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Search head clustering is a Splunk feature that allows a group of search heads to share configurations, apps, and knowledge objects, and to provide high availability and scalability for searching. Search head clustering has the following characteristics:A deployer is required. A deployer is a Splunk instance that distributes the configurations and apps to the members of the search head cluster. The deployer is not a member of the cluster, but a separate instance that communicates with the cluster master.At least three search heads are needed. A search head cluster must have at least three search heads to form a quorum and to ensure high availability. If the cluster has less than three search heads, it cannot function properly and will enter a degraded mode.The deployer must have sufficient CPU and network resources to process service requests and push configurations. The deployer is responsible for handling the requests from the cluster master and the cluster members, and for pushing the configurations and apps to the cluster members. Therefore, the deployer must have enough CPU and network resources to perform these tasks efficiently and reliably.Search heads do not need to meet the high-performance reference server requirements, as this is not a mandatory condition for search head clustering. The high-performance reference server requirements are only recommended for optimal performance and scalability of Splunk deployments, but they are not enforced by Splunk.

When building a deployment plan, the architect should perform the following tasks:Use case checklist. A use case checklist is a document that lists the use cases that the deployment will support, along with the data sources, the data volume, the data retention, the data model, the dashboards, the reports, the alerts, and the roles and permissions for each use case.A use case checklist helps to define the scope and the functionality of the deployment, and to identify the dependencies and the requirements for each use case1 Inventory data sources. An inventory of data sources is a document that lists the data sources that the deployment will ingest, along with the data type, the data format, the data location, the data collection method, the data volume, the data frequency, and the data owner for each data source.An inventory of data sources helps to determine the data ingestion strategy, the data parsing and enrichment, the data storage and retention, and the data security and compliance for the deployment1Review network topology. A review of network topology is a process that examines the network infrastructure and the network connectivity of the deployment, along with the network bandwidth, the network latency, the network security, and the network monitoring for the deployment.A review of network topology helps to optimize the network performance and reliability, and to identify the network risks and mitigations for the deployment1Installing Splunk apps is not a task that the architect should perform when building a deployment plan, as it is a task that the administrator should perform when implementing the deployment plan.Installing Splunk apps is a technical activity that requires access to the Splunk instances and the Splunk configurations, which are not available at the planning stage

Splunk indexing is read/write intensive, as it involves reading data from various sources, writing data to disk, and reading data from disk for searching and reporting. Therefore, it is important to select the appropriate disk storage solution for each deployment, based on the performance, reliability, and cost requirements. The recommended RAID setup for Splunk indexers is RAID 10 (1 + 0), as it provides the best balance of performance and reliability. RAID 10 combines the advantages of RAID 1 (mirroring) and RAID 0 (striping), which means that it offers both data redundancy and data distribution.RAID 10 can tolerate multiple disk failures, as long as they are not in the same mirrored pair, and it can improve the read and write speed, as it can access multiple disks in parallel2High performance SAN (Storage Area Network) can be used for Splunk indexers, but it is not recommended, as it is more expensive and complex than local disks. SAN also introduces additional network latency and dependency, which can affect the performance and availability of Splunk indexers.SAN is more suitable for Splunk search heads, as they are less read/write intensive and more CPU intensive2 NFS (Network File System) should not be used for storing hot and warm buckets, as it can cause data corruption, data loss, and performance degradation. NFS is a network-based file system that allows multiple clients to access the same files on a remote server. NFS is not compatible with Splunk index replication and search head clustering, as it can cause conflicts and inconsistencies among the Splunk instances. NFS is also slower and less reliable than local disks, as it depends on the network bandwidth and availability.NFS can be used for storing cold and frozen buckets, as they are less frequently accessed and less critical for Splunk operations2 Virtualized environments are not usually preferred over bare metal for Splunk indexers, as they can introduce additional overhead and complexity. Virtualized environments can affect the performance and reliability of Splunk indexers, as they share the physical resources and the network with other virtual machines. Virtualized environments can also complicate the monitoring and troubleshooting of Splunk indexers, as they add another layer of abstraction and configuration.Virtualized environments can be used for Splunk indexers, but they require careful planning and tuning to ensure optimal performance and availability2 

All of the options are possible causes of a crash in Splunk.According to the Splunk documentation1, incorrect ulimit settings can lead to file descriptor exhaustion, which can cause Splunk to crash or hang.Insufficient disk IOPS can also cause Splunk to crash or become unresponsive, as Splunk relies heavily on disk performance2.Insufficient memory can cause Splunk to run out of memory and crash, especially when running complex searches or handling large volumes of data3.Running out of disk space can cause Splunk to stop indexing data and crash, as Splunk needs enough disk space to store its data and logs4.1: Configure ulimit settings for Splunk Enterprise2: Troubleshoot Splunk performance issues3: Troubleshoot memory usage4: Troubleshoot disk space issues

Adding more search peers and making sure forwarders distribute data evenly across all indexers will provide the most search performance improvement when the distributed deployment is approaching its capacity. Adding more search peers will increase the search concurrency and reduce the load on each indexer. Distributing data evenly across all indexers will ensure that the search workload is balanced and no indexer becomes a bottleneck.Replacing the indexer storage to SSD will improve the search performance, but it is a costly and time-consuming option. Adding more search heads will not improve the search performance if the indexers are the bottleneck.Rescheduling slow searches to run during an off-peak time will reduce the search contention, but it will not improve the search performance for each individual search. For more information, see [Scale your indexer cluster] and [Distribute data across your indexers] in the Splunk documentation.

The indexers may have different configurations than the heavy forwarders, which might cause the issue of inconsistently formatted events for a web sourcetype. The heavy forwarders perform parsing and indexing on the data before sending it to the indexers. If the indexers have different configurations than the heavy forwarders, such as different props.conf or transforms.conf settings, the data may be parsed or indexed differently on the indexers, resulting in inconsistent events. The search head configurations do not affect the event formatting, as the search head does not parse or index the data. The data inputs configurations on the forwarders do not affect the event formatting, as the data inputs only determine what data to collect and how to monitor it. The forwarder version does not affect the event formatting, as long as the forwarder is compatible with the indexer. For more information, see [Heavy forwarder versus indexer] and [Configure event processing] in the Splunk documentation.

Search is not locked out when a customer has installed a 500GB Enterprise license and a 300GB, no enforcement license on the same license master. The no enforcement license allows the customer to exceed the license quota without locking search, but violations are still recorded. The customer can ingest up to 800GB of data per day without violating the license, but if they ingest more than that, they will incur a violation. However, the violation will not lock search, as the no enforcement license overrides the enforcement policy of the Enterprise license. For more information, see [No enforcement licenses] and [License violations] in the Splunk documentation.

The deployer distributes apps and non-search related and manual configuration file changes to the search head cluster members. The deployer does not bootstrap a clean Splunk install for a search head cluster, as this is done by the captain. The deployer also does not distribute runtime knowledge object changes made by users across the search head cluster, as this is done by the replication factor. For more information, seeUse the deployer to distribute apps and configuration updatesin the Splunk documentation.

When using the props.conf LINE_BREAKER attribute to delimit multi-line events, the SHOULD_LINEMERGE attribute should be set to false. This tells Splunk not to merge events that have been broken by the LINE_BREAKER. Setting the SHOULD_LINEMERGE attribute to true, auto, or none will cause Splunk to ignore the LINE_BREAKER and merge events based on other criteria. For more information, seeConfigure event line breakingin the Splunk documentation.

A deployment plan should include business continuity and disaster recovery plans, current logging details and data source inventory, and current and future topology diagrams of the IT environment. These elements are essential for planning, designing, and implementing a Splunk deployment that meets the business and technical requirements. A comprehensive list of stakeholders, either direct or indirect, is not part of the deployment plan, but rather part of the project charter. For more information, seeDeployment planningin the Splunk documentation.