A Fast Data Ingestion and Indexing Scheme for Real-Time Log Analytics

Abstract

Structured log data is a kind of append-only time-series data which grows rapidly as new entries are continuously generated and captured. It has become very popular in application domains such as Internet, sensor networks and telecommunications. In recent years, many systems have been developed to support batch analysis of such structured log data. But they often fail to meet the high throughput requirements of real-time log data ingestion and analytics. An efficient index is very important to accelerate log data analytics, and at the meanwhile to support high throughput data loading. This paper focuses on designing a specialized indexing scheme for real-time log data analytics. The solution adopts a dynamic global hash index to partition the tuples into hash buckets. Then the tuples in the hash buckets are sorted and buffered in the sort buffer queue. When the amount of data in the queue reaches a threshold, the data is packed into segments before spilling to the disks. Moreover, an intra-segment index is maintained by meta database. With such an indexing scheme, the database system achieves high throughput and real-time data loading and query performance. As shown in the experiments, the data loading throughput reaches 5 million tuples per second per node. The delay of data loading does not exceed 10 seconds, and a sub-second query performance is achieved for the given queries.

This work is supported by the Outstanding Innovative Talents Cultivation Funded Programs 2014 of Renmin University of China.