Joined 13 years ago
One thing we've seen with DuckDB, which they also mention in their docs, is that it's possible to 'over parallelize'. Beyond some number of cores, the overall workflow measured in wall time actually slows down when you add more CPUs, because of the overhead of parallelizing, despite them running at 100%.
We observe this even if we pull the data from S3 onto the local SSD before processing,
There are a variety of people still use Spark, but this is usually due to constraints on their platform/infrastructure, not because it performs better. For instance people on Databricks can sometimes find it hard to use DuckDB on large machines, or they're just more familiar with Spark.
For a new user who has flexibility over where to run their workloads, we'd always recommend DuckDB. This is particularly the case now that: - Splink 5 (released in September) allows you to run your workload in n chunks, meaning you can distribute it across multiple machines even on DuckDB - DuckDB 2.0 parallelises better on large machines, so throwing a single massive machine at problems is now more viable
In general, after using Spark for a long time, I think for most workloads, it's just not really needed any more. Too complex, too difficult to tune. Perhaps if you have genuinely massive data. I've written a bit more about why I advise against Spark in most instances here: https://www.robinlinacre.com/recommend_duckdb/
Still don't usually see a speedup past about 192vCPU, but prior to 2.0, speed would top out at more like 64vCPU, so this is a big win.
That has enabled us to scale Splink (FOSS record linakge software) to process 1bn records with 10bn record comparisons in 10 mins, which as far as I'm aware makes it the fastest linkage engine of this kind. Most of this performance comes from simply delegating the big calculations to the DuckDB engine! https://moj-analytical-services.github.io/splink/topic_guide...
Thanks to the team for all their work!
So for example, for wind power the consumer effectively pays the CfD strike price, largely irrespective of the spot price.
Fwiw, I do agree we have a problem with power being too expensive, but it's more complex than just the consumer pays the most expensive source.
Another aspect of context that could be relevant to your work may be how people are nested within households. In some record linkage scenarios you can run a linkage on household membership and use this to make person linkages more precise. I imagine an LLM agent could also use this information effectively if it were explicitly provided, but sometimes they miss logical leaps like this
It does not know enough about the records in the context of the overall dataset:
- what is the data quality and to what extent do we expect a errors in some fields - how unusual are certain values such as names in the context of the dataset as a whole, e.g. some names would be very common in some countries but rare in others.
I've written in more detail about this here: https://www.robinlinacre.com/fellegi_sunter_accuracy/
That said for single images the old model was already okayish for prototypes
