Alternatives to Ethereum logo

Alternatives to Ethereum

Dash, Litecoin, Ripple, MySQL, and PostgreSQL are the most popular alternatives and competitors to Ethereum.
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What is Ethereum and what are its top alternatives?

Ethereum, a decentralized platform that enables smart contracts and decentralized applications to be built and operated without any downtime, fraud, control, or interference from a third party. Its key features include the use of smart contracts, which are self-executing contracts with the terms of the agreement between buyer and seller directly written into lines of code. Ethereum also allows the creation and deployment of decentralized applications (dApps) using blockchain technology. However, Ethereum has limitations such as scalability issues and high gas fees.

  1. Binance Smart Chain: Binance Smart Chain is a blockchain platform that enables developers to build decentralized applications. Key features include low transaction fees and fast transaction confirmation times. Pros: Lower fees compared to Ethereum. Cons: Less decentralization.
  2. Cardano: Cardano is a blockchain platform with a focus on sustainability, scalability, and interoperability. Key features include a proof-of-stake consensus algorithm and decentralized governance. Pros: Energy-efficient, scalable. Cons: Still in development stages.
  3. Polkadot: Polkadot is a multi-chain blockchain platform that enables interoperability between different blockchains. Key features include shared security and scalability. Pros: Interoperability, scalability. Cons: Complex ecosystem.
  4. Solana: Solana is a high-performance blockchain platform that is designed for fast and scalable decentralized applications. Key features include a high throughput and low latency. Pros: Fast transaction speeds, low fees. Cons: Limited ecosystem.
  5. Avalanche: Avalanche is a platform for launching decentralized applications and enterprise blockchain deployments. Key features include subnets for faster processing and interoperability. Pros: Scalability, high performance. Cons: Limited adoption.
  6. Tezos: Tezos is a self-amending blockchain platform that enables formal verification of smart contracts. Key features include on-chain governance and a proof-of-stake consensus algorithm. Pros: Upgradable, secure. Cons: Slower development process.
  7. NEAR Protocol: NEAR Protocol is a sharded, developer-friendly blockchain platform for dApps. Key features include low transaction fees and fast finality. Pros: User-friendly, fast transactions. Cons: Limited ecosystem.
  8. Theta Network: Theta Network is a decentralized video delivery network powered by blockchain technology. Key features include high-performance streaming and token rewards for network participants. Pros: Unique use case, rewards system. Cons: Limited scalability for other applications.
  9. Chainlink: Chainlink is a decentralized oracle network that enables smart contracts to securely interact with real-world data. Key features include data reliability and security. Pros: Data connectivity, decentralized. Cons: Limited to data fetching.
  10. Elrond: Elrond is a scalable blockchain platform that aims to provide fast and inexpensive transactions. Key features include adaptive state sharding and a secure proof-of-stake consensus mechanism. Pros: Scalability, efficiency. Cons: Limited adoption and awareness.

Top Alternatives to Ethereum

  • Dash
    Dash

    Dash is an API Documentation Browser and Code Snippet Manager. Dash stores snippets of code and instantly searches offline documentation sets for 150+ APIs. You can even generate your own docsets or request docsets to be included. ...

  • Litecoin
    Litecoin

    It is a peer-to-peer Internet currency that enables instant, near-zero cost payments to anyone in the world. It is an open source, global payment network that is fully decentralized without any central authorities. ...

  • Ripple
    Ripple

    It is an open source protocol which is designed to allow fast and cheap transactions. ...

  • 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. ...

  • Amazon S3
    Amazon S3

    Amazon Simple Storage Service provides a fully redundant data storage infrastructure for storing and retrieving any amount of data, at any time, from anywhere on the web ...

Ethereum alternatives & related posts

Dash logo

Dash

319
63
Gives your Mac instant offline access to 150+ API documentation sets
319
63
PROS OF DASH
  • 17
    Dozens of API docs and Cheat-Sheets
  • 12
    Great for offline use
  • 8
    Works with Alfred
  • 8
    Excellent documentation
  • 8
    Quick API search
  • 5
    Fast
  • 3
    Good integration with Xcode and AppCode
  • 2
    Great for mobile dev work
CONS OF DASH
    Be the first to leave a con

    related Dash posts

    Litecoin logo

    Litecoin

    17
    0
    A cryptocurrency that uses a faster payment confirmation schedule
    17
    0
    PROS OF LITECOIN
      Be the first to leave a pro
      CONS OF LITECOIN
        Be the first to leave a con

        related Litecoin posts

        Ripple logo

        Ripple

        30
        0
        A real-time gross settlement system, currency exchange and remittance network
        30
        0
        PROS OF RIPPLE
          Be the first to leave a pro
          CONS OF RIPPLE
            Be the first to leave a con

            related Ripple posts

            Shirin Hasavari
            Assistant Professor at Morgan State University · | 6 upvotes · 18.5K views
            Shared insights
            on
            RippleRippleHyperledger FabricHyperledger Fabric

            I am a faculty at Morgan State University. I would like to know the differences between Hyperledger Fabric and Ripple. I found a lot of info on Google, but they are not so clear. For example, one use case for the ripple is bank settlements. Can I have more detail about how it works for this use case? I appreciate your response.

            See more
            MySQL logo

            MySQL

            125.4K
            3.8K
            The world's most popular open source database
            125.4K
            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 · 4.1M 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

            98.3K
            3.5K
            A powerful, open source object-relational database system
            98.3K
            3.5K
            PROS OF POSTGRESQL
            • 764
              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 · 11.2M 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

            93.6K
            4.1K
            The database for giant ideas
            93.6K
            4.1K
            PROS OF MONGODB
            • 828
              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

            59.4K
            3.9K
            Open source (BSD licensed), in-memory data structure store
            59.4K
            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 · 2.8M 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 · 11.2M 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
            Amazon S3 logo

            Amazon S3

            53.2K
            2K
            Store and retrieve any amount of data, at any time, from anywhere on the web
            53.2K
            2K
            PROS OF AMAZON S3
            • 590
              Reliable
            • 492
              Scalable
            • 456
              Cheap
            • 329
              Simple & easy
            • 83
              Many sdks
            • 30
              Logical
            • 13
              Easy Setup
            • 11
              REST API
            • 11
              1000+ POPs
            • 6
              Secure
            • 4
              Easy
            • 4
              Plug and play
            • 3
              Web UI for uploading files
            • 2
              Faster on response
            • 2
              Flexible
            • 2
              GDPR ready
            • 1
              Easy to use
            • 1
              Plug-gable
            • 1
              Easy integration with CloudFront
            CONS OF AMAZON S3
            • 7
              Permissions take some time to get right
            • 6
              Requires a credit card
            • 6
              Takes time/work to organize buckets & folders properly
            • 3
              Complex to set up

            related Amazon S3 posts

            Ashish Singh
            Tech Lead, Big Data Platform at Pinterest · | 38 upvotes · 3.3M views

            To provide employees with the critical need of interactive querying, we’ve worked with Presto, an open-source distributed SQL query engine, over the years. Operating Presto at Pinterest’s scale has involved resolving quite a few challenges like, supporting deeply nested and huge thrift schemas, slow/ bad worker detection and remediation, auto-scaling cluster, graceful cluster shutdown and impersonation support for ldap authenticator.

            Our infrastructure is built on top of Amazon EC2 and we leverage Amazon S3 for storing our data. This separates compute and storage layers, and allows multiple compute clusters to share the S3 data.

            We have hundreds of petabytes of data and tens of thousands of Apache Hive tables. Our Presto clusters are comprised of a fleet of 450 r4.8xl EC2 instances. Presto clusters together have over 100 TBs of memory and 14K vcpu cores. Within Pinterest, we have close to more than 1,000 monthly active users (out of total 1,600+ Pinterest employees) using Presto, who run about 400K queries on these clusters per month.

            Each query submitted to Presto cluster is logged to a Kafka topic via Singer. Singer is a logging agent built at Pinterest and we talked about it in a previous post. Each query is logged when it is submitted and when it finishes. When a Presto cluster crashes, we will have query submitted events without corresponding query finished events. These events enable us to capture the effect of cluster crashes over time.

            Each Presto cluster at Pinterest has workers on a mix of dedicated AWS EC2 instances and Kubernetes pods. Kubernetes platform provides us with the capability to add and remove workers from a Presto cluster very quickly. The best-case latency on bringing up a new worker on Kubernetes is less than a minute. However, when the Kubernetes cluster itself is out of resources and needs to scale up, it can take up to ten minutes. Some other advantages of deploying on Kubernetes platform is that our Presto deployment becomes agnostic of cloud vendor, instance types, OS, etc.

            #BigData #AWS #DataScience #DataEngineering

            See more
            Russel Werner
            Lead Engineer at StackShare · | 32 upvotes · 2.8M 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

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