https://community.databricks.com/t5/data-engineering/broadcast-join-failure-in-streaming-failed-to-store-executor/m-p/137579#M50768 <P>Hi Databricks Community,</P><P>I’m running a Structured Streaming job in Databricks with foreachBatch writing to a Delta table.</P><P>Failed to store executor broadcast spark_join_relation_1622863<BR />(size = Some(67141632)) in BlockManager with storageLevel=StorageLevel(memory, deserialized, 1 replicas)</P><P><BR />I understand that Spark may choose a broadcast join when one side of the join is small enough to be sent to all executors.</P><P>How exactly does Spark decide when to perform a broadcast join?<BR />What are the recommended ways to handle or avoid broadcast join memory errors in streaming operations?</P><P>Any suggestions, configuration tips, or best practices would be greatly appreciated.</P><P>Thanks in advance!</P> Tue, 04 Nov 2025 14:10:33 GMT pooja_bhumandla 2025-11-04T14:10:33Z https://community.databricks.com/t5/data-engineering/broadcast-join-failure-in-streaming-failed-to-store-executor/m-p/137579#M50768 <P>Hi Databricks Community,</P><P>I’m running a Structured Streaming job in Databricks with foreachBatch writing to a Delta table.</P><P>Failed to store executor broadcast spark_join_relation_1622863<BR />(size = Some(67141632)) in BlockManager with storageLevel=StorageLevel(memory, deserialized, 1 replicas)</P><P><BR />I understand that Spark may choose a broadcast join when one side of the join is small enough to be sent to all executors.</P><P>How exactly does Spark decide when to perform a broadcast join?<BR />What are the recommended ways to handle or avoid broadcast join memory errors in streaming operations?</P><P>Any suggestions, configuration tips, or best practices would be greatly appreciated.</P><P>Thanks in advance!</P> Tue, 04 Nov 2025 14:10:33 GMT https://community.databricks.com/t5/data-engineering/broadcast-join-failure-in-streaming-failed-to-store-executor/m-p/137579#M50768 pooja_bhumandla 2025-11-04T14:10:33Z https://community.databricks.com/t5/data-engineering/broadcast-join-failure-in-streaming-failed-to-store-executor/m-p/137630#M50784 <P>Greetings&nbsp;<a href="https://community.databricks.com/t5/user/viewprofilepage/user-id/170125">@pooja_bhumandla</a>&nbsp;, here are some helpful hints and tips.</P> <H2>Diagnosis</H2> <P>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.</P> <H2>How Spark Decides on Broadcast Joins</H2> <P>Spark automatically performs a broadcast join when one side of the join meets these criteria:</P> <P>- The estimated table size is below `spark.sql.autoBroadcastJoinThreshold` (default: **10MB**)<BR />- The join type is compatible (INNER, CROSS, LEFT OUTER, RIGHT OUTER, LEFT SEMI, LEFT ANTI)<BR />- Spark's cost-based optimizer determines it's the most efficient strategy</P> <P>The problem is Spark's size estimator can underestimate actual DataFrame sizes, especially after transformations, leading to broadcast attempts that exceed actual memory capacity.</P> <H2>Recommended Solutions</H2> <H3>1. Disable Auto-Broadcast for Streaming Jobs</H3> <P>Set the threshold to -1 to prevent automatic broadcasting:</P> <P>```python<BR />spark.conf.set("spark.sql.autoBroadcastJoinThreshold", -1)<BR />```</P> <H3>2. Use Join Hints to Force SortMergeJoin</H3> <P>Within your `foreachBatch` function, explicitly prevent broadcasting:</P> <P>```python<BR />from pyspark.sql.functions import broadcast</P> <P>def process_batch(batch_df, batch_id):<BR /># Use NO_BROADCAST_HASH hint<BR />result = batch_df.join(other_df.hint("merge"), "key")<BR />result.write.format("delta").mode("append").save(path)<BR />```</P> <H3>3. Collect Table Statistics</H3> <P>Before joins, gather accurate statistics to improve Spark's size estimation:</P> <P>```sql<BR />ANALYZE TABLE your_table COMPUTE STATISTICS<BR />```</P> <H3>4. Increase Executor Memory</H3> <P>If broadcasts are necessary, ensure sufficient memory:</P> <P>```python<BR />spark.conf.set("spark.executor.memory", "8g")<BR />spark.conf.set("spark.executor.memoryOverhead", "2g")<BR />```</P> <H3>5. Cache Tables Strategically</H3> <P>If repeatedly joining with the same table in `foreachBatch`, cache it beforehand to stabilize memory estimates.</P> <H2>Best Practice for Streaming</H2> <P>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.</P> <P>Hope this helps, Louis.</P> Tue, 04 Nov 2025 18:22:22 GMT https://community.databricks.com/t5/data-engineering/broadcast-join-failure-in-streaming-failed-to-store-executor/m-p/137630#M50784 Louis_Frolio 2025-11-04T18:22:22Z