cx-data-pipeline

Data Pipeline Skill

Use this skill when configuring how Coralogix processes, enriches, and transforms data. It covers parsing rules (extract structured fields from raw logs), enrichments (add context from lookup tables), Events2Metrics (derive metrics from log/span events), and recording rules (precompute PromQL expressions).

CLI Commands

Key flags:

• All create/update operations use --from-file <path> (or - for stdin)
• All commands support -o json for structured output and -p <profile> for profile selection
• cx parsing-rules update and cx recording-rules update require both --from-file and the rule group ID
• cx enrichments custom search requires --id <table-id> and --query <text>
• cx parsing-rules bulk-delete requires --ids <id1> <id2> ...

Working with JSON Payloads

These commands use complex JSON structures. Always template from an existing resource to avoid format errors:

# 1. Get an existing resource as a template
cx parsing-rules get <rule-group-id> -o json > template.json
 
# 2. Modify the template (change fields, remove the ID for create operations)

This pattern applies to all create/update operations across all 4 commands. It prevents payload format errors that are the #1 cause of failed attempts.

Parsing Rules Workflow

1. List Existing Rules

2. Get a Template

3. Create New Rule Group

Edit the template for your new service, then:

4. Verify Parsing

Query recent logs to confirm fields are extracted (load cx-telemetry-querying for log querying):

5. Check Usage Limits

Enrichment Workflow

1. List Enrichment Rules

2. Create Custom Enrichment Table (if needed)

table-definition.json must use the v5 JSON shape (inline file content, not multipart file=@...):

For updates, include customEnrichmentId (number) plus the same fields.

3. Add Enrichment Rules

enrichment-rules.json must use requestEnrichments (not enrichments from list output). Each enrichmentType is an object, not a string:

Other types: {"aws": {"resourceType": "ec2"}}, {"suspiciousIp": {}}, {"customEnrichment": {"id": 1}}.

4. Search Custom Table Data

5. Verify Enriched Fields

Query logs on hot storage (FrequentSearch tier) to confirm enriched fields appear. Avoid querying archive for verification - ingestion delays can cause false negatives.

Events2Metrics Workflow

E2M derives Prometheus metrics from log/span events. See references/e2m-schemas.md for the full JSON wire format, enums, and cardinality rules.

How E2M is computed (read this first)

E2M aggregates events as they stream through the real-time ingestion pipeline into metric series (~1-min resolution). It is forward-only — metrics start from the moment the E2M is created; there is no backfill.

All ingested data flows through the pipeline; a TCO policy routes each stream into a tier, and the tier decides what's possible:

The axis is tier / processing level — NOT "Frequent Search vs archive" (Medium is archive and E2M works on it). Do not tell users to "point E2M at archive instead of Frequent Search" — that is incorrect.

1. Design the metric

Choose logs2metrics vs spans2metrics, the source field(s) + aggregations, and labels (with cardinality in mind — see references/e2m-schemas.md). To scope the E2M to a dataset, set the optional dataSource field to "<dataspace>/<dataset>"; this requires the account feature e2m_dataset_source_enabled (otherwise the API rejects it with "dataSource is not enabled for this company"). Omit it for the standard logs/spans stream.

2. Size it: check limits & cardinality

The labels-cardinality endpoint is a draft forecast — given proposed labels + query it returns the per-day distinct-permutation count over the last 7 days, so you can size a design before creating it. But cx e2m labels-cardinality currently takes no arguments, so it sends no draft and returns an empty list (a CLI gap — it can't forecast yet). Until that's wired up, forecast via the UI or estimate permutations manually (product of distinct label values) and set permutationsLimit. Never use high-cardinality fields (IDs, raw URLs, IPs) as labels. Note the forecast only sees Frequent-Search (High-tier) data.

3. Template from an existing definition

Only cx e2m get returns the full payload ({"e2m": {...}}); list prints a summary. Extract .e2m and drop read-only fields:

4. Create the E2M

5. Verify the metric

Confirm series are being produced (load cx-telemetry-querying for metrics querying):

Troubleshooting: E2M produces no metric series

1. Check the source data's TCO tier — if it's routed to Low/compliance (or blocked), E2M cannot run. Fix with a TCO change (cx tco list / cx-cost-optimization), not an E2M change.
2. Verify the query matches streaming data — run the E2M's lucene filter as a live cx logs/cx spans query and confirm it returns recent results. Note cx logs queries Frequent-Search (High-tier) by default; for a Medium-tier (archive) source add --tier archive, since the data won't appear in a default Frequent-Search query even though E2M still produces series.
3. Remember it's forward-only — no series exist for data ingested before the E2M was created.

Cost optimization: convert High-tier logs to metrics

When the aggregated/metric view is what the customer most cares about, convert High-tier logs → metrics, then downgrade the raw logs High → Medium. Medium still supports E2M/alerts/dashboards and costs less (S3 archive, no hot storage) — you keep cheap, detailed metrics while dropping expensive Frequent-Search retention.

1. Find high-volume High-tier sources: cx usage summary / cx tco list (see cx-cost-optimization).
2. Confirm which fields drive dashboards/alerts (see cx-telemetry-querying).
3. Build + verify the E2M first (steps above).
4. Then change the TCO policy to move the raw logs High → Medium. Keep data on High or Medium (both support E2M); do not drop it to Low/compliance if metrics or alerts are still needed.

Recording Rules Workflow

1. List Existing Recording Rules

2. Get a Template

3. Create Recording Rule Group

4. Verify with PromQL

Confirm the precomputed metric is available (load cx-telemetry-querying for metrics querying):

Key Principles

• Always template from existing - cx <command> get <id> -o json > template.json before any create
• Verify after create - query logs/metrics to confirm the pipeline change took effect
• Use -o json - all payload inspection and creation should use JSON output
• Check limits first - cx parsing-rules usage-limits and cx e2m limits before creating to avoid hitting caps
• Bulk operations - use cx parsing-rules bulk-delete --ids for cleanup, not individual deletes

Additional Resources

Reference Files

• references/e2m-schemas.md - Complete Events2Metrics JSON wire format: type/aggType enum values, logsQuery/spansQuery filters, metric labels & fields, the TCO-tier compute model, cardinality/permutations sizing, and gotchas

Related Skills

• cx-telemetry-querying - discover what data is available before configuring pipeline, and verify parsing results, enriched fields, and E2M metric series via log/metrics queries
• cx-cost-optimization - find high-volume High-tier sources worth converting to metrics, and move the raw logs High→Medium (TCO) after the E2M is verified