You then use your current location — or type in a pick-up address — and type in where you want to go. It will also show you a map of where the closest vehicles are. It will even give you the drop-off times of each vehicle. You can also tap the small informational icon next to your ride description. All of this can be done within the app. This tool gives you almost the same functionality as the in-app estimator.
You can then type in your pick-up and drop-off location. Next to each Uber car type will be your estimated fare. You can also opt to tap the informational icon, which shows you the entire breakdown of your fare. If you are signed in, the booking map will show up. Your Uber driver will be on the way shortly.
Our friends over at Ridester have developed their own Uber fare calculator for you to use. The functionalities of the Ridester calculator are very similar to those available through Uber. First, you can see the estimated cost and arrival time for each type of Uber ride. A nice perk is that you have the ability to share this Uber fare with your friends via Facebook and Twitter. Lastly, this Uber fare calculator has additional features to explore such as seeing top destinations and popular Uber trips around the world.
You even have the ability to see Uber trip bookings in real-time on a live map from all over the world. Thus, our modeling ETL jobs use Hudi readers to incrementally fetch only the changed data from the source table and use Hudi writers to incrementally update the derived output table. Now, ETL jobs also finish in less than 30 minutes, providing end-to-end latency of less than one hour for all derived tables in Hadoop.
In order to provide data users of Hadoop tables with different options to access all data or only new or updated data, Hadoop raw tables using Hudi storage format provide two different reading modes:. Figure 6, below, depicts these two reading views for all Hadoop tables stored in Hudi file format:. Users generally alternate between these two table views based on their needs. When they run an ad hoc query to analyze data based on the latest state, they use the latest mode view of the table e.
On the other hand, when a user has an iterative job or query that needs to fetch only changed or new records since its latest execution, they use the incremental mode view. Both views are available for all Hadoop tables at all times, and users can switch between different modes based on their needs.
In addition to providing different views of the same table, we also standardized our data model to provide two types of tables for all raw Hadoop data:. Figure 7, below, depicts how different Hive raw tables are generated for a specific upstream source datastore using the stream of given changelogs:. However, the stream of changelogs may or may not contain the entire row all columns for a given key.
While merged snapshot tables always provide all the columns for a specific key, the changelog history table may be sparse if the upstream stream of changelogs only provides partial row changelogs, a functionality that improves efficiency by avoiding resending the entire row when only one or a few limited column values are changed. Should users want to fetch the changed values from the changelog history table and join it against the merged snapshot table to create the full row of data, we also include the date partition of the same key from the merged snapshot table in the changelog history table.
This allows the two tables to more efficiently join across a specific partition by avoiding a full table scan of the merged snapshot table when joining the two. Figure 8, below, summarizes the relationship between different components of our Big Data platform:. Since rolling out the third generation of our Big Data platform in , users across the company can quickly and reliably access data in Hadoop, but there is always room to grow.
To enhance data quality, we identified two key areas for improvement. First, we want to avoid non-schema-conforming data when some of the upstream data stores do not mandatorily enforce or check data schema before storage e. This results in bad data entering our Hadoop ecosystem, thereby affecting all downstream users also relying on this data. To prevent an influx of bad data, we are transitioning all upstream data stores towards performing mandatory schema checks on data content and rejecting data entries if there are any issues e.
The second area that we found problematic was the quality of the actual data content. While using schemas ensures that data contains correct data types, they do not check the actual data values e.
To improve data quality, we are expanding our schema service to support semantic checks. These semantic checks in other words, Uber-specific data types allows us to add extra constraints on the actual data content beyond basic structural type checking.
We are aiming to reduce raw data latency in Hadoop to five minutes and data latency for modeled tables to ten minutes. We are also expanding our Hudi project to support an additional view mode, which will include the existing read-optimized view, as well as a new real-time view which shows data with latency of just a few minutes.
This real-time view relies on an open source solution and part of Hudi we call Merge-On-Read or Hudi 2. To improve data efficiency, we are moving away from relying on dedicated hardware for any of our services and towards service dockerization. In addition, we are unifying all of our resource schedulers within and across our Hadoop ecosystem to bridge the gap between our Hadoop and non-data services across the company.
This allows all jobs and services to be scheduled in a unified fashion regardless of the medium it will be executed in. As Uber grows, data locality will be a big concern for Hadoop applications, and a successful unified resource manager can bring together all existing schedulers i.
As part of our effort to improve the scalability and reliability of our platform, we identified several issues related to possible edge cases. While our ingestion platform was developed as a generic, pluggable model, the actual ingestion of upstream data still includes a lot of source-dependent pipeline configurations, making the ingestion pipeline fragile and increasing the maintenance costs of operating several thousands of these pipelines.
This project will ensure that information about these specific upstream technologies will only be an additional metadata added to the actual changelog value as opposed to having totally different changelog content and metadata for different data sources and data ingestion will happen regardless of the upstream source.
Finally, our next version of Hudi will allow us to generate much larger Parquet files over one gigabyte compared to our current megabytes by default within a few minutes for all of our data sources.
Compared to fact tables, which grow infinitely over time, dimension tables are always bounded by size e. Dimension tables do not need a special time column.
Table below details the current data types supported in AresDB:. With AresDB, strings are converted to enumerated types enums automatically before they enter the database for better storage and query efficiency.
This allows case-sensitive equality checking, but does not support advanced operations such as concatenation, substrings, globs, and regex matching. We intend to add full string support in the future. AresDB stores all data in a columnar format. The values of each column are stored as a columnar value vector. AresDB stores uncompressed and unsorted columnar data live vectors in a live store. Data records in a live store are partitioned into live batches of configured capacity.
New batches are created at ingestion, while old batches are purged after records are archived. A primary key index is used to locate the records for deduplication and updates. Figure 3, below, demonstrates how we organize live records and use a primary key value to locate them:. The values of each column within a batch are stored as a columnar vector.
AresDB also stores mature, sorted, and compressed columnar data archive vectors in an archive store via fact tables. Records in archive store are also partitioned into batches. Archive batch uses the number of days since Unix Epoch as its batch ID. Records are kept sorted according to a user configured column sort order. The goal of configuring the user-configured column sort order is to:. A column is compressed only if it appears in the user-configured sort order.
We do not attempt to compress high cardinality columns because the amount of storage saved by compressing high cardinality columns is negligible. After sorting, the data for each qualified column is compressed using a variation of run-length encoding. In addition to the value vector and null vector, we introduce the count vector to represent a repetition of the same value.
The upsert batch is a custom, serialized binary format that minimizes space overhead while still keeping the data randomly accessible. When AresDB receives an upsert batch for ingestion, it first writes the upsert batch to redo logs for recovery.
After an upsert batch is appended to the end of the redo log, AresDB then identifies and skips late records on fact tables for ingestion into the live store. As depicted in Figure 6, below, brand new records not seen before based on the primary key value will be applied to the empty space while existing records will be updated directly:.
We periodically run a scheduled process, referred to as archiving, on live store records to merge the new records records that have never been archived before into the archive store.
Archiving will only process records in the live store with their event time falling into the range of the old cut-off time the cut-off time from last archiving process and new cut-off time the new cut-off time based on the archive delay setting in the table schema. Archiving does not require primary key value index deduplication during merging since only records between the old cut-off and new cut-off ranges will be archived.
In this scenario, the archiving interval is the time between two archiving runs, while the archiving delay is the duration after the event time but before an event can be archived.
As shown in Figure 7, above, old records with event time older than the archiving cut-off for fact tables are appended to the backfill queue and eventually handled by the backfill process. This process is also triggered by the time or size of the backfill queue onces it reaches its threshold. Compared to ingestion by the live store, backfilling is asynchronous and relatively more expensive in terms of CPU and memory resources.
Backfill is used in the following scenarios:. Unlike archiving, backfilling is idempotent and requires primary key value-based deduplication. The data being backfilled will eventually be visible to queries. This measure would include not only what Uber drivers take home as income, but also all of the various commissions and fees that accrue to Uber a booking fee assessed per trip and commissions assessed as a share of passenger fees excluding the booking fee.
As we note below, none of the studies on the components of Uber pay provide this measure of total passenger fares. This is the pay measure presented in Cook et al. This is also the information that Uber drivers receive from Uber. Employers of W-2 earners also often provide health insurance and retirement benefits. Uber treats its drivers as independent contractors and provides them with no benefits and does not pay any payroll taxes toward any of the social insurance programs. Therefore, comparisons of Uber driver earnings after expenses to the wages earned by W-2 employees—such as those made by Uber Research Director Jonathan Hall and Alan B.
Krueger of Princeton University Hall and Krueger , 25 —are not apples-to-apples comparisons. The key estimates of the four studies are placed accordingly in Table 1. We see that none of the studies derive a measure of Uber pay that is comparable to the wages of other occupations because none take benefits into account. Cook at el. But Cook et al. Hall compares these various findings to that of the Zoepf et al.
As this discussion about the varied terms used to describe the concepts of pay identified in Table 1 shows, confusion and inconsistencies abound. For instance, the Cook et al. There are a few ways researchers can be clearer in their descriptions of various metrics for Uber driver pay. It is unclear to us why those using Uber administrative data do not automatically deduct Uber commission fees from any metric of Uber driver pay since commission information is clearly available to Uber and its drivers.
Fourth, any metric of Uber driver wages or earnings should explicitly take into account employer-side social insurance taxes and employer voluntary nonwage benefits such as health and pension benefits. Table 2 provides data directly from Cook et al. The table identifies the sources of the data used in our computations and the formulas involved. Before delving into estimates of Uber driver pay, however, it is important to note that one of the complexities in examining anything about Uber drivers is the duality of driver experiences.
The majority of Uber drivers work part-time, driving less than 10 hours a week Plouffe a. On the other hand, a core group of Uber drivers work full time 35 hours a week or more and provide about half the rides offered by Uber.
Given this duality, it is useful to be explicit about who we are referring to when we discuss measures of pay. This dictates that we evaluate Uber pay for someone who requires basic benefits and probably has a car whose main use is Uber driving. The starting point is what Cook et al. This measure of hourly earnings actually understates the actual hourly fares generated because it is the average across drivers not across hours worked.
And, as noted earlier, the measure of earnings before expenses is the driver income reported by Uber in regular reports. These expenses, however, are tax-deductible. So if an Uber driver has a marginal tax rate for income and self-employment taxes of This facilitates a comparison of Uber driver compensation to that of other workers.
The first step toward estimating the Uber driver W-2 equivalent wage is to subtract the mandatory benefits, which are the employer-side payroll taxes for Social Security and Medicare—the additional 7. These remaining benefits account for These BLS data reflect a pool of workers who have decent benefits, limited benefits, or no benefits at all. One of the motivations for pursuing the analysis above is to facilitate wage comparisons between Uber drivers and other workers or opportunities.
We offer a few benchmarks below. This means that Uber drivers, had they been employees and been provided a standard benefits package, would have earned less than what 90 percent of other earners did. However, since Uber drivers are primarily in higher-wage urban areas, this comparison understates how low Uber driver wages are relative to other, comparable workers.
Another possible benchmark is the minimum wage in the locations where Uber drivers work. Unfortunately, we can only compare the minimum wage in a city with the average hourly Uber wage and the average hourly Uber compensation across cities rather than the actual Uber wage in each city.
Uber drivers are not entitled to earn the guaranteed minimum wage in their city or state because they are independent contractors. The comparison with the minimum wage thresholds therefore lets us gauge whether drivers would be provided with minimum wage protections if they indeed were employees. Zoepf et al. We use our data to make two different comparisons to minimum wage benchmarks and present the comparisons in Figure A.
Figure A compares these two Uber driver wage measures with the minimum wages as of January in 20 major Uber markets—the 18 cities, one county, and one state that were included in the Uber-sponsored Benenson Strategy Group survey of Uber drivers in BSG If we look at the Uber driver hourly discretionary compensation wage that excludes only extra payroll taxes but not other benefits from compensation, then Uber driver pay is still below the minimum wage set by nine of the 20 jurisdictions, including the three largest ones: Chicago, Los Angeles, and New York.
As noted earlier, the minimum wage does not apply to Uber drivers because they are currently considered independent contractors rather than employees. If they had been employees, the average wages of Uber drivers in these major cities would have had to be higher to comply with minimum wage laws.
It would also be useful to consider the distribution of wages of Uber drivers, as Zoepf et al. Unfortunately, we do not have distributional data to draw on. There are other possible choices for an analysis such as in Table 2. Cook et al.
Uber and Lyft only cover rideshare drivers during Periods 2 and 3. Period 2 starts once you accept a ride request and are en route to your passenger, and Period 3 starts once your passenger gets into your car.
Uber describes its insurance, as of March , as providing liability insurance for Period 1 but not providing collision and comprehensive insurance and uninsured motorist insurance Scott That is a problem for drivers. There is nothing simple about the choices drivers face regarding insurance. A review of that insurance FAQs blog post illustrates the complexity of the issues. Is that a good estimate?
We can also benchmark Zoepf et al.
0コメント