Splunk Inc.

Poornima Devaraj

Sanjeev Raghavendra Kulkarni

Karthikeyan Ramasamy

Boyang Peng

Min Zhang

Joseph Gabriel Echeverria

Dragoljub Profirovic

Shyam Mundhra

Zhimin Liang

Ivneet Kaur

Paul Jean André Bernier

Venkata SreeKrishna Koganti

Ryan Konrad Yee

Xiangyu Wu

Hardik Shah

Systems and methods are described for customizable data streams in a streaming data processing system. Routing criteria for the customizable data streams are defined by a user, an automated process, or any other process. The routing criteria can be defined using graphical controls. The streaming data processing system uses the routing criteria to determine data that should be used to populate a particular data stream. Further, processing pipelines are customized such that a particular processing pipeline can obtain data from a particular user defined data stream and write data to a particular user defined data stream. Data is routed through the user defined data streams and customized processing pipelines based on a data route. A data route for a set of data may include multiple user defined data streams and multiple processing pipelines. The data route can include a loop of processing pipelines and data streams.

User interface for customizing data streams and processing pipelines

Systems and methods are described herein for synthesizing traces from logs of a distributed computing system. A trace represents a single transaction, such as handling of a user request, on the distributed computing system. The transaction can include multiple underlying operations on the distributed computing system, which are represented as spans within the trace and may be hierarchically arranged within the trace. In instances where a distributed computing system does not provide for tracing natively, a trace can be synthesized from log entries of the distributed computing system. A streaming data processing system can ingest a data stream including log entries, and identify within the data stream those log entries relating to a given transaction. The streaming data processing system can further identify log entries that demark the beginnings and endings of operations for that transaction, and can utilize the identified log entries to build a trace for the transaction.

Streaming synthesis of distributed traces from machine logs

Systems and methods are described for a streaming data processing system that defers processing of some data based on a determined importance of the data. A streaming data processing system can ingest a data stream that contains multiple events, and can extract data field values from individual events and process the data field values to determine event importance. The streaming data processing system can then do further processing and indexing of high importance events, and can generate a storage prefix for each low importance event that determines where to store the low importance event in a data storage system. The streaming data processing system can then process queries by retrieving the indexed high importance events, and can extract the data field values from a high importance event to determine the storage prefix for retrieving corresponding low importance events from the data storage system.

Dynamic storage and deferred analysis of data stream events

Systems and methods are described for tuning parameter values of a processing pipeline in a streaming data processing system. In order to determine an optimal set of parameter values for a particular processing pipeline, a processing pipeline can be implemented with different sets of parameter values. A performance metric can be measured for each implementation to measure the performance of the processing pipeline with regards to a particular set of parameter values. The performance metrics for each implementation can be compared in order to determine optimal performance metrics. The processing pipeline can be implemented based on an optimal set of parameter values that correspond to the optimal performance metrics.

Determining a set of parameter values for a processing pipeline

User defined data stream for routing data to a data destination based on a data route

User interface for customizing data streams

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Fremont, CA, United States of America

Poornima Devaraj