Select Two (Multi-Select) Questions

Using the CLI command 'splunk show cluster-status'.

Running 'splunk show cluster-status' on a cluster member returns replication and search-factor compliance, peer counts and any ongoing fixup. It satisfies the stem by giving a supported CLI method to monitor indexer cluster health directly from a node.

Using the Monitoring Console.

The Monitoring Console ships with cluster-specific dashboards that surface replication and search-factor status, peer health and bucket-fixup activity. It satisfies the stem by providing a supported, graphical method to monitor an indexer cluster's overall state.

Service load is too high causing timeouts.

Excessive search load starves the captain heartbeat, so the member's periodic captain communication times out and it drops out of the cluster. This satisfies the stem's timeout constraint directly: resource contention, not network failure, breaks captain contact.

Network instability.

Network instability interrupts the heartbeat and replication traffic between the member and the captain, causing the member to lose contact. This directly satisfies the stem's scenario as one of the common causes of captain contact loss.

Replication port is blocked by firewall.

Search head cluster members replicate configuration and artefacts to peers over the replication port, so a firewall blocking that port prevents members from reaching the captain and each other. This directly satisfies the stem's cause of losing contact with the captain.

Reviewing the Search Activity dashboard

Reviewing the Search Activity dashboard regularly reveals slow searches, concurrency peaks and resource contention. It satisfies the performance task requirement by surfacing the search-side constraint, enabling tuning of limits and knowledge objects before user experience degrades.

Monitoring index storage usage

Monitoring index storage usage regularly prevents licence violations and search degradation as data grows. It satisfies the performance task requirement by tracking the constraint most likely to cause ingestion blocking, letting administrators adjust retention or add indexers before users are affected.

Data loss if an indexer fails.

A low replication_factor means fewer searchable copies exist across indexers, so a single indexer failure can leave some buckets with no surviving replica, causing permanent loss of that indexed data. This directly satisfies the stem's data-loss risk, since replication is the mechanism protecting against indexer failure.

Inability to meet search factor requirements.

A low replication_factor directly caps how many searchable copies can exist, since the search factor cannot exceed the replication factor. With too few replicated buckets, Splunk cannot create the required number of searchable copies, so the cluster fails to satisfy its search factor requirement.

Increased downtime during peer maintenance.

With too few replicas, taking a peer offline for maintenance leaves insufficient copies to maintain searchability, forcing the cluster to block searches until the peer returns. This directly satisfies the stem's downtime constraint: replication_factor below the number of concurrent peer failures tolerated causes searchable copies to drop below the required threshold.

Memory usage

Memory usage exposes indexer resource pressure: sustained high heap consumption triggers search-time degradation and can cause queue backup. Monitoring Console tracks this per indexer instance, letting you scale or rebalance before ingestion latency breaches the pipeline's throughput requirement.

Disk I/O latency

Disk I/O latency directly governs indexer throughput, since indexing writes buckets to disk and search reads them back. Elevated latency signals storage contention or failing disks, which stalls the indexing pipeline and breaches the ingestion SLA the Monitoring Console is designed to protect.

CPU utilization

CPU utilization indicates whether the indexer is saturated by indexing and search load. Sustained high values signal insufficient processing capacity, making it a critical health metric alongside memory, disk and indexing throughput in the Monitoring Console.

The security key ('pass4SymmKey') is mismatched

Cluster peers authenticate with the shared pass4SymmKey; if the indexer's key differs from the master's, the peer handshake fails and the node never appears as a cluster member. A mismatch therefore directly prevents reporting, satisfying the stem's requirement for a likely cause.

The management port is blocked by a firewall

Firewalls blocking TCP port 8089 prevent the indexer from reaching the Cluster Master's management interface, which carries heartbeats and configuration replication. Splunk's clustering and distributed management traffic relies exclusively on this port, so a blocked path directly explains the missing reporting.

The indexer instance is not running the 'cluster-member' role

An indexer only joins a cluster when its server.conf declares the cluster-member role. Without that role, the instance runs as a standalone indexer, so it never contacts the Cluster Master and cannot report, matching the stem's scenario.

The peers will continue to index incoming data normally

During maintenance mode, peers keep indexing incoming data normally, so ingestion continues uninterrupted. The cluster simply suspends replication and searchability of new buckets until the maintenance window ends, meaning the constraint satisfied is continuous data ingestion while replication and search factors are temporarily relaxed.

The Master will not alert on any missing buckets

With fix-up suspended, the Cluster Master also stops generating alerts about missing or under-replicated buckets, since those alerts derive from the same fix-up monitoring cycle. Operators must therefore check replication health manually before exiting Maintenance Mode.

The Cluster Master will not perform bucket fix-up activities

Maintenance Mode suspends the Cluster Master's bucket fix-up routines, so it stops searching for and remediating under-replicated or missing buckets. This is a deliberate implication: fix-up resumes once Maintenance Mode is disabled and the cluster returns to normal operation.

TSIDX files

TSIDX files hold the time-series index that maps timestamped buckets to raw events, enabling Splunk to locate data within a specified time range. They reside in the index directory alongside bucket metadata, not in the search head or configuration directories.

Raw data files (compressed)

Raw data files (compressed) reside in the index directory because Splunk Enterprise stores each index's bucket contents there, including the compressed journal and rawdata files. This satisfies the stem's requirement for items held within the index directory, as distinct from configuration files kept under $SPLUNK_HOME/etc.

coldPath

coldPath is the directory storing buckets that have rolled from warm to cold, holding older searchable data on cheaper storage. Together with homePath and thawedPath it forms the three configurable index path components, directly answering which parts constitute the Splunk index directory structure.

homePath

homePath is the writable directory holding the index's hot and warm bucket directories, where newly indexed data initially lands. It is one of the three path settings (with coldPath and thawedPath) that define an index's directory structure, satisfying the question's requirement for a genuine component.

thawedPath

Stores restored buckets.

Incorrect pass4SymmKey on one of the nodes

Cluster members authenticate replication traffic using the shared pass4SymmKey secret. A mismatch on any single node prevents that member from joining the replication channel, so configuration bundles never propagate, directly producing the search head cluster replication failures the stem describes.

Network firewall blocking port 8089

Search head cluster members replicate configuration bundles and artefacts over port 8089, the Splunk management port. A firewall blocking that port between members halts replication traffic entirely, which is a frequent cause of the replication failures described in the stem.

Disk I/O throughput

Disk I/O throughput directly governs indexing and search performance, since indexers constantly read and write bucket data. In a multisite cluster, replication and searchable copy factors multiply this load across sites, so sustained throughput per indexer must be sized against peak ingest plus inter-site replication traffic.

Inter-site network latency and bandwidth

Inter-site network latency and bandwidth directly govern replication and search-fanout traffic between peer sites, so they constrain how many indexers each site needs and whether replication factor can be satisfied across sites. Ignoring them risks searchable copies falling behind or bucket replication failures, breaching the cluster's multisite resilience requirement.

The total number of indexer peers

Each indexer peer adds storage, CPU and memory load, and replication and search factors scale with peer count. Sizing must therefore account for the total number of indexer peers across all sites, directly driving aggregate hardware requirements.

Managing bucket replication.

Bucket replication is a master-level function: the master coordinates replication of buckets across peer indexers, instructing peers to copy and search replicated data. This satisfies the stem's requirement for a genuine Indexer Cluster Master feature, distinct from search head or peer responsibilities.

Monitoring indexer health.

The master monitors indexer health, tracking peers' status and triggering fixups when peers go down or fall behind on replication. This directly satisfies the stem's requirement for a genuine Indexer Cluster Master feature, separating it from search head or peer duties.

Use data models and acceleration

Data model acceleration precomputes and stores summarised results in tsidx files, so searches against the model read compact prebuilt summaries instead of raw events. This avoids repeated full-dataset scans, directly addressing the large-dataset performance constraint.

Filter searches using indexed fields

Indexed fields let Splunk discard events before reading raw data, so the search engine scans only matching buckets rather than the full dataset. This sharply reduces the volume processed, which is the core constraint when datasets are large.

The indexers have high CPU usage

Splunk search performance depends on indexer CPU headroom for search processing and result aggregation. Sustained high CPU usage starves search processes, queuing and slowing queries across the cluster, directly explaining the slow search performance described in the stem.

Search concurrency limits are being reached on the search head

When concurrent searches exceed the configured limits on a search head, additional searches queue rather than run, so users perceive slow performance. Raising concurrency limits or adding search heads addresses this constraint, which is a common cause of cluster-wide search slowdown.

The indexer cluster is currently re-balancing buckets

Bucket re-balancing moves index buckets between peers to equalise data distribution. During this process, indexers consume I/O and CPU serving bucket movement, and search affinity is disrupted, so searches run slower across the cluster until re-balancing completes.

Object storage (SmartStore)

SmartStore uses object storage as the remote tier for warm and cold buckets, while indexers cache hot buckets locally. It satisfies the constraint of cost-effective, scalable retention for indexer data without relying solely on attached disks.

Local attached storage

Locally attached storage, such as internal disks or SAN volumes, provides the block-level I/O and low latency Splunk indexers require for hot and warm buckets. It satisfies the constraint that indexer data needs performant, directly accessible storage for active searching.

Excessive data model acceleration jobs

Background summary searches are CPU intensive.

Complex, inefficient search queries

Complex transformations consume search-side CPU.

High ingestion volume

Parsing and indexing require significant CPU.

maxDataSize

maxDataSize sets the size threshold for a hot bucket; once the bucket reaches that volume, Splunk rolls it to warm. This satisfies the stem's question by naming the size-based condition that triggers the hot-to-warm transition.

maxHotSpanSecs

maxHotSpanSecs caps how long a bucket may remain hot, measured by the age span of its data. Reaching that time limit rolls the bucket to warm, satisfying the stem's question by naming the time-based condition.

Perform a mock upgrade in a staging environment

A mock upgrade in staging exercises the full upgrade path, including app compatibility and configuration migrations, without risking production. It satisfies the stem's system-health constraint by exposing failures, rollback gaps and performance regressions before the change window opens on live indexers.

Verify compatibility with all installed apps and add-ons

Splunk upgrades break when installed apps and add-ons are incompatible with the new version, causing search failures or indexing outages. Verifying compatibility against the target release before upgrading satisfies the system-health constraint by preventing post-upgrade breakage across search heads, indexers and forwarders.

Perform a full backup of the indexer buckets and configuration files

Backing up indexer buckets and configuration files preserves indexed data and settings, enabling restore if the upgrade fails or corrupts components. It satisfies the system-health constraint by guaranteeing a rollback point before any irreversible migration of buckets or configuration occurs.

Search concurrency

Concurrent searches compete with indexing for the same indexer disk bandwidth, so higher search concurrency increases read I/O and can slow bucket operations. This satisfies the stem's factor requirement because search load directly affects the disk I/O demands placed on indexer peers.

Daily ingestion volume

Higher daily ingestion volume forces more indexer disk writes, bucket creation and merging, directly raising sustained I/O load. This satisfies the stem's factor requirement because ingestion throughput determines how much data must be written and indexed per day across the cluster.

Storage latency/type

Storage latency and type determine how quickly indexers complete bucket reads and writes, so slower disks or higher-latency tiers directly constrain achievable I/O throughput. This satisfies the stem's factor requirement by naming the underlying hardware characteristic that governs indexer disk performance.

server.conf

In server.conf, the `[general]` stanza's `parallelIngestionPipelines` setting controls how many ingestion pipelines run per indexer, directly governing CPU parallelism during indexing. Raising it scales throughput across cores, satisfying the stem's indexer performance tuning requirement, while `[clustering]` options manage replication load.

limits.conf

limits.conf governs indexer resource ceilings, including parallel ingestion pipelines, search concurrency and per-result limits. Tuning these values directly controls how much CPU and memory the indexer allocates to indexing versus searching, making it central to indexer performance tuning.

indexes.conf

indexes.conf controls per-index settings such as maxHotBuckets, frozenTimePeriodInSecs and bucket sizing. These determine how aggressively the indexer rolls and freezes buckets, which governs disk I/O and search efficiency, making it essential for indexer performance tuning.