  Recent years have seen massive time-series data generated in many areas. This
different scenario brings new challenges, particularly in terms of data
ingestion, where existing technologies struggle to handle such massive
time-series data, leading to low loading speed and poor timeliness. To address
these challenges, this paper presents MatrixGate, a new and efficient data
ingestion approach for massive time-series data. MatrixGate implements both
single-instance and multi-instance parallel procedures, which is based on its
unique ingestion strategies. First, MatrixGate uses policies to tune the slots
that are synchronized with segments to ingest data, which eliminates the cost
of starting transactions and enhance the efficiency. Second, multi-coroutines
are responsible for transfer data, which can increase the degree of parallelism
significantly. Third, lock-free queues are used to enable direct data transfer
without the need for disk storage or lodging in the master instance. Experiment
results on multiple datasets show that MatrixGate outperforms state-of-the-art
methods by 3 to 100 times in loading speed, and cuts down about 80% query
latency. Furthermore, MatrixGate scales out efficiently under distributed
architecture, achieving scalability of 86%.
