQuery UI, jQuery Mobile, Bootstrap wherever required.

Service: I have planned to use Java as the main business layer language as I have 7+ years of experience on this I believe I can do better work using Java than other languages. In addition, I'm thinking to use the stacks Node.js.

Database and ORM: I'm gonna pick MySQL as DB and Hibernate as ORM since I have a piece of good knowledge and also work experience on this combination.

Search Engine: I need to deal with a large amount of product data and it's in-detailed info to provide enough details to end user at the same time I need to focus on the performance area too. so I have decided to use Solr as a search engine for product search and suggestions. In addition, I'm thinking to replace Solr by Elasticsearch once explored/reviewed enough about Elasticsearch.

Host: As of now, my plan to complete the application with decent features first and deploy it in a free hosting environment like Docker and Heroku and then once it is stable then I have planned to use the AWS products Amazon S3, EC2, Amazon RDS and Amazon Route 53. I'm not sure about Microsoft Azure that what is the specialty in it than Heroku and Amazon EC2 Container Service. Anyhow, I will do explore these once again and pick the best suite one for my requirement once I reached this level.

Build and Repositories: I have decided to choose Apache Maven and Git as these are my favorites and also so popular on respectively build and repositories.

Additional Utilities :) - I would like to choose Codacy for code review as their Startup plan will be very helpful to this application. I'm already experienced with Google CheckStyle and SonarQube even I'm looking something on Codacy.

Happy Coding! Suggestions are welcome! :)

Thanks, Ganesa

See more
NIDHISH PUTHIYADATH
Lead Software Engineer at EDIFECS · | 1 upvote · 307.9K views

Material Design for Angular Angular 2 Node.js TypeScript Spring-Boot RxJS Microsoft SQL Server Hibernate Spring MVC

We built our customer facing portal application using Angular frontend backed by Spring boot.

See more
Flyway logo

Flyway

287
561
33
Version control for your database
287
561
+ 1
33
PROS OF FLYWAY
  • 13
    Superb tool, easy to configure and use
  • 9
    Very easy to config, great support on plain sql scripts
  • 6
    Is fantastic and easy to install even with complex DB
  • 4
    Simple and intuitive
  • 1
    Easy tool to implement incremental migration
CONS OF FLYWAY
  • 3
    "Undo Migrations" requires pro version, very expensive

related Flyway posts

Miguel Suarez

Flyway vs Liquibase #Migration #Backwards-compatible

We were looking for a tool to help us integrating the migration scripts as part of our Deployment. At first sight both tools look very alike, are well integrated with Spring, have a fairly frequent development activity and short release cycles.

Liquibase puts a lot of emphasis on independence with the DB, allowing you to create the scripts on formats like JSON and YML, abstracting away from SQL, which it's also supported. Since we only work with one DB type across services we wouldn't take much advantage of this feature.

Flyway on the other hand has the advantage on being actively working on the integration with PostgreSQL 11, for it's upcoming version 6. Provides a more extensive set of properties that allow us to define what's allowed on what's not on each different environment.

Instead of looking for a tool that will allow us to rollback our DB changes automatically, we decided to implement backwards-compatible DB changes, for example adding a new column instead of renaming an existing one, postponing the deletion of the deprecated column until the release has been successfully installed.

See more
Shared insights
on
FlywayFlywayPostgreSQLPostgreSQLMSSQLMSSQL

MSSQL to PostgreSQL database migration can be done through Flyway? Please advise the steps if possible.

See more
Spring Data logo

Spring Data

599
407
0
Provides a consistent approach to data access – relational, non-relational, map-reduce, and beyond
599
407
+ 1
0
PROS OF SPRING DATA
    Be the first to leave a pro
    CONS OF SPRING DATA
      Be the first to leave a con

      related Spring Data posts

      Остап Комплікевич

      I need some advice to choose an engine for generation web pages from the Spring Boot app. Which technology is the best solution today? 1) JSP + JSTL 2) Apache FreeMarker 3) Thymeleaf Or you can suggest even other perspective tools. I am using Spring Boot, Spring Web, Spring Data, Spring Security, PostgreSQL, Apache Tomcat in my project. I have already tried to generate pages using jsp, jstl, and it went well. However, I had huge problems via carrying already created static pages, to jsp format, because of syntax. Thanks.

      See more
      jOOQ logo

      jOOQ

      108
      97
      1
      A light database-mapping software library
      108
      97
      + 1
      1
      PROS OF JOOQ
      • 1
        Easy dsl
      CONS OF JOOQ
        Be the first to leave a con

        related jOOQ posts

        MySQL logo

        MySQL

        124.8K
        105.6K
        3.8K
        The world's most popular open source database
        124.8K
        105.6K
        + 1
        3.8K
        PROS OF MYSQL
        • 800
          Sql
        • 679
          Free
        • 562
          Easy
        • 528
          Widely used
        • 490
          Open source
        • 180
          High availability
        • 160
          Cross-platform support
        • 104
          Great community
        • 79
          Secure
        • 75
          Full-text indexing and searching
        • 26
          Fast, open, available
        • 16
          Reliable
        • 16
          SSL support
        • 15
          Robust
        • 9
          Enterprise Version
        • 7
          Easy to set up on all platforms
        • 3
          NoSQL access to JSON data type
        • 1
          Relational database
        • 1
          Easy, light, scalable
        • 1
          Sequel Pro (best SQL GUI)
        • 1
          Replica Support
        CONS OF MYSQL
        • 16
          Owned by a company with their own agenda
        • 3
          Can't roll back schema changes

        related MySQL posts

        Nick Rockwell
        SVP, Engineering at Fastly · | 46 upvotes · 3.9M views

        When I joined NYT there was already broad dissatisfaction with the LAMP (Linux Apache HTTP Server MySQL PHP) Stack and the front end framework, in particular. So, I wasn't passing judgment on it. I mean, LAMP's fine, you can do good work in LAMP. It's a little dated at this point, but it's not ... I didn't want to rip it out for its own sake, but everyone else was like, "We don't like this, it's really inflexible." And I remember from being outside the company when that was called MIT FIVE when it had launched. And been observing it from the outside, and I was like, you guys took so long to do that and you did it so carefully, and yet you're not happy with your decisions. Why is that? That was more the impetus. If we're going to do this again, how are we going to do it in a way that we're gonna get a better result?

        So we're moving quickly away from LAMP, I would say. So, right now, the new front end is React based and using Apollo. And we've been in a long, protracted, gradual rollout of the core experiences.

        React is now talking to GraphQL as a primary API. There's a Node.js back end, to the front end, which is mainly for server-side rendering, as well.

        Behind there, the main repository for the GraphQL server is a big table repository, that we call Bodega because it's a convenience store. And that reads off of a Kafka pipeline.

        See more
        Tim Abbott

        We've been using PostgreSQL since the very early days of Zulip, but we actually didn't use it from the beginning. Zulip started out as a MySQL project back in 2012, because we'd heard it was a good choice for a startup with a wide community. However, we found that even though we were using the Django ORM for most of our database access, we spent a lot of time fighting with MySQL. Issues ranged from bad collation defaults, to bad query plans which required a lot of manual query tweaks.

        We ended up getting so frustrated that we tried out PostgresQL, and the results were fantastic. We didn't have to do any real customization (just some tuning settings for how big a server we had), and all of our most important queries were faster out of the box. As a result, we were able to delete a bunch of custom queries escaping the ORM that we'd written to make the MySQL query planner happy (because postgres just did the right thing automatically).

        And then after that, we've just gotten a ton of value out of postgres. We use its excellent built-in full-text search, which has helped us avoid needing to bring in a tool like Elasticsearch, and we've really enjoyed features like its partial indexes, which saved us a lot of work adding unnecessary extra tables to get good performance for things like our "unread messages" and "starred messages" indexes.

        I can't recommend it highly enough.

        See more
        PostgreSQL logo

        PostgreSQL

        97.8K
        81.9K
        3.5K
        A powerful, open source object-relational database system
        97.8K
        81.9K
        + 1
        3.5K
        PROS OF POSTGRESQL
        • 763
          Relational database
        • 510
          High availability
        • 439
          Enterprise class database
        • 383
          Sql
        • 304
          Sql + nosql
        • 173
          Great community
        • 147
          Easy to setup
        • 131
          Heroku
        • 130
          Secure by default
        • 113
          Postgis
        • 50
          Supports Key-Value
        • 48
          Great JSON support
        • 34
          Cross platform
        • 33
          Extensible
        • 28
          Replication
        • 26
          Triggers
        • 23
          Multiversion concurrency control
        • 23
          Rollback
        • 21
          Open source
        • 18
          Heroku Add-on
        • 17
          Stable, Simple and Good Performance
        • 15
          Powerful
        • 13
          Lets be serious, what other SQL DB would you go for?
        • 11
          Good documentation
        • 9
          Scalable
        • 8
          Free
        • 8
          Reliable
        • 8
          Intelligent optimizer
        • 7
          Transactional DDL
        • 7
          Modern
        • 6
          One stop solution for all things sql no matter the os
        • 5
          Relational database with MVCC
        • 5
          Faster Development
        • 4
          Full-Text Search
        • 4
          Developer friendly
        • 3
          Excellent source code
        • 3
          Free version
        • 3
          Great DB for Transactional system or Application
        • 3
          Relational datanbase
        • 3
          search
        • 3
          Open-source
        • 2
          Text
        • 2
          Full-text
        • 1
          Can handle up to petabytes worth of size
        • 1
          Composability
        • 1
          Multiple procedural languages supported
        • 0
          Native
        CONS OF POSTGRESQL
        • 10
          Table/index bloatings

        related PostgreSQL posts

        Simon Reymann
        Senior Fullstack Developer at QUANTUSflow Software GmbH · | 30 upvotes · 10.7M views

        Our whole DevOps stack consists of the following tools:

        • GitHub (incl. GitHub Pages/Markdown for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
        • Respectively Git as revision control system
        • SourceTree as Git GUI
        • Visual Studio Code as IDE
        • CircleCI for continuous integration (automatize development process)
        • Prettier / TSLint / ESLint as code linter
        • SonarQube as quality gate
        • Docker as container management (incl. Docker Compose for multi-container application management)
        • VirtualBox for operating system simulation tests
        • Kubernetes as cluster management for docker containers
        • Heroku for deploying in test environments
        • nginx as web server (preferably used as facade server in production environment)
        • SSLMate (using OpenSSL) for certificate management
        • Amazon EC2 (incl. Amazon S3) for deploying in stage (production-like) and production environments
        • PostgreSQL as preferred database system
        • Redis as preferred in-memory database/store (great for caching)

        The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:

        • Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
        • Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
        • Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
        • Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
        • Scalability: All-in-one framework for distributed systems.
        • Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.
        See more
        Jeyabalaji Subramanian

        Recently we were looking at a few robust and cost-effective ways of replicating the data that resides in our production MongoDB to a PostgreSQL database for data warehousing and business intelligence.

        We set ourselves the following criteria for the optimal tool that would do this job: - The data replication must be near real-time, yet it should NOT impact the production database - The data replication must be horizontally scalable (based on the load), asynchronous & crash-resilient

        Based on the above criteria, we selected the following tools to perform the end to end data replication:

        We chose MongoDB Stitch for picking up the changes in the source database. It is the serverless platform from MongoDB. One of the services offered by MongoDB Stitch is Stitch Triggers. Using stitch triggers, you can execute a serverless function (in Node.js) in real time in response to changes in the database. When there are a lot of database changes, Stitch automatically "feeds forward" these changes through an asynchronous queue.

        We chose Amazon SQS as the pipe / message backbone for communicating the changes from MongoDB to our own replication service. Interestingly enough, MongoDB stitch offers integration with AWS services.

        In the Node.js function, we wrote minimal functionality to communicate the database changes (insert / update / delete / replace) to Amazon SQS.

        Next we wrote a minimal micro-service in Python to listen to the message events on SQS, pickup the data payload & mirror the DB changes on to the target Data warehouse. We implemented source data to target data translation by modelling target table structures through SQLAlchemy . We deployed this micro-service as AWS Lambda with Zappa. With Zappa, deploying your services as event-driven & horizontally scalable Lambda service is dumb-easy.

        In the end, we got to implement a highly scalable near realtime Change Data Replication service that "works" and deployed to production in a matter of few days!

        See more
        MongoDB logo

        MongoDB

        93.2K
        80.4K
        4.1K
        The database for giant ideas
        93.2K
        80.4K
        + 1
        4.1K
        PROS OF MONGODB
        • 827
          Document-oriented storage
        • 593
          No sql
        • 553
          Ease of use
        • 464
          Fast
        • 410
          High performance
        • 255
          Free
        • 218
          Open source
        • 180
          Flexible
        • 145
          Replication & high availability
        • 112
          Easy to maintain
        • 42
          Querying
        • 39
          Easy scalability
        • 38
          Auto-sharding
        • 37
          High availability
        • 31
          Map/reduce
        • 27
          Document database
        • 25
          Easy setup
        • 25
          Full index support
        • 16
          Reliable
        • 15
          Fast in-place updates
        • 14
          Agile programming, flexible, fast
        • 12
          No database migrations
        • 8
          Easy integration with Node.Js
        • 8
          Enterprise
        • 6
          Enterprise Support
        • 5
          Great NoSQL DB
        • 4
          Support for many languages through different drivers
        • 3
          Schemaless
        • 3
          Aggregation Framework
        • 3
          Drivers support is good
        • 2
          Fast
        • 2
          Managed service
        • 2
          Easy to Scale
        • 2
          Awesome
        • 2
          Consistent
        • 1
          Good GUI
        • 1
          Acid Compliant
        CONS OF MONGODB
        • 6
          Very slowly for connected models that require joins
        • 3
          Not acid compliant
        • 2
          Proprietary query language

        related MongoDB posts

        Jeyabalaji Subramanian

        Recently we were looking at a few robust and cost-effective ways of replicating the data that resides in our production MongoDB to a PostgreSQL database for data warehousing and business intelligence.

        We set ourselves the following criteria for the optimal tool that would do this job: - The data replication must be near real-time, yet it should NOT impact the production database - The data replication must be horizontally scalable (based on the load), asynchronous & crash-resilient

        Based on the above criteria, we selected the following tools to perform the end to end data replication:

        We chose MongoDB Stitch for picking up the changes in the source database. It is the serverless platform from MongoDB. One of the services offered by MongoDB Stitch is Stitch Triggers. Using stitch triggers, you can execute a serverless function (in Node.js) in real time in response to changes in the database. When there are a lot of database changes, Stitch automatically "feeds forward" these changes through an asynchronous queue.

        We chose Amazon SQS as the pipe / message backbone for communicating the changes from MongoDB to our own replication service. Interestingly enough, MongoDB stitch offers integration with AWS services.

        In the Node.js function, we wrote minimal functionality to communicate the database changes (insert / update / delete / replace) to Amazon SQS.

        Next we wrote a minimal micro-service in Python to listen to the message events on SQS, pickup the data payload & mirror the DB changes on to the target Data warehouse. We implemented source data to target data translation by modelling target table structures through SQLAlchemy . We deployed this micro-service as AWS Lambda with Zappa. With Zappa, deploying your services as event-driven & horizontally scalable Lambda service is dumb-easy.

        In the end, we got to implement a highly scalable near realtime Change Data Replication service that "works" and deployed to production in a matter of few days!

        See more
        Robert Zuber

        We use MongoDB as our primary #datastore. Mongo's approach to replica sets enables some fantastic patterns for operations like maintenance, backups, and #ETL.

        As we pull #microservices from our #monolith, we are taking the opportunity to build them with their own datastores using PostgreSQL. We also use Redis to cache data we’d never store permanently, and to rate-limit our requests to partners’ APIs (like GitHub).

        When we’re dealing with large blobs of immutable data (logs, artifacts, and test results), we store them in Amazon S3. We handle any side-effects of S3’s eventual consistency model within our own code. This ensures that we deal with user requests correctly while writes are in process.

        See more
        Redis logo

        Redis

        59.2K
        45.5K
        3.9K
        Open source (BSD licensed), in-memory data structure store
        59.2K
        45.5K
        + 1
        3.9K
        PROS OF REDIS
        • 886
          Performance
        • 542
          Super fast
        • 513
          Ease of use
        • 444
          In-memory cache
        • 324
          Advanced key-value cache
        • 194
          Open source
        • 182
          Easy to deploy
        • 164
          Stable
        • 155
          Free
        • 121
          Fast
        • 42
          High-Performance
        • 40
          High Availability
        • 35
          Data Structures
        • 32
          Very Scalable
        • 24
          Replication
        • 22
          Great community
        • 22
          Pub/Sub
        • 19
          "NoSQL" key-value data store
        • 16
          Hashes
        • 13
          Sets
        • 11
          Sorted Sets
        • 10
          NoSQL
        • 10
          Lists
        • 9
          Async replication
        • 9
          BSD licensed
        • 8
          Bitmaps
        • 8
          Integrates super easy with Sidekiq for Rails background
        • 7
          Keys with a limited time-to-live
        • 7
          Open Source
        • 6
          Lua scripting
        • 6
          Strings
        • 5
          Awesomeness for Free
        • 5
          Hyperloglogs
        • 4
          Transactions
        • 4
          Outstanding performance
        • 4
          Runs server side LUA
        • 4
          LRU eviction of keys
        • 4
          Feature Rich
        • 4
          Written in ANSI C
        • 4
          Networked
        • 3
          Data structure server
        • 3
          Performance & ease of use
        • 2
          Dont save data if no subscribers are found
        • 2
          Automatic failover
        • 2
          Easy to use
        • 2
          Temporarily kept on disk
        • 2
          Scalable
        • 2
          Existing Laravel Integration
        • 2
          Channels concept
        • 2
          Object [key/value] size each 500 MB
        • 2
          Simple
        CONS OF REDIS
        • 15
          Cannot query objects directly
        • 3
          No secondary indexes for non-numeric data types
        • 1
          No WAL

        related Redis posts

        Russel Werner
        Lead Engineer at StackShare · | 32 upvotes · 2.6M views

        StackShare Feed is built entirely with React, Glamorous, and Apollo. One of our objectives with the public launch of the Feed was to enable a Server-side rendered (SSR) experience for our organic search traffic. When you visit the StackShare Feed, and you aren't logged in, you are delivered the Trending feed experience. We use an in-house Node.js rendering microservice to generate this HTML. This microservice needs to run and serve requests independent of our Rails web app. Up until recently, we had a mono-repo with our Rails and React code living happily together and all served from the same web process. In order to deploy our SSR app into a Heroku environment, we needed to split out our front-end application into a separate repo in GitHub. The driving factor in this decision was mostly due to limitations imposed by Heroku specifically with how processes can't communicate with each other. A new SSR app was created in Heroku and linked directly to the frontend repo so it stays in-sync with changes.

        Related to this, we need a way to "deploy" our frontend changes to various server environments without building & releasing the entire Ruby application. We built a hybrid Amazon S3 Amazon CloudFront solution to host our Webpack bundles. A new CircleCI script builds the bundles and uploads them to S3. The final step in our rollout is to update some keys in Redis so our Rails app knows which bundles to serve. The result of these efforts were significant. Our frontend team now moves independently of our backend team, our build & release process takes only a few minutes, we are now using an edge CDN to serve JS assets, and we have pre-rendered React pages!

        #StackDecisionsLaunch #SSR #Microservices #FrontEndRepoSplit

        See more
        Simon Reymann
        Senior Fullstack Developer at QUANTUSflow Software GmbH · | 30 upvotes · 10.7M views

        Our whole DevOps stack consists of the following tools:

        • GitHub (incl. GitHub Pages/Markdown for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
        • Respectively Git as revision control system
        • SourceTree as Git GUI
        • Visual Studio Code as IDE
        • CircleCI for continuous integration (automatize development process)
        • Prettier / TSLint / ESLint as code linter
        • SonarQube as quality gate
        • Docker as container management (incl. Docker Compose for multi-container application management)
        • VirtualBox for operating system simulation tests
        • Kubernetes as cluster management for docker containers
        • Heroku for deploying in test environments
        • nginx as web server (preferably used as facade server in production environment)
        • SSLMate (using OpenSSL) for certificate management
        • Amazon EC2 (incl. Amazon S3) for deploying in stage (production-like) and production environments
        • PostgreSQL as preferred database system
        • Redis as preferred in-memory database/store (great for caching)

        The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:

        • Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
        • Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
        • Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
        • Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
        • Scalability: All-in-one framework for distributed systems.
        • Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.
        See more