fix: accept GeoJSON strings for Edm.GeographyPoint in AzureSearchWriter by chon3806 · Pull Request #2556 · microsoft/SynapseML

chon3806 · 2026-04-25T19:18:23Z

Summary

AzureSearchWriter failed with 400 Bad Request when a user provided an Edm.GeographyPoint value as a StringType column containing GeoJSON. The string was JSON-escaped during serialization, so Azure AI Search received a quoted string instead of a spatial object:

"location":"{\"type\":\"Point\",\"coordinates\":[...]}"   // rejected
"location":{"type":"Point","coordinates":[...]}           // expected

Fix

Added convertGeographyPointToStruct in AzureSearch.scala, mirroring the existing convertDateTimeToISO8601 handling. For every top-level index field declared as Edm.GeographyPoint, if the corresponding DataFrame column is a StringType, it is parsed via from_json (with mode = FAILFAST) into the canonical struct<type: string, coordinates: array<double>> before checkSchemaParity and to_json. Struct-based inputs continue to work unchanged.

FAILFAST was chosen so malformed GeoJSON surfaces as a loud SparkException at row materialization rather than silently nulling out the field (the default PERMISSIVE behavior of from_json). This is consistent with AzureSearchWriter.write defaulting to fatalErrors = true.

Tests

Added in SearchWriterSuitePart2.scala:

End-to-end (live Azure AI Search): Handle GeoJSON GeographyPoint fields supplied as strings — writes documents with a StringType location column and verifies the index ingests them (any 400 would throw via fatalErrors = true).
Unit (no network):
- convertGeographyPointToStruct parses GeoJSON strings into structs
- convertGeographyPointToStruct leaves struct columns untouched
- convertGeographyPointToStruct fails fast on malformed GeoJSON instead of silently nulling

Compatibility

Private helper only (private[ml]) — no public API changes.
Existing struct-based GeographyPoint usage path is unaffected.
No new dependencies.

Review feedback addressed

All 3 Copilot review comments have been addressed in 95a7ae1:

Switched from_json to FAILFAST mode so malformed GeoJSON is not silently nulled.
Clarified Scaladoc that conversion applies to top-level fields only (parity with convertDateTimeToISO8601).
Added unit tests proving the parse / passthrough / fail-fast behavior, since the repo has no public per-document fetch helper for richer e2e content assertions.

…er (microsoft#2420) Azure AI Search expects spatial values as GeoJSON objects, but when users supplied a StringType column the writer JSON-escaped the entire string and the service rejected the request with HTTP 400. Convert string GeographyPoint columns into the canonical struct<type, coordinates> shape via from_json before serialization, mirroring the existing Edm.DateTimeOffset handling. Existing struct-based input is unchanged.

github-actions · 2026-04-25T19:18:33Z

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Copilot

Pull request overview

This PR fixes Azure AI Search ingestion failures when AzureSearchWriter is given Edm.GeographyPoint values in StringType columns containing GeoJSON by parsing those strings into the expected struct shape before JSON serialization.

Changes:

Added a private normalization step to parse StringType GeoJSON into struct<type:string, coordinates:array<double>> for Edm.GeographyPoint index fields.
Wired the new normalization into AzureSearchWriter.prepareDF before schema parity checks and request serialization.
Added an end-to-end Scala test covering GeographyPoint values supplied as strings.

Reviewed changes

Copilot reviewed 2 out of 2 changed files in this pull request and generated 3 comments.

File	Description
cognitive/src/main/scala/com/microsoft/azure/synapse/ml/services/search/AzureSearch.scala	Adds GeoJSON string → struct conversion for `Edm.GeographyPoint` fields and applies it during DF preparation.
cognitive/src/test/scala/com/microsoft/azure/synapse/ml/services/search/split2/SearchWriterSuitePart2.scala	Adds an end-to-end test that writes docs with GeoJSON GeographyPoint stored as strings.

Copilot · 2026-04-25T19:23:24Z

+        currentDF.schema(fieldName).dataType match {
+          case StringType =>
+            currentDF.withColumn(fieldName,
+              when(col(fieldName).isNotNull, from_json(col(fieldName), geoStructType))


from_json defaults to permissive parsing, so malformed/invalid GeoJSON strings will be converted to null silently. That can lead to documents being ingested with a missing location (or unexpected null updates) instead of surfacing an error. Consider using from_json with mode=FAILFAST (or otherwise explicitly validating the parse result) so invalid GeoJSON fails loudly rather than being dropped.

Suggested change

when(col(fieldName).isNotNull, from_json(col(fieldName), geoStructType))

when(

col(fieldName).isNotNull,

from_json(col(fieldName), geoStructType, Map("mode" -> "FAILFAST"))

)

Done in 95a7ae1 -- switched to from_json(..., Map("mode" -> "FAILFAST")) and added a unit test (convertGeographyPointToStruct fails fast on malformed GeoJSON instead of silently nulling) that asserts a SparkException on materialization.

Copilot · 2026-04-25T19:23:24Z

+   * For each field declared as `Edm.GeographyPoint` in the index, if the corresponding
+   * DataFrame column is a `StringType`, parse it into the canonical
+   * `struct<type:string, coordinates:array<double>>` so that downstream `to_json`
+   * emits a proper GeoJSON object. Columns that are already structured are left as-is.
+   *
+   * @param df DataFrame with potential GeographyPoint columns
+   * @param indexJson JSON string containing the index schema
+   * @return DataFrame with string GeographyPoint columns converted to GeoJSON structs
+   */
+  private def convertGeographyPointToStruct(df: DataFrame, indexJson: String): DataFrame = {
+    val geoStructType = StructType(Seq(
+      StructField("type", StringType),
+      StructField("coordinates", ArrayType(DoubleType))
+    ))
+    val geoFields = parseIndexJson(indexJson).fields
+      .filter(_.`type` == "Edm.GeographyPoint")
+      .map(_.name)
+    geoFields.foldLeft(df) { (currentDF, fieldName) =>


The Scaladoc says this runs "for each field declared as Edm.GeographyPoint in the index", but the implementation only inspects parseIndexJson(indexJson).fields (top-level fields) and won’t convert GeographyPoint values nested inside complex types. Either clarify the doc to state it’s top-level only, or extend the traversal to nested fields so behavior matches the comment.

Done in 95a7ae1 -- updated the Scaladoc to state '''top-level''' explicitly and noted that the scope mirrors convertDateTimeToISO8601 (which has the same top-level-only limitation). Extending nested-field traversal felt out-of-scope for this bug fix; happy to do it as a follow-up if preferred.

Copilot · 2026-04-25T19:23:24Z

+    retryWithBackoff(assertSize(in, 2))
+


This test only asserts document count after the write. If GeoJSON parsing were to fail and produce null (or otherwise lose the spatial payload), Azure Search could still ingest the documents and this test would pass. To make the test validate the intended behavior, consider fetching the stored documents (or querying/selecting location) and asserting the location field is present and shaped as a GeoJSON object (e.g., has type and coordinates).

Addressed in 95a7ae1. Two complementary guarantees now make the count check meaningful:

The new FAILFAST parsing mode raises a SparkException on malformed GeoJSON before the request is even built (covered by convertGeographyPointToStruct fails fast on malformed GeoJSON ...).

AzureSearchWriter.write runs with fatalErrors=true by default, so any 400 from the service throws a RuntimeException and fails the test before assertSize is reached.

Combined with the new unit tests that assert the converted struct shape and parsed coordinates directly, a count of 2 is only achievable if the documents were accepted as valid spatial objects. The repo's existing search tests follow the same assertSize-only pattern and there's no helper for fetching individual documents, so I kept the e2e style consistent.

…or GeographyPoint conversion - Replace unicode em-dash with ASCII to avoid encoding/scalastyle surprises. - Replace 'microsoft#2420' in Scaladoc (member-reference syntax) with a full URL. - Make convertGeographyPointToStruct private[ml] so it can be exercised by in-tree unit tests without requiring live Azure Search credentials. - Add two non-network unit tests covering the string->struct rewrite, null preservation, and the no-op path for already-structured columns.

chon3806 · 2026-04-25T19:25:04Z

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@microsoft-github-policy-service agree

- Use Spark's FAILFAST parsing mode in from_json so malformed GeoJSON surfaces an explicit exception instead of being silently coerced to null and shipped to Azure Search. - Clarify Scaladoc to state the conversion is top-level-only (mirroring convertDateTimeToISO8601), and document the FAILFAST behavior. - Add a unit test asserting malformed GeoJSON fails fast at materialization. - Document why the end-to-end test's count assertion is sufficient: with fatalErrors=true (default) any service-side rejection throws, so a passing count proves the documents were accepted as valid spatial objects.

chon3806 · 2026-04-25T19:31:07Z

Hi @BrendanWalsh — friendly nudge on this one when you have a moment. The CLA is signed, the semantic title check passes, and the Copilot review's 3 comments have all been addressed in 95a7ae1. CI hasn't run yet (fork PR — needs a maintainer to approve workflows). Happy to address any further feedback. Thanks!

Copilot AI review requested due to automatic review settings April 25, 2026 19:18

Copilot started reviewing on behalf of chon3806 April 25, 2026 19:18 View session

Copilot AI reviewed Apr 25, 2026

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fix: accept GeoJSON strings for Edm.GeographyPoint in AzureSearchWriter#2556

fix: accept GeoJSON strings for Edm.GeographyPoint in AzureSearchWriter#2556
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Summary

Fix

Tests

Compatibility

Review feedback addressed

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