Splunk Inc.

Signalfx, Inc.

Cisco Technology, Inc.

Arijit Mukherji

Rajesh Raman

Phillip Liu

Kris Grandy

Jack William Lindamood

Shmuel Shaffer

Labhesh Patel

Maxime Petazzoni

Gerardo Chaves

Christopher A White

Elango Ganesan

Michael Freed

John F Wakerly

Lawrence Rolfe Kreeger

Rituraj Kirti

Rohit Dewan

Song Lu

Uday Sagar Shiramshetty

Jennifer Williamson

Tianyu Wang

Edward Crossman

Jack Lindamood

Philip Liu

A data analysis system processes data generated by instrumented software. The data analysis system receives data streams generated by instances of instrumented software executing on systems. The data analysis system also receives metadata describing data streams. The data analysis system receives an expression based on the metadata. The data analysis system receives data of data streams for each time interval and computes the result of the expression based on the received data values. The data analysis system repeats these steps for each time interval. The data analysis system may quantize data values of data streams for each time interval by generating an aggregate value for the time interval based on data received for each data stream for that time interval. The data analysis system evaluates the expression using the quantized data for the time interval.

Real-time reporting based on instrumentation of software

According to embodiments, a data stream including a plurality of time series data is received and metadata objects are extracted from the data stream. The metadata objects are associated with metrics time series (MTS) objects. The metadata objects and MTS objects are stored via separate in-memory data structures in a logical database. The in-memory data structures include information that correlates the metadata objects with the MTS objects. Any updates to the metadata objects will stay with the metadata objects and do not propagate to the MTS objects. A logical in-memory join may be performed to associate the metadata objects with the appropriate MTS object according to the in-memory data structures when a query for an MTS object is received.

Optimized storage of metadata separate from time series data

An instrumentation analysis system processes data streams by executing instructions specified using a data stream language program. The data stream language allows users to specify a search condition using a find block for identifying the set of data streams processed by the data stream language program. The set of identified data streams may change dynamically. The data stream language allows users to group data streams into sets of data streams based on distinct values of one or more metadata attributes associated with the input data streams. The data stream language allows users to specify a threshold block for determining whether data values of input data streams are outside boundaries specified using low/high thresholds. The elements of the set of data streams input to the threshold block can dynamically change. The low/high threshold values can be specified as data streams and can dynamically change.

Dynamically changing input data streams processed by data stream language programs

An analysis system receives data streams generated by instances of instrumented software executing on external systems. The analysis system evaluates an expression using data values of the data streams over a plurality of time intervals. For example, the analysis system may aggregate data values of data streams for each time interval. The analysis system determines whether or not a data stream is considered for a time interval based on when the data value arrives during the time interval. The analysis system determines a maximum expected delay value for each data stream being processed. The analysis system evaluates the expression using data values that arrive before their maximum expected delay values. The analysis system also determines a failure threshold value for a data stream. If a data value of a data stream fails to arrive before the failure threshold value, the analysis system marks the data stream as dead.

Real-time processing of data streams received from instrumented software

A system processes data stream language expressions that combine result data streams from multiple data stream language sub-expressions. The system determines a set of fixed dimensions based on static analysis of the data stream language sub-expression. The system determines a union set representing a union of the sets of fixed dimensions. The system determines at execution time of the data stream language expression, a plurality of sets of data streams. Each set of data stream corresponds to a data stream language sub-expression from the plurality of data stream language expressions. The system correlates data streams across the plurality of sets of data streams based on the union set. The system determines result data streams for the data stream language expression by combining data values of correlated data streams.

Combining data streams generated by instrumented software using metadata correlation

Representing result data streams based on execution of data stream language programs

Quantization of data streams of instrumented software

Growing community of inventors

Fremont, CA, United States of America

Arijit Mukherji