Nabil developed a novel set data structure that efficiently tracks large quantities of (mostly) sequential values. Instead of storing the set entries individually, they are grouped into closed intervals with new insertions either falling into an existing interval or creating a new one.

By tagging messages passing through our large scale data streams with sequential IDs, we’re able to leverage that data structure to monitor or improve the integrity of our data. We identify data loss in real-time by counting the gaps in the sequence, and eliminate duplication errors altogether.

Mehmet introduced improvements to our cluster resource allocation algorithm so that priorities of jobs that have waited too long to get resources are adjusted on the fly and they are moved to the front of the queue.

This helped us address the starvation issues in Quantflow MapReduce cluster, and make sure that SLAs in our stacks are not missed even when the cluster is under heavy demand.