Error Handling in Talend Jobs: Patterns That Keep Your Pipelines Running

Last updated: June 2025

Why Error Handling Matters More Than You Think

A Talend job that crashes loudly at 2 AM is annoying. A Talend job that silently drops 40,000 rows and reports "success" is dangerous. We've seen both in production. The loud crash gets fixed by morning. The silent failure? That one gets discovered three weeks later when a finance report doesn't reconcile.

Every production Talend job needs a deliberate error handling strategy -- not the default behavior of "crash and hope someone notices." This guide covers the specific components, patterns, and architecture we use across 50+ production jobs to catch errors early, log them properly, and recover gracefully.

Talend's Error Handling Components

Talend provides five components specifically designed for error management. Each one serves a different purpose, and most production jobs need at least three of them.

tLogCatcher

Catches Java exceptions and component warnings during job execution. It captures the component name, error priority (WARN, ERROR, FATAL), error code, and the full stack trace message. Drop it on your canvas and connect it to a logging flow -- it'll intercept any uncaught exception thrown by any component in the job.

Critical gotcha: tLogCatcher only catches Java-level exceptions. If tDBOutput inserts a row that violates a database constraint, the database returns an error to Talend, but tLogCatcher won't see it unless the component is configured to throw on error. For data-level issues like constraint violations, duplicate keys, or type mismatches, you need reject links -- the red output connector on database and file components.

tStatCatcher

Records runtime statistics for each component: start time, end time, duration, and row count. It doesn't catch errors directly, but it's essential for post-mortem analysis. When something goes wrong at 3 AM, you want to know exactly which component was running and how long it had been processing.

tFlowMeterCatcher

Tracks row counts at connection points between components. This is your data reconciliation tool. If you send 100,000 rows into a tMap and only 85,000 come out, you need to know that -- and tFlowMeterCatcher records it automatically.

tWarn and tDie

tWarn writes a warning message to the log without stopping the job. tDie stops the job immediately with an error code and message. Use tWarn for data quality issues that need attention but aren't fatal. Use tDie when continuing would cause data corruption -- like when a critical lookup table returns zero rows.

Building a Centralized Error Logging Table

Logging errors to the console is fine for development. In production, errors need to go to a database table that you can query, alert on, and trend over time.

CREATE TABLE etl_error_log (
    error_id        BIGINT IDENTITY PRIMARY KEY,
    job_name        VARCHAR(200)  NOT NULL,
    component_name  VARCHAR(200),
    execution_id    VARCHAR(100)  NOT NULL,
    error_timestamp TIMESTAMP     DEFAULT CURRENT_TIMESTAMP,
    error_code      VARCHAR(50),
    error_message   VARCHAR(4000),
    severity        VARCHAR(20)   DEFAULT 'ERROR',
    row_data        VARCHAR(4000),
    source_table    VARCHAR(200),
    target_table    VARCHAR(200),
    row_count       BIGINT        DEFAULT 0
);

Every job writes to this table through a shared sub-job. The execution_id is a UUID generated at job start (use java.util.UUID.randomUUID().toString() in a tJava component) so you can correlate all errors from a single run. The row_data column stores the serialized row that caused the error -- invaluable for debugging data quality issues without re-running the entire job.

Die on Error vs. Continue on Error

Every Talend component with database or file interaction has this setting. Choosing wrong causes real pain.

When to Use Die on Error

• Financial transaction loads where partial data is worse than no data
• Master data updates that other downstream jobs depend on
• Any job where row order matters (parent-child inserts)
• Jobs writing to tables with referential integrity constraints

When to Use Continue on Error

• Log ingestion where dropping a few malformed records is acceptable
• Dimension loads where you want to collect all bad rows for review
• API data pulls where individual record failures shouldn't stop the batch

Common mistake: Setting continue-on-error on every component and never checking the reject output. Your job shows a green "OK" in the monitoring console, but the error log table has 12,000 rejected rows. We've watched teams run like this for months before someone noticed the target table was missing 15% of its data.

Try-Catch Pattern with tRunJob

Talend doesn't have a native try-catch block, but you can build one using tRunJob and the On Component Error trigger. The pattern works like this: your parent job calls child jobs via tRunJob. If the child job fails, the On Component Error trigger fires, routing execution to your error handling flow.

tRunJob_ChildETL
   |
   |-- [On Component OK] --> tJava_LogSuccess
   |
   |-- [On Component Error] --> tJava_CaptureError
                                   |
                                   --> tDBOutput_ErrorLog
                                   |
                                   --> tSendMail_AlertTeam

The tJava_CaptureError component grabs the error details using ((String)globalMap.get("tRunJob_1_ERROR_MESSAGE")) and passes them to both the error logging table and an email alert. This isolates the failure -- the child job crashes, but the parent job stays alive to run cleanup logic or proceed to the next step.

Email Notifications on Failure

Connect tSendMail to your On Component Error triggers. Include the job name, error message, timestamp, and a direct link to your monitoring dashboard in the email body. Keep the subject line parseable: [TALEND-FAIL] job_name - component_name - YYYY-MM-DD HH:MM. This format lets you set up email rules and filter alerts by job.

Send to a team distribution list, not an individual. People go on vacation. Set up a secondary alert to Slack or Teams using tRESTClient if your primary email channel is unreliable.

Retry Patterns for Transient Errors

Database connection timeouts, API rate limits, and network blips are transient -- they'll succeed if you wait and try again. Permanent errors like authentication failures or missing tables won't. Your retry logic needs to distinguish between these.

int maxRetries = 3;
int retryCount = 0;
boolean success = false;
long baseDelay = 2000; // 2 seconds

while (!success && retryCount < maxRetries) {
    try {

In the Main section, place your actual operation. In the End section:

        success = true;
    } catch (java.sql.SQLTransientException e) {
        retryCount++;
        if (retryCount >= maxRetries) throw e;
        Thread.sleep(baseDelay * (long)Math.pow(2, retryCount));
        System.out.println("Retry " + retryCount + "/" + maxRetries);
    } catch (Exception e) {
        throw e; // Don't retry permanent errors
    }
}

The exponential backoff (2s, 4s, 8s) prevents hammering a struggling service. Cap retries at 3-5 attempts. Log every retry so you can identify components that frequently need retries -- those are candidates for infrastructure investigation.

Graceful Shutdown and Checkpoint Tables

When a job processes 2 million rows and fails at row 1.8 million, you don't want to reprocess all 2 million. Build a checkpoint table that tracks the last successfully processed batch.

CREATE TABLE etl_checkpoint (
    job_name        VARCHAR(200) PRIMARY KEY,
    last_processed  TIMESTAMP,
    last_batch_id   BIGINT,
    row_count       BIGINT,
    status          VARCHAR(20)  -- RUNNING, COMPLETED, FAILED
);

At job start, read the checkpoint. If the status is FAILED, resume from last_batch_id instead of the beginning. Update the checkpoint after each successful batch commit. On graceful shutdown (On Component Error trigger), write the current position before exiting. This turns a 4-hour reprocessing nightmare into a 15-minute recovery.

Error Classification: Handle Each Type Differently

Not all errors deserve the same response. We classify errors into three categories:

• Data quality errors: Bad dates, null required fields, constraint violations. Action: reject the row, log it, continue processing. Review daily.
• Infrastructure errors: Connection timeouts, disk full, out of memory. Action: retry with backoff, then fail the job. Page the on-call engineer.
• Logic errors: Unexpected nulls from lookups, row counts that don't reconcile, schema changes in source. Action: stop the job immediately. These need developer investigation before restarting.

Route each category to a different severity level in your error logging table and configure alerting accordingly. Data quality errors get a daily digest email. Infrastructure errors get a Slack alert. Logic errors get a PagerDuty notification.

Reusable Error Handling Sub-Job Template

Build one error handling sub-job and call it from every production job via tRunJob. The template accepts context variables for job_name, component_name, error_message, severity, and row_data. It writes to the centralized error table, sends alerts based on severity, and updates the checkpoint table status to FAILED.

Store this sub-job in a shared project reference so all teams use the same error handling infrastructure. When you need to change the alert destination or add a new logging field, you change it in one place. If you're still managing error handling with NullPointerException debugging on a case-by-case basis, a centralized sub-job will save you dozens of hours per month.

Key Takeaways

• Silent failures are worse than loud crashes -- always log errors to a queryable table, not just the console
• tLogCatcher handles Java exceptions; reject links handle data-level errors. You need both.
• Die-on-error for critical data; continue-on-error only when you're actively processing rejects
• Retry transient errors with exponential backoff; fail fast on permanent errors
• Checkpoint tables turn 4-hour reprocessing jobs into 15-minute restarts
• Classify errors (data/infrastructure/logic) and route each type to the right alert channel
• Build one reusable error handling sub-job and share it across all projects

Frequently Asked Questions