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BigData 11 errors

Big Data & Pipeline Errors

Spark, Kafka, Airflow, Snowflake, Flink and Databricks failures.

Understanding BigData errors

Distributed data errors are rarely about the line of code that threw them. An executor OOM, a failed checkpoint or a stuck consumer group are usually symptoms of data skew, insufficient partitioning, or a downstream system applying backpressure. The diagnostic instinct that pays off here is to look at the distribution of work across tasks before looking at the exception.

How to debug BigData errors

  1. Open the Spark UI (or Flink dashboard) and sort tasks by duration and shuffle read size. If one task is an order of magnitude larger than the median, you have skew, not a memory shortage.
  2. Check the driver log and at least one executor log. The exception the driver reports is often a downstream consequence of the first executor failure.
  3. For streaming, measure consumer lag over time rather than at a point (kafka-consumer-groups --describe). Lag that grows linearly means throughput, lag that spikes and recovers means a poison message or a GC pause.
  4. Confirm whether the job failed or was killed. YARN, Kubernetes and Databricks all kill containers that exceed memory limits, and the resulting message looks like a crash rather than an eviction.
  5. Before increasing memory, try increasing partitions. Doubling spark.sql.shuffle.partitions is cheaper and more often correct than doubling executor memory.

Tools worth reaching for

  • Spark UI
  • kafka-consumer-groups
  • Airflow task logs
  • EXPLAIN / query profile

All 11 BigData errors

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