I was sent this: Relational Algebra Is the Root of SQL Problems which is really brilliant and provides some helpful concrete examples of stuff SQL is really bad at.
The accompanying email was filled with nonsense about how important and world-changing SQL was.
I can't disagree. Back when disk was very expensive and very small, the SQL-based join strategies where essential for micro-managing every bit of data. Literally. Every Bit.
And then we would denormalize the structure for performance reasons. Because we always knew the SQL was terrible at a fairly large number of things.
Those days are behind us. We can now chose to use a document database, and make our lives simpler. Storage is relatively inexpensive, and the labor to normalize and denormalize data doesn't create significant value. The need to write stored procedures to turn a single conceptual operation into a bunch of inserts and updates was a symptom that this wasn't the best approach.
I've had many "But what about..." conversations regarding document databases.
"What about ad-hoc queries in SQL?"
"What about joins?"
- It's a space-saving technique. We don't need the overheads to save the space. The "update anomalies" still require careful design, and may lead to some decomposition of data into multiple documents. But the ruthless normalization shouldn't be seen as a requirement.
"What about the schema?"
- It's brittle and schema migration creates a lot of low-value labor. We can use Python JSONSchema to validate documents. See NoSQL Database doesn't Mean No Schema.
Transactional v. AnalyticalIt requires some care to understand the distinction between "transactional" and "analytical" uses for data. While folks try to leverage this distinction, it's a spectrum not a distinction.
A lot of data collection is a simple sequence of event documents. These have no sensible state change, so they're not really transactional. They are often created by concurrent processes where locking prevents corruption, so transactions *seem* helpful. Except, of course, the file system writes can be trivially sharded by process ID and then unified later. And all document databases serialize document writes from multiple client processes, so there's no value to writing a relational database.
Some data operations are properly stateful. By normalizing our tables, moving from consistent state to consistent state is made complex. Which requires a defined transaction as a work-around. And don't get me started on replication and two-phase commit as yet another layer of complexity on top of transactions.
A document database allows us to skip over 1NF. We can think of a document as being a row in a table where the data types are complex data structures involving mappings, sequences, strings, numbers, booleans, and nulls. (See JSON Schema.) A lot of multi-step SQL transactions are operations on several children of a common parent. If the parent was persisted as a single document, there wouldn't be multiple operations, an atomic MongoDB update operation can make complex rewrites to a complex document.
We can contrive a design where state changes must be coordinated and the data cannot be colocated in a single document. It's not difficult to stipulate enough requirements to make single documents difficult. The presence of these contrived requirement, however, doesn't suddenly invalidate document datastores for transactional data. In the SQL world, the idea of long-running and reversible long-running transactions has always been a horrible problem. Allowing stacked "undo" for the user means either creating a chain of Memento objects that can recover previous state, or having numerous flags and indicators on each record, allowing the state to be reversed. Some design problems are really hard. And the SQL model seems to make them harder.
The core ACID concepts of always consistent is -- in practice -- nonsense. As soon as we have to consider "isolation levels" and "read consistency" it becomes clear that there is no consistent state unless all transactions and queries are serialized via exclusive "whole database" locking. Competent DBA's know that long-running analytic queries performed concurrently with transactional updates can't use locking, and must tolerate inconsistencies in the database.
It's common practice to do data extracts so that analytic queries aren't working against the (inconsistent) transactional data. In this case, the frequency of extracts is the timing of "eventual consistency" promised by the BASE concept.
Bottom Line: Relational ACID rules are almost always broken in practice by read consistency rules and extracts to analytic databases. Analytical data is always based on eventual consistency expectations. The batch extracts means "eventually" is measured in hours. A document data store can often create consistency in milliseconds. (MongoDB primary failure, voting, and secondary promotion to primary relies on a 10-second heartbeat, so it takes time to discover and repair.)
AlsoA second email detailed their amazement (Amazing! Wow! Unbelievable! You Must Inform The World Of This!) that analytic processing of data is actually faster and simpler using the file system. The very idea of HDFS was so amazing that they were amazed.
Somehow, the idea of the raw filesystem as being really, really fast was the source of much amazement.
I'm glad they're making an effort to catch up. I'm glad they're seeing the relational model as a bad choice that has a limited number of use cases. Mostly, relational databases are useful for an organization can't write API's to handle the integrity issues.
To SQL or NoSQL? That's the database question | Ars Technica