Understanding Real World Data Corruptions in Cloud Systems

Abstract

Big data processing is one of the killer applications for cloud systems. MapReduce systems such as Hadoop are the most popular big data processing platforms used in the cloud system. Data corruption is one of the most critical problems in cloud data processing, which not only has serious impact on the integrity of individual application results but also affects the performance and availability of the whole data processing system. In this paper, we present a comprehensive study on 138 real world data corruption incidents reported in Hadoop bug repositories. We characterize those data corruption problems in four aspects: 1) what impact can data corruption have on the application and system? 2) how is data corruption detected? 3) what are the causes of the data corruption? and 4) what problems can occur while attempting to handle data corruption? Our study has made the following findings: 1) the impact of data corruption is not limited to data integrity, 2) existing data corruption detection schemes are quite insufficient: only 25% of data corruption problems are correctly reported, 42% are silent data corruption without any error message, and 21% receive imprecise error report. We also found the detection system raised 12% false alarms, 3) there are various causes of data corruption such as improper runtime checking, race conditions, inconsistent block states, improper network failure handling, and improper node crash handling, and 4) existing data corruption handling mechanisms (i.e., data replication, replica deletion, simple re-execution) make frequent mistakes including replicating corrupted data blocks, deleting uncorrupted data blocks, or causing undesirable resource hogging.