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Databases & messaging · #aerospike
Learn how Aerospike secondary indexes speed up non‑primary‑key queries, what memory and latency costs they add, and how to decide which bins to index.
Enable Aerospike’s Strong Consistency mode to get linearizable reads and writes. This guide shows how to configure the namespace, set the client read policy, measure performance impact, and decide when SC is right for your workload.
Learn how to configure Aerospike Strong Consistency (SC) mode to eliminate stale reads and ensure linearizability for critical data during network partitions.
Choosing between Aerospike's memory and device namespaces is a capacity-versus-latency decision with a hidden RAM cost: the primary index always lives in memory.
Learn when Aerospike server-side predicate filtering reduces network payload alongside secondary indexes, and why it doesn't replace indexing.
Aerospike's Hybrid Memory Architecture keeps indexes in DRAM and stores data blobs directly on NVMe flash, bypassing the filesystem and page cache. Here is what that means for latency, endurance, and capacity planning.
Learn how to implement and optimize Aerospike secondary indexes to retrieve records by non-key bins, including Java client examples and critical memory considerations.
Latency spikes plus rising evictions in Aerospike usually mean namespace memory pressure. A diagnostic guide: symptoms, cause table, ordered asinfo checks, targeted fixes, and escalation criteria.
When concurrent writes to the same Aerospike record cause duplicate deductions enabling strong-consistency mode with ALL commit and consistency levels eliminates race conditions at the cost of higher latency and increased network usage