Alternatives to Oracle PL/SQL

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'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

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.

I just finished the very first version of my new hobby project: #MovieGeeks. It is a minimalist online movie catalog for you to save the movies you want to see and for rating the movies you already saw. This is just the beginning as I am planning to add more features on the lines of sharing and discovery

For the #BackEnd I decided to use Node.js , GraphQL and MongoDB:

1. Node.js has a huge community so it will always be a safe choice in terms of libraries and finding solutions to problems you may have

2. GraphQL because I needed to improve my skills with it and because I was never comfortable with the usual REST approach. I believe GraphQL is a better option as it feels more natural to write apis, it improves the development velocity, by definition it fixes the over-fetching and under-fetching problem that is so common on REST apis, and on top of that, the community is getting bigger and bigger.

3. MongoDB was my choice for the database as I already have a lot of experience working on it and because, despite of some bad reputation it has acquired in the last months, I still believe it is a powerful database for at least a very long list of use cases such as the one I needed for my website

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 initially started out with Heroku as our PaaS provider due to a desire to use it by our original developer for our Ruby on Rails application/website at the time. We were finding response times slow, it was painfully slow, sometimes taking 10 seconds to start loading the main page. Moving up to the next "compute" level was going to be very expensive.

We moved our site over to AWS Elastic Beanstalk , not only did response times on the site practically become instant, our cloud bill for the application was cut in half.

In database world we are currently using Amazon RDS for PostgreSQL also, we have both MariaDB and Microsoft SQL Server both hosted on Amazon RDS. The plan is to migrate to AWS Aurora Serverless for all 3 of those database systems.

Additional services we use for our public applications: AWS Lambda, Python, Redis, Memcached, AWS Elastic Load Balancing (ELB), Amazon Elasticsearch Service, Amazon ElastiCache

I am a Microsoft SQL Server programmer who is a bit out of practice. I have been asked to assist on a new project. The overall purpose is to organize a large number of recordings so that they can be searched. I have an enormous music library but my songs are several hours long. I need to include things like time, date and location of the recording. I don't have a problem with the general database design. I have two primary questions:

1. I need to use either MySQL or PostgreSQL on a Linux based OS. Which would be better for this application?
2. I have not dealt with a sound based data type before. How do I store that and put it in a table? Thank you.

I need to add a DBMS to my stack, but I don't know which. I'm tempted to learn SQLite since it would be useful to me with its focus on local access without concurrency. However, doing so feels like I would be defeating the purpose of trying to expand my skill set since it seems like most enterprise applications have the opposite requirements.

To be able to apply what I learn to more projects, what should I try to learn? MySQL? PostgreSQL? Something else? Is there a comfortable middle ground between high applicability and ease of use?

While I love and use PostgreSQL , I would definitely recommend having a look at SQLite as well. It can be a solid database for lots of applications and it brings some advantages in terms of handling: you don't need a server running, which makes things like testing, deploying or backing up databases much easier. Through the ORM in Django you are one abstraction level away from your database anyway and switching later on is definitely an option, but I believe SQLite is very good for pretty much all the small applications you can think of.

We primarily use MariaDB but use PostgreSQL as a part of GitLab , Sentry and Nextcloud , which (initially) forced us to use it anyways. While this isn't much of a decision – because we didn't have one (ha ha) – we learned to love the perks and advantages of PostgreSQL anyways. PostgreSQL's extension system makes it even more flexible than a lot of the other SQL-based DBs (that only offer stored procedures) and the additional JOIN options, the enhanced role management and the different authentication options came in really handy, when doing manual maintenance on the databases.

I'm researching what Technology Stack I should use to build my product (something like food delivery App) for Web, iOS, and Android Apps. Please advise which technologies you would recommend from a Scalability, Reliability, Cost, and Efficiency standpoint for a start-up. Here are the technologies I came up with, feel free to suggest any new technology even it's not in the list below.

For Mobile Apps -

1. native languages like Swift for IOS and Java/Kotlin for Android
2. or cross-platform languages like React Native for both IOS and Android Apps

For UI -

1. React

For Back-End or APIs -

1. Node.js
2. PHP

For Database -

1. PostgreSQL
2. MySQL
3. Cloud Firestore
4. MariaDB

Thanks!