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Petr Zapletal

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Change Data Capture in Distributed SystemsScale By The Bay 20192019-11-13
Compares three practical CDC patterns — polling with status timestamps (simple but wasteful and brittle), triggers/store procedures (low-latency but…
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Adopting GraalVMScale By The Bay 20182018-11-15
Evaluation of GraalVM covers concrete steps and pain points: SBT can use the Graal plugin and JVMCI flags to compile Scala projects and yields about 10%…
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InterviewScale By The Bay 20182018-11-15
Building a global real-time streaming platform that serves thousands of concurrent viewers for products like ESPN+ and MLB and is being extended toward…
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Petr Zapletal InterviewScale By the Bay 20172017-11-16
Focuses on cost and state at scale: uses the SMACK stack (Spark, Mesos, Akka, Cassandra, Kafka), targets systems with millions of concurrent users, and…
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Top Mistakes When Writing Reactive ApplicationsScala Days Copenhagen 20172017-05-30
Covers concrete mistakes and fixes for Akka systems: Java serialization bloats footprints and caused a client to wait ~1 minute for persistent actors, so…
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Distributed Real-Time Stream Processing: Why and HowScala Days Berlin 20162016-06-15
Compares Apache Storm, Trident, Spark Streaming, Samza, Apex and Flink, showing Spark Streaming uses 1s microbatches while Storm and Flink are native streams…
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Distributed Real Time Stream Processing: Why and HowScala Days Berlin 20162016-06-15
Compares concrete systems — Spark Streaming (micro-batch), Flink (native streaming), Storm/Trident, Samza, Apex and Kafka — and shows how each implements state…
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