Due to me being kind of a big deal around here, I was sent to Google Next 18 last week. It’s a two-and-a-half-day conference in San Francisco, all about Google Cloud. I made some exciting discoveries, which I will share with you, and also went to some talks or something.
When we started using Google BigQuery – almost five years ago now – it didn’t have any partitioning functionality built into it. Heck, queries cost $20 p/TB back then too for goodness’ sake! To compensate for this lack of functionality and to save costs, we had to manually shard our tables using the well known
_YYYYMMDD suffix pattern just like everyone else. This works fine, but it’s quite cumbersome, has some hard limits, and your SQL can quickly becomes unruly.
Then about a year ago, the BigQuery team released ingestion time partitioning. This allowed users to partition tables based on the load/arrival time of the data, or by explicitly stating the partition to load the data into (using the
$ syntax). By using the
_PARTITIONTIME pseudo-column, users were more easily able to craft their SQL, and save costs by only addressing the necessary partition(s). It was a major milestone for the BigQuery engineering team, and we were quick to adopt it into our data pipelines. We rejoiced and gave each other a lot of high-fives.
Shine’s good friend Felipe Hoffa from Google was in Melbourne recently, and he took the time to catch up with our resident Google Developer Expert, Graham Polley. But, instead of just sitting down over a boring old coffee, they decided to take an iconic tram ride around the city. To make it even more interesting, they tested out some awesome Google Cloud technologies by using their phones to spin up a Cloud Dataflow cluster of 50 VMs, and process over 10 billion records of data in under 10 minutes! Check out the video they recorded:
Here in Australia, we do a lot of work on Google Cloud Platform for one of the country’s largest ISPs, Telstra. Most of that work involves building data pipelines and running analytics off the back of them for their Media business unit. As you can well imagine, they generate a huge amount of data on a daily basis. We use tools like BigQuery, Cloud Dataflow and Data Studio to wrangle, manage, and understand that data.
On one such project for Telstra, we saw an opportunity to delete three code repositories and finally rid ourselves of some of the headaches associated with maintaining those applications, all the while saving money on the operational costs.
We were able to replace the system comprising these repos with two new Google Cloud Platform services:
In this blog post, I’ll introduce you to those new services that Google have spun up, and how we were able to use them to replace our legacy applications. Who doesn’t like a good spring clean, huh?
Post update: My good friend Lak over at Google has come up with a fifth option! He suggests using Cloud Dataprep to achieve the same. You can read his blog post about that over here. I had thought about using Dataprep, but because it actually spins up a Dataflow job under-the-hood, I decided to omit it from my list. That’s because it will take a lot longer to run (the cluster needs to spin up and it issues export and import commands to BigQuery), rather than issuing a query job directly to the BigQuery API. Also, there are extra costs involved with this approach (the query itself, the Dataflow job, and a Dataprep surcharge – ouch!). But, as Lak pointed out, this would be a good solution if you want to transform your data, instead of issuing a pure SQL request. However, I’d argue that can be done directly in SQL too 😉
Not so long ago, I wrote a blog post about how you can use Google Apps Script to schedule BigQuery jobs. You can find that post right here. Go have a read of it now. I promise you’ll enjoy it. The post got quite a bit of attention, and I was actually surprised that people actually take the time out to read my drivel.
It’s clear that BigQuery’s popularity is growing fast. I’m seeing more content popping up in my feeds than ever before (mostly from me because that’s all I really blog about). However, as awesome as BigQuery is, one glaring gap in its arsenal of weapons is the lack of a built-in job scheduler, or an easy way to do it outside of BigQuery.
That said however, I’m pretty sure that the boffins over in Googley-woogley-world are currently working on remedying that – by either adding schedulers to Cloud Functions, or by baking something directly into the BigQuery API itself. Or maybe both? Who knows!
Recommendation systems are found under the hood of many popular services and websites. The e-commerce and retail industry use them to increase their sales, the music services provide interesting songs to their listeners, and the news sites rank the daily articles based on their readers interests. If you really think about it, recommendation systems can be used in pretty much every area of daily life. For example, why not automatically recommend better choices to house investors, guide your friends in your hometown without you being around, or suggest which company to apply to if you are looking for a job.
All pretty cool stuff, right!
But, recommendation systems need to be a lot smarter than a plain old vanilla software. In fact, the engine is made up of multiple machine learning modules that aim to rank the items of the interests for the users based on the users preferences and items properties.
In this blog series, you will gain some insight on how recommendation systems work, how you can harness Google Cloud Platform for scalable systems, and the architecture we used when implementing our music recommendation engine on the cloud. This first post will be a light introduction to the overall system, and my follow up articles will subsequently deep dive into each of the machine learning modules, and the tech that powers them.
Do you have some data that needs to be fed into BigQuery but the output must be split between multiple destination tables? Using a Cloud Dataflow pipeline, you could define some side outputs for each destination table you need, but what happens when you want to write to date partitions in a table and you’re not sure what partitions you need to write to in advance? It gets a little messy. That was the problem I encountered, but we have a solution.
No food reviews here I’m afraid
This year I was incredibly lucky to score a coveted ticket to YOW! in beautiful Melbourne. I was also asked to be a track host for a couple of sessions, so that was quite an honour too. This post is a whirlwind wrap-up of the conference, and only includes my favourite talks from the two day event. If you’re hoping to hear detailed reviews on how the coffee/food/WiFi/venue was, then you’ll be greatly disappointed (it was all great BTW).
Do you recoil in horror at the thought of running yet another mundane SQL script just so a table is automatically rebuilt for you each day in BigQuery? Can you barely remember your name first thing in the morning, let alone remember to click “Run Query” so that your boss gets the latest data refreshed in his fancy Data Studio charts, and then takes all the credit for your hard work?
Well, fear not my fellow BigQuery’ians. There’s a solution to this madness.
Yes, it’s Google Apps Script to the rescue.
Disclaimer: all credit for this goes to the one and only Felipe Hoffa. He ‘da man!
I have been using BigQuery for over 2 years now at Shine. I’ve found it to be a great tool that is both incredibly fast and able to handle some of our largest workloads. We are processing terabytes of data per day, and each day an extra billion records are added to the store.
But unfortunately this growth is also increasing our costs of running queries. While BigQuery is extremely fast and parallel, it comes at the cost of needing to scan and pay for every record of the columns you are querying. Without the indexes offered by conventional databases, a full table scan is needed for each query. Not only that but when you query large amounts of data the speed of your query slows down:In this post I’ll talk about how we used table partitions to increase the performance of our queries and avoid query slowdowns.