![]() ![]() The team creates Uber’s marketplace-related technologies, such as dispatch, pricing, and incentives. She then transitioned to a role at Uber, building and leading their Marketplace Optimization Data Science organization. After receiving tenure at Cornell, she spent her sabbatical at Microsoft Research, where she developed travel time prediction methods for use in Bing Maps. There, she developed collaborative relationships with several ambulance organizations, and focused her work on statistical methods for ambulance decision support systems. in statistics from Duke University, after which she became a faculty member in the School of Operations Research and Information Engineering at Cornell. We also highlight several key practical challenges and directions of future research from a practitioner's perspective.ĭawn Woodard received her Ph.D. We show using data from Uber that by jointly optimizing dynamic pricing and dynamic waiting, price variability can be mitigated, while increasing capacity utilization, trip throughput, and welfare. Then we link the two levers together by studying a pool-matching mechanism called dynamic waiting that varies rider waiting and walking before dispatch, which is inspired by a recent carpooling product Express Pool from Uber. We also discuss approaches used to predict key inputs into those algorithms: demand, supply, and travel time in the road network. We survey methods for dynamic pricing and matching in ride-hailing, and show that these are critical for providing an experience with low waiting time for both riders and drivers. Optimization Engine : The Gairos Optimization Engine optimizes Gairos’ ingestion pipelines, Elasticsearch cluster/index settings, and RT-Gairos, based on query insights and system statistics.Ride-hailing platforms like Uber, Lyft, Didi Chuxing, and Ola have achieved explosive growth, in part by improving the efficiency of matching between riders and drivers, and by calibrating the balance of supply and demand through dynamic pricing.Query Analyzer : Gairos Query Analyzer analyzes queries collected from RT-Gairos and provides some insights for our optimization engine.It serves as a gateway to all Elasticsearch clusters. RT-Gairos (Real-time-Gairos): RT-Gairos is the Gairos query service.Elasticsearch Clusters : These clusters store output data from Gairos-Ingestion pipelines.Gairos-Ingestion : The Gairos-Ingestion component ingests data from different data sources and publishes events to Gairos.Apache Kafka : We use Apache Kafka as a message queue system for events in services, RT-Gairos queries and Gairos platform metrics and events.Clients : Gairos clients could be a service, a dashboard, a data analyst etc.If multiple nodes are done at the same time and a shard is only available in these nodes. It could be due to disk failures or other hardware failures. These nodes are having hotspot issues, in other words, they are handling more shards or more read/write traffic than our resources (CPU/Memory/Network) can reasonably handle. It could be due to various reasons: the network is not stable, the size of metadata is too big to manage, etc. ![]() Some heavy query causes the whole Elasticsearch cluster to slow down.If the data is not used any more, it will be better to free up resources for other use cases. Once a use case is onboarded to Gairos, there is no automatic way to check usage for these use cases. Some data sources are not used anymore.Since it is a multi-tenant system, some sudden traffic spikes may affect some queries running in the same cluster. Query performance degrades due to traffic spikes from some clients.If any component in the pipeline slows down, it may cause some lagging and SLA misses. SLA (service level agreement) is usually very tight from a couple of seconds to a few minutes. It is a generic challenge for all real-time pipelines. For example, if the input data volume doubles for one use case, it may affect the data availability for other use cases. Some dramatic change in one use case may affect all other use cases in that cluster. Multiple use cases sharing the same cluster can cause the cluster to become unstable.
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