  Many data science applications like social network analysis use graphs as
their primary form of data. However, acquiring graph-structured data from
social media presents some interesting challenges. The first challenge is the
high data velocity and bursty nature of the social media data. The second
challenge is that the complex nature of the data makes the ingestion process
expensive. If we want to store the streaming graph data in a graph database, we
face a third challenge -- the database is very often unable to sustain the
ingestion of high-velocity, high-burst data. We have developed an adaptive
buffering mechanism and a graph compression technique that effectively
mitigates the problem. A novel aspect of our method is that the adaptive
buffering algorithm uses the data rate, the data content as well as the CPU
resources of the database machine to determine an optimal data ingestion
mechanism. We further show that an ingestion-time graph-compression strategy
improves the efficiency of the data ingestion into the database. We have
verified the efficacy of our ingestion optimization strategy through extensive
experiments.
