Troubleshooting

If something isn’t working, read logs first. Lucille logs are detailed and will usually identify where the problem is coming from. See Log Analysis for a guide to reading Lucille logs.

Debugging Failed Documents

The run summary reports how many documents failed, but not which ones or why. Here’s the workflow for investigating failures.

Step 1: Find the failed document IDs and reasons

Search the logs for failure messages. These are logged at ERROR level by the DocLogger. All document failures use a standardized "Document FAILED" prefix, so you can find every failure in one pass and then drill into specific types:

# All document failures (any cause)
grep "Document FAILED" lucille.log

# Pipeline failures (a stage threw StageException)
grep "Document FAILED during pipeline processing:" lucille.log

# Indexer failures (search backend rejected the document)
grep "Document FAILED during indexing:" lucille.log

# Poison pills (distributed mode — exceeded worker.maxRetries)
grep "Document FAILED: retry count exceeded" lucille.log

Each pipeline failure is followed by a stack trace identifying the stage and exception. Each indexer failure includes the backend’s rejection reason. See Which documents failed? for more detail.

Step 2: Understand the failure reason

Common causes:

If the reason is clear from the error message (e.g., “field X is missing”), fix the pipeline config or source data and re-run.

Step 3: Inspect the document’s contents

If you need to see what fields and values the document had at the point of failure:

Add a Print stage before the failing stage. Print logs document contents to a file or stdout. Use conditions to limit output to the specific document:

{
  class: "com.kmwllc.lucille.stage.Print"
  conditions: [{ fields: ["id"], values: ["the-failing-doc-id"] }]
}

Re-run with a CSV indexer. Swap your indexer to CSV and re-run. Documents that would have been rejected by the real backend are written to the CSV file where you can inspect their fields. This helps with indexer rejections but not pipeline failures (the document never reaches the indexer if a stage throws).

indexer { type: "CSV" }
csv { path: "./debug-output.csv", columns: ["id", "title", "problematic_field"] }

Re-run in test mode (Java). Use Runner.runInTestMode() with the same config and source data. After the run, inspect documents programmatically:

RunResult result = Runner.runInTestMode(config);
List<Document> docs = result.getDocsSentForIndexing("my-connector");
Document problem = docs.stream()
    .filter(d -> d.getId().equals("the-failing-doc-id"))
    .findFirst().orElse(null);

Consume the Kafka fail topic (distributed mode). Documents that exceeded worker.maxRetries are sent to the {pipeline}_fail topic with their full serialized content. Consume the topic to inspect the document.

Step 4: Fix and verify

Once you understand the cause:

• Missing or malformed field — add a stage earlier in the pipeline to clean or populate the field, or add conditions so the failing stage skips documents without the required data.
• Index mapping mismatch — update the index mapping to accept the field type, or add a stage to convert the field to the expected type.
• Stage bug — fix the stage code and add a test case for the problematic input.

Re-run with the fix and confirm the failure count drops to zero in the run summary.

Build & Java Environment Issues

Java Command Not Found / Wrong Version

Symptoms:

• java: command not found.
• Lucille fails with version errors.

Fix:

• Ensure Java 17+ is installed and on PATH.
• Ensure JAVA_HOME points to a JDK (not a JRE).

java -version
echo $JAVA_HOME

Example output:

java -version

java version "22.0.1" 2024-04-16
Java(TM) SE Runtime Environment (build 22.0.1+8-16)
Java HotSpot(TM) 64-Bit Server VM (build 22.0.1+8-16, mixed mode, sharing)

echo $JAVA_HOME

/Library/Java/JavaVirtualMachines/jdk-22.jdk/Contents/Home

If unset, refer to the installation guide.

Configuration Issues

Missing or Misspelled Keys

Symptoms:

• Configuration does not contain key “name”.
• Stage/Indexer throws for missing required property.

Fix:

• Compare with component SPEC docs and ensure required fields exist and are correctly cased.

Invalid Types or List/Map Shapes

Symptoms:

• Expected NUMBER got STRING.

Fix:

• Match the SPEC type exactly and convert scalars to lists/maps when required.

Kafka / Messaging

Cannot Connect to Kafka

Symptoms:

• Timed out waiting for connection from Kafka.
• Connection to Kafka could not be established.

Fix:

• Verify your kafka.bootstrapServers in your config.
• Check Kafka connectivity to ensure it is reachable.

Solr / Elasticsearch / OpenSearch Connectivity

Cannot Connect to Solr / Elasticsearch / OpenSearch

Symptoms:

• Failed to talk to the search cluster.
• Connection refused or host not found.

Fix:

• Ensure that the URL/port in your config is correct.
• Ensure that the service is reachable from the machine running Lucille.

Certificate / Authorization Problems

Symptoms:

• Handshake failure, certificate not trusted, or unauthorized.

Fix:

• Test connection with auth/TLS disabled for debugging.
• Verify that credentials are correct.

Schema / Mapping mismatches

Symptoms:

• Bulk index partially fails and some documents are rejected.
• Mapping or parsing exceptions.

Fix:

• Read the exact field and reason in the error details and fix that field in your pipeline or mapping.

Timeouts

Lucille has a default timeout specified in runner.connectorTimeout of 24 hours. You can override this timeout in the configuration file. This may be necessary in the case of very large or long-running jobs that may take more than 24 hours to complete.