The magic behind Uber’s data-driven success
Uber, the ride-hailing large, is a family identify worldwide. All of us acknowledge it because the platform that connects riders with drivers for hassle-free transportation. However what most individuals don’t notice is that behind the scenes, Uber isn’t just a transportation service; it’s an information and analytics powerhouse. Day-after-day, tens of millions of riders use the Uber app, unwittingly contributing to a fancy net of data-driven choices. This weblog takes you on a journey into the world of Uber’s analytics and the vital function that Presto, the open supply SQL question engine, performs in driving their success.
Uber’s DNA as an analytics firm
At its core, Uber’s enterprise mannequin is deceptively easy: join a buyer at level A to their vacation spot at level B. With a couple of faucets on a cellular machine, riders request a journey; then, Uber’s algorithms work to match them with the closest out there driver and calculate the optimum worth. However the simplicity ends there. Each transaction, each cent issues. A ten-cent distinction in every transaction interprets to a staggering $657 million yearly. Uber’s prowess as a transportation, logistics and analytics firm hinges on their capability to leverage knowledge successfully.
The pursuit of hyperscale analytics
The size of Uber’s analytical endeavor requires cautious choice of knowledge platforms with excessive regard for limitless analytical processing. Contemplate the magnitude of Uber’s footprint.1 The corporate operates in additional than 10,000 cities with greater than 18 million journeys per day. To keep up analytical superiority, Uber retains 256 petabytes of information in retailer and processes 35 petabytes of information daily. They help 12,000 month-to-month energetic customers of analytics operating greater than 500,000 queries each single day.
To energy this mammoth analytical enterprise, Uber selected the open supply Presto distributed question engine. Groups at Fb developed Presto to deal with excessive numbers of concurrent queries on petabytes of information and designed it to scale as much as exabytes of information. Presto was capable of obtain this stage of scalability by fully separating analytical compute from knowledge storage. This allowed them to concentrate on SQL-based question optimization to the nth diploma.
What’s Presto?
Presto is an open supply distributed SQL question engine for knowledge analytics and the info lakehouse, designed for operating interactive analytic queries towards datasets of all sizes, from gigabytes to petabytes. It excels in scalability and helps a variety of analytical use circumstances. Presto’s cost-based question optimizer, dynamic filtering and extensibility by user-defined capabilities make it a flexible instrument in Uber’s analytics arsenal. To realize most scalability and help a broad vary of analytical use circumstances, Presto separates analytical processing from knowledge storage. When a question is constructed, it passes by a cost-based optimizer, then knowledge is accessed by connectors, cached for efficiency and analyzed throughout a collection of servers in a cluster. Due to its distributed nature, Presto scales for petabytes and exabytes of information.
The evolution of Presto at Uber
Starting of an information analytics journey
Uber started their analytical journey with a conventional analytical database platform on the core of their analytics. Nevertheless, as their enterprise grew, so did the quantity of information they wanted to course of and the variety of insight-driven choices they wanted to make. The price and constraints of conventional analytics quickly reached their restrict, forcing Uber to look elsewhere for an answer.
Uber understood that digital superiority required the seize of all their transactional knowledge, not only a sampling. They stood up a file-based data lake alongside their analytical database. Whereas this side-by-side technique enabled knowledge seize, they shortly found that the info lake labored nicely for long-running queries, however it was not quick sufficient to help the near-real time engagement vital to take care of a aggressive benefit.
To handle their efficiency wants, Uber selected Presto due to its capability, as a distributed platform, to scale in linear vogue and due to its dedication to ANSI-SQL, the lingua franca of analytical processing. They arrange a few clusters and commenced processing queries at a a lot quicker pace than something that they had skilled with Apache Hive, a distributed data warehouse system, on their knowledge lake.
Continued excessive progress
As the usage of Presto continued to develop, Uber joined the Presto Basis, the impartial governing physique behind the Presto open supply mission, as a founding member alongside Fb. Their preliminary contributions had been based mostly on their want for progress and scalability. Uber targeted on contributing to a number of key areas inside Presto:
Automation: To help rising utilization, the Uber group went to work on automating cluster administration to make it easy to maintain up and operating. Automation enabled Uber to develop to their present state with greater than 256 petabytes of information, 3,000 nodes and 12 clusters. In addition they put course of automation in place to shortly arrange and take down clusters.
Workload Administration: As a result of completely different sorts of queries have completely different necessities, Uber made positive that site visitors is well-isolated. This permits them to batch queries based mostly on pace or accuracy. They’ve even created subcategories for a extra granular method to workload administration.
As a result of a lot of the work completed on their knowledge lake is exploratory in nature, many customers wish to execute untested queries on petabytes of information. Massive, untested workloads run the chance of hogging all of the assets. In some circumstances, the queries run out of reminiscence and don’t full.
To handle this problem, Uber created and maintains pattern variations of datasets. In the event that they know a sure person is doing exploratory work, they merely route them to the sampled datasets. This fashion, the queries run a lot quicker. There could also be inaccuracy due to sampling, however it permits customers to find new viewpoints throughout the knowledge. If the exploratory work wants to maneuver on to testing and manufacturing, they’ll plan appropriately.
Safety: Uber tailored Presto to take customers’ credentials and go them right down to the storage layer, specifying the exact knowledge to which every person has entry permissions. As Uber has completed with a lot of its additions to Presto, they contributed their safety upgrades again to the open supply Presto mission.
The technical worth of Presto at Uber
Analyzing complicated knowledge sorts with Presto
As a digital native firm, Uber continues to broaden its use circumstances for Presto. For conventional analytics, they’re bringing knowledge self-discipline to their use of Presto. They ingest knowledge in snapshots from operational methods. It lands as uncooked knowledge in HDFS. Subsequent, they construct mannequin knowledge units out of the snapshots, cleanse and deduplicate the info, and put together it for evaluation as Parquet information.
For extra complicated knowledge sorts, Uber makes use of Presto’s complicated SQL options and capabilities, particularly when coping with nested or repeated knowledge, time-series knowledge or knowledge sorts like maps, arrays, structs and JSON. Presto additionally applies dynamic filtering that may considerably enhance the efficiency of queries with selective joins by avoiding studying knowledge that might be filtered by be a part of situations. For instance, a parquet file can retailer knowledge as BLOBS inside a column. Uber customers can run a Presto question that extracts a JSON file and filters out the info specified by the question. The caveat is that doing this defeats the aim of the columnar state of a JSON file. It’s a fast strategy to do the evaluation, however it does sacrifice some efficiency.
Extending the analytical capabilities and use circumstances of Presto
To increase the analytical capabilities of Presto, Uber makes use of many out-of-the-box capabilities supplied with the open supply software program. Presto supplies an extended listing of capabilities, operators, and expressions as a part of its open supply providing, together with normal capabilities, maps, arrays, mathematical, and statistical capabilities. As well as, Presto additionally makes it straightforward for Uber to outline their very own capabilities. For instance, tied intently to their digital enterprise, Uber has created their very own geospatial capabilities.
Uber selected Presto for the pliability it supplies with compute separated from knowledge storage. In consequence, they proceed to broaden their use circumstances to incorporate ETL, data science, knowledge exploration, on-line analytical processing (OLAP), knowledge lake analytics and federated queries.
Pushing the real-time boundaries of Presto
Uber additionally upgraded Presto to help real-time queries and to run a single question throughout knowledge in movement and knowledge at relaxation. To help very low latency use circumstances, Uber runs Presto as a microservice on their infrastructure platform and strikes transaction knowledge from Kafka into Apache Pinot, a real-time distributed OLAP knowledge retailer, used to ship scalable, real-time analytics.
In response to the Apache Pinot web site, “Pinot is a distributed and scalable OLAP (On-line Analytical Processing) datastore, which is designed to reply OLAP queries with low latency. It might ingest knowledge from offline batch knowledge sources (equivalent to Hadoop and flat information) in addition to on-line knowledge sources (equivalent to Kafka). Pinot is designed to scale horizontally, in order that it will possibly deal with massive quantities of information. It additionally supplies options like indexing and caching.”
This mix helps a excessive quantity of low-latency queries. For instance, Uber has created a dashboard referred to as Restaurant Supervisor by which restaurant house owners can take a look at orders in actual time as they’re coming into their eating places. Uber has made the Presto question engine connect with real-time databases.
To summarize, listed here are a few of the key differentiators of Presto which have helped Uber:
Velocity and Scalability: Presto’s capability to deal with large quantities of information and course of queries at lightning pace has accelerated Uber’s analytics capabilities. This pace is important in a fast-paced trade the place real-time decision-making is paramount.
Self-Service Analytics: Presto has democratized data entry at Uber, permitting knowledge scientists, analysts and enterprise customers to run their queries with out relying closely on engineering groups. This self-service analytics method has improved agility and decision-making throughout the group.
Information Exploration and Innovation: The pliability of Presto has inspired knowledge exploration and experimentation at Uber. Information professionals can simply take a look at hypotheses and achieve insights from massive and numerous datasets, resulting in steady innovation and repair enchancment.
Operational Effectivity: Presto has performed an important function in optimizing Uber’s operations. From route optimization to driver allocation, the power to research knowledge shortly and precisely has led to value financial savings and improved person experiences.
Federated Information Entry: Presto’s help for federated queries has simplified knowledge entry throughout Uber’s varied knowledge sources, making it simpler to harness insights from a number of knowledge shops, whether or not on-premises or within the cloud.
Actual-Time Analytics: Uber’s integration of Presto with real-time knowledge shops like Apache Pinot has enabled the corporate to supply real-time analytics to customers, enhancing their capability to watch and reply to altering situations quickly.
Neighborhood Contribution: Uber’s energetic participation within the Presto open supply group has not solely benefited their very own use circumstances however has additionally contributed to the broader improvement of Presto as a strong analytical instrument for organizations worldwide.
The facility of Presto in Uber’s data-driven journey
In the present day, Uber depends on Presto to energy some spectacular metrics. From their newest Presto presentation in August 2023, right here’s what they shared:
Uber’s success as a data-driven firm is not any accident. It’s the results of a deliberate technique to leverage cutting-edge applied sciences like Presto to unlock the insights hidden in huge volumes of information. Presto has develop into an integral a part of Uber’s knowledge ecosystem, enabling the corporate to course of petabytes of information, help numerous analytical use circumstances, and make knowledgeable choices at an unprecedented scale.
Getting began with Presto
In the event you’re new to Presto and wish to test it out, we suggest this Getting Started web page the place you may attempt it out.
Alternatively, if you happen to’re able to get began with Presto in manufacturing you may take a look at IBM watsonx.data, a Presto-based open knowledge lakehouse. Watsonx.knowledge is a fit-for-purpose knowledge retailer, constructed on an open lakehouse structure, supported by querying, governance and open knowledge codecs to entry and share knowledge.
Request a live demo here to see Presto and watsonx.data in action
1 Uber. EMA Technical Case Examine, sponsored by Ahana. Enterprise Administration Associates (EMA). 2023.
