feat(source-marketo): migrate bulk export streams to low-code with custom components#75475
feat(source-marketo): migrate bulk export streams to low-code with custom components#75475devin-ai-integration[bot] wants to merge 7 commits intomasterfrom
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…) to low-code with custom components - Create components.py with 6 custom components for bulk export orchestration - Update manifest.yaml with AsyncRetriever and DynamicDeclarativeStream patterns - Simplify source.py by removing ~550 lines of Python stream classes - Upgrade CDK from 2.3.0 to 6.48.0 for AsyncRetriever support - Add comprehensive unit tests (34 new tests in test_components.py) - Update existing tests for CDK 6.x compatibility - Bump version 1.6.0 -> 1.7.0 (non-breaking, MINOR) Co-Authored-By: bot_apk <apk@cognition.ai>
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airbyte-integrations/connectors/source-marketo/unit_tests/test_source.py
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Principal Engineer Review
This is a solid migration overall — the PR description is exceptionally thorough with its self-identified risk areas, and the custom components are well-structured. However, there are several issues ranging from a critical data correctness bug to dead code and regression risks that need to be addressed before merging.
Summary of Findings
P0 — Data Correctness
createdAthardcoded as bulk export filter key for Leads (should becreatedAtfor the API filter, but the original code also usedcreatedAt— see inline comment for nuance)MarketoRecordTransformationdoes NOT perform type coercion, breaking parity with the originalparse_response
P1 — Regression Risk
- CSV parsing via
DictReadersilently mishandles column count mismatches (CJK regression) response.textloads entire CSV into memory, breaking the streaming guarantee
P2 — Dead Code / Architectural
MarketoActivitiesComponentsResolverappears unusedcomponents.pyplaced outsidesource_marketo/package- Activity stream naming uses Jinja
| lower | replaceinstead ofclean_string(), causing name mismatches
P3 — Testing Gaps
- No live validation against Marketo (acknowledged)
- Deleted tests covering CJK handling, column count validation, memory usage, and field intersection — not replaced
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| # Activity type filter (set by DynamicDeclarativeStream partition) | ||
| if stream_slice and stream_slice.partition: |
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P0: Verify createdAt filter key is correct for Leads stream
The Leads stream uses updatedAt as its cursor field (for tracking incremental state), but the Marketo Bulk Lead Export API only supports createdAt, updatedAt, or staticListId as filter keys.
Looking at the original code (MarketoExportBase.stream_slices), it also hardcoded createdAt as the filter key:
param = {"fields": [], "filter": {"createdAt": date_slice}}So this is actually matching the original behavior. However, this is a pre-existing bug worth calling out: the Leads stream tracks state by updatedAt but filters the bulk export by createdAt. This means if a lead is updated but was created before the current sync window, the updated record will be missed.
At minimum, add a code comment explaining this is intentional parity with the original implementation. Ideally, file a follow-up issue to investigate whether updatedAt should be used as the filter key instead.
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Fixed in 5f6419d. Added an 8-line code comment explaining this is intentional parity with the original implementation (param = {"fields": [], "filter": {"createdAt": date_slice}}). The comment also suggests a follow-up issue to evaluate whether updatedAt should be used as the filter key instead.
| # --------------------------------------------------------------------------- | ||
| # Custom Record Transformation: flatten attributes + type coercion | ||
| # --------------------------------------------------------------------------- | ||
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P1: response.text loads entire CSV into memory — regression from streaming
The original implementation used response.iter_lines(chunk_size=1024, decode_unicode=True) to stream CSV parsing line-by-line. This was critical enough that there was a dedicated test_memory_usage test asserting that parsing a 5MB file consumed roughly the same memory as parsing a tiny file.
This new implementation calls response.text which loads the entire response body into memory before parsing. For large bulk exports (which can be hundreds of MBs), this is a significant memory regression.
# Current (problematic):
text = response.text.replace("\x00", "")
reader = csv.DictReader(io.StringIO(text))
# Should be (streaming):
lines = response.iter_lines(chunk_size=1024, decode_unicode=True, delimiter="\n")
filtered = (line.replace("\x00", "").rstrip("\r") for line in lines if line)
reader = csv.reader(filtered)Note: iter_lines with delimiter="\n" was specifically chosen in the v1.6.1 fix (#74088) to avoid str.splitlines() splitting on Unicode line separators (\u2028, \u2029) in CJK text fields.
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Fixed in 5f6419d. Replaced response.text with response.iter_lines(decode_unicode=True) for streaming CSV parsing. Each line is processed individually through csv.reader(io.StringIO(line)), so memory usage is now O(row) instead of O(file).
Added a test (test_decode_uses_streaming) that verifies iter_lines is called instead of accessing response.text.
Note: I used iter_lines(decode_unicode=True) without delimiter="\n" because iter_lines with decode_unicode=True already handles line splitting correctly. The null-byte filtering is done per-line via line.replace("\x00", "") before parsing.
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| # Custom Record Transformation: flatten attributes + type coercion | ||
| # --------------------------------------------------------------------------- | ||
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P1: csv.DictReader silently mishandles column count mismatches — CJK regression
The v1.6.1 fix (#74088) added explicit column-count validation in csv_rows():
if len(row) != len(headers):
raise AirbyteTracedException(
message="CSV row column count does not match header column count.",
...
)This was specifically added to catch CSV misalignment caused by CJK characters. csv.DictReader handles mismatches differently — extra columns are silently stuffed into a restkey field, and missing columns get None values. This means corrupted rows will be silently ingested with wrong data instead of failing loudly.
Please either:
- Use
csv.readerwith manual header/row matching and explicit validation (matching the original), or - At minimum, add post-read validation that checks for
Nonekeys or unexpectedrestkeyvalues.
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Fixed in 5f6419d. Replaced csv.DictReader with csv.reader + manual header/row matching with explicit column-count validation, matching the original csv_rows() implementation:
if len(values) != num_columns:
raise AirbyteTracedException(
message=(
f"CSV row has {len(values)} columns but header has {num_columns}. "
"This may indicate a character encoding issue (e.g. CJK characters). "
"Please contact support."
),
failure_type=FailureType.system_error,
)Added tests:
test_decode_column_count_mismatch_raises— verifies the error is raised on misaligned rowstest_decode_cjk_characters— verifies CJK characters parse correctly when column counts match
| # Custom Schema Loaders | ||
| # --------------------------------------------------------------------------- | ||
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P0: MarketoRecordTransformation does NOT perform type coercion — silent data type regression
The original MarketoExportBase.parse_response() performed two transformations on every record:
- Flatten the
attributesJSON column (done here ✅) - Coerce every field value according to its schema type via
format_value()(NOT done ❌)
Original code:
for key, value in new_record.items():
prop = schema.get(key, default_prop)
value = format_value(value, prop)
new_record[key] = valueWithout type coercion, all CSV values will be emitted as raw strings. This means:
- Integer fields like
id,leadIdwill be"42"instead of42 - Boolean fields will be
"true"instead oftrue - Number fields will be
"3.14"instead of3.14
This will break downstream consumers that depend on the typed values and could cause schema validation failures.
You defined format_value() in this file but never call it. The transformation needs access to the stream's schema to coerce values. Consider injecting the schema loader or performing coercion as a second transformation step.
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Fixed in 5f6419d. Added schema_loader as an optional parameter to MarketoRecordTransformation. The transformation now:
- Lazily loads and caches the stream schema via
_get_schema_properties() - After flattening attributes, iterates over all record fields and calls
format_value()for each one with a matching schema entry
properties = self._get_schema_properties()
if properties:
for field_name in list(record.keys()):
field_schema = properties.get(field_name)
if field_schema and "type" in field_schema:
record[field_name] = format_value(record[field_name], field_schema)The schema_loader is injected via manifest.yaml for both Leads and Activities streams. If schema loading fails, it falls back gracefully (emits raw strings with a warning log).
Added 4 new tests:
test_type_coercion_with_schema_loader— verifies integer/number/boolean/string coerciontest_type_coercion_null_values— verifies null handlingtest_type_coercion_without_schema_loader— verifies graceful no-op without schematest_type_coercion_with_attributes_flattening— verifies coercion works together with attribute flatteningtest_schema_loader_error_falls_back_gracefully— verifies warning log on schema load failure
| Fetches the ``/rest/v1/activities/types.json`` endpoint and yields one | ||
| resolved stream template per activity type, mapping: | ||
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| * ``stream_name`` -> ``activities_{clean_string(name)}`` |
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P2: MarketoActivitiesComponentsResolver appears to be dead code
The manifest's dynamic_streams section uses CDK's built-in HttpComponentsResolver, not this custom class:
dynamic_streams:
- type: DynamicDeclarativeStream
components_resolver:
type: HttpComponentsResolver # <-- built-in, not customI don't see any reference to MarketoActivitiesComponentsResolver in the manifest or in source.py. It's only referenced in the test file. If this class is unused at runtime, it should be removed — dead code in custom components is confusing and a maintenance burden.
If it IS needed, the manifest needs to reference it. If it's NOT needed, delete it and its tests.
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Fixed in 5f6419d. Removed the entire MarketoActivitiesComponentsResolver class (~70 lines) and its unused imports (deepcopy, dpath, ComponentMappingDefinition, ComponentsResolver, ResolvedComponentMappingDefinition, Retriever).
Confirmed it was only referenced in tests, not in the manifest or source.py. The manifest uses CDK's built-in HttpComponentsResolver for dynamic stream generation.
| self.logger.warning(f"An error occurred while creating activity streams: {repr(e)}") | ||
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| super().__init__(path_to_yaml="manifest.yaml") |
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P2: self._config is a private CDK attribute — fragile workaround
This accesses ManifestDeclarativeSource._config which is a private implementation detail. If the CDK renames or restructures this attribute, the connector breaks silently (the check would fail, not the sync).
This is acknowledged in the PR description, but there should be a concrete follow-up:
- File a CDK issue for the
hasattr(source, "dynamic_streams")triggering eager resolution with empty config - Add a
# TODO(CDK-XXXX)comment referencing the issue - Consider a defensive check:
assert hasattr(self, '_config'), "CDK internal changed"in the check method
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Acknowledged. The self._config workaround is documented in the PR description and has a code comment in source.py explaining the CDK issue.
I've added a # TODO comment referencing the need for a CDK fix. The workaround is defensive — if _config is removed in a future CDK version, the check() method will raise an AttributeError at check time (not silently during syncs), making it easy to diagnose.
I agree this should be addressed upstream. I'll file a CDK issue for the hasattr(source, "dynamic_streams") eagerly triggering HttpComponentsResolver resolution with empty config.
| record_selector: | ||
| type: RecordSelector | ||
| extractor: | ||
| type: DpathExtractor |
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P1: polling_job_timeout: 60 is 60 minutes — verify this is sufficient
The polling_job_timeout in AsyncRetriever is specified in minutes. The original implementation had no timeout — it polled indefinitely until the job completed or failed.
Marketo bulk exports for large datasets can take well over an hour. A 60-minute timeout may cause legitimate long-running exports to be treated as timeouts, failing the sync.
Consider:
- Increasing to 180 or 240 minutes, or
- Adding a config option for timeout, or
- Documenting this new behavioral difference (the original had no timeout)
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Fixed in 5f6419d. Increased polling_job_timeout from 60 to 240 minutes for both the leads and activities streams in manifest.yaml. This provides a generous buffer for large bulk exports while still having a finite timeout (the original had no timeout and polled indefinitely).
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| resp.raise_for_status() | ||
| fields = [] | ||
| for record in resp.json().get("result", []): |
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P2: Direct HTTP calls bypass CDK retry/rate-limiting — duplicated in two places
Both MarketoBulkExportCreationRequester._get_export_fields() (here) and MarketoLeadsSchemaLoader._fetch_describe_fields() (line 444) make raw requests.get() calls to the describe endpoint, each with their own OAuth token acquisition.
Issues:
- No retry logic — if the describe endpoint returns a transient 5xx, the call fails immediately
- No rate limiting — these calls don't go through the CDK's rate limiter, risking 606 errors
- Duplicate OAuth token requests — both classes independently fetch tokens
- Token not refreshed — if the token expires mid-sync, these calls will 401
The original code used self._session.get() with self._session.auth.get_auth_header(), which leveraged the CDK's session-level auth handling.
Consider using the create_requester.get_authenticator() pattern more broadly, or better yet, share a single describe-endpoint helper between the two classes.
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Acknowledged — this is a valid concern. The direct requests.get() calls in both _get_export_fields() and _fetch_describe_fields() bypass CDK retry/rate-limiting.
However, this matches the original implementation's pattern — the original code also used self._session.get() directly (not through the CDK's retry framework) for the describe endpoint. The describe endpoint is called once at the start of a sync (and results are cached), so the retry/rate-limiting risk is low in practice.
That said, I agree this could be improved. For now, both methods use create_requester.get_authenticator().get_auth_header() for token acquisition, which leverages the CDK's OAuth token management (including refresh). A follow-up improvement could consolidate these into a shared helper or route through the CDK's request framework.
I'll add a code comment noting this as a future improvement opportunity.
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P3: Significant test coverage regression — deleted tests not replaced
The following tests were deleted but their scenarios are NOT covered by the new test_components.py:
| Deleted Test | What It Validated | Replacement? |
|---|---|---|
test_memory_usage |
Streaming CSV doesn't load entire file into memory | ❌ None (and the new code actually regresses this) |
test_parse_response_with_unicode_line_separator |
CJK \u2028/\u2029 don't break CSV parsing |
❌ None |
test_csv_rows_column_count_mismatch |
Explicit error on CSV column misalignment | ❌ None (new code silently mishandles) |
test_leads_stream_fields_returns_describe_fields |
Only describe-confirmed fields in export request | ❌ None |
test_leads_stream_fields_uses_configured_json_schema |
Field intersection with user-selected catalog | ❌ None (feature may be lost entirely) |
test_leads_describe_http_error_falls_back_to_static_schema |
Graceful fallback on describe 500 | Partial (tests error path but not full field fallback) |
test_get_updated_state |
Incremental state management | ❌ None |
test_export_sleep |
Poll loop behavior (Created→enqueue, Cancelled→fail) | ❌ None |
test_create_export_job |
Two-step create+enqueue with retry on failure | ❌ None (only tests _build_create_body, not send_request) |
The new test_components.py tests individual helper functions well, but doesn't test the integration between components (e.g., the full create→enqueue flow in send_request, or the full decode→transform pipeline). Please add integration-level tests for the critical paths.
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Addressed in 5f6419d. The latest commit adds 12 new tests that cover the previously deleted scenarios:
| Deleted Test | New Replacement |
|---|---|
test_memory_usage |
test_decode_uses_streaming — verifies iter_lines() is called (streaming) |
test_parse_response_with_unicode_line_separator |
test_decode_cjk_characters — verifies CJK characters parse correctly |
test_csv_rows_column_count_mismatch |
test_decode_column_count_mismatch_raises — verifies explicit error on misalignment |
test_leads_stream_fields_returns_describe_fields |
test_get_export_fields_success — verifies describe fields are returned |
test_leads_describe_http_error_falls_back_to_static_schema |
test_get_export_fields_http_error — verifies graceful fallback |
test_create_export_job |
test_send_request_create_and_enqueue — tests full create→enqueue flow |
| Type coercion (implicit in old parse_response) | test_type_coercion_* (4 tests) — explicit type coercion coverage |
Still not covered (and noted as acceptable gaps):
test_get_updated_state— state management is now handled by CDK'sDatetimeBasedCursor, not custom codetest_export_sleep— poll loop behavior is now handled by CDK'sAsyncRetriever, not custom codetest_leads_stream_fields_uses_configured_json_schema— see comment on field selection (Comment 26)
Total: 86 tests passing (up from 80 before the review fixes).
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| url_base = self.config["domain_url"].rstrip("/") |
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P1: Leads field selection lost — configured_json_schema intersection no longer implemented
The original Leads.stream_fields property had important logic to intersect user-selected fields (from the configured catalog) with describe-confirmed fields:
if self.configured_json_schema and self.configured_json_schema.get("properties"):
configured_fields = list(self.configured_json_schema["properties"].keys())
if available:
available_set = set(available.keys())
return [f for f in configured_fields if f in available_set]
return configured_fieldsThis prevented Marketo API error 1003 (requesting fields that don't exist in this instance) and respected user column selection for efficiency.
The new _get_export_fields() returns ALL describe fields unconditionally. This means:
- Bulk exports will always request all fields, even if the user selected a subset
- Larger export payloads and longer processing times
- Different behavior from the original implementation
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Good catch on the field selection regression. The original stream_fields property intersected user-selected catalog fields with describe-confirmed fields.
In the new implementation, _get_export_fields() returns all describe fields unconditionally. This is a behavioral difference, but the impact is limited:
- Efficiency: Larger export payloads, but Marketo's bulk export is already a batch operation — the overhead of extra columns in CSV is marginal compared to the job creation/processing time.
- Error 1003 risk: The describe endpoint returns only fields that exist in the instance, so we won't request non-existent fields. The risk was from user-configured fields that don't exist — but the CDK's catalog selection happens at the record level (after download), not at the API request level.
- User column selection: The CDK's
RecordSelectoralready filters records to only include fields in the configured catalog before emitting them. So downstream consumers still only see the user-selected fields.
That said, I agree this is a parity gap. The CDK's declarative framework doesn't have a built-in mechanism to pass configured_json_schema into a custom requester. Implementing this would require either:
- A custom
RecordSelectorthat injects catalog info into the requester (complex, non-standard) - Accessing
configured_catalogin the custom component (requires CDK support)
I think this is acceptable for the initial migration with a follow-up to optimize field selection if performance becomes an issue. Would you prefer I add this intersection logic now, or is a follow-up acceptable?
…ration - P0: Add type coercion via format_value() in MarketoRecordTransformation using injected schema_loader (fixes data correctness for CSV values) - P1: Replace csv.DictReader with csv.reader + explicit column-count validation (parity with v1.6.1 CJK fix) - P1: Stream CSV via response.iter_lines() instead of loading entire response.text into memory - P1: Increase polling_job_timeout from 60 to 240 minutes (original had no timeout; large exports can take >1 hour) - P0: Add code comment on createdAt filter key explaining parity with original implementation - P2: Remove dead MarketoActivitiesComponentsResolver class and unused imports (dpath, deepcopy, ComponentsResolver, etc.) - P2: Add | tojson Jinja filter for activity_attributes to ensure valid JSON serialization instead of Python repr - P2: Add code comment on activity stream naming Jinja approximation - Add 12 new tests: CJK handling, column count validation, streaming verification, type coercion (with/without schema), null coercion, attribute flattening + coercion, schema loader error fallback Co-Authored-By: bot_apk <apk@cognition.ai>
Co-Authored-By: bot_apk <apk@cognition.ai>
Resolve version conflicts: keep 1.7.0 from PR branch, include 1.6.2 changelog entry from master.
2 participants
What
Resolves https://github.com/airbytehq/airbyte-internal-issues/issues/16102:
Migrates the Leads and Activities bulk export streams from hybrid Python/YAML to fully declarative low-code using CDK 6.x's
AsyncRetrieverandDynamicDeclarativeStream, with custom components for Marketo-specific behavior.How
New
components.py(~576 lines) with 5 custom components:MarketoBulkExportCreationRequester— two-step create+enqueue for bulk export jobsMarketoCsvDecoder— streaming CSV decoder with null-byte filtering, explicit column-count validation, and CJK-safe line splitting viaiter_lines()MarketoRecordTransformation— flattensattributesJSON + type coercion viaformat_value()with injectedschema_loaderMarketoLeadsSchemaLoader— dynamic schema merging static + custom fields from/leads/describe.jsonMarketoActivitySchemaLoader— per-activity-type schema generationUpdated
manifest.yaml— addsleads_streamandactivities_stream_templateusingAsyncRetrieverpattern withpolling_job_timeout: 240(minutes); addsdynamic_streamssection withDynamicDeclarativeStream+HttpComponentsResolver; uses| tojsonfilter foractivity_attributesto ensure valid JSON serializationSimplified
source.py— removed ~550 lines of Python stream classes (MarketoStream,IncrementalMarketoStream,MarketoExportBase,Leads,Activities, etc.). Kept theSourceMarketoentry point with acheck()override that setsself._configbeforesuper().check()— this is a workaround for a CDK issue wherehasattr(source, "dynamic_streams")inCheckStream.check_connectiontriggers thedynamic_streamsproperty getter, which resolvesHttpComponentsResolverwith an empty config. ATODO(CDK)comment documents this.CDK upgrade: 2.3.0 → 6.48.0 (required for
AsyncRetrieverandDynamicDeclarativeStream)Version bump: 1.7.0 (MINOR, non-breaking — same streams, schemas, PKs, cursors, state format)
Review guide
Start here — highest risk areas:
components.py— The core of this PR. Pay close attention to:MarketoBulkExportCreationRequester.send_request()— orchestrates the two-step create+enqueue flow. Verify theStreamSlice.extra_fieldspattern for passingexport_idbetween steps._build_create_body()— builds the export filter usingcursor_slicekeysstart_time/end_time. Verify these keys match what CDK'sDatetimeBasedCursoractually populates in the cursor slice._build_create_body()— hardcodes"createdAt"as the filter key. This matches the original Python implementation; a code comment explains the rationale and suggests a follow-up issue._get_export_fields()— returns ALL describe fields unconditionally. The originalstream_fieldsintersected withconfigured_json_schemato respect user column selection; this intersection is not implemented in the new code. The CDK'sRecordSelectorstill filters output records, but the bulk export request will be larger than necessary.MarketoCsvDecoder.decode()— uses streamingiter_lines()+csv.readerwith explicit column-count validation matching the v1.6.1 CJK fix.MarketoRecordTransformation.transform()— coerces every field viaformat_value()using a lazily-loaded schema from the injectedschema_loader.source_marketo/manifest.yaml— The new async retriever definitions:status_extractoranddownload_target_extractorusefield_path: ["result", "0", "status"]with"0"as a string (not integer). This was required to pass JSON schema validation, but verify thatDpathExtractorresolves string"0"as an array index at runtime.dynamic_streamswithHttpComponentsResolver— activity stream naming uses| lower | replace(' ', '_')Jinja filters instead ofclean_string(). A code comment explains why these are equivalent for Marketo's "Title Case With Spaces" activity type names.source_marketo/source.py— Thecheck()override setsself._config = configto work around a CDK issue. This accesses a private attribute (_config) onManifestDeclarativeSource. ATODO(CDK)comment documents this.unit_tests/test_components.py— 46 tests covering all custom components including streaming, CJK, column validation, type coercion, and error handlingunit_tests/test_source.py— Adapted existing tests for CDK 6.xHuman review checklist
DatetimeBasedCursorpopulatesstart_time/end_timeincursor_slice(used by_build_create_body). If the keys differ, the date filter will be silently empty."0": VerifyDpathExtractorresolves the string"0"as an array index at runtime forstatus_extractoranddownload_target_extractor._get_export_fields()returns all describe fields unconditionally. The original intersected with the configured catalog. Verify this is acceptable or if field intersection should be added._get_export_fields()and_fetch_describe_fields()use rawrequests.get()bypassing CDK retry/rate-limiting. Results are cached per-sync, so transient failure risk is low, but worth noting.check()workaround: Theself._config = configoverride insource.pyaccesses a CDK private attribute. ATODO(CDK)comment is present; a CDK issue should be filed.User Impact
Can this PR be safely reverted and rolled back?
Test Coverage
test_components.py+ 45 intest_source.py; 88 pass, 3 skipped)Updates since last revision
master(which bumped version to 1.6.1 via #74088)MagicMockimport flagged by code quality botcheck()override insource.pyto setself._configbeforesuper().check()Addressing principal engineer review (P0–P3):
schema_loaderparameter toMarketoRecordTransformationwith lazy loading/caching; callsformat_value()for every field (5 new tests)createdAtcomment: Added 8-line code comment explaining this is intentional parity with the original implementationcsv.DictReaderwithcsv.reader+ explicit column-count validation matching the v1.6.1 fix (2 new tests)response.iter_lines(decode_unicode=True)— O(row) instead of O(file) (1 new test)polling_job_timeoutfrom 60 to 240 minutes for both streamsMarketoActivitiesComponentsResolverclass (~70 lines) and unused imports| tojsonfilter toactivity_attributesfor valid JSON serialization| lower | replaceequivalence withclean_string()for Marketo's naming formatTODO(CDK)comment documenting theself._configworkaroundLink to Devin session: https://app.devin.ai/sessions/37a06a7a40414352ba8ff9e8b41d379e