Building Uber’s Fulfillment Platform for Planet-Scale using Google Cloud Spanner
You’re seeing information for Japan . To see local features and services for another location, select a different city. Show more Introduction The Fulfillment Platform is a foundational Uber domain that enables the rapid scaling of new verticals. The platform handles billions of database transactions each day, ranging from user actions (e.g., a driver starting a trip) and system actions (e.g., cre
Engineering SQL Support on Apache Pinot at Uber
Uber leverages real-time analytics on aggregate data to improve the user experience across our products, from fighting fraudulent behavior on Uber Eats to forecasting demand on our platform. As Uber’s operations became more complex and we offered additional features and services through our platform, we needed a way to generate more timely analytics on our aggregated marketplace data to better und
Evolving Michelangelo Model Representation for Flexibility at Scale
You’re seeing information for Japan . To see local features and services for another location, select a different city. Show more Michelangelo, Uber’s machine learning (ML) platform, supports the training and serving of thousands of models in production across the company. Designed to cover the end-to-end ML workflow, the system currently supports classical machine learning, time series forecastin
Consistent Data Partitioning through Global Indexing for Large Apache Hadoop Tables at Uber
Consistent Data Partitioning through Global Indexing for Large Apache Hadoop Tables at Uber Data serves little purpose if we cannot find it. Looking up individual records in the 100-plus petabytes of data accumulated at Uber lets us perform updates and gather useful insights to help improve our services, such as delivering more accurate ETAs to riders and showing eaters their favorite food options
Engineering Data Analytics with Presto and Parquet at Uber
Data / MLEngineering Data Analytics with Presto and Apache Parquet at UberJuly 11, 2017 / Global From determining the most convenient rider pickup points to predicting the fastest routes, Uber uses data-driven analytics to create seamless trip experiences. Within engineering, analytics inform decision-making processes across the board. As we expand to new markets, the ability to accurately and qui
Uber Engineering’s Incremental Processing Framework on Hadoop
You’re seeing information for Japan . To see local features and services for another location, select a different city. Show more With the evolution of storage formats like Apache Parquet and Apache ORC and query engines like Presto and Apache Impala, the Hadoop ecosystem has the potential to become a general-purpose, unified serving layer for workloads that can tolerate latencies of a few minutes
H3: Uber’s Hexagonal Hierarchical Spatial Index
Grid systems are critical to analyzing large spatial data sets, partitioning areas of the Earth into identifiable grid cells. With this in mind, Uber developed H3, our grid system for efficiently optimizing ride pricing and dispatch, for visualizing and exploring spatial data. H3 enables us to analyze geographic information to set dynamic prices and make other decisions on a city-wide level. We us
Introducing Kraken, an Open Source Peer-to-Peer Docker Registry
You’re seeing information for Japan . To see local features and services for another location, select a different city. Show more A Docker registry’s primary purpose is to store and distribute Docker images. This may seem like a relatively trivial task, but with a large-scale compute cluster like Uber’s, it can easily turn into a scaling bottleneck. In computing environments with multiple regions
Uber’s Big Data Platform: 100+ Petabytes with Minute Latency
Data / ML, EngineeringUber’s Big Data Platform: 100+ Petabytes with Minute LatencyOctober 17, 2018 / Global Uber is committed to delivering safer and more reliable transportation across our global markets. To accomplish this, Uber relies heavily on making data-driven decisions at every level, from forecasting rider demand during high traffic events to identifying and addressing bottlenecks in our
Herb: Multi-DC Replication Engine for Uber's Schemaless Datastore | Uber Engineering Blog
Data / ML, EngineeringHerb: Multi-DC Replication Engine for Uber’s Schemaless DatastoreJuly 25, 2018 / Global Schemaless, Uber’s fault-tolerant and scalable datastore, supports the 600-plus cities where we operate and the 15 million rides per day that occur on our platform, not to mention Uber Eats, Uber Freight, and other business lines. Since 2014, we have deployed more than 50 Schemaless instan
Service-Oriented Architecture: Scaling the Uber Engineering Codebase As We Grow
You’re seeing information for Japan . To see local features and services for another location, select a different city. Show more Like many startups, Uber began its journey with a monolithic architecture, built for a single offering in a single city. At the time, all of Uber was our UberBLACK option and our “world” was San Francisco. Having one codebase seemed “clean” at the time, and solved our c
Queryparser, an Open Source Tool for Parsing and Analyzing SQL
You’re seeing information for Japan . To see local features and services for another location, select a different city. Show more In early 2015, Uber Engineering migrated its business entities from integer identifiers to UUID identifiers as part of an initiative towards using multiple active data centers. To achieve this, our Data Warehouse team was tasked with identifying every foreign-key relati
Why Uber Engineering Switched from Postgres to MySQL
We’ve represented the old version in red and the new row version in green. Under the hood, Postgres uses another field holding the row version to determine which tuple is most recent. This added field lets the database determine which row tuple to serve to a transaction that may not be allowed to see the latest row version. With Postgres, the primary index and secondary indexes all point directly
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Why We Leverage Multi-tenancy in Uber's Microservice Architecture