A plumbing problem started this dispute. But NBC Bay Area Responds helped flush it all out for a Santa Clara woman.

The central question here is: who pays when an emergency repair is needed?

Liz Gong told us that a shower knob fell apart while renting a house in South Lake Tahoe. The homeowner (a.k.a. host) hired a plumber to fix it.

And later charged Liz $180.

“I told the host that I did not agree with that and that it was their responsibility to keep their rental home in working order.”

Gong disputed the bill with Airbnb and got nowhere.

So, she contacted us. And we reached out to Airbnb for her.

An Airbnb spokesman said Liz’s initial complaint “wasn’t handled by the right team.”

Ultimately, Airbnb agreed with liz – that the plumbing flaw wasn’t her responsibility.

It refunded her stay in full — plus the money she was charged for the plumber.

That’s $1,786 back in her pocket.

“This guest is getting a full refund, just as she should have in the first place,” Airbnb said in a statement. “We have your back in the very unusual event that a problem occurs.”

Liz is pleased with the refund, but frustrated with the process.

“An apology and a refund does not overlook the fact that I had to get NBC involved for them to review the case,” she wrote. “They probably would NOT have done anything further had I not contacted your team.”

One last note: Liz says she submitted a negative review that was later taken down.

Airbnb told us that was a mistake, too. Liz’s frank appraisal of the Tahoe home will be re-posted for future renters to consider.

The post Airbnb Person Will get $180 Plumber Invoice – NBC Bay Space appeared first on DAILY SAN FRANCISCO BAY NEWS.

Imagine an Airbnb guest flooding your entire home from a clogged toilet, leaving you without a home and responsible for footing the massive bill. That’s what a San Francisco host says happened to her earlier this year, and she’s been fighting Airbnb to get reimbursed since.

Erika Gemzer, a career coach who uses X under the handle ErikaCoaches, posted on the platform the nightmare she’s been living since renting a unit above her apartment to a guest who clogged the toilet with feces and baby wipes on April 14.

If only a plunger could have fixed the problem.

Instead, Gemzer says the guest, who had checked in for a one-month stay and left early without warning, damaged the valve that manages water flow from the tank to the bowl.

“A perfect storm,” she said.

The valve damage led to water running for at least 15 hours, ruining half her building with fecal water. Gemzer, 12 weeks pregnant at the time, contacted Airbnb, who allegedly told her she first needed to complete the repairs and then file a claim against the guest.

Gemzer says she spent “dozens of hours” trying to reach someone on the phone at Airbnb with no luck as she scrambled to get a construction estimate for the damage to meet the company’s 14-day claim window. Just the estimate to dry out the house, not including rebuilding it, was $130,000, Gemzer says.

The guest declined to pay the cost, so Gemzer was officially put into Airbnb’s claim system. She says over the course of six weeks, she exchanged about 93 emails with the company and its third-party adjuster. Airbnb refused to discuss the claim until the adjuster completed their investigation, but Gemzer says the adjuster never came to her home and it took them seven weeks to hire a plumber.

The plumber confirmed the issues were caused by the clog and broken valve. Gemzer says Airbnb emailed her alluding to doubt about the origin of the baby wipes and feces and hinted at toilet maintenance issues. The company offered her $6,000, Gemzer says, even though at this point in the saga she owed $52,743, including $19,451 in lost revenue for canceled bookings.

The user says she continued to go back and forth with Airbnb representatives since the disaster plumbing issue and has moved three times. She has no clue when her home will be livable again and says she’s on the hook for $300,379 in repair costs.

Airbnb offered her a “final offer” of about $31,000, she says.

Gemzer is sharing her story in the hope that Airbnb will help, particularly as she faces the challenge of securing temporary accommodation for herself, her two pets, and her soon-to-arrive baby.

“I truly cannot imagine that a company like Airbnb intends to leave hosts homeless and otherwise high and dry in their darkest moments,” she says. “Especially when the source of the hardship was 100% caused by guest damages.”

The post Wild Airbnb Saga Ends With Host ‘Pregnant, Homeless and $300,000 in Debt’ appeared first on DAILY SAN FRANCISCO BAY NEWS.

By Doug Milnes

Now that summer’s officially started, many people are looking forward to much-needed vacations. However, with travel costs on the rise, finding an affordable Airbnb has become both a puzzle and an art. To help you in your search for cost-saving summer rentals, MoneyGeek analyzed data for over 200,000 Airbnb listings across 19 U.S. vacation hotspots and explored how location, party size, amenities and room type impact costs.

Key findings

• Among the 19 U.S. travel destinations analyzed, the median price of an Airbnb is $154 per night. Hawaii listings have the highest median nightly cost ($294), while Portland, Oregon, offers the most affordable accommodations ($100).
• By choosing a private room, instead of a full home or apartment, you could cut your costs in half. A little extra privacy will cost you an additional $70 in a one-bedroom rental.
• While higher-capacity rooms tend to be more expensive, larger groups may experience more savings overall. When pitching in to rent a unit with a group of four, expect to save around 17% per person compared to a group of two.
• Access to fitness equipment or a pool will increase the cost of your stay by nearly 50%. In cities like New York, you could even pay double for rentals that offer these amenities.

MoneyGeek

How Airbnb prices vary across top tourist destinations

The research found that the median cost of an Airbnb across 19 popular U.S. travel destinations is $154 per night. However, where you travel significantly impacts the cost of your vacation accommodations. The price of a room on the sun-soaked shores of Hawaii is the highest among the locations the study analyzed at $294. Nashville was home to the second-most expensive rentals, with a median nightly cost of $198, followed by San Diego ($186).

The analysis also found that the way prices fluctuate based on the total capacity of rentals differs depending on your location. For example, while Nashville took the second spot for the highest overall Airbnb costs, San Francisco and New Orleans tied for the second-most expensive accommodations for 1-2 travelers; in New Orleans and San Francisco, renting a smaller-sized Airbnb will typically set you back $110 per night, while it costs just $90 in Nashville.

Across the U.S., Airbnbs that accommodate 3-4 people are typically 66% more expensive than those that accommodate 1-2 guests. This price increase is highest in Las Vegas’ Clark County, where travelers pay twice as much for these larger accommodations.

The table compares Airbnb prices across 19 major U.S. tourist destinations by median nightly cost overall and for different unit capacities.

MoneyGeek

Most expensive Airbnb neighborhoods in New York, Los Angeles, and Chicago

The study also broke down nightly median accommodation costs for the three most populated cities in the country: New York, Los Angeles and Chicago.

MoneyGeek

The premium for personal space is $70: How less privacy can lead to more savings on Airbnb

Imagine having an entire Airbnb to yourself — a private retreat where you can spread out, enjoy uninterrupted relaxation and experience the luxury of complete solitude. While the appeal is undeniable, this level of personal space comes with a premium price tag, often leading guests to pay more for the privilege of privacy.

For more personal space in a one-bedroom Airbnb rental, visitors can expect to pay roughly $70 more. The study calculated this expense by comparing the median prices of entire homes or apartments ($145 per night) and private rooms ($75 per night). By giving up a little privacy, you could see savings of nearly 50%. If you’re seeking even cheaper alternatives, rentals with shared bathrooms are available at just $60 a night.

MoneyGeek

Planning to swim or work out? Expect to pay nearly 50% more

Airbnb pricing doesn’t just hinge on location — amenities like beach, pool and gym access also wield considerable influence.

Our analysis compared median prices of rentals with specific amenities to those without and found that listings with beach access are 61% more expensive.

Pool availability is the second-most expensive amenity, costing vacationers 48% more. But this cost increase isn’t the same everywhere: for instance, the cost of rentals with pool access skyrockets to more than twice the usual rate (125%) in New York City.

Fitness enthusiasts looking for gym access can expect around a 47% increase in booking costs. In Portland, Oregon, the price tag on rentals with gym access goes up by 84%.

You’ll also likely pay a premium if there’s a dishwasher in your rental unit; Airbnbs with dishwashers typically cost 21% more than those without them.

Tips to save on your Airbnb bookings

It’s no secret that traveling today is expensive. The researchers recommend following these cost-saving tips when looking for Airbnb accommodations to enjoy the vacation you want without breaking the bank.

1. Boost your party size.

A group of four can anticipate 17% savings per person compared to pairs. On average, a 2-person unit charges $105 per night, while accommodating four nudges the price up by 65% to $173 — letting you entertain twice as many folks for less.

2. Opt for private rooms or units with shared bathrooms.

The cost of renting a full home averages $185 per night, while a private room or a shared-bathroom unit only costs $79 or $60 per night, respectively.

3. Embrace washing dishes by hand.

Dishwasher-equipped units demand a 21% premium. In Hawaii, you may even find yourself paying 85% more for rentals with dishwashers. Opting for accommodations without a dishwasher could lead to significant savings.

4. Utilize credit cards.

Familiarize yourself with the benefits of travel credit cards and reward credit cards to make your money work for you. Between welcome offers, cash back rewards and opportunities to accumulate points and miles, there are many ways you can utilize credit cards to make your vacation cost-effective.

Methodology

The study analyzed data from Inside Airbnb to provide insights on Airbnb costs in 19 U.S. tourist destinations and offer tips for maximizing savings on Airbnb bookings.

The study examined several data points, such as price, location, unit capacity, available amenities and room type. Then, the study calculated median rates across these variables to demonstrate how prices vary by location.

The study also applied a linear regression model to the data and used Shap feature importance to identify the amenities with the highest impact on price. To calculate the percentage impact of amenities on price, the study compared the median prices of listings with a specific amenity to those without it.

This story was produced by MoneyGeek and reviewed and distributed by Stacker Media.

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Transcript

Liu: I live in the beautiful city of San Francisco. One thing to know about the city is it’s very hilly. Even after three years of living in the city, I still run into this issue where every time I’m trying to get from point A to point B in the city, whether it’s for dinner or for a walk, I’ll look at Google Maps and be like, that looks easy. When I actually get on my way, at some point, I will realize that up ahead is a hill with a non-trivial incline. That happens a lot in SF because it’s hilly in so many places. When I think about it, this uphill journey resembles Airbnb’s very own journey to service oriented architecture. In a sense that we started out on this flat and straightforward path of migrating to SOA, then realized that there’s a surprisingly steep and interesting learning curve beyond that. I will share with you four key lessons that we have learned along the way.

Background

My name is Selina. I’m a software engineer at Airbnb.

Lessons from Airbnb’s Journey to SOA – Invest In Common Infra Early

Here are the four key lessons from our journey to SOA and beyond. Let’s dive straight into the first one, which is to invest in common infrastructure early in your SOA journey, because this will help to turbocharge your migration from monolith to SOA. To give you more context, let’s spend some time first on why and how we went about our migration. Back in 2008, just like most small scrappy startups, Airbnb started out with a single web application written using the Ruby on Rails framework with a model view controller paradigm. Over the years, as Airbnb expanded its global footprint product offering and engineering team, the single web app grew into a massive bloated monolith. Since it is developed using the Ruby on Rails framework, internally, we call it the monorail. As more engineers jumped onto the monorail, it was becoming slow, unstable, hard to maintain, and a single point of failure. In 2018, we launched a company-wide initiative to migrate our tech stack from this slow moving monorail, to service oriented architecture. Similar to the MVC, model view controller paradigm, we decided to decompose the monolith into, first of all, presentation services that render data in a user friendly format for frontend clients to consume. Then we also have mid-tier services that are grouped around shared business concerns and can be used in multiple contexts. Then, under those, there are data services that encapsulate multiple databases in a uniform data layer.

A Case Study – Host Reservation Details (HRD)

To make this abstract diagram a little bit more concrete, let’s use Host Reservation Details, or HRD, as a case study. Before I explain what HRD is, I want you to meet Maria. Maria is a new Airbnb host based in Colombia. She just published her listing on Airbnb last week, and today, she received her first ever booking request. When she opens her host inbox on the Airbnb website, she sees a message from the guest, Christopher, and this panel on the right here that displays key information about the trip being requested by the guest. This right panel here is the HRD or the Host Reservation Details. Taking a better look at it, we see that it has these call to action buttons that prompt the host to either accept or decline the booking request. It also pulls information from a myriad of sources in our system, including users, listings, payments, and reviews. In fact, there’s a lot more information that it offers below the fold as well.

To migrate this product feature to our SOA paradigm, we basically broke down the product logic into, first of all, a presentation service that handles view layer business logic such as formatting and translations. Then we have a few mid-tier services that handle write operations such as confirming or declining a booking request. Then we have data services down the stack that encapsulate different product entities such as reservations, users, and listings. Of course, there are many other services on each layer, and we interact with many of them every time we serve a request.

Let’s call our reservation presentation service Ramen, because, who doesn’t like Ramen? Going back to this diagram, we do interact with a lot of services every time we serve just a single product service like HRD. You can imagine that to serve all the product services we own, we need a pretty sizable number of SOA microservices to power them. What made this monumental task of migrating all the complex logic and building out our vast SOA ecosystem possible are the building blocks that our infra team provided that allowed us to build services with confidence and speed. Let me walk you through some of them.

API Framework

First, we have this in-house API framework built on the Thrift language. It is used by all Airbnb services to define clean API and to talk to each other. Let’s say that as part of our business logic, Ramen has to read data from the Tofu service. To serve that data, the Tofu engineer only has to define the endpoint in simple Thrift language, and the framework will auto-generate the endpoint logic in Tofu that handles stuff like schema validation and observability metrics. Secondly, a multi-threaded RPC client that the Ramen service can use to talk to Tofu service. It handles stuff like error propagation, circuit breaker requests, retries. This means that engineers can focus on implementing the core business logic and not spend time worrying about the plumbing details of inter-service communication. Based on the framework, we also developed productivity tools such as this API Explorer, where engineers can browse different services, figure out which endpoint to call, and even use an API playground like this one to figure out how to call the endpoints.

Powergrid

Second, we have Powergrid, which is a Java library built at Airbnb that makes it super easy to run code in parallel. Under the hood, Powergrid helps us organize our code as a directed acyclic graph, or a DAG, where each node is a function or a task. We can use it to model each of our service endpoint as a data flow with a request as the input and the response as the output of the data flow. Because it handles multi-threading and concurrency, we can easily schedule chunks of code to run in parallel. Let’s take the host sending a special offer to the guest as an example. In this write operation in Ramen service, there’s a bunch of checks and validation that have to be performed before we allow the host to send a special offer. Using Powergrid, we will basically first take the listing ID from the request body, use it to fetch information about the listing from the listing data service. Then we’ll pass the information down to a bunch of downstream services for validating, which will happen in parallel. Then after that, we will aggregate all the validation responses, make sure that we got a green light from everyone, before we write back to the special offer data service to actually send it to the guest. This Powergrid library provides a few benefits. First of all, it provides low latency for performing network IO operations concurrently, which really makes a difference when your endpoint has multiple downstream dependencies. It also offers granular metrics for each node, which helps us to pinpoint bottlenecks in the data flow. These benefits help to ensure that our code is performant, observable, and easy to reason about.

OneTouch

Third, we have OneTouch, which is a framework and a toolkit built on top of Kubernetes that allows us to manage our services transparently and to deploy to different environments efficiently. This framework has two key aspects. The first part is the principle that all service configurations should live in one place in Git. For instance, other configurations for the Ramen service will live in an infrastructure folder that lives right alongside the source code folder. From there, we can easily configure stuff like CPU resources, deploy environments, alerts and service dependencies. Then the second aspect to the OneTouch framework is this magical k tool, which is a command line tool built on top of Kubernetes that allows us to deploy our services to different environments on the Kubernetes clusters. If I just type k all on a command line, it will automatically generate the configs, build the app, and deploy it to a remote cluster. If you think about it, it’s actually just like making a bowl of ramen where first you generate the bowl, which is the configs. Then you build the ramen, which is the actual application. Then you deploy the entire bowl of ramen with garnishes, and that is the end product. Because all environments are deployed the same way, we can easily make five beautiful bowls of ramen soup for different environments. From the service governance perspective, it makes it super easy for everyone to orchestrate, deploy, and diagnose a service, because there’s only one place to look and one process to learn.

Spinnaker

Lastly, we have Spinnaker, which is an open source continuous delivery platform that we use to deploy our services. It provides safe and repeatable workflows for deploying changes to production. One aspect that has been especially helpful for us is the automated Canary analysis. In this step of the deploy pipeline, we basically deploy both the old and the new snapshots to two temporary environments, namely, Baseline and Canary. Then we route a small percentage of production traffic to them. Then, key metrics about these environments such as error rates, are automatically ingested and fed into a statistical test that produce an aggregate score for the Canary, which has the new snapshot as measured against Baseline which has the old snapshot. Based on the score, the analysis tool will decide whether to fail or promote the Canary environment to the next stage in the deploy pipeline, which, for example, could be deployed to production or running more integration tests. In SOA, where so many services are deployed every single day, this helps us to release code changes at scale with confidence and ease.

Thanks to all these infra pieces, we were able to migrate our core product functionality to SOA within the span of two to three years, with an architecture like this. Of course, there are many other services that are not shown in this diagram. In total, we have more than 400 services running in production with more than 1000 deploys every single day.

What Does the Post-Migration World Look Like?

After all this work, you could think that we can now take a long nap in front of the computer just like this cat. We are not done. In fact, we haven’t started climbing the metaphorical hill that I mentioned in the beginning. What we realize is that with a sprawling architecture like ours, there are some important SOA tradeoffs that we need to consider before we continue walking in the same direction. For instance, it could sometimes take more time for engineers to ship a feature with SOA, because engineers need to acquaint themselves with and make changes in multiple services. Then they also have to deploy these different services in order before they can push out a feature. There’s also significant overhead in maintaining each of these services. On one hand, our system has become more reliable and highly available, because if one service is down, other parts of the SOA can still function. On the other hand, the proliferation of services along the stack also led to more developer friction. Furthermore, even though now our services are loosely coupled, it means that certain patterns of logic have to be repeated across different services, since it’s now harder to share code across service boundaries than before.

To continue the comparison, on one hand, our services are now individually scalable, which means that we can fine-tune our resource allocation by service instead of scaling the entire monolith. On the other hand, we face fragmentation and inconsistency in business logic, which makes it harder for us to have a full picture of what’s going on in the system. Lastly, on one hand, we achieved business agility by separating different parts of the product into different services that can basically iterate in parallel at the same time. On the other hand, it’s also easy to end up with a complicated dependency graph if there’s no rules around who can call who. That’s what happened to us where due to unconstrained call patterns between services where basically anyone can call anyone else, we have this dependency graph with unruly arrows pointing in every single direction. This is unideal and potentially dangerous, especially when there are circular dependencies, which can make relationships between services hard to visualize and understand. On a good day, this could make it hard for a new engineer to ramp up on the system. On a bad day, when there’s an outage, this could make it difficult to debug errors and to mitigate user impact. In a tangled dependency graph like this, highly stable services can also be easily brought down by more volatile services. These are all flip sides of the same SOA coin that we have to grapple with.

Simplify Service Dependencies

To address these issues, we took a few steps, starting with simplifying service dependencies, which is our second takeaway. We designed our architecture to be a tiered tech stack consisting of presentation, mid-tier, and data services. The motivation was to separate services into layers as shown in this diagram, based on their technical priorities. As we go up the stack towards application and UI layers, the primary consideration here is iteration speed and schema flexibility, leading to more specific and fast changing API. On the other hand, if we go down the stack towards platform and infra layers, they need to have more generalized API and higher reliability requirements, since their blast radius is bigger, which means that if they go down, many other services will be impacted. For an SOA to be reliable and resilient, it’s imperative that stable services do not depend on more volatile services. Conceptually speaking, this means that a higher tier service can call a lower tier, but not vice versa.

However, the problem with our SOA system was that there was not enough service governance and dependency management to enforce this fundamental principle and to restrict who can call who. Hence, to enforce a topology-driven layer architecture, we introduce service blocks at a platform layer. You can think of each block as a collection of services with related business functionalities. For instance, the listing block, as shown here, will encapsulate both the data and business logic that inform core listing attributes. Then it will expose a simple, consistent read/write API to upstream clients through the facade. Under the hood, the facade will help to orchestrate the coordination between the underlying data and business logic services, while providing a layer of abstraction that conceals the underlying complexity from the clients. We also enforce a strict topology by prohibiting clients from calling any of the internal services, as well as prohibiting blocks from having circular dependencies with each other. With such a high level abstraction, it’s much easier for upstream clients to discover and leverage our core functionality at the platform layer. It is also much easier for us to manage block dependencies and maintain high levels of stability.

Platformize Data Hydration

We also spent some time platformizing data hydration. Going back to this diagram again, you can see that we have quite a number of presentation services. If we zoom into any of these typical presentation services, there are usually three common and straightforward functions that these services perform, including, first, which is data fetching and hydration from different downstream services. For example, Ramen service calls thousands of services to hydrate data for Host Reservation Details. Second, there’s also a simple transformation of data. Using the same example, Ramen service will transform and merge data from all these thousands of downstream services into something that the client expects. Then third, there’s also permission checks that we have to perform before proceeding with more complex business logic.

As time went on, we realized that engineers were spending a lot of time on these three common functions, resulting in a lot of duplicated code and wasted productivity. Our approach to this problem is to introduce a platformized data access layer that provides a single consolidated GraphQL schema, stitching together different entities across all of Airbnb’s data, for instance, listings, users, and reservations. Then, it also serves as one platform to host all the mundane data fetching and hydration logic rather than requiring duplication of this logic across many different presentation services. Together with a more complex presentation logic on the left and the write logic on the right, which we will ignore for this presentation, this common data access layer will eventually replace our old presentation services. Then the service blocks below the layer will also replace the old data and services. You can say that with this effort, we continue to simplify service dependencies.

Going back to the layer itself, it is this service building column that we call Viaduct, because it’s like a conduit for the data flowing through our entire system. In essence, it is an enhanced GraphQL engine that’s built in the language of Kotlin, that reimagines the way data is fetched in SOA, by going from service oriented to data oriented hydration. What do I mean by that? To give you a more concrete example, instead of writing code to explicitly call reservation data service to get reservation data, the caller will instead write a declarative query on the reservation entity. They will even fetch the associated listing and guest user data using the same query. Such GraphQL queries are made possible by our GraphQL schema that’s enriched with special directives. For example, the ServicePoweredType annotation with its templated fields allows us to associate a GraphQL type with a service endpoint, where the response from the service will be automatically used to hydrate the GraphQL type. Here you can see that we are linking the ReservationDataService endpoint, getReservationsByIds to the reservation GraphQL type.

As another example, the ServicePoweredType key annotation allows us to link different types together. For instance, the guestId on the reservation type can link to a fully-fledged user type that is implicitly fetched from the user block service. How the magic happens is that at build time, Viaduct translates these templated directives into auto-generated code, as represented by the field and type resolvers in this diagram, that basically takes care of the inter-service data fetching and simple data transformations. A resolver in GraphQL terminology is basically a function that outputs the value of a given field or type. In this diagram, the third-party GraphQL library is responsible for first parsing the incoming query, and then calling the resolvers for every field that we can process. In turn, the field resolvers will call the type resolvers to fetch data from downstream services. Aside from these directives, there’s also a privacy directive that wires in permission checks automatically, and ownership directive that makes it easy for us to find code reviewers and to route alerts to the right teams for all these different types at Airbnb.

Another cool directive is the derivedField directive, which allows developers to create new fields computed from one or more existing fields. For example, this HostFacingStatus field is used in Host Reservation Details page to call out important stages or milestones of a reservation to a host. For instance, it can take on the string values of pending payment, checking out today, review guests. To compute this status, we need to consult listing reviews and payment data, which will come from other parts of the GraphQL schema. With the derivedField annotation, we can tell Viaduct to resolve this derivedField using the business logic defined in the HostFacingStatusProvider class.

Going into this class, this provider class basically implements a standardized interface, and overrides two key methods. The first one is this getFragments method that allows us to define data dependencies for this DerivedField in terms of a GraphQL query. You can see here that we are fetching data from listings, reviews, and payment. Then, second, we have this resolve method, where we will write the actual business logic for resolving the DerivedField. What actually happens when a DerivedField gets resolved is that it makes additional field resolver calls, it can be multiple calls, to satisfy its data requirements. In effect, it creates its own GraphQL engine, which can instantiate field resolvers the same way that the other fields do.

Such an abstraction makes it easy for engineers to add all sorts of presentation logic without worrying about the nitty-gritty details of data fetching. You can imagine that when a query asks for multiple fields, especially multiple DerivedFields, it is easy to have overlapping data dependencies within the query. For instance, here you see that three field resolvers might end up needing data from the listing block service. To account for such scenarios, Viaduct has this built-in capability to batch the requests that it sends to downstream services in an optimized manner, making sure that it is not making more calls than necessary. The batching logic here is encapsulated by this dataloader wrapper around each of the type resolvers.

Optimized Batching

Under the hood, Viaduct relies heavily on coroutines in the Kotlin language, which you can basically think of as lightweight threads. Within a single Viaduct request, each of these field resolvers will trigger a coroutine to resolve its field value. Some of the field resolvers might end up calling the same dataloaders. For example, here, you can see that the first two field resolvers both call the listing dataloader. Same for the bottom two field resolvers, which call the user dataloader. It is important to highlight here that the loadById method calls here don’t trigger immediate data loading, but are rather suspended, which means that it can be paused and resumed at a later time. This suspension feature is a feature of the Kotlin coroutines. Viaduct with its special coroutine dispatcher will basically wait until all the coroutines for this particular request have reached their suspension point, meaning that no more resolution is possible for that request, before dispatching the actual data loading requests to downstream services in batch. Here you can see that Viaduct has aggregated all the listing IDs in one call to the listing block service, and all the user IDs in one call to the user service respectively. Once the data comes back, Viaduct will pass back the data to the origin of your resolver coroutines, which will then resume with the results.

To go even one step further, we also added an intra-request cache, which basically keys on the global ID and the type of data to make sure that within the lifecycle of a single request to Viaduct, the same data doesn’t get requested from its source more than once. For instance, the same listing ID, or the same user ID does not get requested more than once. These two measures, optimized batching and caching for data loading, prevents unconstrained funnel of requests to downstream services.

Example – Listing Details Page

To give you a real-life example, this is the listing details page that gets used to check out a listing and book the listing. Naturally, this page depends a lot on listing data. Before with our first iteration of SOA, the presentation service responsible for hydrating this page will end up triggering 11 calls all together to the listing block service through our SOA maze. After migrating most of our presentation logic to Viaduct, then the number of calls is reduced down to one. You can see how with batching and caching, Viaduct helps to improve performance and saves us computing costs by reducing the number of network requests needed to serve a single page. In addition, we also have an online IDE built on top of the open source graphical library that makes it super easy for engineers to explore the schema, issue actual queries, and to inspect the data fetched. It is also the primary way that we use to test our local code changes, since the IDE can be easily hooked up to local and test environments as well.

All in all, a GraphQL schema that’s enriched with these configuration driven directives, allows developers to easily create types, construct an entire graph representing Airbnb’s central schema, and to fetch data easily without having to navigate our vast constellation of services. This means that product engineers can focus on product innovation, instead of writing the same data hydration code over again.

Unify Client-Facing API

Lastly, as we continue to evolve our SOA, we also decided to unify our client-facing API. In our original SOA diagram, each presentation service is usually maintained by a different product team. The implication of this is that each presentation service tends to define its own client-facing API and solve problems its own way. There wasn’t really a common set of best practices, and people sometimes ended up reinventing the wheel. This often resulted in inconsistent if not buggy user experience and lots of spaghetti code that is not really reusable.

UI Platform

Our solution to the problem is UI Platform, which is a unified, opinionated, server-driven UI system. To quickly visualize how it works, this is what the user sees on HRD, and this is what the UI Platform sees where everything on the page is broken down into a standardized section. Our idea is for the content and styling of the UI within each of these sections to be driven by the backend. This leaves the frontend with just a thin layer of logic that’s responsible for rendering these sections. In this diagram, on the presentation backend, you see that we expose a common schema to the client. You can see that each of the frontend clients has a UI Platform runtime that is responsible for interpreting the API responses from the backend, and rendering it into UI for the user.

Taking a deeper look into the standardized API response, you can see that it is broken down into two parts. First, we have a registry of all the sections that’s needed for a page. Then, second, is the screen structure, which expresses the layout of the sections on the page. For instance, we can say that we want the header section to be at the top of the page, and the card section to be right below it. Zooming further into each of these sections, here we have the schema definition on the left with a concrete example on the right. The example here is the user highlight section in HRD, as indicated by the section ID. Focusing just on the key attributes, we first have section data which represent the data model for the section. For example, here we have a list of user info, including where the user lives. Then, second, we have the UIComponentType field, which is an enum that is used to refer to the frontend UI component that should be used to present the data. In this case, we want to render the data as a bullet list. One thing to call out here is that it is possible for one section data type to be rendered by different UI component types, which gives us more flexibility and variation in UI design. More importantly, all these sections should be totally reusable across different services. For example, this user highlight section here can be shared between a guest facing and a host facing surface.

There are a few other key features of the UI Platform. First, we have support for different layouts and placement of sections on the page, which provides flexibility and range for product design needs. Second, with deferred sections, we can opt to delay the loading of more expensive sections to a second network request, which helps to improve the initial page load time and user experience overall. This is especially helpful for mobile clients that can have weaker internet signals than desktop web. Lastly, the framework also logs impression and UI actions of each section automatically, which is helpful for measuring user engagement when we launch a new feature. To make the developer experience easier, we also build out a web tool that allows backend engineers to visualize their API response in real time by copy pasting their API response payload into the textbox in a tool.

In summary, by driving the UI from the backend server, we can basically establish a clear schema contract between frontend and backend, and maintain a repository of robust, reusable UI components that makes it easier for us to prototype a feature. In addition, with pre-built sections, we could also easily push new content to the clients without waiting for the long mobile app release cycles on app stores. Since no client code changes are needed with pre-built sections. Lastly, by centralizing the business logic and presentation logic in the backend, instead of having it scattered across clients, we could also ensure a consistent user experience across all frontend platforms.

Where on the Spectrum?

On the spectrum of how server driven we are, we have been leaning very close to the side of fully server driven UI. Basically, after piloting the UI Platform for a few product launches, we noticed some issues. First of all, by driving the presentation logic from the backend, it makes it hard for the different frontend clients to leverage native improvements or building capabilities from their native OS platforms. For instance, the navigation styles or haptic feedback on Android and iOS, are done very differently and it is hard to server drive these things differently in a centralized manner from the backend.

Second, the UI Platform makes it easy for us to reuse existing sections to build new pages. However, for a brand new section, designing the schema and building it out involves close collaboration between the client and backend engineers initially. As our company continues to invest in the ambitious product roadmap, we need to find a way to decouple this very tight-knit frontend and backend workflows to speed up our iteration. We basically decided to scale back our server-driveness and move towards the middle of the spectrum where the UI Platform will become a thin screen building layer in our frontend architecture. In this version, the section will basically remain the primitive for screen building, but now sections can be orchestrated with either server-side logic or client logic. Meaning that some sections can remain server driven, but for the client-driven sections, the frontend will handle the UI presentation logic, while the backend will handle the business logic. This pivoting in our frontend strategy will hopefully give us more flexibility in supporting new products and give us more synergy and speed in developer collaboration.

Recap

First, we have the first lesson, which is to invest in common infrastructure early to turbocharge our initial SOA migration. In our case, we leveraged both open source and in-house technologies to lay a foundation for our SOA. Second, as you continue to expand and scale your architecture, you can consider to start simplifying your service dependency graph for long term stability. For us, we did it with service blocks at the platform layer. Third, platformize common patterns such as data hydration, so that product engineers can focus on solving new and important problems. For us, we built a Viaduct service to do the job. Lastly, unify client-facing API into a robust system of flexible orchestration and reusable parts to support product iteration. For us, we use the UI Platform to standardize our API.

One overarching theme in the progression of these takeaways is that we continue to streamline and fine-tune our layers of abstraction based on the way we work and the way we build our product. Starting from the infra layer with the common building blocks, to platform layer with the service blocks, and then to application and UI layer with Viaduct and UI Platform. What informed these stepwise improvements, were the common pain points experienced by engineers and end users. It’s true that sometimes it means that we have to undo our earlier work. That is fine. It is really hard to get everything right the first time anyways. The point is to keep evolving the architecture to improve developer experience and to serve prevailing business needs.

Conclusion

Going back to the metaphorical hill in SF again, when we set out to migrate to SOA, we were not expecting our path to include this steep climb up ahead. The lessons along the way were enriching, and the learning curve was in fact quite an exciting and fulfilling ride. We can’t say for sure that we have made it to the top of the hill, but when we survey our current tech stack, we begin to see that SOA is not a fixed destination. Instead, like a real city, it is constantly changing and evolving into something more resilient and lasting.

Questions and Answers

Wells: What’s the next thing you’re thinking we need to make a change here to improve stuff?

Liu: At Airbnb, we are now focusing a lot on further improving developer productivity now that we have this complex system of so many services being deployed and features being launched at the same time. Something that we are trying to speed up is a local developing environment. We used to have all these staging environments, test environments, and developers used to just push their local changes to one of the test environments and issue some request to make sure that their changes work as expected. Those test environments are static and they are maintained by the service owner.

Right now we are trying to pilot something different that is basically allowing any developer working on any service to just spin up a private group of cloud applications to basically run your changes on, and those can spin up or ramp down based on developer demand. It’s like an on-demand test environment that is connected to the wider staging environment of the different services in the ecosystem. In that way, hopefully we can speed up the deploy time, because in the past, when we deploy to specific test environments, we still have to go through CI checks and stuff like that. With a more private group that have safety boundaries around it, we can make things faster and bypass some of the checks. That’s something that we have been tinkering on and trying to get more adoption of within Airbnb.

Wells: You talk about service oriented architecture. I’ve been around long enough to remember we had service oriented architecture and we started talking about microservices. What do you see as the difference between the two? Why do you say service oriented architecture when you’re talking about these changes?

Liu: I think it’s just a process of how we socialize the idea within our company. I think, tech leaders at that time, they really just used the word SOA to push the concept wider to every corner of the company. I think partially it might be because, at the start, we were wary of splitting our logic into tiny services. We wanted to avoid using microservices, just because the term might be a little bit misleading or biased in just how it’s understood by most people, when they first hear it. It’s probably just like the process of how we communicate it, more than anything else.

Wells: Basically, microservices sound like they’re just going to be far too small.

Liu: From your perspective, is there a difference?

Wells: There isn’t. I really don’t think so. I think probably, it’s a good instinct not to get hang up on the size of services. Because where we built microservices architecture at the “Financial Times,” I think we did build far too many services, because we thought, they’re supposed to be small.

What are the biggest benefits of the presented service oriented architecture at Airbnb compared to the monorail? Scalability.

Liu: Scalability, for sure. I think the most important part is how it’s mapped to how our teams are divided. In very broad strokes, we have the guest facing teams and host facing teams, and we work on the logic on our website separately for those things. In terms of just iterating, and pushing out new features, breaking our monolith into different services allow us to just do things in parallel, instead of having just one long deploy train on this monolithic application. A guest change might delay a host change and stuff like that. I think that’s the main benefit.

Wells: Downsides?

Liu: Just fragmentation of logic. Right now we are moving from being fragmented and having a lot of services to consolidating some of this logic. We’re trying to start from the more bottom layers of our tech stack, so starting from the platform layers, we’re trying to group all these very commonly used data and logic into bigger service blocks, instead of having multiple services re-implementing the same thing.

Wells: My experience is definitely you end up moving things around as you realize you’re changing them all at the same time.

In addition to Ruby, what programming languages have you added to your stacks?

Liu: Ruby was used in the monolith. Right now we use a lot of Java in our backend services, and some Kotlin sometimes as well. For the data side of things, we use Scala.

Wells: Was the monorail completely decommissioned?

Liu: Unfortunately not. That’s one of the fine print in our presentation. There’s a different level of challenge when it comes to migration. There are just going to be some stuff that’s really hard to migrate out, or stuff that is important but not that critical, and no one has the time to migrate them out. Right now, monorail still serves some very legacy traffic. Another reason is some very old frontend clients, especially mobile clients like iOS and Android might still be talking to monorail endpoints. For those reasons, we have to keep it around for a while longer.

Wells: Is there a plan to say we’re going to get rid of it, or is it just we’ll keep a certain amount of it?

Liu: Our goal is definitely to decommission it completely. I’m just not sure how long it will take. There’s going to be a very long tail that might just take maybe a couple more years for the monorail to completely disappear. It’s not critical anymore. It’s just going to be a small component in our SOA. It will become one of the services basically.

Wells: How does the GraphQL layer talk with the service layer? Does it use Thrift?

Liu: Yes, that’s correct.

See more presentations with transcripts

The post Airbnb at Scale appeared first on DAILY SAN FRANCISCO BAY NEWS.

SAN FRANCISCO — A San Francisco woman was arrested this week after police said she flew to Detroit and rented an Airbnb in a nearby suburb to meet with a teen.

Stephanie Sin, 32, rented an Airbnb in Royal, Oak, Michigan, authorities said, according to media outlets. She ordered an Uber to the home of a 15-year-old in nearby Novi to pick him up, authorities said. The teen told his parents when the ride arrived, and they called the police.

Officials suspect she talked to the teen for more than a year.

Find out what’s happening in San Franciscowith free, real-time updates from Patch.

Online court records showed she was booked into the Oakland County jail Monday night on sex offense charges and using a computer in commission of a crime.

Novi police said Sin met the boy on an app when he was 14, WJBK-TV reported. Their talks escalated from the app to text messages and became increasingly more explicit, police said.

Find out what’s happening in San Franciscowith free, real-time updates from Patch.

Police told WJBK that Sin did not appear surprised when she was arrested.

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San Francisco Woman Rents Airbnb Near Detroit To Meet Teen, Police Say

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SAN FRANCISCO (CBS SF/AP) — San Francisco-based Airbnb will allow its employees to live and work almost anywhere around the world, fully embracing a remote work policy to attract staff and ensure flexibility.

The San Francisco short-term stay company said late Thursday that under the new policy, employees can work from the office, home or during their travels to 170 countries.

READ MORE: Two Dead After Truck Careens Off Cliff Into Ocean At Pescadero State Beach

Staff will still have to meet in person for regular team meetings and events, CEO Brian Chesky said in a message to employees. Salaries won’t change if employees decide to move.

Employees can spend up to three months working in each country they visit every year but they will still need a permanent address for tax and payroll reasons, which involves a “mountain of complexities,” but Chesky said the company is working on an open-source solutions.

The new policy will put the company in a better position to hire and retain the best people by not “limiting the talent pool to a commuting radius around our offices,” Chesky said. Remote work and flexibility “will become the predominant way that we all work 10 years from now,” he said.

Millions of people switched to working remotely during the pandemic to limit potential exposure to COVID-19. Companies are beginning to ask those workers to come back to the office, at least on a hybrid basis, including other tech companies such as Facebook parent Meta and Microsoft.

Think tank Joint Venture Silicon Valley says workplace flexibility is going to be the new norm.

“The interesting shift is that this has come to be classified now as a worker right — not a privilege but a right. In other words, it’s a perk that has come to be expected and I think the world is just going to be organized that way,” said President and CEO Russell Hancock.

READ MORE: Spring Rain, Quirky Climate Wipes Out Early Strawberry Crop in Monterey County

“There will be ripple effects in the economy. We’ll see that in the corporate kitchens, we’ll see that in the other corporate services that used to be provided on a full-time basis, now they’re only being provided on an occasional basis,” Hancock said.

Small businesses may struggle to get foot traffic.

This week the San Francisco Controller’s Office released a report that found that more than 20 percent of office space is sitting empty in the city, up from 7% in January 2020.

Jonathan Perez of Emeryville currently works from home for a for-profit education institution.

“If I’m getting paid the lifestyle of San Francisco — which is a high lifestyle — and I could move to the Midwest and get paid a really nice job, salary and then buy a house in the Midwest,” he said. “So it would actually incentivize me to maybe not live here because it’s very expensive here.”

Rita Gilliland is visiting San Francisco from Houston where she’s already spotted a lot of California license plates around town.

“If you’re the person moving from California — yeah, absolutely it’s good for you. Cost of living is significantly different in Houston,” she said. “So anybody who’s coming from California to Houston, if they’re going to have their same pay, they’re going to be doing pretty well. So it’s probably a good perk for them.”

MORE NEWS: Coroner: San Jose Rookie Officer De’Jon Packer Died Of Fentanyl Overdose

KPIX correspondent Betty Yu contributed to this report

The post Airbnb Embracing Distant Workforce; Staff Allowed To Work And Stay Anyplace – CBS San Francisco appeared first on DAILY SAN FRANCISCO BAY NEWS.

Oakland News Now – Tag Cloud – Former Airbnb executive on why he’s moving to cryptocurrency market

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The post Former Airbnb govt on why he is transferring to cryptocurrency market | Oakland Information Now appeared first on DAILY SAN FRANCISCO BAY NEWS.

The billionaire founder and CEO of Airbnb has announced that he’ll be working from a variety of rental apartments from now on – while hopping from city to city.

Brian Chesky, 40, who is currently worth an estimated $12.3 billion, took to Twitter yesterday to announce his alternative work style.

In a series of tweets, he outlined where he would be staying over the next few weeks and why he chose to operate in so many different locations.

He wrote: “As of today I live on Airbnb. I will be staying in a different town or city every few weeks.

Brian Chesky, 40, who currently has an estimated fortune of $12.3 billion, announced on Twitter yesterday that he would be staying in a series of Airbnbs while hopping around town

In a series of tweets, Chesky explained that he planned to start in Atlanta but would be visiting San Francisco — and other places — while staying in a number of Airbnbs

“This week I’m in Atlanta. I will return to San Francisco often, but for now my home will be somewhere on Airbnb.

‘Why do I do this? I think the pandemic has changed travel the most since the advent of commercial flying. For the first time, millions of people can now live anywhere.”

Chesky went on to explain that remote work has “unleashed” many people from the requirement to work in an office every day.

“We see this in our data,” he added. “From July through September, one in five nights booked on Airbnb was for stays of a month or longer, and almost half of the nights booked were for stays of a week or longer.

“Last year, 100,000 Airbnb guests booked stays of three months or longer. I think the biggest travel trend in 2022 will be people sprawling out into thousands of cities and towns, staying for weeks, months, or even whole seasons at a time.

Chesky went on to explain that remote work has freed many people from the need to work in an office every day (stock image of Airbnb logo).

“More people will start living abroad, others will travel all summer, and some will even give up their rental contracts and become digital nomads.

“Cities and countries will compete to attract these remote workers and this will lead to a redistribution of the places people travel and live.

“This trend is something of a decentralization of life and changing the identity of travel. That’s why I live on Airbnb. It will be fun, but more importantly, it will help us improve the experience for people who can now live anywhere.’

According to the Future Workforce Report, the number of US citizens working from home is projected to increase to 36.2 million by 2025.

And with fewer staff needing to be in the office due to the Covid pandemic, people took the opportunity to get more exercise.

Airbnb reported record profits of $2.2 billion in the third quarter of 2021 — a 36 percent increase compared to the same period in 2019 before the pandemic.

Last year, Airbnb quietly removed a clause from its 40-page Terms of Service that allowed hosts and guests to sue the company for sexual assault and harassment in its listings

Chesky founded Airbnb in 2008 with roommates Nathan Blecharczyk and Joe Gebbia — both of whom own smaller stakes in the company than Chesky.

In 2020, the company went public, with the share price rising to $144, more than doubling its original value.

Last year, Airbnb quietly removed a clause from its 40-page Terms of Service that allowed hosts and guests to sue the company for sexual assault and harassment in its listings.

The clause — which said customers who claimed sexual assault or sexual harassment occurred in an Airbnb building must seek a judge to settle the dispute — had been buried in the terms for more than a decade.

The 150 million Airbnb users agreed to this clause when they registered for the site.

The 150 million Airbnb users agreed to the previous clause when they registered for the site. In a statement released alongside the change last year, Airbnb said it was to “build trust” by “doing the right thing on the rare occasions when something goes wrong.”

In a statement released in August 2021, Airbnb said, “We announce that in the next version of the Airbnb Terms of Service, the arbitration provisions will no longer apply to claims of sexual assault or sexual harassment by hosts or guests.”

“We believe survivors should be able to make claims in the forum most appropriate to them,” the statement said.

It also encouraged “industry peers in the travel and hospitality space to consider similar moves for their respective communities.”

Airbnb concluded the statement by assuring hosts and guests that it “builds trust” by “doing the right thing on the rare occasions when something goes wrong.”

The post Billionaire Airbnb CEO says he’ll work remotely shifting from metropolis to metropolis each two weeks appeared first on DAILY SAN FRANCISCO BAY NEWS.

They all made it, from Salesforce to Uber to Twitter. It was pure magic, a show that was produced with tremendous hype. Now they’re all trying to get out at the same time.

By Wolf Richter for WOLF STREET.

When Airbnb reported a net loss of $ 1.17 billion for the first quarter last week, the company announced in its letter to shareholders that that loss included an expense of $ 113 million, which it expects if it tries to get one We terminate the San Francisco office lease we consider no longer necessary given our restructuring and cost-cutting efforts. “

The $ 113 million cost is an estimate of the difference between what would be incurred by subletting the space to new tenants and what the landlord and associated costs would have to pay over the remainder of the lease term. But that’s a cheaper way out than leaving it empty and paying the landlord until the lease ends.

The fascinating thing about it is how much office space Airbnb had secured with long-term office rents during the years of so-called office shortages, which it had never used, and which it had by renting office space that it did not need .

Airbnb’s disclosure pertains to the 287,000 square feet (sf) at 650 Townsend St. south of the market. According to the San Francisco Business Times, Airbnb rented the space in 2017 for a term of nine years. But it only took 170,000 sf of it at a time. The rest had remained empty all these years.

This brings the total area of ​​office space in San Francisco that Airbnb is currently trying to rent to CHF 427,000, spread over three buildings. That’s roughly half the total Airbnb office space in San Francisco.

They all made it, from Salesforce to Uber to Twitter. They were looking for the office space they envisioned for the future, assuming everything would grow at an endless rate. And by renting office space that they didn’t need and often didn’t occupy, they also exacerbated the office shortage and drove office rents into the ridiculous realm.

It was pure magic, a show that was produced with tremendous hype. And now they’re all trying to rip that vacant office space – from Salesforce, Uber, and Twitter down – by sublet it at the same time, to make those office dreams come true and create San Francisco’s worst. ever historical office flooding.

In San Francisco, the number of subleases exploded to a new all-time high of 8.9 million square feet in the first quarter, according to real estate services company Savills. The overall availability rate (subletting and direct leasing from landlords) rose to 23.6% of the total office space. And the asking rents for class A fell by 15% year-on-year.

However, the decline in asking rents is not an appropriate measure in this environment, as the actual rental contracts are 10 to 15% below the asking rents and, in addition, enormous incentives are negotiated, e.g. B. Improvement grants of up to $ 80 per person, according to John McNellis of McNellis Partners, more than pre-covid deals and an additional three months of free rental.

Leasing activities in the first quarter decreased by 85% compared to the first quarter of 2019. Of the few signed leasing contracts, more than half were for the extension of leasing contracts. In this environment, Airbnb tries to sublet office space that is not needed.

Airbnb share [ABNB] has had a tough time over the past few weeks, and given the $ 1.17 billion loss in the first quarter, it makes sense for the company to try to cut costs. Since the closing high on Feb.11, the stock is currently down 38% to $ 136 (data via YCharts):

In order to get the employees in San Francisco back to the office and not work from everywhere, Airbnb will develop a hybrid model.

“We want to live the lifestyle everywhere. By modeling lifestyle everywhere, we can give our employees more flexibility, ”said CEO Brian Chesky during the call for results (transcript via Seeking Alpha).

“I told our employees that they wouldn’t have to return to the office until next September. And even if we ask people to come back, they will be much more flexible than before, ”he said.

“People are not expected to return to the office five days a week every week. We believe that this is not how most workplaces will work in the 21st century, ”he said.

“We also think personal collaboration is important. So we want to strike a balance between modeling lifestyle everywhere and enabling personal creative collaboration. And that’s exactly what we’re designing, ”he said.

They all do it. Dropbox announced a $ 400 million fee in February for leaving its San Francisco headquarters. After Salesforce announced in February that it would switch to a hybrid work-from-anywhere model, the company announced in March that it had canceled the lease for 325,000 sf of space in an undeveloped tower in the Transbay region. And it brought its 225,000 SF to the sublet market at 350 Mission St. Uber and Twitter, along with a number of others, have brought large chunks of office space to the sublet market. And since they all do, it’s hard to see a recovery in the San Francisco office market.

Airbnb will eventually be able to find a tenant if the rent is low enough, considering how lavishly these office buildings have been furnished. If the rent is low enough, companies will leave their old digs when the lease ends and move in for a big upgrade, possibly for less. The flight to quality. This will cause the vacancy rates to gradually move downwards. However, given the length of typical commercial leases, this takes years.

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