Alternatives to Amazon RDS

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.

Looker , Stitch , Amazon Redshift , dbt

We recently moved our Data Analytics and Business Intelligence tooling to Looker . It's already helping us create a solid process for reusable SQL-based data modeling, with consistent definitions across the entire organizations. Looker allows us to collaboratively build these version-controlled models and push the limits of what we've traditionally been able to accomplish with analytics with a lean team.

For Data Engineering, we're in the process of moving from maintaining our own ETL pipelines on AWS to a managed ELT system on Stitch. We're also evaluating the command line tool, dbt to manage data transformations. Our hope is that Stitch + dbt will streamline the ELT bit, allowing us to focus our energies on analyzing data, rather than managing it.

Docker containers on Mesos run their microservices with consistent configurations at scale, along with Aurora for long-running services and cron jobs.

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.

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.

Hi. We are planning to develop web, desktop, and mobile app for procurement, logistics, and contracts. Procure to Pay and Source to pay, spend management, supplier management, catalog management. ( similar to SAP Ariba, gap.com, coupa.com, ivalua.com vroozi.com, procurify.com

We got stuck when deciding which technology stack is good for the future. We look forward to your kind guidance that will help us.

We want to integrate with multiple databases with seamless bidirectional integration. What APIs and middleware available are best to achieve this? SAP HANA, Oracle, MySQL, MongoDB...

ASP.NET / Node.js / Laravel. ......?

Please guide us

I recently started a new position as a data scientist at an E-commerce company. The company is founded about 4-5 years ago and is new to many data-related areas. Specifically, I'm their first data science employee. So I have to take care of both data analysis tasks as well as bringing new technologies to the company.

1. They have used Elasticsearch (and Kibana) to have reporting dashboards on their daily purchases and users interactions on their e-commerce website.

2. They also use the Oracle database system to keep records of their daily turnovers and lists of their current products, clients, and sellers lists.

3. They use Data-Warehouse with cockpit 10 for generating reports on different aspects of their business including number 2 in this list.

At the moment, I grab batches of data from their system to perform predictive analytics from data science perspectives. In some cases, I use a static form of data such as monthly turnover, client values, and high-demand products, and run my predictive analysis using Python (VS code). Also, I use Google Datastudio or Google Sheets to present my findings. In other cases, I try to do time-series analysis using offline batches of data extracted from Elastic Search to do user recommendations and user personalization.

I really want to use modern data science tools such as Apache Spark, Google BigQuery, AWS, Azure, or others where they really fit. I think these tools can improve my performance as a data scientist and can provide more continuous analytics of their business interactions. But honestly, I'm not sure where each tool is needed and what part of their system should be replaced by or combined with the current state of technology to improve productivity from the above perspectives.

PostgreSQL Heroku Heroku Postgres Node.js Knex.js

Last week we rolled out a simple patch that decimated the response time of a Postgres query crucial to Checkly. It quite literally went from an average of ~100ms with peaks to 1 second to a steady 1ms to 10ms.

However, that patch was just the last step of a longer journey:

1. I looked at what API endpoints were using which queries and how their response time grew over time. Specifically the customer facing API endpoints that are directly responsible for rendering the first dashboard page of the product are crucial.

2. I looked at the Heroku metrics such as those reported by heroku pg:outlier and cross references that with "slowest response time" statistics.

3. I reproduced the production situation as best as possible on a local development machine and test my hypothesis that an composite index on a uuid field and a timestampz field would reduce response times.

This method secured the victory and we rolled out a new index last week. Response times plummeted. Read the full story in the blog post.

I could spin up an Amazon EC2 instance and install PostgreSQL myself, review latest configuration best practices, sort Amazon EBS storage for data, set up a snapshot process etc.

Alternatively I could use Amazon RDS, Amazon RDS for PostgreSQL or Heroku Postgres and have most of that work handled for me, by a team of world experts...

We use Go for the first-off due to our knowledge of it. Second off, it's highly performant and optimized for scalability. We run it using dockerized containers for our backend REST APIs.

For Frontend, we use React with Next.js at vercel. We use NextJS here mostly due to our need for Server Side Rendering and easier route management.

For Database, we use MySQL as it is first-off free and always has been in use with us. We use Google Cloud SQL from GCP that manages its storage and versions along with HA.

All stacks are free to use and get the best juice out of the system. We also use Redis for caching for enterprise-grade apps where data retrieval latency matters the most.

As far as the backend goes, we first had to decide which database will power most of Daily services. Considering relational databases vs document datbases, we decided that the relational model is a better fit for Daily as we have a lot of connections between the different entities. At the time MySQL was the only service available on Google Cloud SQL so this was out choice. In terms of #backend development Node.js powers most of our services, thanks to its amazing ecosystem there are a lot of modules publicly available to shorten the development time. Go is for the light services which are all about performance and delivering quickly the response, such as our redirector service.

We are embarking on a project of building a Django web application on Microsoft Azure. The debate holding us back is whether to with Azure SQL Database or Azure Database for PostgreSQL. From all the tutorials and video tutorials they use Azure Database for PostgreSQL but one team member is insisting on Azure SQL Database. Please advise of what to consider if I capitulate what database do I need to install locally to get the project moving.

Hi, I am trying to build a billing system for utilities. It will have a web app and a mobile app too. The USP of this system would be that the mobile application would support offline syncing, basically, let's say while doing the payment the internet goes down then when it's back the payment goes through. Basically, some features could work offline. So I am confused as to which DB to go for. A relational one like Azure SQL Database or a non-relational one like MongoDB?

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.

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!