Machine Learning in Google Bigquery

http://ai.googleblog.com/2018/07/machine-learning-in-google-bigquery.html

199 49 8 years ago

ai.googleblog.com

mooneater• 8 years ago

Only logistic and linear regression for now. For use cases it covers, this will save a lot of plumbing.

Independent of DL, I'm curious why these two regression cases haven't been made available in a SQL-like interface until now. Kudos to Google for putting this in the hand of folks who otherwise just use SQL.

mark_l_watson• 8 years ago

Good point! I wouldn’t be surprised to see a Postgres plugin for this. Wouldn’t get the vast scalability but it would offer convenience.

YurtleTheTurtle• 8 years ago

Oracle has had linear regression for well over a decade now.

cottonseed2• 8 years ago

Linear regression makes a lot of sense since it can be done streaming in a single pass. We have it as an aggregator in Hail: https://hail.is/docs/devel/aggregators.html#hail.expr.aggreg...

I wonder how they're doing logistic regression. It doesn't have a closed form and normally solved with an iterative, multi-pass optimization. edit: Read more closely. They're using (non-stochastic) batch gradient descent.

djhworld3• 8 years ago

We're trialling BigQuery at the moment, pushed about 130TB into it last week (about ~300 billion rows) and have been blown away by the performance of it.

It's a bit of a shame they never released a paper on how it works, well, nothing since Dremel anyway. And Collosus isn't public either.

I can already see this in built ML stuff being useful for trialling models, especially as it's built right into the SQL.

puzzle• 8 years ago

There is information about Colossus: the original GFS paper, an interview with Sean Quinlan with McKusick on Colossus improvements, as well as a talk by Denis Serenyi on more of the gory details, e.g. rebalancing.

There was also a public talk on D, the GFS chunkserver replacement, but there don't seem to be recordings or slides from that.

kyrra• 8 years ago

There have been some blog posts about it, but you are right, no whitepapers.

https://cloud.google.com/blog/big-data/2016/04/inside-capaci...

https://cloud.google.com/blog/big-data/2016/01/bigquery-unde...

minimaxir3• 8 years ago

At the Reddit talk at Google Next, the VP of Engineering noted that they moved to BigQuery and saved in both performance and cost.

We at BuzzFeed have seen similar results for migrating to BigQuery (and we did a talk about it at Next too, although it’s not online yet)

minimaxir3• 8 years ago

Reddit talk: https://youtu.be/tKISLQ87GO8

BuzzFeed talk: https://youtu.be/74PKZmL-Qjs

antonvs• 8 years ago

How do you find the querying costs?

It seems like for many use cases, you'd have to pay quite a bit of attention to partitioning the database in a way that minimizes query costs.

minimaxir3• 8 years ago

The relatively new date-partitioning feature for tables has cut down query costs/time significantly.

gipp2• 8 years ago

Cost (or rather bytes scanned) is sent back from the API on any queries or dry run/validation calls. There's a common chrome extension to put it in the legacy ui, and I believe the beta ui has it baked in (as part of validation)

djhworld3• 8 years ago

For this trial we are transferring some data from AWS, which is already partitioned in S3 by quite a few dimensions, including DAY.

As BQ only supports DAY partitioning, I had to create multiple tables that represent the other dimensions, but it seems to work OK, if a little clunky when you have to use wildcards/_TABLE_SUFFIX

vgt7• 8 years ago

Bq pm here. No papers, yes, but lots of talks and blog posts. Here's a list:

https://medium.com/@thetinot/bigquery-required-reading-list-...

Happy to answer any questions!

polskibus• 8 years ago

How would you compare it to a traditional OLAP solutions (MS SQL Analysis Services in OLAP model (not tabular), Essbase etc.) in terms of :

- multidimensional / hierarchy modeling for analytical purposes

- permissioning model (roles with element-level granularity, ie. User A - allow Country=USA, User B - allow Product=Bike)

- historical modeling (built-in slowly-changing dimensions support)

What is the underlying storage model? Is it a column store ? Is it closer to traditional row-based stores?

Are you taking advantage of GPUs or other dedicated hardware to accelerate BQ?

nrjames2• 8 years ago

Did you all work together with the Firebase team on the recent schema change? We're scrambling to mitigate the ~2x cost increase that caused for us, since it blew our analytics budget out of the water.

jimmytucson3• 8 years ago

This blog post from 2 years ago from AWS has Redshift beating BigQuery pretty handily on TPC-H and TPS-DS: https://aws.amazon.com/blogs/big-data/fact-or-fiction-google...

Has anything changed on BigQuery since then that would warrant rerunning those benchmarks? If Amazon’s largest cluster outperforms BigQuery then is your decision about which service to use just a cost calculation of dedicated per month for Redshift vs. projected units scanned per month on BigQuery?

djhworld3• 8 years ago

Thanks for the links, will definitely take a look.

I suspect you might be hearing some feedback from us soon w.r.t this trial, assuming the google people we are working with relay that back to you?

ceocoder• 8 years ago

We (Sojern) have been using BigQuery since late 2015, we moved from Hive. It has been an amazing experience in terms of cost savings (managed infra, operational cost of a hadoop cluster), and performance. We use it across the org from analytics, ingestion, reporting, ML, and many more uses.

salimmadjd• 8 years ago

This is very exciting for us, even at its nascent limited ability point.

Compared to many players in different verticals, our data is small. But in our vertical of asthma care, we probably have one of the largest (possibly the largest) asthma data.

We've been looking at different way of plumbing the data to automate and run some rudimentary analysis on it, since we found BigQuery a bit limiting. Now seeing this announcement, it could be a great start for us.

I hope this is just a start and people like us can send Google Cloud team a wish list as we come across various needs. Good job and thanks to everyone behind this release.

cottonseed2• 8 years ago

Can I ask more about the structure of the data and what you'd like to do with it? My email is in my profile.

hotpotjunkie• 8 years ago

Good, I hope this trend of shoving ML into SQL (instead of the other way around) continues. I always thought it was silly that every "data wrangling" system like Pandas and R needed to (poorly) re-invent SQL.

halflings• 8 years ago

Unless you present clear arguments, I'd refrain from saying that Pandas is "poorly re-inventing SQL".

Pandas is now the standard for data analysis (as long as things fit into memory). It's much much easier to debug than a SQL command. You can write operations as a succession of small logical steps (instead of one huge query that is hard to debug).

It's raw Python, so you can do something like:

df.groupby('movie_id').agg(dict(ratings='median', price=lambda p : np.percentile(p, .95))).plot.bar(bins=50)

alexgmcm• 8 years ago

Yeah, also in Pandas you can do stuff that otherwise requires writing a custom reducer or UDAF in which case you aren't using SQL anyway.

I just use SQL to grab and if necessary aggregate the data and then do everything else in Pandas - using Python custom reducers to deployed trained models although we are migrating to GCP now so soon that won't be necessary.

akadien2• 8 years ago

I appreciate how convenient it is to have statistical analysis tools available directly in BQ SQL, but are linear and logistic regression really considered "machine learning"?

azurezyq• 8 years ago

Actually most of online ads ctr prediction machine learning systems are still logistic regression based until very recently. It's simple but very effective.

Eridrus• 8 years ago

Yes, it definitely is.

Xorlev• 8 years ago

Just because it's not a neural net doesn't make it not ML. Also, can echo the other folks here: linear models and basic logistic regression are still competitive.

akadien2• 8 years ago

I wholeheartedly agree with you. I'm suggesting that it might be time to have the counter conversation that just because it has numerical statistical analysis does not make it ML. I've been around the field for a couple of decades and understand linear regression is covered in the first pages of chapter one. If that is the standard, then Excel can claim to have had machine learning capabilities for years.

spocklivelong• 8 years ago

Does anyone here tried Druid? I hear at Druid performance is much better in terms of response time, especially for arbitratry queries over large set of dimensions. Did anyone do an in-depth comparison between Bq vs Druid?

Machine Learning in Google Bigquery

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