Cisco Technology, Inc.

Appdynamics, Inc.

Arjun Iyer

Yuchen Zhao

Nima Haddadkaveh

Eric Shawn Vandenberg

Eric Vandenberg

Instead of processing a query as-is, the query is chunked or broken down into a sequence of smaller chunked queries and the chunked results of those smaller queries are streamed back to the requester. Chunking the query and streaming the chunked results can substantially decrease the user's time to value when running a query by returning some immediate results for display which are refined and eventually converge on the full results as each chunked query runs.

Dynamic streaming of query responses

In one aspect, a regular expression is automatically generated based on user input for fields that are desired to be extracted from log lines. The input may be received by user through an interface provided by a machine such as a controller. The input may identify one or more fields within a log line that should be extracted. Multiple instances of potential regular expression portions may be generated based on the user input, and different portions are combined together to determine if they achieve the desired extraction. Once a complete regular expression is generated based on user input, a user may provide additional input to identify examples or counterexamples of log line fields that satisfy or don't satisfy the user's intended extraction.

Automatic generation of regular expression based on log line data

Clusters of log lines are identified based on log line templates. The log line templates are based on a punctuality pattern for a log line. Clusters of log lines that match each punctuality pattern can be identified based on comparisons between the log lines. The comparison may determine the similarity of the log lines and ultimately identify whether the log lines are close enough to be clustered. The comparison may be based on generated n-grams for the log lines and performing a hash on the n-grams. The resulting cluster information may be communicated to a user in an interface.

Log event summarization for distributed server system

A system that utilizes a plurality of time series of metric data to more accurately detect anomalies and model and predict metric values. Streams of time series metric data are processed to generate a set of independent metrics. In some instances, the present system may automatically analyze thousands of real-time streams. Advanced machine learning and statistical techniques are used to automatically find anomalies and outliers from the independent metrics by learning latent and hidden patterns in the metrics. The trends of each metric may also be analyzed and the trends for each characteristic may be learned. The system can automatically detect latent and hidden patterns of metrics including weekly, daily, holiday and other application specific patterns. Anomaly detection is important to maintaining system health and predicted values are important for customers to monitor and make planning and decisions in a principled and quantitative way.

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San Mateo, CA, United States of America

Arjun Iyer