How Yahoo Optimizes Apache Spark with Flexible VMs
As a global media and technology company connecting hundreds of millions of users to finance, sports, and entertainment platforms, Yahoo operates a massive data infrastructure where analytics workloads must run continuously at high speed. In deadline-driven data environments, relying on fixed virtual machine (VM) configurations creates a brittle system; if a specific machine shape faces a regional capacity constraint, cluster provisioning in Managed Service for Apache Spark (formerly Dataproc) can experience delays and stall critical data pipelines.
Yahoo utilizes flexible VMs in Managed Service for Apache Spark clusters to automatically absorb these resource fluctuations by defining a ranked list of acceptable VM shapes. This allows the system to dynamically search regional zones and maintain pipeline execution without manual intervention. To search for capacity across a region, teams must also enable Auto-Zone placement.
This optimization builds on Yahoo's broader data modernization journey, which involved migrating on-premises Hadoop and big data...
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