![]() This process is known as Index Replication. Indexer Clusters are a cluster of Indexers that provide high availability for replicating external data and maintain multiple copies of data for disaster recovery purposes. ![]() Resources are interchangeable across the cluster, so a particular dashboard is available on all Search Heads in the cluster for example, enabling High Availability and load balancing. Search Head Clusters enable multiple search heads to form a cluster that acts as a shared resource for searching, apps, dashboards, etc. To achieve High Availability and scalability for performance requirements, Splunk has a scalable architecture. There is also a further type of component, known as a Deployment Server, which is an instance configured to distribute updates and apps to other Splunk instances. When a search is requested, it is sent to Search Peers, which are Search Indexers, and the results are then retrieved and presented back to the user.Ī basic diagram of a Splunk Architecture therefore, could be as follows: They can be either Dedicated Search Heads, which do not have indexes of their own and therefore only handles searches, or they can be standard Search Heads which can also handle indexing. Search Head – The Search Head offers search management functionality, and distributes searches to the Search Indexers. For its part, the indexing pipeline segments data, indexes it, then writes it to disk. While parsing, it can extract default fields for indexing, some data analysis such as determining character set and line termination, can add timestamps or identify them, and most importantly in a sensitive environment can mask sensitive information in the data. In each pipeline various actions can be carried out. Data sent to an indexer is passed into two pipelines, a parsing pipeline that breaks data into chunks and sends it into an indexing pipeline. Search Indexer – A search indexer processes data and can store it in the Primary Splunk Data Store. There are two types of forwarders: Universal Forwarders which are minimum Splunk instances capable only of forwarding data, and Heavy Forwarders which have a larger footprint and can both parse data before forwarding, route data based on rules and criteria, and can also index data locally as well as forwarding it on. They offer robust functionalities for tagging and encrypting data, compressing it, and have configurable buffering to enable throughput management. Forwarders and collect methods can interact with many data sources, log files, http rest, scripted inputs, NoSQL, Hadoop, etc for data ingestion / collection. The primary components in a Splunk Architecture include:įorwarders – these are instances of Splunk that are used to forward data from sources to Indexers where they are processed and stored. That will be discussed more in the use cases, but for now a quick look at a typical Splunk Architecture and the components needed to get it all working. In a Big Data environnement, this movement generally involves moving data from various sources, to a Data Lake, but as well as being a transport service, Splunk makes a pretty good destination all in itself due to its strong analytic offerings. Architecture OverviewĪs mentioned, one of the core roles of Splunk is to move machine generated (or via add-ins, any) data. This article looks at the architecture and use cases for Splunk, and a quick look at the BI capabilities offered by its Analytics dashboards. Having said that, its use is prevalent across a wide swathe of companies and entreprises, and it integrates nicely with data on Hadoop via a companion product known as Hunk. Splunk is a product made by the company of the same name since 2003, so it is a commercial offering and is not part of the Apache Hadoop stack. This is the core functionality offered by Splunk, and on the way it offers some impressive Business Intelligence capabilities on the data it moves. Big data involves getting big amounts of data from where it is made (or stored), to where it is analysed.
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