transformWithStateInPandas. Invalid pickle opcode when updating ValueState with large (float) array

[VaDim]

I am getting an error when the entity I need to store in a ValueState is a large array (over 15k-20k items). No error (and works correctly) if I trim the array to under 10k samples. The same error is raised when using it as a value for MapState or as an item in a ListState.
I tried multiple runs to see if there's a hard threshold when the error shows up - but there doesn't seem to be one; i.e. it can fail at 16k but it can also be ok and fail with the next record at 15k.

schema="samples ARRAY<FLOAT>"

  File "/databricks/spark/python/pyspark/worker.py", line 2232, in main
    process()
  File "/databricks/spark/python/pyspark/worker.py", line 2224, in process
    serializer.dump_stream(out_iter, outfile)
  File "/databricks/spark/python/pyspark/sql/pandas/serializers.py", line 1292, in dump_stream
    super().dump_stream(flatten_iterator(), stream)
  File "/databricks/spark/python/pyspark/sql/pandas/serializers.py", line 600, in dump_stream
    return ArrowStreamSerializer.dump_stream(
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/databricks/spark/python/pyspark/sql/pandas/serializers.py", line 118, in dump_stream
    for batch in iterator:
  File "/databricks/spark/python/pyspark/sql/pandas/serializers.py", line 593, in init_stream_yield_batches
    for series in iterator:
  File "/databricks/spark/python/pyspark/sql/pandas/serializers.py", line 1289, in flatten_iterator
    for pdf in iter_pdf:

...

  File "/databricks/spark/python/pyspark/sql/streaming/stateful_processor.py", line 68, in update
    self._valueStateClient.update(self._stateName, newValue)
  File "/databricks/spark/python/pyspark/sql/streaming/value_state_client.py", line 96, in update
    raise PySparkRuntimeError(f"Error updating value state: " f"{response_message[1]}")
pyspark.errors.exceptions.base.PySparkRuntimeError: Error updating value state: invalid pickle opcode: 0

[mark_ott · Accepted solution]

The error you’re facing, specifically PySparkRuntimeError: Error updating value state: invalid pickle opcod, usually points to a serialization (pickling) problem when storing large arrays in Flink/Spark state such as ValueState, ListState, or MapState.

Root Cause

• State in streaming applications: Frameworks like Apache Flink (and Spark's Structured Streaming stateful ops) keep user state in a serialized form. The underlying serialization method (often Python’s pickle or Arrow) has practical or hard size limits, and very large objects (such as arrays with thousands of floats) can trigger serialization errors.

• Pickle limitations: Pickle itself doesn’t have an explicit documented size cap, but in distributed systems like PySpark, network buffers, JVM/Python boundaries, and other system-level constraints often result in unpredictable failure thresholds. That’s why you see failures at different slightly varying array sizes.

• Arrow and Pandas Serializers: PySpark frequently uses Arrow under the hood, which introduces its own limits and quirks, particularly with large or deeply nested objects in stateful streaming operators.

Strategies to Address the Issue

1. Avoid Large Arrays In State

• Store statistics instead of raw data: Instead of persisting the full array, try to only keep aggregate data (mean, sum, histograms, sample points).

• Use an external store: Offload large data to an external fast store (e.g., Redis, Cassandra, Delta Lake), and store a reference (such as a key or ID) in ValueState. Retrieve the array as needed.

2. Chunk or Compress the Data

• Chunk the array: Split the array into smaller sublists, and store each chunk separately (possibly with sequence numbers or keys).

• Compression: Serialize and compress the array manually (e.g., with zlib or numpy.savez_compressed), but be aware this might just push the limits rather than solve underlying serialization boundaries.

3. Check Cluster Configuration

• Increase buffer size: If using Arrow, increase related buffer or batch sizes, though this often only gives slight improvements.

• Python/PySpark config: Ensure spark.driver.memory, spark.executor.memory, and Arrow batch/serialization configs are sufficiently high.

4. Validate Data Cleanliness

• Check array content: Large arrays with unexpected nested or corrupt objects can break pickle/Arrow serialization. Ensure your data are pure float arrays.

References

• [PySpark mailing list - Serialization large objects]

• [Apache Flink mailing list - ValueState size limits]

Direct Recommendations

• Don’t persist huge arrays in operator state; keep it as lean as possible.

• If unavoidable, switch to an external, scalable key-value store for raw data.