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Elasticsearch & Full Text Search Systems · Lesson

Profiling and Slow Query Logs

Diagnose performance problems by profiling individual queries and capturing slow operations with the slow log so you can find and fix the real bottlenecks.

Profiling and Slow Query Logs is a free Elasticsearch & Full Text Search Systems lesson on CoddyKit — lesson 4 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the Elasticsearch & Full Text Search Systems learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.

Measure Before You Optimize

Guessing at performance fixes wastes time. Elasticsearch gives you two precise tools: the Profile API for dissecting a single query, and slow logs for catching expensive operations in production.

The Profile API

Add "profile": true to any search request. Elasticsearch returns a detailed timing breakdown of how the query executed on each shard.

GET my_index/_search
{
  "profile": true,
  "query": { "match": { "title": "elasticsearch" } }
}

Reading Query Timings

The profile output lists each Lucene query and the time spent in phases like build_scorer, next_doc, and score. A surprisingly slow phase points you straight to the culprit.

Rewrite Time

Watch the rewrite_time. Wildcard, prefix, and range queries can rewrite into thousands of terms, inflating this number. If rewrite dominates, reconsider the query type or use a keyword/edge-ngram field.

Profiling Aggregations

The profile output also has an aggregations section. It shows initialize, collect, and build_aggregation times, helping you spot a costly high-cardinality terms aggregation.

Profile API Caveats

Profiling adds overhead and does not capture network or coordination time. Use it for relative comparison between query versions, not as an absolute production latency figure.

The Search Slow Log

The slow log records queries that exceed configurable time thresholds, per shard. It separates the query phase from the fetch phase and uses warn/info/debug/trace levels.

PUT my_index/_settings
{
  "index.search.slowlog.threshold.query.warn": "1s",
  "index.search.slowlog.threshold.fetch.warn": "500ms"
}

The Indexing Slow Log

A parallel indexing slow log catches documents that take too long to index, useful for spotting expensive pipelines or oversized documents.

PUT my_index/_settings
{
  "index.indexing.slowlog.threshold.index.warn": "1s"
}

Per-Shard Logging

Slow log thresholds apply per shard, not per request. A query slow on one shard but fast overall will still be logged for that shard, helping you find a single hot or unbalanced shard.

Acting on Findings

Once you identify a slow pattern, common fixes are: add a filter to narrow the dataset, replace heavy wildcard queries, increase shard count for hot indices, or pre-aggregate data. Always re-profile to confirm the gain.

A Workflow

Production loop: slow log surfaces a bad query, you reproduce it with the Profile API, identify the costly phase, apply a fix, and verify. This data-driven cycle beats guesswork every time.

Quick Check

Test your understanding of performance diagnostics.

Recap

You learned to diagnose performance:

  • The Profile API breaks down query and aggregation timings phase by phase.
  • Watch rewrite_time for expanding wildcard/range queries.
  • Search and indexing slow logs catch expensive operations per shard in production.
  • Follow a measure-fix-verify loop instead of guessing.

Frequently asked questions

Is the “Profiling and Slow Query Logs” lesson free?

Yes — the full text of “Profiling and Slow Query Logs” is free to read here on the web, and the Elasticsearch & Full Text Search Systems course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the Elasticsearch & Full Text Search Systems course, upgrade to CoddyKit PRO.

What will I learn in “Profiling and Slow Query Logs”?

Diagnose performance problems by profiling individual queries and capturing slow operations with the slow log so you can find and fix the real bottlenecks. You practise Elasticsearch & Full Text Search Systems with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.

Do I need any experience to start Elasticsearch & Full Text Search Systems?

No prior experience is required. Elasticsearch & Full Text Search Systems on CoddyKit is structured for beginners through advanced learners; this is — lesson 4 of 4, so you can start here or from the beginning and move at your own pace.

How long does the “Profiling and Slow Query Logs” lesson take?

Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.

Can I write and run code in this Elasticsearch & Full Text Search Systems lesson?

Yes. Every Elasticsearch & Full Text Search Systems lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.

All lessons in this course

  1. Query Optimization Strategies
  2. Indexing Performance Best Practices
  3. Caching and Concurrency
  4. Profiling and Slow Query Logs
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