Most products at ByteDance, e.g., TikTok, Douyin, and Toutiao, naturally generate massive amounts of graph data. To efficiently store, query and update massive graph data is challenging for the broad range of products at ByteDance with various performance requirements. We categorize graph workloads at ByteDance into three types: online analytical, transaction, and serving processing, where each workload has its own characteristics. Existing graph databases have different performance bottlenecks in handling these workloads and none can efficiently handle the scale of graphs at ByteDance. We developed ByteGraph to process these graph workloads with high throughput, low latency and high scalability. There are several key designs in ByteGraph that make it efficient for processing our workloads, including edge-trees to store adjacency lists for high parallelism and low memory usage, adaptive optimizations on thread pools and indexes, and geographic replications to achieve fault tolerance and availability. ByteGraph has been in production use for several years and its performance has shown to be robust for processing a wide range of graph workloads at ByteDance.

Compared to the wide application of LoRa(Long Range) in the ground Internet of Things, there is little research about LoRa in the LEO satellites Internet of Things. The focus of this paper is to study the LoRa (Long Range) adaptability in the Internet of Things(IoT) of LEO (Low Earth Orbit) satellites. First, this paper analyses whether LoRa modulation technology can be applied to LEO satellite Internet of Things scenarios. Then, the adaptability of LoRa protocol is analyzed according to the characteristics of the IoT system of LEO satellites like LoRa network architecture, access mechanism, bandwidth and so on. And suggestions for improving inapplicable characteristics are given.