Broadcast Join Failure in Streaming: Failed to store executor broadcast in BlockManager

[pooja_bhumandla]

Hi Databricks Community,

I’m running a Structured Streaming job in Databricks with foreachBatch writing to a Delta table.

Failed to store executor broadcast spark_join_relation_1622863
(size = Some(67141632)) in BlockManager with storageLevel=StorageLevel(memory, deserialized, 1 replicas)

I understand that Spark may choose a broadcast join when one side of the join is small enough to be sent to all executors.

How exactly does Spark decide when to perform a broadcast join?
What are the recommended ways to handle or avoid broadcast join memory errors in streaming operations?

Any suggestions, configuration tips, or best practices would be greatly appreciated.

Thanks in advance!

[Louis_Frolio]

Greetings @pooja_bhumandla , here are some helpful hints and tips.

Diagnosis

Your error indicates that a broadcast join operation is attempting to send ~64MB of data to executors, but the BlockManager cannot store it due to memory constraints. This commonly occurs in Structured Streaming with `foreachBatch` when Spark automatically decides to broadcast a DataFrame that exceeds available executor memory.

How Spark Decides on Broadcast Joins

Spark automatically performs a broadcast join when one side of the join meets these criteria:

- The estimated table size is below `spark.sql.autoBroadcastJoinThreshold` (default: **10MB**)
- The join type is compatible (INNER, CROSS, LEFT OUTER, RIGHT OUTER, LEFT SEMI, LEFT ANTI)
- Spark's cost-based optimizer determines it's the most efficient strategy

The problem is Spark's size estimator can underestimate actual DataFrame sizes, especially after transformations, leading to broadcast attempts that exceed actual memory capacity.

Recommended Solutions

1. Disable Auto-Broadcast for Streaming Jobs

Set the threshold to -1 to prevent automatic broadcasting:

```python
spark.conf.set("spark.sql.autoBroadcastJoinThreshold", -1)
```

2. Use Join Hints to Force SortMergeJoin

Within your `foreachBatch` function, explicitly prevent broadcasting:

```python
from pyspark.sql.functions import broadcast

def process_batch(batch_df, batch_id):
# Use NO_BROADCAST_HASH hint
result = batch_df.join(other_df.hint("merge"), "key")
result.write.format("delta").mode("append").save(path)
```

3. Collect Table Statistics

Before joins, gather accurate statistics to improve Spark's size estimation:

```sql
ANALYZE TABLE your_table COMPUTE STATISTICS
```

4. Increase Executor Memory

If broadcasts are necessary, ensure sufficient memory:

```python
spark.conf.set("spark.executor.memory", "8g")
spark.conf.set("spark.executor.memoryOverhead", "2g")
```

5. Cache Tables Strategically

If repeatedly joining with the same table in `foreachBatch`, cache it beforehand to stabilize memory estimates.

Best Practice for Streaming

For Structured Streaming jobs, disabling auto-broadcast (`-1`) is generally recommended since streaming micro-batches can have unpredictable sizes, and SortMergeJoin handles dynamic data volumes more reliably.

Hope this helps, Louis.