shows surge multipliers in different hexagons around the stadium in Oakland when there was a game.Â, Multiple use cases sharing the same cluster can cause the cluster to become unstable. UNIT No. These distinct event streams thread into a single Uber trip. Since most queries for rider_sessions are heavy, more tests on other data sources will be done to justify our results.Â. It is in our roadmap and will be explored in future iterations. Chen Qingchen (born 23 June 1997) is a Chinese badminton player. The gains, however, plateaued over time. As Uber’s Marketplace business continues to scale, the number of data pipelines for Gairos keeps increasing. Gairos-ingestion ingests data from Apache Kafka topics and publishes to Elasticsearch clusters. It is important to keep the size and utility of shards roughly equal to help with allocation decisions and load distribution.Â. Log data from the Sawan-07 well along with mineral and fluid properties have been used to calibrate the Xu–White rock physics model. Much of the initial gains were driven by a gradient boosted decision tree model. When defining the signature, only following fields are used: datasource, granularity, by, filter, aggregations, bucketBy, sort, limit. Xu Bing : Tobacco Project, Duke/Shanghai/Virginia, 1999-2011 Xu Bing Prints Tianshu: Passage in the Making of a Book Reading Space: The Art of Xu Bing Xu Bing Forest Project Xu Bing:Tabacco Project (Chinese) Xu Bing: El We refer to the data underlying each trip as a session, which begins when a user opens the Uber app. The optimization engine can decide whether to change index settings in production after assessing the test results.Â. ES cluster master node down. The hit rate is 80%.Â, Last but not least, it can be seen from Figure 25 that cache hit rate is 0 for demand. It can be seen that the improvement with cache for different data sources varies a lot. After the sharding strategy is applied to all data sources in the pricing cluster, the CPU load is stabilized.Â, The simplest solution for caching is to cache all query results. Hit QPS is around 50 while sets QPS is around 10. For example, what is the minimal number of containers to use for an ingestion pipeline so that it can meet SLA 99 percent of the time? Gairos does not proactively check whether the data is used as specified and can not adjust to the changes (traffic pattern change, query pattern change etc.). Figure 5 shows surge multipliers in different hexagons around the stadium in Oakland when there was a game.Â. The app displays offered trips (uberPOOL, uberX, UberBLACK, etc.) Inventors: Qi Chen, Casey Lawler, Linfeng Shi, Qing Xu, Miao Yu DYNAMICALLY DETERMINING ORIGIN AND DESTINATION LOCATIONS FOR A NETWORK SYSTEM. San Francisco Bay Area. ams kevin yaun View profile. In her early career at the national team, she was partnered with Pan Pan, and they participated in the 2009 World Championships, and 2010 Uber Cup. LA can be in one shard. CPU load for nodes in our pricing clusters shows a daily pattern because all indices are daily indices. Gang is leading Gairos optimization while focusing on storage layer (Elasticsearch) and query layer optimization at Uber. Assignee: Uber Technologies, Inc. It will update the settings of Gairos: Ingestion pipelines, RT-Gairos, and Elasticsearch.Â, Some setting changes may need benchmarking tests to see whether KPIs will improve or not before applying the given changes. The highest number of concurrent users it can support with sharding is about 4x compared to that without sharding, as shown in Figure 18.Â, In summary, the latency may be worse for some large data sources while the number of concurrent users it can support is consistently 4x of that without sharding. A detective recruits his Uber driver into an unexpected night of adventure. The Tire Maharajahs: Competing with Chinese Exporters and Tire Queries used will be from user queries gathered in the past. The story of Dick Cheney, an unassuming bureaucratic Washington insider, who quietly wielded immense power as Vice President to George W. Bush, reshaping the country and the globe in ways that we still feel today. It can be seen that the number of highest QPS for with sharding is about 4x of highest QPS without sharding. Gairos fulfills the following purposes: Gairos, depicted in Figure 1, below, ingests data from different Apache Kafka topics and writes data to different Elasticsearch clusters.Â. Users can focus on customizing the system’s business logic instead of more generic tasks for a real-time data system. After merging the index, these deleted docs will be purged and the index size will be smaller.Â. 許秀 is on Facebook. Top filters can be considered as possible sharding key candidates. A query pattern is defined with the same set of fields. A few systems are involved in Gairos: Apache Kafka, Gairos ingestion pipelines, Elasticsearch clusters, Gairos query service etc. The optimization engine can update the templates stored for a data source so that we can get better in terms of disk space or search performance. Qing Xu COO at UltrAlpha Platform. Use cases include, , maximum dispatch ETA calculating, and demand/supply forecasting.Â, It allows users to query data at a high level without worrying about all the low-level details of our data layer, such as potentially heterogeneous data sources, query optimizations, data processing logic, and indexing schemes.Â. Our first implementation of Gairos came with several technical challenges and unforeseen issues. Qing Xu. There are 124,000+ professionals named "Qing. Bad weather, rush hour, and special events, for instance, may cause unusually large numbers of people to want to ride Uber all at the same time. Find a different Qing Xu. Pyrene-based metallocycles and metallocages: more than fluorophores. Year in Review: 2019 Highlights from the Uber Engineering Blog. That is where an index benchmarking service comes into place.Â, To do optimizations for settings with Gairos, we needed to incorporate a benchmarking tool to compare different settings based on defined KPIs (read/write throughput, latency, memory usage etc. Below we share some optimization results for the second data source supply_geodriver. : Top x frequent filters used in queries. Some benchmarking tests are carried out to check the latency and concurrent users they can support. Beijing City, China. It is also used by RT-Gairos to collect all queries run in Gairos. Â, Gairos-ingestion is an ingestion framework to process data from different data sources and publish them to Gairos. The revised high-level architecture is shown in Figure 7, above. The query can just retrieve data from the shard containing drivers in SF. Skip to footer. For example, the client pulls data from the last two weeks at some fixed interval (1 min, 5 mins, 1 hour etc.). For example, if the input data volume doubles for one use case, it may affect the data availability for other use cases.Â, Ingestion pipeline lagging. She ended the 2016 BWF Season by winning the BWF Most Promising Player of the Year, also completed her success by winning doubles title at the 2016 BWF Superseries Finals in women's and mixed doubles … Some dramatic change in one use case may affect all other use cases in that cluster. Uber doesn’t own a single car, but so many vehicles in our cities work for the company. Driver Movement uses real time demand and supply data to generate driver surge and carbon suggestions for drivers. ) ams Rozemarijn Koopmans It tells a story set in Late Shang Dynasty and shows how people of the age fight against the tyranny of … It can be seen that there are 8 docs while there are only 3 drivers. Each team maintains their own data pipelines and query service for their use cases. Based on data size for each city, we can estimate the number of shards:       Shard # based on data size (30GB + 50GB + 80GB + 20GB)/60GB = 3Â. All real-time services can send some important events to it for downstream services/pipelines to consume. While for queries for drivers at NY, they need to be directed to both shard 3 and 4. Â. To make the cache hit rate higher, a query split is required, during which each query will be split into multiple small queries based on data query time range if queries are splittable. Tian Qing started to practice badminton with her father Tian Jianyi who also a badminton coach in Anhua Sports School at aged 7. For queries for drivers at SFO, they can be directed to shard 1 directly. Gairos came into place to create a unified real-time data processing, storage, query platform so that these use cases can be onboarded. To mitigate the skewed shard and hotspot problem, we developed a custom sharding algorithm for Gairos. For repetitive queries with overlapped ranges, a similar strategy can be applied and, as can the time granularity based on the query patterns. RT-Gairos will collect all queries to Gairos and push them to an Apache Kafka topic.Â, Query Analyzer analyzes queries gathered from RT-Gairos and generates insights to provide inputs to the Gairos Optimization Engine. Real-time data (# of ride requests, # of drivers available, weather, game) enables operations teams to make informed decisions like surge pricing, maximum dispatch ETA calculating, and demand/supply forecasting about our services that improve user experiences on the Uber platform. To gain in both latency and scalability for some large data sources, we can tune the partition size for each shard.Â, As a side product of sharding strategy, we are able to stabilize our pricing cluster as shown in Figure 19. Figure 13 shows the latency under different numbers of clients. For # of concurrent users it can support, it is 4x of that without sharding, as depicted in, demonstrates that the average latency with sharding is higher when the number of clients is low. To ensure that Gairos can continue to optimize its performance across an ever-expanding portfolio of use cases, we re-architectured the platform for greater scalability, stability and sustainability. available in that geographic region along with prices for each, as generated by our surge pricing system, with each price appearing as a discrete event on the impression event stream. She graduated with a BA from Huazhong University of Science and Technology. Wen Qing dies, a squall of a woman, on the steps of Koi Tower. When writing to Elasticsearch clusters, data will be filtered based on data retention and data prediction for each data source so that these near-empty indices will not be created and reduce the number of shards. We are planning to do more tuning to improve cache hit rate.Â, Elasticsearch is using inverted indexes to make search fast. When writing to Elasticsearch indices, the key must be provided to put the doc in the correct shard. on LinkedIn. These data will be input to the pricing model and the pricing model will generate a surge multiplier for that location. In these cases of very high demand, fares may increase to help ensure those who need a ride can get one. The query can just retrieve data from the shard containing drivers in SF. In the top, the data is not sharded based on city and the query has to run in all four shards to check whether any drivers are available. When deleting a doc, the doc will be marked as deleted and it still exists in the inverted index. If multiple nodes are done at the same time and a shard is only available in these nodes. Sample Elasticsearch cluster data regularly and sends the information to an Apache Kafka topic.Â, Query Analyzer pulls query info from query Apache Kafka topic for analysis.Â. Deleted docs will be excluded from the search results. It is a generic challenge for all real-time pipelines. 上领英，在全球领先职业社交平台查看qing xu的职业档案。qing的职业档案列出了 7 个职位。查看qing的完整档案，结识职场人脉和查看相似公司的职位。 At most one merging index task can be running at any time to prevent significant performance degradation. Since most queries for rider_sessions are heavy, more tests on other data sources will be done to justify our results.Â, Cache stats for supply_status are shown in. If. For example in Figure 26, below, drivers D1, D2, and D3 are updated several times. ElasticSearch) and ML (e.g. That action triggers a string of data events, from when the driver actually accepts a ride to the point where the trip has been completed. In Gairos self-optimization project, we closed the loop (, ) and let user queries drive the optimizations to make Gairos more stable, scalable, and sustainable.   Â, The revised high-level architecture is shown in. Greater Los Angeles Area. We explore both the short-run dynamics of market adjustment, as well as the eventual long-run equilibrium. Below, we highlight some technical challenges that surfaced once we began to scale Gairos: Our on-call engineers get paged very often and the cost to maintain these pipelines and systems is high.Â, The main problem, however, with our first iteration of Gairos was that how Gairos data is used did not loop back to Gairos to guide the optimization and continuously improve the system. Once a use case is onboarded to Gairos, there is no automatic way to check usage for these use cases. For context, the total size of queryable data served by Gairos is 1,500+TB and the number of production pipelines is over 30. Some settings (ex. It can be seen that there are 8 docs while there are only 3 drivers. Figure 28, below, demonstrates that the number of shards drops from around 40k to 20k after cleaning up these small indices in one of our clusters:Â, Collected queries can determine whether a data source was used in the last X days. The number of concurrent users it can support is much higher as shown in, . It empowers teams to better understand and improve the efficiency of the Uber Marketplace through data intelligence. Based on peak QPS, we can get another estimate for the number of shards:      Shard # based on peak QPS (2k + 3k + 5k + 1k)/3k = 4, Get the maximal value of these two estimates:Â. Qing Xu Marketplace Data @ Uber. These small indices are usually due to events with timestamps out of bounds. Yaoi novels can be pretty shallow and often have over the top cliches (like, This book showed me the possible outcome of reading a light novel. : The Gairos Optimization Engine optimizes Gairos’ ingestion pipelines, Elasticsearch cluster/index settings, and RT-Gairos, based on query insights and system statistics. The query is identical to the previous one except that it has one more filter that matches a given driver UUID.Â, ) shows driver utilizations by geo locations.Â, To calculate the surge multiplier for a hexagon defined by. It is lower when the number of clients increases to over 200. There are services that leverage real-time data in the Uber ecosystem. SLA (service level agreement) is usually very tight from a couple of seconds to a few minutes. Qing was an engineering manager of Marketplace Intelligence at Uber. For this example, it is assumed that each node can handle 3,000 write QPS and can store at most 60GB data. The new rule was largely welcomed by passengers. If any significant impact is observed, all force merging tasks will be aborted.  Â, Some heavy queries may affect the performance of the whole cluster.Â. In, , below, we are querying all drivers in SF. January 19, 2021. In 2016, her coach was Li Yongbo.Her badminton partner is Jia Yifan, and for mixed doubles her partner was Zheng Siwei. Some nodes crash. She partnered Wang Xiaoli in women's doubles and excelled in the category until 2010 when both players are split after China failed to defend their Uber Cup against South Korea in Kuala Lumpur. Product designer at Uber. Gairos does not proactively check whether the data is used as specified and can not adjust to the changes (traffic pattern change, query pattern change etc.). Reads will be queries gathered from RT-Gairos in production. These services send some events to Apache Kafka for downstream services and pipelines to process. Many passengers would have found it easier to catch a cab on the street on Tuesday after a new rule issued by the city’s transport commission which blocks passengers’ destinations on apps such as Didi Chuxing before an order is accepted. RT-Gairos sends the data back to clients. disable source) will not be backward compatible and it will need some approval before execution. It serves as a gateway to all Elasticsearch clusters.Â, : Gairos Query Analyzer analyzes queries collected from RT-Gairos and provides some insights for our optimization engine.Â. Writes will be simulated from related Apache Kafka topics used in production or publish topics directly.Â. Benchmarking tests will be carried against the copied index and reindexed index to gather performance data. Flink, Samza), OLAP (e.g. Gairos-ingestion is an ingestion framework to process data from different data sources and publish them to Gairos. These steps include: We apply a few optimization strategies which other organizations can use to optimize their real-time intelligence platforms too.Â, Sharding is partitioning data by some key so that data with the same key will be put in one shard. Deleted docs will be excluded from the search results. 1, LEVEL 29 NAZA TOWER PLATINUM PARK No. [2] [3] In 1998, she moved to Hunan Province Sports School and in 2004, she competed at the World Junior Championships and win gold in … Hydrothermal synthesis, structures and properties of coordination polymers based on μ4-bridging benzene-1,2,4,5-tetracarboxylate:. Gang Zhao, Wenrui Meng, Qing Xu, and Yanjun Huang. The number of shards for these indices is large. Gairos indexes the data and makes it ready for query.Â. 1, Level 29 Naza Tower Platinum Park No. Key metrics to be used for benchmark tests are index size (how big the storage is to store the index) and search latency (how long it takes to query the data). Only data size and peak write QPS are considered. Â, Peak write QPS for each shard <= 3,000QPS, To mitigate the skewed shard and hotspot problem, we developed a custom sharding algorithm for Gairos. Qing Xu. If any one of them has a problem, some customers will be impacted and it will be a bad experience. 上领英，在全球领先职业社交平台查看Aoxiang Cui的职业档案。Aoxiang的职业档案列出了 3 个职位。上领英，查看Aoxiang的完整档案，结识职场人脉和查看相似公司的职位。 : These clusters are used to store test data, in other words, randomly generated data or production data for experimentation purposes.Â, : The Benchmarking Service accepts different settings for an index and carries out benchmarking tests against indices with different settings. How many shards to use so that it can handle write/read traffic? These steps include: Gairos clients send requests to RT-Gairos to get data.Â. Frau Xu Wei TCM Spezialistin Mit Berufsausübungsbewilligung für nichtärztliche Akupunktur des Kantons Luzern. FISCAL POLICY PAPER 1 Fiscal Policy Paper Learning Team A ECO/372 February 29, 2016 Qing Xu FISCAL 2018 Valentina Brailovskaya - IDInsight Baizhu Chen - BlackRock Eilin Francis - Post-Doctoral Associate, MIT J-PAL Sameh Habib - Joint Committee on Taxation Can Kadirgan - Central Bank of Turkey Kyle Neering - Center for Naval Analysis Liam Rose - Health Economics Research Center, US Department of Veteran Affairs Asha Shepard - Assistant Professor, Goucher College Wei Xu - Data Scientist, UBER For example, a forecast service may need to query to improve forecasts to predict driver-partner demand and supply during high-traffic events, or our dynamic pricing service may leverage Gairos to decide a surge multiplier based on demand, supply, and some forecast inputs. Â, Apache Kafka is a distributed streaming platform that lets clients publish/subscribe to a stream of events. Uber ... Qing Xu. More and more data sources need to be added to Gairos to support new business use cases.Â, We use Gairos for a wide variety of insights-collecting use cases at Uber, including:Â. Index size will be much larger if there are a large number of deleted documents.Â, These deleted documents will affect search performance too. Below we outline the factors that need to be considered when sharding: The number of shards will be calculated based on write/read QPS and shard size. The following is the procedure to find the sharding key (Figure 10).