Alternatives to Amazon SimpleDB

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.

Our most popular (& controversial!) article to date on the Uber Engineering blog in 3+ yrs. Why we moved from PostgreSQL to MySQL. In essence, it was due to a variety of limitations of Postgres at the time. Fun fact -- earlier in Uber's history we'd actually moved from MySQL to Postgres before switching back for good, & though we published the article in Summer 2016 we haven't looked back since:

The early architecture of Uber consisted of a monolithic backend application written in Python that used Postgres for data persistence. Since that time, the architecture of Uber has changed significantly, to a model of microservices and new data platforms. Specifically, in many of the cases where we previously used Postgres, we now use Schemaless, a novel database sharding layer built on top of MySQL (https://eng.uber.com/schemaless-part-one/). In this article, we’ll explore some of the drawbacks we found with Postgres and explain the decision to build Schemaless and other backend services on top of MySQL:

https://eng.uber.com/mysql-migration/

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!

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.

We are in the process of building a modern content platform to deliver our content through various channels. We decided to go with Microservices architecture as we wanted scale. Microservice architecture style is an approach to developing an application as a suite of small independently deployable services built around specific business capabilities. You can gain modularity, extensive parallelism and cost-effective scaling by deploying services across many distributed servers. Microservices modularity facilitates independent updates/deployments, and helps to avoid single point of failure, which can help prevent large-scale outages. We also decided to use Event Driven Architecture pattern which is a popular distributed asynchronous architecture pattern used to produce highly scalable applications. The event-driven architecture is made up of highly decoupled, single-purpose event processing components that asynchronously receive and process events.

To build our #Backend capabilities we decided to use the following: 1. #Microservices - Java with Spring Boot , Node.js with ExpressJS and Python with Flask 2. #Eventsourcingframework - Amazon Kinesis , Amazon Kinesis Firehose , Amazon SNS , Amazon SQS, AWS Lambda 3. #Data - Amazon RDS , Amazon DynamoDB , Amazon S3 , MongoDB Atlas

To build #Webapps we decided to use Angular 2 with RxJS

#Devops - GitHub , Travis CI , Terraform , Docker , Serverless

Back in 2014, I was given an opportunity to re-architect SmartZip Analytics platform, and flagship product: SmartTargeting. This is a SaaS software helping real estate professionals keeping up with their prospects and leads in a given neighborhood/territory, finding out (thanks to predictive analytics) who's the most likely to list/sell their home, and running cross-channel marketing automation against them: direct mail, online ads, email... The company also does provide Data APIs to Enterprise customers.

I had inherited years and years of technical debt and I knew things had to change radically. The first enabler to this was to make use of the cloud and go with AWS, so we would stop re-inventing the wheel, and build around managed/scalable services.

For the SaaS product, we kept on working with Rails as this was what my team had the most knowledge in. We've however broken up the monolith and decoupled the front-end application from the backend thanks to the use of Rails API so we'd get independently scalable micro-services from now on.

Our various applications could now be deployed using AWS Elastic Beanstalk so we wouldn't waste any more efforts writing time-consuming Capistrano deployment scripts for instance. Combined with Docker so our application would run within its own container, independently from the underlying host configuration.

Storage-wise, we went with Amazon S3 and ditched any pre-existing local or network storage people used to deal with in our legacy systems. On the database side: Amazon RDS / MySQL initially. Ultimately migrated to Amazon RDS for Aurora / MySQL when it got released. Once again, here you need a managed service your cloud provider handles for you.

Future improvements / technology decisions included:

Caching: Amazon ElastiCache / Memcached CDN: Amazon CloudFront Systems Integration: Segment / Zapier Data-warehousing: Amazon Redshift BI: Amazon Quicksight / Superset Search: Elasticsearch / Amazon Elasticsearch Service / Algolia Monitoring: New Relic

As our usage grows, patterns changed, and/or our business needs evolved, my role as Engineering Manager then Director of Engineering was also to ensure my team kept on learning and innovating, while delivering on business value.

One of these innovations was to get ourselves into Serverless : Adopting AWS Lambda was a big step forward. At the time, only available for Node.js (Not Ruby ) but a great way to handle cost efficiency, unpredictable traffic, sudden bursts of traffic... Ultimately you want the whole chain of services involved in a call to be serverless, and that's when we've started leveraging Amazon DynamoDB on these projects so they'd be fully scalable.

Fontumi focuses on the development of telecommunications solutions. We have opted for technologies that allow agile development and great scalability.

Firebase and Node.js + FeathersJS are technologies that we have used on the server side. Vue.js is our main framework for clients.

Our latest products launched have been focused on the integration of AI systems for enriched conversations. Google Compute Engine , along with Dialogflow and Cloud Firestore have been important tools for this work.

Git + GitHub + Visual Studio Code is a killer stack.

We are building a social media app, where users will post images, like their post, and make friends based on their interest. We are currently using Cloud Firestore and Firebase Realtime Database. We are looking for another database like Amazon DynamoDB; how much this decision can be efficient in terms of pricing and overhead?

We have an in-house build experiment management system. We produce samples as input to the next step, which then could produce 1 sample(1-1) and many samples (1 - many). There are many steps like this. So far, we are tracking genealogy (limited tracking) in the MySQL database, which is becoming hard to trace back to the original material or sample(I can give more details if required). So, we are considering a Graph database. I am requesting advice from the experts.

1. Is a graph database the right choice, or can we manage with RDBMS?
2. If RDBMS, which RDMS, which feature, or which approach could make this manageable or sustainable
3. If Graph database(Neo4j, OrientDB, Azure Cosmos DB, Amazon Neptune, ArangoDB), which one is good, and what are the best practices?

I am sorry that this might be a loaded question.

Google Maps lets "property owners and their authorized representatives" upload indoor maps, but this appears to lack navigation ("wayfinding").

MappedIn is a platform and has SDKs for building indoor mapping experiences (https://www.mappedin.com/) and ESRI ArcGIS also offers some indoor mapping tools (https://www.esri.com/en-us/arcgis/indoor-gis/overview). Finally, there used to be a company called LocusLabs that is now a part of Atrius and they were often integrated into airlines' apps to provide airport maps with wayfinding (https://atrius.com/solutions/personal-experiences/personal-wayfinder/).

I previously worked at Mapbox and while I believe that it's a great platform for building map-based experiences, they don't have any simple solutions for indoor wayfinding. If I were doing this for fun as a side-project and prioritized saving money over saving time, here is what I would do:

• Create a graph-based dataset representing the walking paths around your university, where nodes/vertexes represent the intersections of paths, and edges represent paths (literally paths outside, hallways, short path segments that represent entering rooms). You could store this in a hosted graph-based database like Neo4j, Amazon Neptune , or Azure Cosmos DB (with its Gremlin API) and use built-in "shortest path" queries, or deploy a PostgreSQL service with pgRouting.

• Add two properties to each edge: one property for the distance between its nodes (libraries like @turf/helpers will have a distance function if you have the latitude & longitude of each node), and another property estimating the walking time (based on the distance). Once you have these values saved in a graph-based format, you should be able to easily query and find the data representation of paths between two points.

• At this point, you'd have the routing problem solved and it would come down to building a UI. Mapbox arguably leads the industry in developer tools for custom map experiences. You could convert your nodes/edges to GeoJSON, then either upload to Mapbox and create a Tileset to visualize the paths, or add the GeoJSON to the map on the fly.

*You might be able to use open source routing tools like OSRM (https://github.com/Project-OSRM/osrm-backend/issues/6257) or Graphhopper (instead of a custom graph database implementation), but it would likely be more involved to maintain these services.

Context: I wanted to create an end to end IoT data pipeline simulation in Google Cloud IoT Core and other GCP services. I never touched Terraform meaningfully until working on this project, and it's one of the best explorations in my development career. The documentation and syntax is incredibly human-readable and friendly. I'm used to building infrastructure through the google apis via Python , but I'm so glad past Sung did not make that decision. I was tempted to use Google Cloud Deployment Manager, but the templates were a bit convoluted by first impression. I'm glad past Sung did not make this decision either.

Solution: Leveraging Google Cloud Build Google Cloud Run Google Cloud Bigtable Google BigQuery Google Cloud Storage Google Compute Engine along with some other fun tools, I can deploy over 40 GCP resources using Terraform!

Check Out My Architecture: CLICK ME

Check out the GitHub repo attached

We are building a social media app, where users will post images, like their post, and make friends based on their interest. We are currently using Cloud Firestore and Firebase Realtime Database. We are looking for another database like Amazon DynamoDB; how much this decision can be efficient in terms of pricing and overhead?