PostgreSQL vs MySQL - A Real Developer Rates an AI Podcast

In the spirit of YouTube videos like a real hacker rates hacking scenes in movies, real chef rates fine dining movie scenes etc, I’m a real developer, and I’m going to listen to an AI-generated podcast comparing two database technologies and see what I think the podcast gets right and wrong. My viewpoint and knowledge is limited too, while I consider myself to have some expertise, I am interested to find out how well an AI podcast app explains this difficult and nuanced topic.

Disclaimer: This is an AI-generated podcast created by Continua AI.

I will put my human-generated commentary after each section, but let's start with the transcript. Here's the episode link if you'd like to listen along.

Transcript:

Part 1: introduction

Clara: Hello and welcome to the very first episode of Technology Deep Dive: Enterprise Tech at Scale! I'm Clara Chen, co-founder of Continua AI.

Leo: And I'm Leo Jimenez, the other half of Continua AI. We're thrilled to kick off this 10-episode podcast series exploring the foundational technologies that power today's most innovative companies.

Clara: That's right, Leo. Each episode, we'll deep-dive into complementary technologies, examining their strengths, trade-offs, and implementation strategies at scale. Our goal is to help our listeners understand these systems at a level that would impress even the most technical interviewer.

Leo: And speaking of interviews, if you're preparing for technical interviews, this series is designed with you in mind. Clara and I formed Continua AI specifically to help people ace technical interviews, and we're bringing that same practical approach to this podcast.

Clara: Today's episode is all about "RDBMS Powerhouses: PostgreSQL vs. MySQL." We'll explore the strengths and use cases of the two most popular relational database systems, examining real-world implementations at companies like Instagram and Uber.

Leo: And before we dive in, a quick note for our listeners. You can use episode chat for any questions about the content in today's episode. Just look for that feature in your podcast app, and we'll try to address as many questions as possible in future episodes.

Clara: Perfect! Shall we jump into those core architectural differences, Leo?

My Commentary

In the first section, the hosts are just introducing themselves and the topic. I notice that they introduce themselves as the co-founder of Continua AI rather than what they really are: AI podcast hosts. That's a little presumptuous of them! Back to the transcript:

Part 2: MVCC

Leo: Absolutely! Let's start with one of the fundamental differences between PostgreSQL and MySQL: their Multi-Version Concurrency Control implementations, or MVCC for short.

Clara: MVCC is such a critical concept. Essentially, it's how these databases handle concurrent access to data. PostgreSQL implements MVCC by creating a new version of a row whenever it's updated, while keeping the old version available for transactions that might still need it.

Leo: Right, and this approach means PostgreSQL never blocks readers with writers, which is a huge advantage for read-heavy workloads. MySQL, on the other hand, handles this differently depending on the storage engine you're using.

Clara: That's an important distinction. MySQL offers different storage engines, each with its own performance characteristics. InnoDB, which is the default since MySQL 5.5, does support MVCC, but its implementation differs from PostgreSQL's.

Leo: Exactly. MySQL's InnoDB uses a combination of MVCC and traditional locking mechanisms. It maintains a rollback segment where it stores the old versions of modified rows, but it also uses row-level locks for write operations.

Clara: And that's why we see different performance characteristics depending on your workload. PostgreSQL often takes the lead with more complex writes and intricate queries, while MySQL has traditionally excelled in read-heavy scenarios.

Leo: Transaction isolation levels are another key difference. Both support the standard isolation levels—Read Uncommitted, Read Committed, Repeatable Read, and Serializable—but they implement them differently.

Clara: PostgreSQL's default is Read Committed, while MySQL's InnoDB default is Repeatable Read. This affects data consistency guarantees and can impact application behavior if you're not careful.

Leo: Another major architectural difference is in their extension ecosystems. PostgreSQL is known for its extensibility—it allows you to create custom data types, operators, and even write functions in different programming languages.

Clara: Absolutely! PostgreSQL's extensibility is one of its greatest strengths. You can add functionality like PostGIS for spatial data or TimescaleDB for time-series without changing the core engine. This 'platform' approach has contributed significantly to PostgreSQL's growing popularity.

Leo: MySQL, while less extensible in terms of core functionality, has its own strengths with its rich ecosystem of tools, particularly those optimized for web applications. This has made it the backbone of countless websites over the years.

Clara: I think it's worth noting that these architectural differences really highlight the different design philosophies. PostgreSQL emphasizes standards compliance, extensibility, and data integrity, while MySQL prioritizes speed, ease of use, and practical features for common use cases.

Leo: That's a great summary. And these different philosophies directly influence when you might choose one over the other for your specific application needs.

My Commentary

The very first difference they point out is MVCC, which is crucial to understand. Actually, this is the biggest misunderstanding many developers have of PostgreSQL - they think that, when you use transactions, this prevents any of your queries from doing anything concurrent, and so as long as it happens in a transaction, it will be totally serial and you simply won't have concurrency control to worry about. In reality, PostgreSQL allows writes and reads to be happening concurrently, and the only guarantee it offers is about what happens within the scope of a particular transaction, not what might be happening concurrently while that transaction is running. From the official docs:

Unlike most other database systems which use locks for concurrency control, Postgres maintains data consistency by using a multiversion model. This means that while querying a database each transaction sees a snapshot of data (a database version) as it was some time ago, regardless of the current state of the underlying data. This protects the transaction from viewing inconsistent data that could be caused by (other) concurrent transaction updates on the same data rows, providing transaction isolation for each database session.

In my experience, often this key difference is overlooked because many applications don't actually need transactional consistency. Often we build applications using relational databases because they're battle-tested and we associate them with high performance (although in reality they are usually the cause of performance headaches rather than their solution... but I digress). So, although we often have the wrong picture in mind of how PostgreSQL works, it doesn't bite us until we reach a point where we actually do need transactional semantics (meaning: I need to change rows X, Y, Z in a certain order, and no one else can change the data in those rows until I’m finished, or else the relational order will no longer be coherent).

Long story short, I think the podcast does an excellent job highlighting the most important difference first. The other differences they discuss around extensibility are fair enough to mention, too - you can also use PostgreSQL as a timeseries DB and as a GIS DB as well. The other big selling point I have seen come up the most is the JSON support in PostgreSQL, although perhaps that trend has now come and gone. At enterprise scale, though, I find it is rarely a big benefit that a service that is specialized toward doing one thing well also does another thing, since I would rather just use the right tool for each job - however my experience using TimescaleDB has been positive and GIS non-existent so these may actually be cases that are very well suited to Postgres and not purely "add-ons".

Part 3: Instagram and Sharding

Clara: Now let's dive into a fascinating real-world example: Instagram's PostgreSQL sharding strategy. Instagram is one of the most high-profile PostgreSQL users, and they've scaled to billions of users with it.

Leo: It's an impressive case study in database scaling. Instagram started with a single PostgreSQL instance, but as they grew to hundreds of millions of users, they needed a way to horizontally scale their database layer.

Clara: Their solution was a custom sharding middleware layer. Essentially, they split their data across multiple PostgreSQL instances based on user ID. This allowed them to scale horizontally while maintaining the ACID properties they needed within each shard.

Leo: What I find particularly interesting is that they didn't abandon PostgreSQL for a NoSQL solution, which was a common path for companies facing similar scaling challenges. Instead, they leveraged PostgreSQL's strengths while building around its limitations.

Clara: Exactly! They recognized that for their workload, which required strong consistency and complex relationships between entities like users, photos, and comments, PostgreSQL was still the right choice.

Leo: Their sharding approach is quite elegant. They use what's called 'logical sharding' where application code determines which database shard to query based on the user ID. This keeps the database layer itself relatively simple.

Clara: And for read scaling, Instagram implements read/write splitting techniques. They direct write operations to the primary database instances while routing read queries to replicas. This significantly increases their read capacity without affecting write performance.

Leo: They've also developed sophisticated monitoring and alerting systems for database health. When you're operating at Instagram's scale, being able to quickly identify and respond to performance issues is crucial.

Clara: Absolutely. They monitor metrics like query performance, replication lag, connection counts, and disk usage. This gives them early warning signs of potential problems before they affect users.

Leo: I think there's an important lesson here for anyone in an interview situation discussing database scaling. The solution isn't always to switch to a completely different technology—sometimes it's about building the right architecture around a proven technology like PostgreSQL.

My Commentary

Ah, database sharding. A favorite topic in technical interviews, knowing the general principle behind this is important, but I think this is given way too much precedence in this podcast. If you've only just introduced PostgreSQL and you're already needing to shard it, you probably are wishing you did not use PostgreSQL. Businesses only do this when they're very sick (that is, when it would be too much work to migrate to something that actually is designed to go horizontal in this way).

What I mean is, if you can safely shard your relational database in a way that doesn't break your application semantics, then you actually didn't need to use a relational database in the first place. Relational databases are critical for when you need to define a relational order on the world and maintain that order, even in interesting circumstances like partial failures, network partitions, out of order writes, schema migrations, all the little interesting nuances that make it difficult to conform the real world to your idealized abstraction of what it should be (your database's relational schema).

I think Clara and Leo are reflecting a real bias that human developers have, too - we want to talk about interesting, difficult stories about how we made systems do things they weren't designed to do in some novel way. If you had used a suitable architecture from the jump, there wouldn't be anything to talk about in a technical interview. We often choose a technology knowing that we'll have to monkey-patch and replace it at scale, and we're generally ok with that decision.

And hey, for the audience that says that relational database sharding actually is something that is desirable and normal to do, there's a whole product out there you can use. But don't look at me, because I have never actually seen a problem for which this was the best solution.

Part 4: PostgreSQL to MySQL

Clara: Speaking of interesting case studies, let's talk about Uber's journey with database technologies. They actually went in the opposite direction of Instagram—they migrated from PostgreSQL to MySQL.

Leo: It's a fascinating story that highlights how different workloads can lead to different technology choices. Uber initially built their platform on PostgreSQL, but as they scaled to millions of rides globally, they encountered challenges.

Clara: The primary issues they faced were related to scaling write operations and achieving the level of high availability they needed for their global operation. They found that PostgreSQL's replication capabilities at the time weren't meeting their needs.

Leo: Right, this was back around 2016. PostgreSQL's replication features have improved significantly since then, but at the time, MySQL offered more mature replication capabilities, particularly for Uber's geographically distributed workload.

Clara: The technical challenges during the transition were enormous. They had to migrate billions of rows of data while maintaining service availability. They couldn't just take Uber offline for a weekend!

Leo: They developed a sophisticated dual-writing system where writes went to both the old PostgreSQL system and the new MySQL system simultaneously, with verification to ensure consistency. This allowed them to gradually cut over services with minimal disruption.

Clara: The performance improvements they achieved were significant. They reported better query performance, more efficient replication, and improved availability. MySQL's InnoDB storage engine was particularly well-suited to their transaction patterns.

Leo: One of the key lessons from Uber's migration was the importance of matching database technology to specific workload characteristics. They weren't making a statement that MySQL is universally better than PostgreSQL—just that it was better for their particular needs.

Clara: That's such an important point for interview preparation. When asked about database choices, the answer is almost always it depends on the specific requirements and constraints. There's rarely a one-size-fits-all solution.

Leo: Exactly. And Uber's case teaches us to be pragmatic rather than dogmatic about technology choices. They made a data-driven decision based on benchmarks and their specific scaling challenges.

My Commentary

I think the nuance presented here is really excellent. If I was in a systems design interview with a candidate and they explained both that there are good reasons to move from PostgreSQL to MySQL and good reasons to do the opposite (and could back it up), I would be really impressed by that.

Clara and Leo seem a little too impressed by dual-write migrations but I really appreciate that they are talking up an unseen and usually un-trumpeted part of working on large production systems: migrations. Migrations are a part of life and for many of us, they're the larger part of life.

Part 5: Indexing

Clara: Let's shift gears and talk about advanced indexing and query optimization techniques. This is an area where both PostgreSQL and MySQL offer powerful features, but with some key differences.

Leo: Absolutely! And this is definitely a topic that comes up in technical interviews. Starting with PostgreSQL, one of its standout features is support for partial indexes.

Clara: Partial indexes are incredibly powerful. Instead of indexing an entire table, you can create an index that only includes rows matching a certain condition. This can make your indexes much smaller and more efficient.

Leo: For example, if you have an orders table and you frequently query for 'pending' orders, you could create a partial index that only includes pending orders, rather than indexing the entire table.

Clara: PostgreSQL also supports expression indexes, where you index the result of an expression rather than just the column values. This is great for optimizing queries that use functions or expressions in their WHERE clauses.

Leo: A common example is case-insensitive searches. You could create an index on LOWER(email) to optimize queries that need to find email addresses regardless of case.

Clara: MySQL has been catching up in this area with functional indexes in recent versions, but PostgreSQL still has the edge in terms of flexibility.

Leo: Both databases support covering indexes, which include all the data needed to satisfy a query without having to look up the actual table data. These are called 'index-only scans' in PostgreSQL.

Clara: Index-only scans can dramatically improve performance for certain types of queries. If all the data you need is in the index itself, the database can avoid the extra step of fetching the full row from the table.

Leo: Query plan analysis is crucial for optimization in both databases. PostgreSQL's EXPLAIN ANALYZE command gives you detailed information about how a query is executed, including actual timing information.

Clara: MySQL has similar functionality with its EXPLAIN command, though historically PostgreSQL's query planner has been more sophisticated, especially for complex joins and subqueries.

Leo: One common performance pitfall candidates should know about is not understanding how NULL values are handled in indexes. In both databases, NULL values don't follow normal comparison rules, which can lead to unexpected query performance.

Clara: Another pitfall is not considering the impact of multi-column indexes. The order of columns in an index is crucial—it should match the way your queries filter and sort data.

Leo: And one more that I see trip up candidates: assuming that more indexes are always better. Each index adds overhead to write operations, so there's a tradeoff between read and write performance.

Clara: Great point. The key is to index strategically based on your actual query patterns, not just adding indexes indiscriminately.

My Commentary

Some good stuff in here, some not-so-good stuff. Partial indexes are a really specialty tool that you would only reach for if you have a pretty exact mastery of which queries you are seeing and which you are not seeing.

For me, these are all solutions in search of a problem. I would start with a discussion of what kinds of problems you commonly see, and then move on to how to diagnose them, and lastly how to fix them. As it is, we're kind of just throwing some tools at you to mention in interviews without really giving you the context you need to learn when you might reach for those tools.

EXPLAIN ANALYZE is a great diagnostic tool when you already know what you're looking for, but what I find myself using far more often in real life are the built in tools in AWS and GCP to figure out what my problems actually are before I start slinging around indices - but I suppose that is a viewpoint biased from working with cloud providers, which not everyone does.

Part 5: HA

Leo: Now let's talk about high-availability architectures, which is crucial for any production database deployment.

Clara: Absolutely. Both PostgreSQL and MySQL offer robust replication capabilities, but they approach it differently. In PostgreSQL, the primary replication mechanism is called 'streaming replication,' which works at the WAL (Write-Ahead Log) level.

Leo: Right, PostgreSQL's streaming replication sends WAL records to replica servers as they're generated, allowing replicas to stay very close to the primary in terms of data currency.

Clara: MySQL has traditionally used a binary log (binlog) based replication system. The primary server writes changes to its binary log, and replicas read from this log to apply the same changes.

Leo: MySQL has long been praised for its straightforward replication, making it easy to offload read traffic and distribute those queries across multiple servers. This has been one of its key strengths in web applications.

Clara: Both databases now support synchronous replication options, where the primary waits for confirmation from one or more replicas before considering a transaction committed. This gives you stronger durability guarantees.

Leo: For failover mechanisms, PostgreSQL historically required more manual intervention or third-party tools, while MySQL's Group Replication and InnoDB Cluster provide more automated failover capabilities.

Clara: PostgreSQL has been catching up with extensions like Patroni and tools like pg_auto_failover, which provide automated failover. The PostgreSQL community has really focused on improving these operational aspects in recent versions.

Leo: Connection pooling is another critical component of high-availability architectures. Tools like PgBouncer for PostgreSQL and ProxySQL for MySQL help manage database connections efficiently.

Clara: These connection poolers prevent the database from being overwhelmed with too many connections, which is especially important during traffic spikes or when recovering from an outage.

Leo: When it comes to data consistency during network partitions, both databases prioritize consistency over availability by default, following the CP side of the CAP theorem.

Clara: That's true, though with careful configuration, you can adjust this balance somewhat. For example, with PostgreSQL's synchronous_commit settings or MySQL's transaction isolation levels.

Leo: One approach I've seen work well in practice is having a primary database in each data center with asynchronous replication between data centers, combined with synchronous replication within each data center.

Clara: That's a great pattern. It gives you strong local consistency while still allowing operations to continue if there's a problem with the connection between data centers.

My Commentary

Again, I think it's a bit of a miss to talk about PostgreSQL HA without mentioning cloud providers, since the big ones provide HA configurations of PostgreSQL, so realistically this isn't a problem you need to think about unless you decide to host your database yourself (in your datacenter perhaps, or a k8s cluster that lives wherever). Maybe this is just my own limited viewpoint speaking but HA disaster recovery hasn't been a real concern in the past few years (if anything, my concern has been too many backups or too many cross-region replicas that are in place for disaster recovery increasing costs, rather than too few).

I think the discussion of connection pooling here is a big miss. Clara and Leo breeze over it as if connection pooling is a "feature" that lets you handle traffic spikes and helps you recover from outages, but in my experience, connection pooling for PostgreSQL is absolutely necessary for a database that is appropriately sized for the amount of traffic that your application actually does. To me, connection pooling is one of the top items to consider when deploying PostgreSQL, because connections are so heavyweight and expensive (compared to a similarly resourced MySQL database). If you deploy PostgreSQL without any connection pooling plan, it will bite you, and it will take down your app.

Leo: One approach I've seen work well in practice is having a primary database in each data center with asynchronous replication between data centers, combined with synchronous replication within each data center.

I'm a little skeptical that Leo has seen this work well in practice. This sounds like a nightmare in practice that would only work well if you've tested every inch of it and are extremely judicious in vetting changes to the stable configuration after you've done so. What I mean is, this is really dependent on how your app actually works. Sure, you can continue to make local changes if those changes can be safely merged back into the global primary when it is restored, but again - if this is possible, then you don't actually need to have a relational database.

Clara: That's a great pattern. It gives you strong local consistency while still allowing operations to continue if there's a problem with the connection between data centers.

Yeah, that's a great pattern. It lets you have CAP all at once with no downsides! Let's deploy it to prod and go enjoy a long vacation with Pagerduty on silent!

Part 6: Interview Questions

Clara: Let's wrap up with some interview preparation strategies specifically focused on RDBMS topics. These databases come up in almost every technical interview for backend or data roles.

Leo: Absolutely! First, let's discuss some common RDBMS interview questions and how to approach them. One frequent question is: "What are the differences between PostgreSQL and MySQL, and when would you choose one over the other?"

Clara: That's a classic! A strong answer would touch on what we've discussed today: PostgreSQL's stronger ACID compliance, rich feature set, and extensibility versus MySQL's speed, simplicity, and optimization for web applications.

Leo: Exactly. You'd want to mention that PostgreSQL might be preferred for complex applications with advanced data types or where data integrity is critical, while MySQL might be chosen for high-traffic web applications where read performance is paramount.

Clara: Another common question is about indexing strategies. Interviewers might ask, "How would you approach indexing a table with X million rows that needs to support these specific query patterns?"

Leo: For that type of question, demonstrate your understanding of different index types (B-tree, hash, GIN, etc.), when to use compound indexes, and how to analyze query plans to identify bottlenecks.

Clara: System design considerations are huge in interviews. Questions like, "How would you design a database architecture to support a global application with X million users?" test your knowledge of sharding, replication, and high-availability strategies.

Leo: Right, and this is where real-world examples like Instagram's and Uber's approaches can be really valuable to reference. Show that you understand the tradeoffs involved in different scaling strategies.

Clara: Performance troubleshooting scenarios are also common. An interviewer might say, "A query that used to take milliseconds now takes seconds. How would you diagnose and fix the issue?"

Leo: For that, walk through a systematic approach: examining query plans, checking for schema or data distribution changes, looking at system resources, and considering index optimizations or query rewrites.

Clara: To demonstrate deep technical knowledge of database internals, be prepared to explain concepts like MVCC, WAL (Write-Ahead Logging), and how different transaction isolation levels actually work under the hood.

Leo: And don't forget about data modeling questions! Being able to design a normalized schema for a given problem domain is still a fundamental skill that many interviewers look for.

Clara: Absolutely. The best way to prepare is through practice—actually working with these databases, optimizing queries, and understanding how they behave under different conditions.

Leo: One approach I recommend is to set up both PostgreSQL and MySQL locally, load them with some sample data, and experiment with the different features and optimizations we've discussed today.

Clara: And remember, when answering database questions in interviews, always consider the specific requirements of the problem. There's rarely a one-size-fits-all answer in database design and optimization.

My Commentary

PostgreSQL's stronger ACID compliance

This is vague. ACID compliance is a baseline expectation of both databases; stronger how? I will be charitable and assume Leo means that you get more choice in transactional semantics because of the MVCC, but really this is about selectively opting out of ACID when it isn't needed.

You'd want to mention that PostgreSQL might be preferred for complex applications with advanced data types or where data integrity is critical

Again, where data integrity is critical? Did I miss a bulletin somewhere that made MySQL somehow less ACID, less supportive of data integrity (if so, I hope my bank got the same bulletin, since they likely use MySQL to store my account balance...).

Clara: Performance troubleshooting scenarios are also common. An interviewer might say, "A query that used to take milliseconds now takes seconds. How would you diagnose and fix the issue?"

Leo: For that, walk through a systematic approach: examining query plans, checking for schema or data distribution changes, looking at system resources, and considering index optimizations or query rewrites.

I think this is basically right - what I'd really love to hear in a technical interview would be the realization that oftentimes, it's not the query itself that is slow, it's some other query that is making your query slow. I think people get very hyper-focused on indexes because they are very "in" the CS curricula and feel very tractable, but in my professional experience the things that tend to make queries slow are other queries that are somehow locking the rows you need or deadlocking your transaction, that kind of thing. Or, to put it another way, it's not so much the query is slow as it is that the system is slow - the query is just as symptom.

Wrap-up

If you enjoyed this episode, check out the whole series on Continua. There are some other interesting episodes in there like Redis vs Memcached, Snowflake vs BigQuery and MongoDB vs CouchDB. I think these are genuinely really good food for thought to get you thinking about these different technologies and great jumping-off points to learning new things.