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Today, I would like to talk to you about Uber feedback open source software, many people may not know much about it. In order to make you understand better, the editor summarized the following content for you. I hope you can get something from this article.

On the morning of May 10, local time, travel giant Uber was listed on the New York Stock Exchange under the symbol "UBER". This may be the largest IPO in the US stock market in 2019. Issues that have nothing to do with technology, such as the performance of Uber's IPO and the company's huge losses, are left to Wall Street experts to analyze. We only talk about open source software that supports Uber technology.

AresDB

AresDB is an open source real-time analysis storage engine and query engine based on GPU operation by Uber. Low query latency, high data refresh rate and efficient memory and disk storage management. AresDB requires the support of CUDA Toolkit.

Fusion.js

Uber's Web platform team developed Fusion.js, an open source Web framework for simplifying Web development and building high-performance lightweight Web applications.

Petastorm

Petastorm is an open source data access library developed by Uber ATG (Advanced Technologies Group). This library can be used to evaluate stand-alone or distributed training and deep learning models directly based on data sets in TB Parquet format.

M3

M3 is a Uber open source measurement platform based on the distributed timing database M3DB, which aggregates 500 million metrics per second and stores these results continuously at the rate of 20 million strokes per second.

Uber JVM Profiler

JVM Profiler is an open source distributed profiler by the Uber Engineering team that collects performance and resource usage metrics to provide services for further analysis.

RIBs

RIBs, which stands for Router, Interactor and Builder, is the cross-platform architecture behind many mobile applications of Uber.

AthenaX

AthenaX is the internal flow analysis platform of Uber, which supports both technical and non-technical customers of Uber, ensuring that it can run comprehensive production-level flow analysis tasks using structured query language (SQL).

Jaeger

Jaeger is Uber's distributed tracking system, and the polling function in the Jaeger client library is designed to solve these problems. By moving the decision on the appropriate sampling strategy to the tracking back end, we eliminate the need for service developers to guess the appropriate sampling rate.

Chaperone

As an Kafka audit system, Chaperone monitors the integrity and latency of data flows. Audit metrics are persisted in the database for Kafka users to quantify the loss of their topics.

Ludwig

This is an open source toolkit based on the Google TensorFlow framework. With Ludwig, users can develop deep learning without writing any more code.

Hudi

Hudi aims to solve the inefficient problem of ingestion and ETL pipelines that need to insert update and incremental consumption primitives in Uber big data ecosystem.

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