bigquery under the hood
It's really, really inexpensive. Separation of storage and compute specifically offers a wide range of benefits to BigQuery users. To support the ever-expanding range of workloads, such as data engineering, complex analysis, Spark and AI/ML processing, the BigQuery team developed dynamic concurrency with query queues to remove all practical limits on concurrency and eliminate the administrative burden. So if you follow me on Twitter-- which, by the way, you should-- I've been learning a lot of machine learning lately. And I think it's very cool. By taking care of everything except the very highest layer, BigQuery can do whatever gives users the best experience possiblechanging compression ratios, caching, replicating, changing encoding and data formats, and so on. Our assignment algorithm makes sure that Google Cloud projects within the same Google Cloud organization are assigned to the same subset of zones in every region. And I'm like, what? New Blog Series - BigQuery Explained: An Overview In other words, unlike a traditional DB, data is stored on Google disks in columnar format rather than in row format. Multi-tenancy means that a single instance of the . But he was only using a couple of those columns, and so he was reading all this extra data. What is Google BigQuery | Whatagraph to state, ?] FRANCESC: Now, users no longer have to scan for idle time periods or periods of low usage to optimize when to submit their workload requests. JORDAN: Ooh! FRANCESC: And you also kind of-- there's this concept that when you have large data sets that you need to bring the compute to the data rather than the data to the compute. Basically, if there's a last minute thing you want to make sure is on the podcast, what can go there? Up until 2015, BigQuery's storage engine was ColumnIO. Awesome. FRANCESC: So you heard kind of like the engineering point and now maybe the customer facing point that I like to think about is imagine you have those big old IBM Blue supercomputers that used to be famous in the '80s and the '90s. So we've been talking about the compute side. There's unicorns. Google BigQuery Architecture for Data Engineers So, for instance, if you want to develop like-- I don't know-- like, a store, you're going to have search, and buy, and stuff like that, right? Is this running on Kubernetes? Today well dive deeper and discuss what it takes to build something this fast. And so he just switched this data to a columnar format and wrote a little SQL engine on top of it. All right, well, Francesc, thank you once again for joining me on this wonderful episode today. So yeah, you save on reading the columns that you don't care about. So you mentioned something about Dremel, which is kind of cool. But essentially, a bunch of us in the Seattle office had gotten pulled off of other products that we were working on, because the site director wanted us to build a data marketplace. Sports . Posted by. Borg assigns server resources to jobs; the job in this case is the Dremel cluster. BigQuery Under the Hood with Tino Tereshko and Jordan Tigani Aja Hammerly, known as Thagomizer on the internet, has written a series of blog posts where she builds a game that apparently I knew, but I did not know the name of it. FRANCESC: So we have a really fast in-memory shuffle that also gets into the process. And this allows many databases to use column storage now. Cool. Ridiculously fast in fact. And the query master gets the query. For existing tables, the BigQuery team added a background process to gradually migrate existing fixed file size tables into adaptive tables, to migrate customers existing tables to the performance-efficient adaptive tables. MARK: And we also have Jordan Tigani, software engineer. Analytical queries containing WITH clauses encompassing common table expressions (CTE) often reference the same table through multiple subqueries. More worker capacity is allocated to Left or the Right side of the join depending on where the data skew is detected (Left side has data skew in task 2; Right side has data skew in task 1). The second parameter is an "emitter" function. That sounded really bad. MARK: I think we have a pretty advanced version of that that builds on top of sort of the standard column store and allows us to A, compress better, and B, read less data. BigQuery is Google's take on a distributed analytical database. But I need to build a bot to do that. Were always expanding our capacity footprint to support customer growth and onboard new customers. And I'm actually going to be speaking about it during the Cloud Summit that will happen in Sydney, which I think it's on the day this episode's coming out, which is Wednesday. All I have is a first name here. And sort of the total throughput of that is in the petabit range, which is pretty massive amount of data. Google Cloud storage is built on top of Blobstore, which is built on top of Colossus, which uses Bigtable, which uses Colossus. Machines crash, power supplies fail, network switches die, and a myriad of other problems can occur while running a large production datacenter. I want to be able to talk to my phone and make it do things. And now I'm the engineering lead of the BigQuery product. To reiterate, we simply created a Google Cloud project, enabled billing, and ran this SQL query in BigQuery. And the redundancy that you have across a row is pretty small in general. So what sometimes the storage engine does is it looks at how the data is stored physically inside of BigQuery and says, well, maybe it can be better, right? Some good blogs on BigQuery. And what does it take to achieve BigQuery speeds? FRANCESC: We can opt them into all kinds of crazy, weird dogfood that we can't do with customers that pay us real money. Because I have no clue. And that's really how they knew they were on the new stuff instead of the old stuff. BigQuery relies on Colossus, Googles latest generation distributed file system. So what day is this coming out? Topic Bigquery . If the files are too small, theres overhead in reading and managing the metadata for the larger number of small files being read. There is no such thing as one bad query taking down the entire service. I want to talk to to my phone like its J.A.R.V.I.S. Google Cloud Data Analytics Services Cheat Sheet - Jayendra's Cloud Yeah, awesome. Do you need to have a cluster of computers? And for the compute side of things, basically, what we're doing is rather than running one really big query, there's thousands of them, or I don't know how many, but like, many, many of those little jobs running all around that BigQuery tree. Depending on tables being queried on join columns, the skew may be on the table column referenced on the left side of the join or the right side. If every machine can talk to every other machine at 10 Gbps, racks dont matter. They really all kind of fit into that same bucket. So those examples-- for instance, you could say, I would like to buy-- I don't know-- Pixel XL, right? BigQuery, like most large data processing systems, has developed a rich store of information on the file contents, which is stored in the header of each Capacitor file. I'll take a break or something, because it's going to be very intense. This operational model gives BigQuery some interesting options. I have a little bit of a clue, but I'm still really lost. It's worth noting that she steps through all the logic, but she has a deployment for App Engine that is public with an API. Or maybe we can do some other improvements here. It's a weird mix. MARK: Google BigQuery (Google BigQuery) has a very different lineage than Redshift and Azure SQLDW. Whatever. FRANCESC: FRANCESC: When querying large fact tables, there is a strong likelihood that data may be skewed, meaning data is distributed asymmetrically over certain key values, creating unequal distribution of the data. Maybe you have telephone numbers, and the prefix is always the same. What about you? Very good, very, very excited. MARK: So we essentially have excess capacity on the compute side. BigQuery Under the Hood with Tino Tereshko and Jordan Tigani Sep 13 '17 play Have you ever wanted to know what powers BigQuery under the hood? Now I am the big data lead for a relatively new organization called Office of the CTO in Google Cloud. It's a little hard to do without diagrams, and just by kind of describing the data flow. He did it in like a couple of afternoons, whatever. Googles Jupiter network can deliver 1 Petabit/sec of total bisection bandwidth, allowing us to efficiently and quickly distribute large workloads. Reading from and writing to BigQuery tables maintained in storage files can become inefficient quickly if workloads had to scan all the files for every table. Separation of storage and compute in BigQuery - Medium I will be attending Strange Loop, one of my favorite places in the world, on the 28th of September. BigQuery with Bigtable: The GA release of Cloud Bigtable to BigQuery federation supports pushdown of queries for specific row keys to avoid full table scans. To solve this, the BigQuery team built a duplicate CTE detection mechanism in the query optimizer.This algorithm reduces resource usage substantially allowing more shuffle capacity to be available to be shared across queries. Yeah. However, just like with any other sensibly-operated distributed service, there are times when idle capacity is available: In-place silo-less data sharing with Google BigQuery. Someone unplugs a rack in the datacenter in the middle of running your query, and youll never notice the difference. So BigQuery! Hi Guys, What is the cheapest way to restore a BQ table to a previous state (not necessarily dropped table, just changed some records)? Customer data is replicated between these zones, and there is fast automatic failover to the secondary zone if the primary zone is experiencing issues. But as customers moving from traditional data warehouses started bringing in smaller data sets on the order of gigabytes and terabytes the default big file sizes were no longer the optimal form factor for these smaller tables. To get the best performance for workloads that read/write data in datasets belonging to different projects, ensure that the projects are in the same Google Cloud org. That makes scanning data much quicker. . MARK: MARK: TINO: We know that customers use BigQuery with other cloud services to allow data analysts to ingest from or query other data sources with their BigQuery data. Maybe it has certain permissions, or certain startup scripts, or other things like that. Under the hood, BigQuery employs a vast set of multi-tenant services like Dremel, Colossus, Jupiter and Borg. We had an episode on Borg. BigQuery has a built-in storage optimizer that continuously analyzes and optimizes data stored in storage files within Capacitor using various techniques: Compact and Coalesce: BigQuery supports fast INSERTs using SQL or API interfaces. TINO: Hey, yay! But otherwise, yeah, thanks for having me. The data would sort of sit in our cloud. It's talking about tight providers in Deployment Manager. He is also heading to Australia in October for GDG Devfest Melbourne and Game Connect Asia Pacific and will be hanging out at Unite Melbourne and PAX Australia. FRANCESC: How BigQuery's Data Ingest Breaks Tech Norms Wonderful. And BigQuery stores data on Colossus. It's really basically like all the cool things of the week, but way more than that, and all in written form. 1 Answer Sorted by: 3 From Bigquery under the hood: Colossus - Distributed Storage BigQuery relies on Colossus, Google's latest generation distributed file system. JavaScript UDFs are executed on . What's in the paper's no longer really the case these days. JORDAN: Whoo! 1. Based on the concepts proposed in their 2021 VLDB paper, Big Metadata: When Metadata is BigData, the BigQuery team developed a distributed metadata system, called CMETA, that features fine-grained column and block-level metadata that is capable of supporting very large tables and that is organized and accessible as a system table. One of the nice things about SQL is it's very parallelizable, where clauses and filters can all be completely parallelized. The wonderful BigQuery Public Datasets program is a great jumping off point, especially with BigQuerys free tier: you get 10GB of storage and 1TB of query every month! TINO: I'll be there speaking at DevFest Melbourne as well as at Game Connect Asia Pacific and probably pottering around at the very least at Unite Melbourne, as well as Pax Australia. It sounds like this would make a really good episode. Press J to jump to the feed. And let's remember that there was also a blog post that they wrote long time ago. How does the magic happen that turns that SQL into computation that potentially spans across multiple computers, and does lots of crazy things? Jupiter networking infrastructure might be the single biggest differentiator in Google Cloud Platform. MARK: A Deep Dive Into Google BigQuery Architecture | Panoply FRANCESC: FRANCESC: FRANCESC: How are you doing? We looked at a bunch of open column formats, including Parquet, which is really, really common in the open source community. The results shared in the VLDB paper demonstrate that query runtimes are accelerated by 5 to 10 for queries on tables ranging from 100GB to 10TB using the CMETA metadata system. This table contains 100 billion rows and is about 7 Terabytes in size. Thank you. Tino is the Big Data Lead for Office of the CTO at Google Cloud, focusing on building strategic relationships with the world's top Enterprises in the interest of sharing and accelerating . And you just give a bunch of examples, like saying, well, if instead of receiving, I want to buy, or say I would like to get, well, that's kind of the same thing. Soon, it's going to be 2018, how terrifying is that? MARK: So I will be at Austin Game Conference on the 25th of September, speaking. Hey, Mark, how are you doing? Anyway, also very excited, because we have two people that were in the BigQuery team before--. Before Google, he worked at a number of star-crossed startups, and also spent time at Microsoft in the Windows kernel team and MSR. The one distinction I want to make here before we move on, guys, is any kind of typical similar technology, when you "stand up a cluster," quote, unquote, you essentially have a process that is really fast. Aggregations can be partially parallelized. Let's do that. Then you're going to need to develop probably convolutional neural network. MARK: FRANCESC: That's actually true. Unexpectedly, it works, yep. This sounds really cool. Cool. So a lot of times, any typical similar technology keeps state in the nodes that are processing themselves. Maybe Tino, you can start. Sure. Dremel dynamically apportions slots to queries on an as needed basis, maintaining fairness amongst multiple users who are all querying at once. BigQuery Under the Hood; BigQuery Continues to Define What it Means to be Fully Managed; Inside Capacitor, BigQuery's Next-Generation Columnar Storage Format; In-memory Query Execution in BigQuery; Life of a BigQuery Streaming Insert; 15 Awesome Things you Probably Didn't Know About BigQuery; Paying it Forward How BigQuery's Data . Yeah, so they're running on Borg. BigQuery is a fully managed, durable, petabyte scale, serverless, highly scalable, and cost-effective multi-cloud data warehouse. So I will not be in Seattle, I will be in Sydney. TINO: In October, I really should book some flights to Australia, because I will also be there. BigQuery automatically runs their requests or schedules them on a queue to run as soon as current running workloads have completed. One key aspect of BigQuery's architecture is that it is multi-tenant; it runs workloads from different customers on compute and storage infrastructure that does not require any customer involvement in capacity planning.