configuring-filters

Configuring Filters

A filter is a record gate that decides which records continue through the pipeline and which are silently dropped. Filters are configured directly on exports, imports, and flow router branches -- not as standalone resources.

Concerns when configuring a filter:

• Placement -- where in the pipeline the filter runs (output filter on export, input filter on lookup, pre-import filter, branch routing condition)
• Mechanism -- expression-based rules (declarative, no code) vs. script-based (full JavaScript control)
• Expression syntax -- prefix-notation S-expressions with operators, field access (extract), type coercions, and logical combinators (and/or/not)
• Settings references -- dynamic filter values pulled from flow, export, import, or integration settings via settings accessor
• Script alternative -- when expression rules cannot handle the logic (multi-step conditionals, date math, external lookups), use a filter script instead

Four Places Filters Appear

Output filter (export filter)

Applied after records are retrieved from the source system. Records that match continue through the flow; records that do not match are silently dropped. Available on all export types.

Input filter (lookup export inputFilter)

Applied to incoming records before they trigger a lookup API call. Records that do not match skip the lookup step entirely -- they pass through without enrichment. This reduces unnecessary API calls. Only relevant when isLookup: true.

Import filter (import filter)

Applied to records before they are sent to the destination system. Records that do not match are dropped and never submitted to the destination. Useful for conditional writes (e.g., only import records with a non-empty email).

Branch input filter (router branch inputFilter)

Applied per-branch in a flow router to route records to different processing paths. Each branch has its own filter expression. The last branch can omit a filter to serve as a catch-all. Configured on the flow's routers[].branches[] entries, not on exports or imports.

Quick Reference

Filter Type Decision Matrix

Expression vs. Script

Prefer expressions -- they are simpler, faster, and do not require a separate script resource. Use scripts only when expression operators cannot handle the logic.

Operator Quick Reference

Field access: ["extract", "fieldName"] for record fields, ["settings", "flow.settingName"] for configuration values.

Type coercions: ["number", ...], ["string", ...], ["boolean", ...], ["epochtime", ...].

Transformations: ["lowercase", ...], ["uppercase", ...], ["floor", ...], ["ceiling", ...], ["abs", ...].

Schema Index

Related Skills

• configuring-exports > Quick Reference -- export configuration where output filter and input filter are wired
• configuring-imports > Quick Reference -- import configuration where import filter is wired
• writing-scripts > Record-Level Processors -- script-based filter hook signature and data shapes
• building-flows > Flow Topologies -- branching routers with per-branch input filters
• writing-handlebars > Quick Reference -- Handlebars in dynamic filter values

How to Configure a Filter

1. Determine filter placement

Where in the pipeline should filtering happen?

• After export retrieval -- use an output filter on the export (filter)
• Before a lookup call -- use an input filter on the lookup export (inputFilter)
• Before import submission -- use a filter on the import (filter)
• For branch routing -- use branch input filters on the flow router

2. Choose expression vs. script

Can the logic be expressed as field comparisons combined with and/or? Use an expression. Need loops, date math, cross-field calculations, or external API calls? Use a script.

3. Build the expression rules

Expression rules use prefix (S-expression) notation. Every rule is an array where the first element is the operator and remaining elements are operands.

Pattern: Single condition

[operator, [extract, fieldName], value]

Pattern: Multiple conditions (all must match)

[and, [condition1], [condition2], ...]

Pattern: Multiple conditions (any must match)

[or, [condition1], [condition2], ...]

Pattern: Field access with type coercion

[operator, [number, [extract, fieldName]], numericValue]

Pattern: Dynamic value from settings

[equals, [string, [extract, fieldName]], [string, [settings, flow.settingName]]]

4. Set the filter on the resource

Use the CLI set command to add or update a filter on an existing export or import. The set command does GET-modify-PUT automatically.

# Set an expression filter on an export (output filter)

For complex expressions, use JSON input:

5. Wire a script filter (when expressions are insufficient)

Create the script resource first, then reference it:

The script function receives options.record and must return true (process) or false (skip). See writing-scripts > Record-Level Processors for the full function signature.

6. Test the filter

Check numSuccess, numIgnore, and numError on the job. Records dropped by a filter show up in numIgnore, not numError.

CLI Commands

Filters are configured on exports and imports, not as standalone resources. Use export and import CRUD commands.

Pre-Submit Checklist

Before creating or updating a filter, verify:

• filter.type matches the config block -- "expression" requires filter.expression, "script" requires filter.script
• expression.version is "1" -- the only supported version
• Operator names are lowercase -- "notempty", not "isNotEmpty" or "NOTEMPTY"
• extract references valid field names -- field names must match the actual record structure; use dot notation for nested fields (e.g., "customer.email")
• Type coercions are applied where needed -- comparing a string field to a number requires ["number", ["extract", "field"]]
• Script filter returns strict boolean -- true or false, not truthy/falsy values
• Input filter is on a lookup export -- inputFilter only applies when isLookup: true

Gotchas

1. Operators are notempty/empty, not isnotempty/isempty. The spec documents both forms but the platform uses the shorter names. Using isnotempty or isempty will silently fail to match.
2. Filtered records are silently dropped, not errored. There is no error log entry for filtered records. They appear as numIgnore in job stats, which makes debugging "missing records" problems harder. Check the filter first when records disappear without errors.
3. Import filter drops records before submission. Records filtered out on an import never reach the destination -- they are not sent, not rejected. This differs from a server-side rejection which would appear in errors.
4. Input filter skips the step, not the record. On a lookup export, a filtered record skips the lookup but continues through subsequent flow steps. It is not removed from the pipeline.
5. Branch filters use the same expression syntax but live on the flow. Branch inputFilter is configured on routers[].branches[] in the flow, not on the export or import. Use celigo flows set or celigo flows update to modify them.
6. Expression and script filters cannot coexist. Setting filter.type to "script" ignores any expression config, and vice versa. Switching types does not auto-clear the other config block.
7. PUT erases omitted fields. Always GET the export/import first, modify the filter, then PUT the complete object. The set command handles this automatically.
8. settings accessor requires scoped prefix. Use flow.settingName, export.settingName, import.settingName, or integration.settingName -- not bare settingName.
9. String comparison is case-sensitive by default. Use ["lowercase", ["string", ["extract", "field"]]] before comparison to make it case-insensitive. The matches operator also does case-sensitive matching.

Common Errors