Alternatives to Stellar logo

Alternatives to Stellar

Tokamak, Ethereum, Ripple, MySQL, and PostgreSQL are the most popular alternatives and competitors to Stellar.
6
28
+ 1
0

What is Stellar and what are its top alternatives?

Stellar is a decentralized payment network that allows for fast, low-cost cross-border transactions using its native cryptocurrency Lumens (XLM). It aims to make money transfers more accessible and efficient, with features like atomic swaps, multi-currency support, and smart contracts. However, Stellar's network can sometimes face congestion issues during high traffic periods, and its governance structure has been criticized for not being fully decentralized.

  1. Ripple: Ripple is known for its digital payment protocol and cryptocurrency XRP. It focuses on fast transactions and low fees, making it a popular choice for cross-border payments. However, Ripple has faced regulatory challenges due to its centralized nature.
  2. Ethereum: Ethereum is a decentralized platform that enables smart contracts and decentralized applications (DApps). It offers a wider range of features compared to Stellar but can be more complex to navigate for beginners.
  3. Cardano: Cardano is a blockchain platform that offers scalability, security, and sustainability. It aims to provide a more efficient and secure alternative to traditional financial systems. However, Cardano is still under development and may face scalability challenges.
  4. EOS: EOS is a blockchain platform known for its scalability and user-friendly interface. It aims to support large-scale DApps and high transaction speeds. However, EOS has faced criticism for its centralized governance model.
  5. Tezos: Tezos is a blockchain platform that focuses on security and formal verification. It allows for on-chain governance and self-amendment, making it a unique alternative to Stellar. However, Tezos has faced some controversy related to its governance structure.
  6. Algorand: Algorand is a blockchain platform that aims to provide fast and secure transactions through its pure proof-of-stake consensus mechanism. It offers features like atomic swaps and multi-signature transactions. However, Algorand is a relatively newer player in the market.
  7. Nano: Nano is a cryptocurrency known for its instant transactions and zero fees. It utilizes a block-lattice structure to achieve scalability and energy efficiency. However, Nano's consensus mechanism has been criticized for being vulnerable to certain attacks.
  8. NEO: NEO is a blockchain platform that focuses on digitizing assets and smart contracts. It offers support for multiple programming languages and high transaction throughput. However, NEO's centralized consensus model has raised some concerns about decentralization.
  9. VeChain: VeChain is a blockchain platform that focuses on supply chain management and tracking. It offers features like IoT integration and tokenization of assets for enhanced transparency. However, VeChain's use case may not directly compete with Stellar's focus on payments.
  10. Chainlink: Chainlink is a decentralized oracle network that aims to connect smart contracts with real-world data. It offers secure and reliable data feeds for various applications. However, Chainlink's focus on oracles may not directly align with Stellar's use case in the payments sector.

Top Alternatives to Stellar

  • Tokamak
    Tokamak

    Fusion Reactor for Rust - Atom Rust IDE

  • Ethereum
    Ethereum

    A decentralized platform for applications that run exactly as programmed without any chance of fraud, censorship or third-party interference. ...

  • 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 ...

Stellar alternatives & related posts

Tokamak logo

Tokamak

2
0
A Rust IDE for Atom
2
0
PROS OF TOKAMAK
    Be the first to leave a pro
    CONS OF TOKAMAK
      Be the first to leave a con

      related Tokamak posts

      Ethereum logo

      Ethereum

      865
      13
      Open source platform to write and distribute decentralized applications
      865
      13
      PROS OF ETHEREUM
      • 7
        Decentralized blockchain, most famous platform for DApp
      • 2
        Resistant to hash power attacks
      • 2
        Rich smart contract execution environment
      • 2
        #2 on capitalization after Bitcoin
      CONS OF ETHEREUM
      • 1
        High fees and lacks scalability

      related Ethereum posts

      Shared insights
      on
      EthereumEthereumParityParity

      Which is the best to use for integrating blockchain techniques into a secure cloud system: Parity, Ethereum, or hyperedge fabric?

      See more
      Shared insights
      on
      EthereumEthereumIPFS IPFS

      Hey! I am building an uber clone using blockchain. I am confused about where do I store the data of the drivers and riders and transaction information. IPFS or Ethereum? or do I store the IPFS URL on Ethereum? What would be the advantages of one over the other?

      See more
      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 · 18.6K 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

          125.6K
          3.8K
          The world's most popular open source database
          125.6K
          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

          98.4K
          3.5K
          A powerful, open source object-relational database system
          98.4K
          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 · 11.6M 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

          93.7K
          4.1K
          The database for giant ideas
          93.7K
          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

          59.5K
          3.9K
          Open source (BSD licensed), in-memory data structure store
          59.5K
          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 · 11.6M 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

          53.3K
          2K
          Store and retrieve any amount of data, at any time, from anywhere on the web
          53.3K
          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

          See more