The search head would receive all the partial results from each indexer and combine them.Note that an indexer itself could not run the second stats command correctly without knowing the results from other indexers as it does not have the full picture. This is also called the 'Map' phase where the task is distributed to get back partial results from each indexer. Each indexer would run the command and return its count by arr_Time, flight_num, date and aircraft_type. Search head would create a remote search to execute the first stats command on indexers. ![]() In a clustered environment the execution steps would be as follows: The first stats command aggregates data at date, flight number and aircraft type level, such that each record denotes a single trip and next stats then counts the number of trips by date and type. ![]() | stats count by arr_Time, flight_num, date, aircraft_type In the second aggregation we will count the number of records by aircraft type and date to get count of unique trips made.Īssuming we can identify a unique flight by flight number and arrival time, a typical search query would look like this: (here 'stats' command is used to aggregate data at the level specified in 'by' clause) index=flight_data At first aggregation we would extract unique trips (by excluding passenger level data) that have taken place by aircraft type and date (such that each record represents a unique trip). To get count of unique trips by aircraft type on each day, we would need to do two step aggregation. Let's assume we have passenger level data for thousands of flights on each day amounting to millions of records over time, with each record having details like Ticket PNR, flight number, date, tail number, aircraft type and several other fields. Let's take an example of airlines flights data where we would like to know how many unique trips were completed on each day for each aircraft type. An Indexer or Search peer, is a node that stores the distributed raw data and runs the remote search to process and return data requested by Search head. A Search head is responsible for accepting a search query from the user, creating a remote search that can be run independently in parallel across Indexers and processing the results returned by Indexers. Splunk Traditional Search in a Distributed Environment OverviewĪ traditional distributed Splunk deployment has Search heads and multiple Indexers (also called Search peers). Let's dive into a simplified lifecycle of traditional search. In this article, I will be talking about Splunk Data Fabric Search (in short DFS, not related to Depth First Search algorithm) that was introduced in Splunk 8 to improve search query performance at an unprecedented scale across Splunk deployments.īefore we look at how DFS improves search performance, we will need to look at how traditional search works in Splunk and when do we start approaching its limits. Initially developed as a log ingestion and monitoring tool used widely in IT operations and Enterprise Security monitoring, Splunk with the vision of "Data-to-Everything" is now being used across several industries to manage various data use cases. Several rest endpoints and SDKs are available for common programming languages (Java, Python, JavaScript etc) which can be used to customize and extend Splunk for various use cases like building an interactive d3 chart or implementing complex machine learning algorithms. Without the need of any external databases or third party solutions, Splunk is a distributed end to end platform for the entire data lifecycle process. Splunk is a big data software platform to ingest, store, process, analyze and visualize machine generated data gathered from various data source such as (but not limited to) websites, applications, sensors, devices etc.
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