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http://traffic.libsyn.com/sedaily/FiverrEngineering.mp3Podcast: Play in new window | Download As the gig economy grows, that growth necessitates innovations in the online infrastructure powering these new labor markets. In our previous episodes about Uber, we explored the systems that balance server load and gather geospacial data. In our coverage of Lyft, we studied Envoy, the service proxy that standardizes communications and load balancing among services. In shows about Airbnb, we talked about the
http://traffic.libsyn.com/sedaily/kubeless_edited.mp3Podcast: Play in new window | Download Modern architectures often consist of containers that run services. Those containers scale up and down depending on the demand for the services. These large software systems often use a technique known as event sourcing, where every change to the system is kept in an event log. When an event on the log is processed, several different data stores might be updated in response.
http://traffic.libsyn.com/sedaily/event_sourcing_edited.mp3Podcast: Play in new window | Download When a user of a social network updates her profile, that profile update needs to propagate to several databases that want to know about such an update–search indexes, user databases, caches, and other services. When Neha Narkhede was at LinkedIn, she helped develop Kafka, which was deployed at LinkedIn to help solve this very problem. Using Kafka as an event queue, LinkedIn adopted