[jeffffff]

yeah redshift is not at all comparable to snowflake. big query is much closer, it's ahead in some areas and in the last year has closed some of the gaps where it wasn't. big query's biggest problem is that it's tied to gcp which is a distant 3rd in cloud marketshare. they have big query omni coming which is multi-cloud but it'll probably be a while before it's comparable to big query in gcp.

[philjohn]

The other problem with BigQuery is that you can very easily write a query that's going to cost you a lot of money to run - with Snowflake you can let it run for an hour or so, and then realise it was a bad idea and you're only out a few credits, a handful of dollars.

The killer feature for me was the query profiler - you can see WHY a query is taking a long time and optimise it - BigQuery just felt like Google were brute forcing the performance, and then charging you accordingly.

When the project I was on switched, the micro-clusters (and the ability to recluster a table) as well as the MERGE semantics beat BigQuery hands down - although those features my be out of beta now (but I've moved on to a new gig).

[m0zg]

That's also a problem that it'd be fairly straightforward for Google to solve by automatically spinning up smaller, entirely separate serving clusters for customers who are worried about such a blowout (for a fee, obvs). It's just the serving tree (+ whatever in-memory storage service they use to do distributed joins nowadays), no need to duplicate the rest of the service. The caveat is, a smaller cluster will favor query optimizations specific to that smaller cluster. Some of those "small cluster" optimizations could hurt query performance when deployed against BQ proper with its tens of thousands of workers.

Also, BQ does explain the query plan to some extent: https://cloud.google.com/bigquery/query-plan-explanation. Not quite at the level of a "regular" SQL DB, but it does give you some info to work with when optimizing queries. If you haven't used it in a while I'd give it another try.

[quirmian]

I believe this is exactly what slot reservations in BigQuery achieve. Instead of paying on-demand pricing that is determined by data read, you purchase a fixed number of “slots” that are shared by queries running within that particular project.

[m0zg]

Ah OK, after reading their docs I see they've changed what "slots" used to mean in Dremel (internal version of BQ). It used to be that slots _guaranteed_ capacity, but did not limit it. Meaning that you could rely on having a certain number of workers in the cluster when you issue a query, but if Dremel had more it'd give you all it's got. Obviously this is not viable when people have to pay per terabyte read, because a ton can be read.

What they have now strikes me as an even better solution to the problem of bankrupting someone with a query IMO. Not sure how pricing compares to redshift et al, but pricing is the easiest thing for Google to change.

[manigandham]

Slots don't control how much data you consume, your query does.

If you need to read a terabyte of data to answer your query then more slots only gets it done faster.

[statusgraph]

BQ Slots lets you do essentially that (pre-commit to a particular cluster size)

[theptip]

I was hitting some rough edges / complexity with BigQuery's MERGE recently, but wasn't able to ascertain any significant difference with Snowflake by scanning their docs briefly -- what aspects of the MERGE semantics are better in Snowflake in your opinion?

Wondering if this is a somewhat new feature in BQ since you used it, or if there's still a feature gap here (e.g. see https://cloud.google.com/blog/products/gcp/performing-large-...).

[5ersi]

BQ has per-project and per-user cost controls. Normally when running new large queries one would run them under a special user with a limit on costs.