Transformations and Data Manipulation

Implement rich transformation pipelines: (1) Unit conversion—transform microseconds to milliseconds, bytes to MB, etc. (2) Aggregation—group by endpoint, sum/avg/min/max queries. (3) Percentile computation—calculate P50, P95, P99 from results. (4) Time-series math—perform math on results: divide timeseries, multiply, add constants. (5) Filtering—filter rows: "only show results where latency > 100ms." (6) Mapping—map values: latency >1s = "red", <100ms = "green". (7) Ordering—sort results by metric, latency, etc. Implement transformation UI: visual blocks representing each transformation. Users can drag/drop to reorder, preview output. This makes transformations discoverable. Create a transformation library: pre-built transformations for common patterns (percentile calculation, unit conversion, aggregation). Users drag/drop library transformations, then customize parameters. For complex transformations, allow scripting (custom JavaScript or Go expressions). Implement preview: as users build transformation pipeline, show sample of transformed output live. Catch errors early. Build a transformation debugging dashboard: shows input → transformation 1 → transformation 2 → ... → output. Users can trace where data is transformed. Store transformation pipelines in Git: export as JSON, version control. Reuse across dashboards. Create documentation with examples: "how to calculate P95 latency", "how to group by service", etc. Provide copy-pasteable transformation configs.

Implement cardinality reduction transformations: (1) Top-K filtering—keep top K series by metric (sort by median, limit 10). (2) Tail aggregation—aggregate remaining series into "others". (3) Percentile filtering—keep only series with values in top percentile (P90+). (4) Bucketing—group series into buckets (histogram-like), aggregate each bucket. (5) Sampling—uniformly sample K series from all results. (6) Thresholding—drop series below threshold value. (7) Cardinality limiting—enforce max cardinality: if >N series, auto-truncate to top N. Implement preview for cardinality reduction: show before/after. "Original: 10K series, After filtering: 15 series." This helps users understand impact. Create smart defaults: analyze incoming data cardinality, auto-suggest appropriate filtering (e.g., "your query returns 5K series; suggest keeping top-50"). Build performance optimization: transformations that reduce cardinality early in pipeline improve overall query performance. Prioritize these. For debugging, provide detailed logging: which series were filtered, why. "Series 'host-5' filtered: median=5ms (rank #500 of 10K)." This helps users understand decisions. Implement "explore filtered data": users can click on "others" aggregation to see which series are included. Drill-down capability. Test cardinality scenarios: query 10K, 100K, 1M series. Verify transformations handle without crashing. Provide runbook: "my dashboard shows only 15 series, but I need to see more. How do I adjust filtering?"

Implement cross-datasource transformation and joining: (1) Multi-query support—allow dashboard panels to combine results from multiple queries (Prometheus + Elasticsearch). (2) Join condition—specify join key: correlate by request_id, trace_id, etc. (3) Inner/outer joins—support inner join (only matching records), left/right/full outer joins. (4) Deduplication—on join key, combine duplicate records. (5) Field mapping—map fields from different datasources to common schema (rename, reorder). (6) Aggregation on join—after join, aggregate: group by request_id, count matching logs, compute metrics. (7) Error handling—handle mismatches gracefully: if Prometheus has request X but Elasticsearch doesn't, how to handle? Implement a join builder UI: visual interface to drag/drop queries and define join conditions. Preview joined results. Create join templates: pre-built joins for common patterns (metrics + logs by trace_id, metrics + events by timestamp). For performance, implement smart join strategies: if one dataset is much larger, use it as the fact table. Index on join key for fast lookups. Build debugging tools: show join statistics (X records from Prometheus, Y from Elasticsearch, Z matched). Highlight unmatched records. Test join scenarios: matching joins, non-matching joins, skewed datasets. Verify correctness and performance. For production use, document join patterns and performance expectations. Create runbook: "joining metrics and logs by trace_id." Provide examples.

Implement transformation debugging and error handling: (1) Null handling—explicit null handling: if CPU is null, use 0, or skip row, or mark as error. (2) Error messages—show clear errors: "CPU_usage is missing for host-1; skipping calculation." (3) Error tracking—count errors per transformation: "50 rows failed calculation due to missing CPU." (4) Partial results—return partial results with error metadata. Users see what worked, what failed. (5) Fallback values—specify fallbacks: if CPU null, use previous value, or default to 0. (6) Validation rules—define validation: "CPU should be 0-100%." Flag out-of-range values. (7) Debug mode—enable verbose debug logging: show intermediate results after each transformation step. Implement error rate monitoring: dashboard showing error rate by transformation. Alert if error rate exceeds threshold (e.g., >10% of results failed). Build drill-down: users can click on error, see sample of failed rows, diagnose issue. Create transformation error recovery UI: when transformation fails, suggest fixes. "Missing CPU values for 5 hosts. Show as 0? Skip row? Use previous value?" For compliance, maintain error audit log: all transformation errors logged with context. Implement transformation unit tests: teams can write tests for their transformations (input → expected output), validate before deployment. Test failure scenarios: missing values, out-of-range values, type mismatches. Verify graceful handling. Create runbook: "debugging transformation errors." Steps for investigating failures.

Implement schema-aware transformations: (1) Schema versioning—track schema version: which fields exist, data types. (2) Field mapping—define alternative field names: if 'request_latency' is missing, try 'latency' or 'response_time'. (3) Type coercion—handle type mismatches: string "123" → number 123. (4) Missing field handling—explicit strategy when field is missing: skip transformation, use default, or error. (5) Schema validation—before executing transformation, validate all required fields exist. Alert if not. (6) Migration guides—when schema changes, provide migration guide for transformations: "field 'error_count' was renamed to 'error_rate'. Update your transformations." (7) Versioned transformations—allow transformations to target specific schema version. Old transformations continue working even if schema evolves. Implement automatic field detection: when users build transformations, system suggests available fields. If field is missing, offer alternatives. Build schema change alerts: when new fields appear in query result, or existing fields disappear, alert and offer transformation update. Create transformation linting: scan transformations for potential schema issues. "Using field 'request_latency', but only 'latency' is available. Did you mean 'latency'?" For rollout, provide dry-run: run transformation against new schema in sandbox, show what would happen. Implement field mapping UI: visual mapping of old fields to new fields during schema evolution. Test schema evolution: simulate adding/removing/renaming fields. Verify transformations adapt gracefully. Create runbook: "schema changed, how do I update my transformations?"

Implement reusable transformation libraries: (1) Transformation functions—define reusable functions: ltv(revenue, months), engagement_tier(activity_score), risk_score(churn_rate). (2) Function registry—centrally store all functions (Git-tracked). Version each function. (3) Library dependencies—transformations can use other transformations as building blocks. (4) Versioned imports—dashboards specify which version of function they use: "use ltv v2.1". Enables evolution without breaking old dashboards. (5) Function testing—unit tests for each function: test inputs → verify outputs. Catch logic bugs before deployment. (6) Documentation—each function has documentation: parameters, output, examples. Teams know how to use. (7) Permission control—sensitive functions (risk scoring) require approval before deployment. Non-sensitive can auto-deploy. Implement IDE-like editing: provide editor for creating/testing transformation functions. Syntax highlighting, auto-complete, inline documentation. Build function versioning: when function logic changes, increment version. Old dashboards stay on v2, new dashboards use v3. Easy evolution. Create central transformation library UI: list all available functions, search, drill into docs. Users browse available options instead of re-implementing. Test transformation functions: run against test data, verify outputs, performance (timeouts for expensive operations). For complex functions, implement gradual rollout: deploy function as v1-beta, let teams opt-in. Collect feedback. Once stable, promote to v1-stable. Create runbook: "adding a new transformation function." Steps for design, implementation, testing, deployment. Document best practices: keep functions small and composable, avoid side effects, ensure deterministic behavior.