For on-demand workloads, this limit is computed dynamically and is not configurable by administrators. You know, there's some bits in here that are proprietary, that we don't usually talk about. And we strive to give every user 2,000 slots, which basically means 2,000 units of individually scheduleable action that can run in parallel, so essentially, 2,000 shards per user. FRANCESC: . I'll take a break or something, because it's going to be very intense. For example, Camanchaca drove 6x faster data processing time, Telus drove 20 faster data processing and reduced $5M in cost, Vodafone saw 70% reduction in data ops and engineering costs, and Crux achieved 10 faster load times. In October, hell be presenting at Velocity London, Google Cloud Summit Paris and Devfest Nantes. The files used to store table data over time may not be optimally sized. This ensures that at execution time, the workers processing data from the table with data skew are proportionally allocated according to the detected skew. And that was just kind of like the nature of this organic buildup. BigQuery will automatically set and manage the concurrency based on reservation size and usage patterns. Google Cloud storage is built on top of Blobstore, which is built on top of Colossus, which uses Bigtable, which uses Colossus. FRANCESC: And so our data is actually stored quite durably within a particular cell. Cool. FRANCESC: But yeah, she explains a little bit how she built all of the things, of course, in Ruby, of course. And we harness the internal Dremel engine in order to make this happen. Great. Analytical queries containing WITH clauses encompassing common table expressions (CTE) often reference the same table through multiple subqueries. BigQuery under the hood - Google Cloud The compute engine of BigQuery, the thing that executes SQL, is actually Dremel, which is this internal service that is ubiquitous at Google. BigQuery for Big data engineers - Master Big Query Internals Each BigQuery region is internally deployed in multiple availability zones. BigQuery gives you access to this incredibly vast supercomputer that Google manages for you called Dremel. Yeah, I really believe so. So you need to know the basics, like Fourier transform and stuff. Does it work like that? FRANCESC: In other words, unlike a traditional DB, data is stored on Google disks in columnar format rather than in row format. TINO: So I have a story that I heard that I don't know if it's true or not. I have so many questions to ask them. So that's in very, very simple terms. 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. And that's it. BigQuery Under the Hood with Tino Tereshko and Jordan Tigani Let's understand what a Capacitor really is, by firstly . And then once the query's done, all those stickies are thrown away, right? MARK: MARK: Today, the background Capacitor process continues to scan the growth of all tables and dynamically resizes them to ensure optimal performance. And I'm going to be talking about the BigQuery storage system and kind of going into more detail that we've shared before about that, how that works, and why we want to have our own storage system. Its designed to be flexible and easy to use. Bigquery under the hood We encourage you to read " BigQuery Under the Hood ," a detailed post on this subject. Oh yeah. The schedule knows when something's happening, and will try to reparallelize the workload, and things like that. I think you win. A single user can get thousands of slots to run their queries. And then we have this fantastic storage engine. BigQuery: Required Reading List MARK: Tino Tereshko - Medium JORDAN: FRANCESC: 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. Borg routes around it, and the software layer is abstracted. And this allows queries to be much more flexible and allows us to be flexible in how we allocate resources. And then you just give examples. BigQuery Under the Hood with Tino Tereshko and Jordan Tigani What does BigQuery look like under the hood (written by me and - Reddit So that is a way. MARK: FRANCESC: New Blog Series - BigQuery Explained: An Overview MARK: I will be attending Strange Loop, one of my favorite places in the world, on the 28th of September. So now I'm curious about-- you were talking about the compute side of things and then the storage side of things. User account menu. And I'll add to that as well. So what we decided was we would build something that would allow you to bring the compute to the data. FRANCESC: TINO: 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. So if you don't want to hear our voices, it's a very good option. JORDAN: 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. Awesome. If the files are too big, then theres overhead in eliminating unwanted rows from the larger files. If every machine can talk to every other machine at 10 Gbps, racks dont matter. That breaks it up into more manageable chunks that can be placed separately. MARK: In this article, we'll try out BQML, learn about its principles and how it works, and then follow an example implementation. I mean, I'm just going to assume it's not just a hard drive sitting on someone's computer under a desk somewhere. That means you can move data around anywhere within the cell extremely quickly. MARK: Awesome. But this didn't really work for complex queries, where you kind of would need to traverse the tree multiple times. Our teams wanted to do more with data to create better products and services, but the technology tools we had werent letting us grow and explore. Well also talk about what BigQuery hides under the hood, leveraging technologies like Borg, Colossus, Jupiter and Dremel. FRANCESC: There is no such thing as one bad query taking down the entire service. It's talking about tight providers in Deployment Manager. This approach is hugely beneficial: This distinction is important. MARK: But you can leverage analytic capacity on top of that storage in very elastic ways. I think the main difference is that we can treat Googlers much worse than we can treat external customers. BigQuery: Basics & Internals: Part # 2 | by Suddhasatwa - Medium By decoupling these components BigQuery provides: This blog post unpacks the what, the how, and the why behind BigQuerys approach to data management. So basically, I want to talk to my phone like JARVIS from "Iron Man." for example, right? And the algorithm to compute those is 10,000 times faster to do it approximately, so I'm just going to use the approximate one. So if you follow me on Twitter-- which, by the way, you should-- I've been learning a lot of machine learning lately. BigQuery is GCP's serverless, highly scalable, and cost effective cloud data warehouse. So, BigQuery can process really big datasets really quickly, but it of course comes with some caveats. Google BigQuery hits the gym and beefs up! - Shine Solutions Group Very good, very, very excited. So now I'm wondering-- you're saying that there's basically a lot of computers running this. TINO: Thank you. Good, thanks. FRANCESC: And BigQuery does that per job very, very quickly, right? But let's maybe take a step back from some of the words like Dremel and stuff that people don't know. And be sure to account for several factors of replication for redundancy. Ooh! And what we realized was that in order to be able to sell large data sets-- and we're Google, we have to deal in large data, that's sort of what we do best-- is you wanted more than just sort of a download link. Constantly monitor and tinker with your storage files to achieve maximum performance. MARK: And you're going to get like JSON messages sent there, and you need to respond to those. So if you want to interact with the API and play "Battleship" against it, it is sitting there. MARK: BigQuery is designed to query structured and semi-structured data using standard SQL. Ridiculously fast in fact. How does the magic happen that turns that SQL into computation that potentially spans across multiple computers, and does lots of crazy things? The branches of the tree are mixers, which perform the aggregation. The storage optimizer analyzes this data and rewrites the files into the right-sized files so that queries can scan the appropriate number of these files, and retrieve data most efficiently. . Hey, Mark, how are you doing? How are those managed? Luckily, both of those things have been fixed. So yeah, you save on reading the columns that you don't care about. Mark is speaking at Austin Game Conference and attending Strangeloop in September. Aggregations can be partially parallelized. Or maybe you have countries, and there's only 10 countries that you deal with. And everybody's queries kind of get time sliced onto some of these clusters. Not to sink the boats. The BigQuery team developed an adaptive algorithm to dynamically assign the appropriate file size, ranging from tens to hundreds of megabytes, to new tables being created in BigQuery storage. And then the data is replicated to multiple locations. So as you may know, in Deployment Manager, you can deploy, say, like a GCE instance or a Kubernetes cluster. So Colossus is our distributed file system. It's just basic calculus. Maybe it has certain permissions, or certain startup scripts, or other things like that. Thank you so much for posting this, Grace. Yeah, I still think it's just magic and unicorns. They just kind of ran a query, and all of a sudden, it was five times faster. FRANCESC: How fast is BigQuery? | Google Cloud Blog Yes. 0 coins. This full-duplex bandwidth means that locality within the cluster is not important. But how fast is BigQuery really? In the example from the last post, the slots are reading 100 Billion rows and doing a regular expression check on each one. Tino holds a Bachelors degree in Applied Mathematics and Economics from University of California - Davis. That's Aja. And I built a very straightforward application to do search on Google Flights directly from your phone. And Borg is what's responsible for if a machine dies, or-- sorry, if one of the shards dies, it'll restart it, and rejoin the cluster. Yeah. For those that do not speak English that well, or grew up in a different country, in Spanish, it's called [? BigQuerys documentation on Quotas and limits for Query jobs states Your project can run up to 100 concurrent interactive queries. BigQuery used the default setting of 100 for concurrency because it met requirements for 99.8% of customer workloads. Well, why don't we go have a chat with our friends, Tino and Jordan, and find out all the magic things that happen underneath the hood in BigQuery? While processing joins, the query engine keeps monitoring join inputs for skewed data. But usually, in a column, there's a lot of redundancy. It's just kind of like a benefit that you get. It's a little hard to do without diagrams, and just by kind of describing the data flow. We've talked a little bit about storage, about how we store, but not where. And also-- just to make sure-- we don't replicate your data out of the country that you're storing your data in. FRANCESC: Any datasets that share the same region can be joined together in a single query. BigQuery under the hood: How zone assignments work - Google Cloud Defer all of their calls. gmail.com, yahoo.com), we can use SUBSTR in combination with STRPOS and LENGTH to dynamically extract everything after the @. 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. Yeah, awesome. 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. And I think, really, the biggest thing, again, comes down to the fact that it really truly is NoOps, right? That makes scanning data much quicker. A logical database storage model, rather than physical. It's fully managed. And sort of six years later, we haven't gotten around to the whole data marketplace thing. Assuming ~100 32-processor machines, one of the servers will fail every day on average, which will take all 3,300 CPUs offline, so youll need extra coordination to handle these failures without slowing down, including deploying additional computing redundancy, preferably across multiple zones. What's the benefit there? BigQuery automatically runs their requests or schedules them on a queue to run as soon as current running workloads have completed. cloud.google.com . So we have today, Tino Tereshko. Thank you for coming. I think that's just sort of an interesting way that that stuff often can develop at Google. So I'm curious about where do we store all of this? MARK: MARK: MARK: [? MARK: So we replicate to a couple of different cells, couple different buildings within a region. The original Dremel paper was sort of a tree shape, where kind of the filters would happen at the lowest level. 1. Now I am the big data lead for a relatively new organization called Office of the CTO in Google Cloud. Tino is the Big Data Lead for Office of the CTO at Google Cloud, focusing on building strategic relationships with the worlds top Enterprises in the interest of sharing and accelerating technological innovation. This allows us to do all kinds of really interesting things when it comes to efficiency and performance. FRANCESC: ). It sounds like this would make a really good episode. (The join is by far the most expensive part of the query, so applying the . The answer comes down to one line in your BigQuery Under the Hood article[0]: "The answer is very simple BigQuery has this much hardware (and much much more) available to devote to your queries for seconds at a time. Could you talk a little bit about the importance of the network in this? MARK: We also have our own scheduler to sort of deal with when to redispatch queries that maybe part of a query was running on a shared and that shard dies, to recognize that, and redispatch it elsewhere. It is super simple. Besides obvious needs for resource coordination and compute resources, Big Data workloads are often throttled by networking throughput. ?]. And then a couple of weeks later, I'll be at Cloud Next Chicago, which is on the 27th of September. Google Cloud Platform architecture Doing great. But while reading metadata for small tables is relatively simple and fast, large (petabyte-scale) fact tables can generate millions of metadata entries. Whatever. It turns SQL queries into execution trees. I want to bring to light a little series written by a person on our team, Alexei. For these queries to generate results quickly the query optimizer needs a highly performant metadata storage system. The failover is designed to be transparent to customers and have no downtime. The leaves of the tree it calls slots, and do the heavy lifting of reading the data from Colossus and doing any computation necessary. And we followed a lot of research papers, and sort of some state of the art ideas that people had and productionized it into our storage format. Tell me how this is possible. To achieve this, the team developed a disaggregated intermediate cache layer called Colossus Flash Cache which maintains a cache in flash storage for actively queried data. Dremel turns your SQL query into an execution tree. Hey, yay! Under the hood. Separation of storage and compute specifically offers a wide range of benefits to BigQuery users. JORDAN: To handle cross-org reads, the algorithm also looks into past query patterns to discover relationships between organizations and make an effort to have at least one common zone between orgs that are related. Yeah. Can you talk us kind of through that step by step? I just assumed it was like magic, and unicorns, and some other things like that. Taking a practical approach to BigQuery slot usage analysis FRANCESC: Because it's a very simple platform that allows you to use machine learning, even though you have no clue of how machine learning works. This capacity is also used to perform the above-mentioned self-optimizing tasks. The Storage Optimizer merges many of these individual files into one, allowing efficient reading of table data without increasing the metadata overhead. That sounded really bad. JORDAN: Then it makes sure that the usage will fit in the currently assigned zones. FRANCESC: And then a couple of the 20% project people started working on it. Today well dive deeper and discuss what it takes to build something this fast. SQL SUBSTR Function | BigQuery Syntax and Examples | Count I want to be able to talk to my phone and make it do things. 1. Most distributed processing systems make a tradeoff between cost (querying data on hard disk) and performance (querying data in memory). MARK: "Un ?]

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