CodingBlocks

Understanding Serial Transactions for Databases like Redis

Apr 3 '23

We’ve got a new / old opening…Allen goes off / on script? Michael denies Joe the “swing” vote, and Joe is all in on AI assistance

Testing for concurrency issues is hard because it’s non-deterministic – basically you get unlucky due to the timing of things

The problems we’ve been discussing the past few episodes have been around since the 1970’s
The answer is always – just use serializable isolation!
Serializable isolation is the strongest isolation
- The database prevents ALL race conditions
  - Even if transactions run in parallel, they’re guaranteed to act/result the same as if they had run one at a time, one after another
If they’re so much better, why have/use weaker isolation levels?

Executing the transactions serially, actually
Two phase locking – was one of the only real available solutions for several decades
Optimistic concurrency control – things like serializable snapshot isolation
We’ll be talking about these in terms of a single node database

The easiest way to get rid of race conditions is to really just run things one after another – no concurrency
This was only implemented for the first time around 2007 – prior the performance was too poor
- This is truly a loop over transactions submitted to the db engine
What changed to make it possible?
- RAM became cheap enough to store entire active datasets in memory – when this is done transactions can execute much faster as you don’t have to wait to load the data from disk
- DB designers concluded that most OLTP transactions are usually short-lived and make a small number of reads and writes – so they can be run on a consistent snapshot using snapshot isolation outside of the serial execution loop
Used by VoltDB/HStore, Redis, and Datomic
- Sometimes single-threaded systems can perform better than concurrent ones simply because there’s no locking
  - However, you’re bound by a single CPU core
  - Transactions will need to be set up differently than in typical concurrent systems

They talked about how the early implementations in db’s had the intention of making the entire flow part of the transaction – to book a flight, a person would be shown a list of flights, they’d choose the one they want, and it’d be stored
- The problem with that approach is it can take a long time for that flow to be completed
- For that reason, web-applications limit transactions to a single web / http request
There can still be situations where the transaction can occur with multiple interactions between the application and the database
- From the application, query to see if the seat is still available on the flight…Ok, it is..now send another query to the db to update the seat to the customer…now query the db again to get any additional information
  - Doing it this way in a serial transaction db would be too slow because there’s too much network latency / waiting
In a single-threaded serial transaction, everything must be done all at once in a stored procedure
- Keeping everything in memory and providing the stored proc everything it needs ensures the transaction is fast without waiting for any network or disk IO
  - Great picture in the book in Figure 7-9 that describes this

They’ve been part of the SQL standard since….1999
- Sometimes get a bad rep
  - Each vendor’s implementation has their own language
  - The book mentions that the SQL language hasn’t kept up with other programming languages and look/are archaic in comparison
  - It’s hard to manage code stored on the database server
    - Harder to debug
    - More difficult to keep in source control
    - More difficult to test
    - More difficult to gather metrics for monitoring
  - Because db’s are typically shared by many applications or a LOT of application code, non-performant stored procedure code can cause massive problems – usually worse problems than poorly written application code
These issues have and can be remedied
- Modern serializable databases use regular programming languages
  - VoltDB – Java/Groovy
  - Datomic – Java/Clojure
  - Redis – Lua
When the database is in memory and the transactions are single threaded, stored procedures can actually be quite good
- Because there’s no IO / networking overhead, transactions can occur quickly on a single thread
VoltDB also executes stored procedures for replication!
- This means the stored procedures have to be deterministic – datetimes have to use deterministic apis

As mentioned before, doing serial transactions means you are limited to a single core of a single CPU
- Read only transactions could occur on a separate thread using snapshot isolation
- If you need high write throughput, the single thread on a single core could be a problematic bottleneck
This is where partitioning comes into play – if you can divvy your data up in a way that would allow transactions to stay within a single partition, then you’ll have the ability to linearly scale your CPU cores/threads to the number of partitions you have
- If your transaction has to go across multiple partitions, then the stored procedure must ensure that each partition is handled appropriately to keep everything serialized properly
- VoltDB can handle multiple partitions
  - Doing cross-partition writes is much slower than single partition writes – VoltDB reports 1k cross-partition writes per second
Determining if transactions can occur on a single partition takes a bit of planning
- key-value data is likely a single partition transaction
- data with multiple secondary indexes will likely require cross-partition transactions

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