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Learn how to diagnose and fix stalled topic compaction in Apache Pulsar, including identifying I/O bottlenecks, misaligned retention policies, and ledger storage issues.
Learn how to enable HDFS erasure coding in Hadoop 3.x to cut storage costs for cold data, with a step‑by‑step example and trade‑offs.
Learn how Nimble’s thin provisioning lets you create logical volumes larger than physical capacity, the steps to enable it, how to confirm it’s working, and what pitfalls to watch for.
Learn how to implement Local-ZFS in Proxmox VE to balance VM disk performance and data redundancy using ZVOLs, mirroring, and ARC memory tuning.
The objective is to evaluate whether erasure coding delivers net storage cost reduction for a low-traffic Hadoop 3.x workload without compromising read performance or increasing reconstruction latency. HDFS erasure coding, as documented in Hadoop 3.x, reduces storage overhead from the default three-way replication to approximately 1.5x for large, infrequentl
Problem Statement OpenSUSE's default installation enables Btrfs snapshot auto-creation via snapper for system and user data. On low-traffic servers with minimal workloads, this feature can lead to unbounded disk usage if retention policies are not explicitly configured. The trade-off between convenience and storage cost becomes critical when running cost-sen
Optimizing Storage for Low-Traffic Workloads In a CouchDB 3.x environment hosting low-traffic databases, operational costs are primarily driven by disk utilization and compute overhead. By default, CouchDB creates databases with clustered semantics, often utilizing a shard count (q) and replica factor (n) designed for high availability and scalability. For s
hadoop
proxmox
opensuse
couchdb