Elastic consulting

We help identify whether CPU pressure is coming from hot shards, expensive queries, indexing spikes, bad mappings, background merges, or cluster allocation issues. From there we tune queries, rebalance workload, review shard strategy, and fix the root cause instead of only treating the symptom.

We troubleshoot heap pressure, long garbage collection pauses, fielddata growth, aggregation-heavy workloads, and shard counts that are too high for the available memory. We can review sizing, JVM settings, query patterns, and index design to reduce memory pressure and make the cluster more stable.

We help diagnose Kafka liveness failures tied to heartbeats, session timeouts, max poll intervals, broker health, or consumers that stop making progress during long processing windows. That includes reviewing broker and consumer settings, deployment behavior, and partition assignments so liveness checks reflect real system health.

We investigate why shards are stuck unassigned, whether the issue is allocation rules, disk thresholds, replica settings, failed recovery, or node-level constraints. We can review allocation explain output, index templates, and recovery behavior to get the cluster back to a healthier state.

We help when the cluster goes red because primaries are unavailable, shard recovery keeps failing, or allocation retries are exhausted. That includes reviewing logs, templates, plugin interactions, recovery settings, and restart sequencing so recovery is done safely and without unnecessary data risk.

We help when nodes fill unevenly, disk watermarks start blocking allocation, or indices flip into read-only protection. We review shard distribution, storage policy, retention, rollover behavior, and watermarks so the cluster can keep indexing without recurring disk incidents.

We help when dynamic mappings create too many fields, index templates are missing the right limits, or cluster state keeps growing because field usage is uncontrolled. We can redesign mappings, tighten templates, normalize field creation, and reduce the operational cost of over-flexible schemas.

We help diagnose slow ingest pipelines, indexing backlogs, refresh overhead, mapping inefficiencies, and write patterns that prevent the cluster from keeping up. That may include tuning bulk behavior, shard counts, refresh cadence, ingest pipelines, and hardware or topology decisions.

We help when search latency is inconsistent because of shard imbalance, query design, routing mistakes, poor caching behavior, or the wrong index structure. We can review query plans, aggregation patterns, routing keys, and shard distribution to improve predictability and response time.

We help configure Redis for the workload you actually have, including memory policy, persistence, replication, latency, and operational maintenance. That covers both day-one setup and cleanup of clusters that have grown into unstable or hard-to-predict behavior over time.

We help diagnose why Redis reaches max memory without evicting the right keys, or why eviction behavior does not match the configured policy. That includes reviewing memory policy, key mix, persistence overhead, fragmentation, and whether the current workload is a good fit for the chosen setup.

We help when write-heavy traffic creates read latency, slowlog noise, or uneven behavior across nodes. We can review pipelining, hot keys, replication, network layout, command usage, and client-side patterns to reduce latency without sacrificing throughput.

We help design cache invalidation and refresh strategies when stale values, TTL expiry spikes, or cache stampedes create instability. That may include write-through, invalidation on update, out-of-band refresh, or other patterns that fit your tolerance for stale data.

We help troubleshoot Cassandra read and write timeout problems, including consistency level behavior, table design, paging, and cluster repair strategy. We can review whether the issue is query design, coordination overhead, node health, or a broader data model mismatch.

We help when tombstones, large partitions, repairs, or compaction behavior start slowing reads and making maintenance windows risky. That includes reviewing delete patterns, table design, repair cadence, compaction strategy, and how data should be modeled for the access pattern.