Hire Elasticsearch Engineers

Elasticsearch is easy to stand up and easy to get quietly wrong, because the defaults will accept almost anything. Documents index, queries return, and nobody notices that the analyser is splitting part numbers on hyphens until a customer searches for one and gets nothing.

Mapping is the decision that matters most and the hardest to change afterwards. Text or keyword, which analyser, whether a field needs both — these determine what is searchable and how, and changing them means a reindex. Getting it wrong is not fatal, but it is expensive at volume and it tends to be discovered by a user rather than a test.

Relevance is the second, and it is where most projects stop too early. “It returns results” is not the bar. The bar is whether the thing the person wanted is in the first three, and answering that requires a set of real queries with known good answers, checked after every change to boosting or analysis. Without that, relevance tuning is superstition — someone adds a boost, it feels better, and something else silently gets worse.

Shard strategy is the third. The common failure is many small indices with five shards each, inherited from an old default, producing thousands of shards and a cluster that spends its time on overhead. The opposite failure — one enormous index that cannot be reindexed inside a maintenance window — is rarer and worse.