Alternatives to TileDB logo

Alternatives to TileDB

Databricks, Snowflake, MongoDB, MySQL, and PostgreSQL are the most popular alternatives and competitors to TileDB.
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What is TileDB and what are its top alternatives?

TileDB is a versatile storage engine that enables efficient management of a wide range of data formats, supporting both dense and sparse multidimensional arrays with its revolutionary format. Its key features include support for cloud storage, seamless data access, high scalability, and compatibility with various programming languages. However, some limitations of TileDB include its relatively steep learning curve and lack of extensive documentation for certain functionalities.

  1. HDF5: HDF5 is a popular open-source data management framework that allows users to manage complex data collections effectively. It offers support for high-performance I/O operations, metadata management, and data compression. Pros of HDF5 include widespread adoption, strong community support, and versatile data management capabilities. However, it can be complex to use for beginners compared to TileDB.
  2. Apache Arrow: Apache Arrow is a cross-language development platform for in-memory data processing. It provides a columnar memory layout that enhances data processing speeds and efficiency. Key features of Apache Arrow include zero-copy data sharing and interoperability across multiple programming languages. However, compared to TileDB, it may have limited support for managing multidimensional arrays.
  3. Zarr: Zarr is a Python library for managing chunked, compressed, N-dimensional arrays. It offers scalability, efficient data storage, and easy integration with existing scientific computing ecosystems. Pros of Zarr include its simplicity and compatibility with NumPy arrays. However, it may lack some advanced features present in TileDB.
  4. Dask: Dask is a flexible parallel computing library in Python that allows users to scale their data analytics workflows efficiently. It enables parallel execution of tasks, out-of-core computing, and distributed computing across multiple machines. Compared to TileDB, Dask provides more extensive support for distributed computing but may not offer the same level of data storage capabilities.
  5. SciDB: SciDB is a scientific database management system designed for multidimensional array data analytics. It supports scalable storage, efficient query processing, and parallel computation for scientific applications. Pros of SciDB include its specialized focus on array data and high performance for analytical workloads. However, it may have a steeper learning curve compared to TileDB.
  6. OpenVDS: OpenVDS is an open-source data storage and access library for volumetric data sets. It provides efficient data compression, out-of-core processing, and support for remote data access. OpenVDS may offer better performance for volumetric data compared to TileDB, but it may have limited support for managing more general multidimensional arrays.
  7. Harp: Harp is a high-performance data management system optimized for handling large-scale scientific data. It offers support for data processing, visualization, and analysis in a distributed computing environment. Pros of Harp include its scalability and parallel computing capabilities. However, compared to TileDB, it may lack some advanced data storage features for multidimensional arrays.
  8. MemSQL: MemSQL is a distributed, in-memory database platform that offers real-time analytics and data processing capabilities. It provides high performance, scalability, and SQL support for analyzing large datasets. Compared to TileDB, MemSQL may excel in real-time analytics but may not offer the same level of specialized support for array data management.
  9. Kinetica: Kinetica is a GPU-accelerated database platform designed for real-time analytics and geospatial data processing. It offers high-speed data processing, in-database analytics, and support for streaming data. Pros of Kinetica include its speed and scalability for handling large volumes of data. However, compared to TileDB, Kinetica may have a narrower focus on specific use cases.
  10. RocksDB: RocksDB is an embeddable, persistent key-value store optimized for fast storage engines. It provides efficient data storage, low-latency access, and support for a wide range of workloads. Pros of RocksDB include its high performance and low overhead for data storage. However, compared to TileDB, RocksDB may not offer the same level of advanced features for managing complex array data structures.

Top Alternatives to TileDB

  • Databricks
    Databricks

    Databricks Unified Analytics Platform, from the original creators of Apache Spark™, unifies data science and engineering across the Machine Learning lifecycle from data preparation to experimentation and deployment of ML applications. ...

  • Snowflake
    Snowflake

    Snowflake eliminates the administration and management demands of traditional data warehouses and big data platforms. Snowflake is a true data warehouse as a service running on Amazon Web Services (AWS)—no infrastructure to manage and no knobs to turn. ...

  • 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. ...

  • 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. ...

  • 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 ...

  • GitHub Actions
    GitHub Actions

    It makes it easy to automate all your software workflows, now with world-class CI/CD. Build, test, and deploy your code right from GitHub. Make code reviews, branch management, and issue triaging work the way you want. ...

TileDB alternatives & related posts

Databricks logo

Databricks

495
749
8
A unified analytics platform, powered by Apache Spark
495
749
+ 1
8
PROS OF DATABRICKS
  • 1
    Best Performances on large datasets
  • 1
    True lakehouse architecture
  • 1
    Scalability
  • 1
    Databricks doesn't get access to your data
  • 1
    Usage Based Billing
  • 1
    Security
  • 1
    Data stays in your cloud account
  • 1
    Multicloud
CONS OF DATABRICKS
    Be the first to leave a con

    related Databricks posts

    Jan Vlnas
    Senior Software Engineer at Mews · | 5 upvotes · 454.9K views

    From my point of view, both OpenRefine and Apache Hive serve completely different purposes. OpenRefine is intended for interactive cleaning of messy data locally. You could work with their libraries to use some of OpenRefine features as part of your data pipeline (there are pointers in FAQ), but OpenRefine in general is intended for a single-user local operation.

    I can't recommend a particular alternative without better understanding of your use case. But if you are looking for an interactive tool to work with big data at scale, take a look at notebook environments like Jupyter, Databricks, or Deepnote. If you are building a data processing pipeline, consider also Apache Spark.

    Edit: Fixed references from Hadoop to Hive, which is actually closer to Spark.

    See more
    Snowflake logo

    Snowflake

    1.1K
    1.2K
    27
    The data warehouse built for the cloud
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    PROS OF SNOWFLAKE
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      Public and Private Data Sharing
    • 4
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    • 4
      Good Performance
    • 4
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    • 3
      Great Documentation
    • 2
      Serverless
    • 1
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    • 1
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    • 1
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    CONS OF SNOWFLAKE
      Be the first to leave a con

      related Snowflake posts

      I'm wondering if any Cloud Firestore users might be open to sharing some input and challenges encountered when trying to create a low-cost, low-latency data pipeline to their Analytics warehouse (e.g. Google BigQuery, Snowflake, etc...)

      I'm working with a platform by the name of Estuary.dev, an ETL/ELT and we are conducting some research on the pain points here to see if there are drawbacks of the Firestore->BQ extension and/or if users are seeking easy ways for getting nosql->fine-grained tabular data

      Please feel free to drop some knowledge/wish list stuff on me for a better pipeline here!

      See more
      Shared insights
      on
      Google BigQueryGoogle BigQuerySnowflakeSnowflake

      I use Google BigQuery because it makes is super easy to query and store data for analytics workloads. If you're using GCP, you're likely using BigQuery. However, running data viz tools directly connected to BigQuery will run pretty slow. They recently announced BI Engine which will hopefully compete well against big players like Snowflake when it comes to concurrency.

      What's nice too is that it has SQL-based ML tools, and it has great GIS support!

      See more
      MongoDB logo

      MongoDB

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      80.6K
      4.1K
      The database for giant ideas
      93.4K
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      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
      MySQL logo

      MySQL

      125.2K
      105.9K
      3.8K
      The world's most popular open source database
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      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

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      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.

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      PostgreSQL logo

      PostgreSQL

      98.1K
      82.1K
      3.5K
      A powerful, open source object-relational database system
      98.1K
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      PROS OF POSTGRESQL
      • 763
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      • 510
        High availability
      • 439
        Enterprise class database
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        Sql
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      • 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

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      Simon Reymann
      Senior Fullstack Developer at QUANTUSflow Software GmbH · | 30 upvotes · 11.1M 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
      Redis logo

      Redis

      59.4K
      45.6K
      3.9K
      Open source (BSD licensed), in-memory data structure store
      59.4K
      45.6K
      + 1
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        Super fast
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        Ease of use
      • 444
        In-memory cache
      • 324
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        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
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        Lists
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        Async replication
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        BSD licensed
      • 8
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        Integrates super easy with Sidekiq for Rails background
      • 7
        Keys with a limited time-to-live
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        Open Source
      • 6
        Lua scripting
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        Strings
      • 5
        Awesomeness for Free
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        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
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        Existing Laravel Integration
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        Channels concept
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        Object [key/value] size each 500 MB
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        Simple
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      • 15
        Cannot query objects directly
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        No secondary indexes for non-numeric data types
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        No WAL

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      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 · 11.1M 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

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      39.8K
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      Store and retrieve any amount of data, at any time, from anywhere on the web
      53.2K
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      • 590
        Reliable
      • 492
        Scalable
      • 456
        Cheap
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        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

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      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

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      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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      GitHub Actions

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      Automate your workflow from idea to production
      23.5K
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      PROS OF GITHUB ACTIONS
      • 8
        Integration with GitHub
      • 5
        Free
      • 3
        Easy to duplicate a workflow
      • 3
        Ready actions in Marketplace
      • 2
        Configs stored in .github
      • 2
        Docker Support
      • 2
        Read actions in Marketplace
      • 1
        Active Development Roadmap
      • 1
        Fast
      CONS OF GITHUB ACTIONS
      • 5
        Lacking [skip ci]
      • 4
        Lacking allow failure
      • 3
        Lacking job specific badges
      • 2
        No ssh login to servers
      • 1
        No Deployment Projects
      • 1
        No manual launch

      related GitHub Actions posts

      Somnath Mahale
      Engineering Leader at Altimetrik Corp. · | 8 upvotes · 1.8M views

      I am in the process of evaluating CircleCI, Drone.io, and Github Actions to cover my #CI/ CD needs. I would appreciate your advice on comparative study w.r.t. attributes like language-Inclusive support, code-base integration, performance, cost, maintenance, support, ease of use, ability to deal with big projects, etc. based on actual industry experience.

      Thanks in advance!

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      Shubham Chadokar
      Software Engineer Specialist at Kaleyra · | 6 upvotes · 118.6K views

      I have created a SaaS application. 1 backend service and 2 frontend services, all 3 run on different ports. I am using Amazon ECR images to deploy them on the EC2 server. My code is on GitHub. I want to automate this deployment process. How can I do this, and What tech stack should I use? It should be in sync with what I am currently using. On merge to master, it should build push the image to ECR and then later deploy again in the EC2 with the latest image. Maybe GitHub Actions or AWS CodePipeline would be ideal. Thanks, Shubham

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