I've been diving into how Spark handles memory with its spill to disk feature, specifically looking at how it uses spark.local.dir for data that doesn't fit in memory. The idea is that data should spill to disk, so ideally, Out of Memory (OOM) errors shouldn't occur. However, there are some situations I'm struggling to understand:
1. During a shuffle between executors, if the receiving executor gets more data than it can hold, shouldn't it just spill to disk?
2. While coalescing with one partition, if a shuffle is triggered and a large chunk of data is gathered by the executor, shouldn't spilling handle that?
3. For a driver executing collect on a massive dataset, it makes sense for OOM to happen since all the data is in memory. But what about the executors?
4. I'm left wondering why OOM errors still occur even if spilling is supposed to work. Can anyone shed light on what specifically causes these OOM errors and how you deal with them?
3 Answers
While spilling helps alleviate some memory pressure, OOMs can still occur because Spark requires memory for various tasks like bookkeeping and serialization. During tasks like shuffles or coalescing, if one task tries to hold onto a massive amount of data before spilling, OOM can still happen. Mitigating this involves tuning settings like `spark.sql.shuffle.partitions`, increasing executor memory, or simply breaking down jobs into smaller tasks. Essentially, spilling acts as a support system but is not a complete substitute for RAM.
One common misunderstanding is thinking of the 'spill to disk' process as a sure-fire solution. Spark indeed tries to offload data to spark.local.dir, but it first fills up the memory and uses buffers to manage data before spilling. If those buffers run out, you can hit an OOM error before any data even writes to disk. Also, not every data structure can spill. Some aggregations must remain in memory, and things like disk pressure or slow I/O can choke the executor during these spill attempts. That's why using tools like Dataflint to monitor memory and spill issues can really help you configure things better rather than just cranking up memory.
Spilling to disk isn't like magically doubling your RAM. If your partition or shuffle block is massive, Spark still requires memory for overhead costs like metadata tracking. That's often where OOM errors sneak in.
Exactly! Remember that spilling is just a backup, not a guarantee against OOM issues. You can still experience spikes where a task needs to handle a big batch or aggregation hash-map in memory prior to spilling; if that exceeds what the JVM can manage, it fails before the spill helps. My personal strategy includes:
- Making partitions smaller and leveraging adaptive query execution for auto-coalescing.
- Avoiding collect on wide or skewed data; aggregating or sampling first helps.
- Keeping an eye on large outputs from UDFs and flattening data structures in advance.
I also adjusted the memoryFraction, but only after addressing the skew. I've found that pairing Databricks with Trino really helps keep memory usage in check, handling more substantial lookups outside of Spark.