Reducing Logging Cost by Two Orders of Magnitude using CLP

Reducing Logging Cost by Two Orders of Magnitude using CLP

摘要

Long, long ago, the amount of data our systems output to logs was small enough that we were able to retain all of the log files. This allowed our engineers to freely analyze the logs, say for troubleshooting our systems or improving applications. But as Uber’s business grew rapidly, the amount of data being logged increased dramatically. And so we were forced to discard log files after just a short period of time, given the prohibitive cost of retaining them–that is, until we integrated CLP into the logging library (Log4j) of our big data platform. In aggregate, CLP achieves a 169x compression ratio on our log data, saving storage, memory, and disk/network bandwidth at every level. As a result, we can now retain all logs at a fraction of the cost, without throwing away any insights, and the compressed logs can be efficiently searched without decompression.