Posts Tagged ‘IanCooper’
[NDCOslo2024] Kafka for .NET Developers – Ian Cooper
In the torrent of event-driven ecosystems, where streams supplant silos and resilience reigns, Ian Cooper, a polyglot architect and Brighter’s steward, demystifies Kafka for .NET artisans. As London’s #ldnug founder and a messaging maven, Ian unravels Kafka’s enigma—records, offsets, SerDes, schemas—from novice nods to nuanced integrations. His hour, a whirlwind of wisdom and wireframes, equips ensembles to embed Kafka as backbone, blending brokers with .NET’s breadth for robust, reactive realms.
Ian immerses immediately: Kafka, a distributed commit log, chronicles changes for consumption, contrasting queues’ ephemera. Born from LinkedIn’s logging ledger in 2011, it scaled to streams, spawning Connect for conduits and Flink for flows. Ian’s inflection: Kafka as nervous system, not notification nook—durable, disorderly, decentralized.
Unpacking the Pipeline: Kafka’s Primal Primitives
Kafka’s corpus: topics as ledgers, partitioned for parallelism, replicated for redundancy. Producers pen records—key-value payloads with headers—SerDes serializing strings or structs. Consumers cull via offsets, groups coordinating coordination, enabling elastic elasticity.
Ian illuminates inroads: Confluent’s Cloud for coddling, self-hosted for sovereignty. .NET’s ingress: Confluent.Kafka NuGet, crafting IProducer for publishes, IConsumer for pulls. His handler: await producer.ProduceAsync(topic, new Message {Key = key, Value = serialized}).
Schemas safeguard: registries register Avro or Protobuf, embedding IDs for evolution. Ian’s caveat: magic bytes mandate manual marshaling in .NET, yet compatibility curtails chaos.
Forging Flows: From Fundamentals to Flink Frontiers
Fundamentals flourish: idempotent producers preclude duplicates, transactions tether topics. Ian’s .NET nuance: transactions via BeginTransaction, committing confluences. Exactly-once semantics, once Java’s jewel, beckon .NET via Kafka Streams’ kin.
Connect catalyzes: sink sources to SQL, sources streams from files—redpanda’s kin for Kafka-less kinship. Flink forges further: stream processors paralleling data dances, yet .NET’s niche narrows to basics.
Ian’s interlude: brighter bridges, abstracting brokers for seamless swaps—Rabbit to Kafka—sans syntactic shifts.
Safeguarding Streams: Resilience and Realms
Resilience roots in replicas: in-sync sets (ISR) insure idempotence, unclean leader elections avert anarchy. Ian’s imperative: tune retention—time or tally—for traceability, not torrent.
His horizon: Kafka as canvas for CQRS, where commands commit, queries query—event sourcing’s engine.