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Vadim Semenov

Data Engineer, Datadog

Vadim Semenov has been working at Datadog for almost 4 years as a software engineer. He helped bring Spark to Datadog and helped build major pipelines that process all metrics data on top of Spark. Previously he worked at Exponential Interactive on scaling main systems, operating Hadoop clusters, working closely with HBase, and building pipelines in MapReduce and Spark. In his free time he teaches high school students programming at CodeNation, and so far he's been to two Taylor Swift concerts.

Vadim Semenov

Sessions / 2019 / 1 talk

  • Tailor-S: Look What You Made Me Do

    This talk is about a service that replaced our previous real-time service that produces all historical metrics data which powers a lot of things at Datadog. This new service is based on Kafka-Connect and Spark (vs. Go previously) and handles 100s TBs/10s T records a day while running on spot instances in a pretty violent environment. We're going to talk about decisions we made, architecture, engineering considerations, various optimizations we had to do, we'll show that it's possible to process trillions of records using Spark, we'll talk about certain savings we got in the result, operational challenges and benefits, and various migrations we had to implement as part of rolling out a new service. Albeit, not everything can be directly used by others but we believe that the general approaches and considerations for building and migrating a system of such scale can be used as a general guidance for building systems of different levels.

Sessions / 2017 / 1 talk

  • Using Apache Spark for processing trillions of records each day at Datadog

    Massively scaling Apache Spark can be challenging, but it’s not impossible. In this session we’ll share Datadog’s path to successfully scaling Spark and the pitfalls we encountered along the way. We’ll discuss some low-level features of Spark, Scala, JVM, and the optimizations we had to make in order to scale our pipeline to handle trillions of records every day. We’ll also talk about some of the unexpected behaviors of Spark regarding fault-tolerance and recovery—including the ExternalShuffleService, recomputing partitions, and Shuffle Fetch failures—which can complicate your scaling efforts.

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