Elasticsearch — Portfolio Projects
Build these projects to demonstrate real skills to employers. Each project is designed to be interview-worthy — something you can walk through in detail.
Project 1: Product Search with Explicit Mappings and Relevance Tuning
Level: Beginner-Intermediate | Time: 2 days
Build a realistic product search feature that demonstrates deliberate mapping decisions and measured relevance tuning, not just "it returns results."
Steps
text + .keyword) wherever a field needs both full-text search and exact-match/aggregation support
bool query combining must (relevance-affecting) and filter (cached, non-scoring) clauses correctly
multi_match with name^3, etc.) to tune relevance, but measure the effect with a small fixed set of test queries before/after each change — don't tune purely by eyeballing one result
_explain on at least one query to understand exactly why a specific document scored the way it did
text, why that one is keyword, why some are both) and each relevance-tuning change with its measured before/after effect
Skills Demonstrated
_explain, not guesswork
must/filter distinction applied correctly in a real bool query
GitHub Repo Name
elasticsearch-product-search
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Project 2: Log Analytics Pipeline with ILM
Level: Intermediate | Time: 2-3 days
Build a realistic log ingestion and analytics setup that manages its own data lifecycle, not just an index that grows forever.
Steps
logs-2026.07.19) and set up an ILM policy with real hot → warm → cold → delete phases and sensible age/size thresholds
date_histogram + terms), p50/p95/p99 latency (percentiles), all using "size": 0 to avoid fetching raw documents unnecessarily
GET /index/_stats (docs.deleted), then run a _forcemerge and document the before/after
Skills Demonstrated
GitHub Repo Name
elasticsearch-log-analytics-ilm
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Project 3: Cluster Health Diagnostics and a Deliberate Failure Drill
Level: Advanced | Time: 3-4 days
Stand up a real multi-node cluster and practice genuine failure diagnosis, not just reading about cluster health colors.
Steps
GET _cluster/allocation/explain to identify the specific, actual reason — not just observe the color
dynamic: false or the flattened type, and document the difference
Skills Demonstrated
_cluster/allocation/explain, not just color-watching
GitHub Repo Name
elasticsearch-cluster-diagnostics-lab
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Tips for Great Elasticsearch Projects
Show that you understand text vs. keyword as a deliberate choice, not default behavior. Every mapping decision in a strong project should be explainable — "this field is keyword because I need to aggregate on it, and text because I also need to search it" is a real answer; "I just used the default" is not.
Measure relevance changes, don't just eyeball one query. A project that shows "before/after with a fixed test query set" is far more convincing than "I added a boost and it looked better."
Elasticsearch is not a relational database. If a project's design implies FOREIGN KEY constraints, JOINs, or full ACID transactions across documents, that's a sign the project is being designed for the wrong tool — Elasticsearch is a search/analytics engine that typically sits alongside a primary database, not a replacement for one.