dbt: Compilation Error (model depends on a node that was not found)

Quick fix

Read the commands before running them. Anything that restarts a service, deletes data or changes permissions should be tried on a non-production system first.

# Resolve the graph and see what dbt knows about
dbt parse
dbt ls --select my_model+

# ref() takes the model NAME (the filename without .sql), not the table name
# source() requires a matching entry in a schema .yml

sources:
  - name: raw
    tables:
      - name: orders

# Build a model and everything it needs
dbt build --select +my_model

How to diagnose 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.

If the quick fix above does not resolve it, work through these steps. They apply to this whole class of error, not just to this one message, which is usually what saves the time.

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

Authoritative references

Primary documentation for this error, worth reading before applying any fix in production.

📖 docs.getdbt.com

Related BigData errors

• Airflow: Scheduler not runningAirflow scheduler process is down or tasks are stuck in queued state.
• Databricks: Cluster terminated unexpectedlyA Databricks cluster was terminated due to spot instance reclamation, out of memory, or cloud…
• Flink: Checkpoint failedApache Flink failed to complete a checkpoint within the timeout. Usually caused by…
• Hadoop: NameNode is in safe modeHDFS NameNode is in safe mode, making the filesystem read-only. This happens at startup until…
• Hive: MetaStore connection failedHive cannot connect to its MetaStore service, which stores table schemas and partition info…
• Kafka: Consumer group rebalancingKafka consumer group is stuck in a rebalance loop. Consumers are frequently joining and…
• Snowflake: Warehouse suspendedAttempting to run query on a suspended warehouse that didn't auto-resume.
• Spark: Executor Lost (OutOfMemoryError)Spark executor ran out of heap memory processing a partition.

See all 11 BigData errors →

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This entry is maintained by hand. If the fix is out of date, incomplete, or you have a better one, email a correction and it will be reviewed.